[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-jiachenli94--Awesome-Interaction-Aware-Trajectory-Prediction":3,"tool-jiachenli94--Awesome-Interaction-Aware-Trajectory-Prediction":61},[4,18,26,36,44,52],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",141543,2,"2026-04-06T11:32:54",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107888,"2026-04-06T11:32:50",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":10,"last_commit_at":50,"category_tags":51,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":53,"name":54,"github_repo":55,"description_zh":56,"stars":57,"difficulty_score":10,"last_commit_at":58,"category_tags":59,"status":17},4292,"Deep-Live-Cam","hacksider\u002FDeep-Live-Cam","Deep-Live-Cam 是一款专注于实时换脸与视频生成的开源工具，用户仅需一张静态照片，即可通过“一键操作”实现摄像头画面的即时变脸或制作深度伪造视频。它有效解决了传统换脸技术流程繁琐、对硬件配置要求极高以及难以实时预览的痛点，让高质量的数字内容创作变得触手可及。\n\n这款工具不仅适合开发者和技术研究人员探索算法边界，更因其极简的操作逻辑（仅需三步：选脸、选摄像头、启动），广泛适用于普通用户、内容创作者、设计师及直播主播。无论是为了动画角色定制、服装展示模特替换，还是制作趣味短视频和直播互动，Deep-Live-Cam 都能提供流畅的支持。\n\n其核心技术亮点在于强大的实时处理能力，支持口型遮罩（Mouth Mask）以保留使用者原始的嘴部动作，确保表情自然精准；同时具备“人脸映射”功能，可同时对画面中的多个主体应用不同面孔。此外，项目内置了严格的内容安全过滤机制，自动拦截涉及裸露、暴力等不当素材，并倡导用户在获得授权及明确标注的前提下合规使用，体现了技术发展与伦理责任的平衡。",88924,"2026-04-06T03:28:53",[14,15,13,60],"视频",{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":87,"forks":88,"last_commit_at":89,"license":90,"difficulty_score":91,"env_os":92,"env_gpu":93,"env_ram":93,"env_deps":94,"category_tags":97,"github_topics":98,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":119,"updated_at":120,"faqs":121,"releases":122},4404,"jiachenli94\u002FAwesome-Interaction-Aware-Trajectory-Prediction","Awesome-Interaction-Aware-Trajectory-Prediction","A selection of state-of-the-art research materials on trajectory prediction","Awesome-Interaction-Aware-Trajectory-Prediction 是一个专注于“交互感知轨迹预测”领域的精选资源库，由斯坦福大学和加州大学伯克利分校的研究者共同维护。在自动驾驶、机器人导航及人群模拟等场景中，准确预测车辆、行人等智能体的未来运动轨迹至关重要，而难点在于如何建模个体之间复杂的相互影响。该资源库正是为了解决这一挑战而生，它系统性地整理了全球最前沿的研究成果，涵盖高质量数据集（如 Waymo、Argoverse）、综述论文、核心算法代码以及评估基准。\n\n其独特亮点在于不仅关注单一目标的运动规律，更重点收录了那些能够理解并推理多智能体间动态交互关系的先进方案，帮助开发者突破传统预测方法的局限。无论是高校研究人员寻找最新文献灵感，还是企业工程师需要复现 SOTA（最先进）模型或获取训练数据，都能在这里找到极具价值的参考。作为一个持续更新的开源项目，它致力于打通学术界与工业界的信息壁垒，为构建更安全、更智能的移动系统提供坚实的技术支撑。","# Awesome Interaction-Aware Behavior and Trajectory Prediction\n![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg) ![Version](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FVersion-2.0-ff69b4.svg) ![LastUpdated](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLastUpdated-2023.09-lightgrey.svg) ![Topic](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTopic-trajectory--prediction-yellow.svg?logo=github)\n\nThis is a checklist of state-of-the-art research materials (datasets, blogs, papers and public codes) related to trajectory prediction. Wish it could be helpful for both academia and industry. (Still updating)\n\n**Maintainers**: [**Jiachen Li**](https:\u002F\u002Fjiachenli94.github.io) (Stanford University); [**Hengbo Ma**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fhengboma\u002F), [**Jinning Li**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjinningli\u002F) (University of California, Berkeley)\n\n**Emails**: jiachen_li@stanford.edu; {hengbo_ma, jinning_li}@berkeley.edu\n\nPlease feel free to pull request to add new resources or send emails to us for questions, discussion and collaborations.\n\n**Note**: [**Here**](https:\u002F\u002Fgithub.com\u002Fjiachenli94\u002FAwesome-Decision-Making-Reinforcement-Learning) is also a collection of materials for reinforcement learning, decision making and motion planning.\n\n\n\nPlease consider citing our work if you found this repo useful:\n\n```\n@inproceedings{li2020evolvegraph,\n  title={EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning},\n  author={Li, Jiachen and Yang, Fan and Tomizuka, Masayoshi and Choi, Chiho},\n  booktitle={2020 Advances in Neural Information Processing Systems (NeurIPS)},\n  year={2020}\n}\n\n@inproceedings{li2019conditional,\n  title={Conditional Generative Neural System for Probabilistic Trajectory Prediction},\n  author={Li, Jiachen and Ma, Hengbo and Tomizuka, Masayoshi},\n  booktitle={2019 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)},\n  pages={6150--6156},\n  year={2019},\n  organization={IEEE}\n}\n```\n\n### Table of Contents\n\n\u003C!-- TOC depthFrom:1 depthTo:6 withLinks:1 updateOnSave:1 orderedList:0 -->\n- [**Datasets**](#datasets)\n\t- [Vehicles and Traffic](#vehicles-and-traffic)\n\t- [Pedestrians](#pedestrians)\n\t- [Sport Players](#sport-players)\n- [**Literature and Codes**](#literature-and-codes)\n\t- [Survey Papers](#survey-papers)\n\t- [Physics Systems with Interaction](#physics-systems-with-interaction)\n\t- [Intelligent Vehicles and Pedestrians](#intelligent-vehicles-and-pedestrians)\n\t- [Mobile Robots](#mobile-robots)\n\t- [Sport Players](#sport-players)\n\t- [Benchmark and Evaluation Metrics](#benchmark-and-evaluation-metrics)\n\t- [Others](#others)\n\t\u003C!-- \u002FTOC -->\n\n## **Datasets**\n### Vehicles and Traffic\n\n|                           Dataset                            |            Agents            |         Scenarios         |        Sensors         |\n| :----------------------------------------------------------: | :--------------------------: | :-----------------------: | :--------------------: |\n|      [Waymo Open Dataset](https:\u002F\u002Fwaymo.com\u002Fopen\u002F)           | vehicles \u002F cyclists \u002F people |\t\t\t\turban \u002F highway     |\tLiDAR \u002F camera \u002F Radar |\n|      [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F)           | vehicles \u002F cyclists \u002F people |\t\t\t\turban \u002F highway     |\tLiDAR \u002F camera \u002F Radar |\n|            [nuScenes](https:\u002F\u002Fwww.nuscenes.org\u002F)             |           vehicles           |           urban           | camera \u002F LiDAR \u002F Radar |\n|           [highD](https:\u002F\u002Fwww.highd-dataset.com\u002F)            |           vehicles           |          highway          |         camera         |\n|           [inD](https:\u002F\u002Fwww.ind-dataset.com\u002F)            |           vehicles           |          highway          |         camera         |\n|           [roundD](https:\u002F\u002Fwww.round-dataset.com\u002F)            |           vehicles           |          highway          |         camera         |\n|          [BDD100k](https:\u002F\u002Fbdd-data.berkeley.edu\u002F)           | vehicles \u002F cyclists \u002F people |      highway \u002F urban      |         camera         |\n|        [KITTI](http:\u002F\u002Fwww.cvlibs.net\u002Fdatasets\u002Fkitti\u002F)        | vehicles \u002F cyclists \u002F people |   highway \u002F rural areas   |     camera \u002F LiDAR     |\n| [NGSIM](https:\u002F\u002Fops.fhwa.dot.gov\u002Ftrafficanalysistools\u002Fngsim.htm) |           vehicles           |          highway          |         camera         |\n|      [INTERACTION](http:\u002F\u002Fwww.interaction-dataset.com\u002F)      | vehicles \u002F cyclists \u002F people | roundabout \u002F intersection |     camera     |\n| [Cyclists](http:\u002F\u002Fwww.gavrila.net\u002FDatasets\u002FDaimler_Pedestrian_Benchmark_D\u002FTsinghua-Daimler_Cyclist_Detec\u002Ftsinghua-daimler_cyclist_detec.html) |           cyclists           |           urban           |         camera         |\n| [Apolloscapes](http:\u002F\u002Fapolloscape.auto\u002F?source=post_page---------------------------) | vehicles \u002F cyclists \u002F people |           urban           |         camera         |\n| [Udacity](https:\u002F\u002Fgithub.com\u002Fudacity\u002Fself-driving-car\u002Ftree\u002Fmaster\u002Fdatasets) |           vehicles           |           urban           |         camera         |\n|      [Cityscapes](https:\u002F\u002Fwww.cityscapes-dataset.com\u002F)       |       vehicles \u002F people       |           urban           |         camera         |\n| [Stanford Drone](http:\u002F\u002Fcvgl.stanford.edu\u002Fprojects\u002Fuav_data\u002F) | vehicles \u002F cyclists \u002F people |           urban           |         camera         |\n|           [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F)            |      vehicles \u002F people       |           urban           |     camera \u002F LiDAR     |\n| [TRAF](https:\u002F\u002Fgamma.umd.edu\u002Fresearchdirections\u002Fautonomousdriving\u002Ftrafdataset)            |      vehicles \u002F buses \u002F cyclists \u002F bikes \u002F people \u002F animals       |           urban           |     camera      |\n|[Aschaffenburg Pose Dataset](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.5724486)               |    cyclists \u002F people     |           urban           |         camera         |\n\n### Pedestrians\n\n|                           Dataset                            |           Agents            |       Scenarios       |    Sensors     |\n| :----------------------------------------------------------: | :-------------------------: | :-------------------: | :------------: |\n| [UCY](https:\u002F\u002Fgraphics.cs.ucy.ac.cy\u002Fresearch\u002Fdownloads\u002Fcrowd-data) |           people           |    zara \u002F students    |     camera     |\n|       [ETH (ICCV09)](https:\u002F\u002Ficu.ee.ethz.ch\u002Fresearch\u002Fdatsets.html)       |           people           |         urban         |     camera     |\n|              [VIRAT](http:\u002F\u002Fwww.viratdata.org\u002F)              |      people \u002F vehicles      |         urban         |     camera     |\n|        [KITTI](http:\u002F\u002Fwww.cvlibs.net\u002Fdatasets\u002Fkitti\u002F)        | vehicles \u002F cyclists \u002F people | highway \u002F rural areas | camera \u002F LiDAR |\n|     [ATC](https:\u002F\u002Firc.atr.jp\u002Fcrest2010_HRI\u002FATC_dataset\u002F)     |           people           |    shopping center    |  Range sensor  |\n| [Daimler](http:\u002F\u002Fwww.gavrila.net\u002FDatasets\u002FDaimler_Pedestrian_Benchmark_D\u002Fdaimler_pedestrian_benchmark_d.html) |           people           |  from moving vehicle  |     camera     |\n| [Central Station](http:\u002F\u002Fwww.ee.cuhk.edu.hk\u002F~xgwang\u002Fgrandcentral.html) |           people           |    inside station    |     camera     |\n| [Town Center](http:\u002F\u002Fwww.robots.ox.ac.uk\u002FActiveVision\u002FResearch\u002FProjects\u002F2009bbenfold_headpose\u002Fproject.html#datasets) |           people           |     urban street     |     camera     |\n| [Edinburgh](http:\u002F\u002Fhomepages.inf.ed.ac.uk\u002Frbf\u002FFORUMTRACKING\u002F) |           people           |         urban         |     camera     |\n|   [Cityscapes](https:\u002F\u002Fwww.cityscapes-dataset.com\u002Flogin\u002F)    |      vehicles \u002F people      |         urban         |     camera     |\n|           [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F)            |      vehicles \u002F people      |         urban         | camera \u002F LiDAR |\n| [Stanford Drone](http:\u002F\u002Fcvgl.stanford.edu\u002Fprojects\u002Fuav_data\u002F) | vehicles \u002F cyclists \u002F people |         urban         |     camera     |\n|           [TrajNet](http:\u002F\u002Ftrajnet.stanford.edu\u002F)            |           people           |         urban         |     camera     |\n|           [PIE](http:\u002F\u002Fdata.nvision2.eecs.yorku.ca\u002FPIE_dataset\u002F)            |           people           |         urban         |     camera     |\n|           [ForkingPaths](https:\u002F\u002Fnext.cs.cmu.edu\u002Fmultiverse\u002Findex.html)            |           people           |         urban \u002F simulation         |     camera     |\n|           [TrajNet++](https:\u002F\u002Fwww.aicrowd.com\u002Fchallenges\u002Ftrajnet-a-trajectory-forecasting-challenge)            |           people           |         urban         |     camera     |\n|[Aschaffenburg Pose Dataset](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.5724486)               |    cyclists \u002F people    |           urban           |         camera         |\n|[Cyclist Top-View Dataset (CTV)](https:\u002F\u002Fwww.ifi-mec.tu-clausthal.de\u002Fctv-dataset)               |    cyclists \u002F people    |           urban           |         camera         |\n\n### Sport Players\n\n|                           Dataset                            | Agents |     Scenarios     | Sensors |\n| :----------------------------------------------------------: | :----: | :---------------: | :-----: |\n|     [Football](https:\u002F\u002Fdatahub.io\u002Fcollections\u002Ffootball)      | people |  football field   | camera  |\n| [NBA SportVU](https:\u002F\u002Fgithub.com\u002Flinouk23\u002FNBA-Player-Movements) | people |  basketball Hall  | camera  |\n|      [NFL](https:\u002F\u002Fgithub.com\u002Fa-vhadgar\u002FBig-Data-Bowl)       | people | American football | camera  |\n\n\n## **Literature and Codes**\n\n### Survey Papers\n\n- Machine Learning for Autonomous Vehicle’s Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions, arXiv preprint arXiv:2307.07527, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.07527.pdf)]\n- Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey, IEEE T-IV, 2023. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10100881)]\n- Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review, IEEE T-ITS, 2023. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10181234)]\n- A Survey on Trajectory-Prediction Methods for Autonomous Driving, IEEE T-IV 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9756903)]\n- A Survey of Vehicle Trajectory Prediction Based on Deep Learning Models, International Conference on Sustainable Expert Systems, ICSES 2022. [[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-19-7874-6_48)]\n- Scenario Understanding and Motion Prediction for Autonomous Vehicles – Review and Comparison, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9733973)]\n- Multi-modal Fusion Technology based on Vehicle Information: A Survey, arXiv preprint arXiv:2211.06080, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.06080.pdf)]\n- Deep Reinforcement Learning for Autonomous Driving: A Survey, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9351818)]\n- Social Interactions for Autonomous Driving: A Review and Perspective, arXiv preprint arXiv:2208.07541, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.07541.pdf)]\n- Generative Adversarial Networks for Spatio-temporal Data: A Survey, ACM T-IST, 2022. [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3474838)]\n- Behavioral Intention Prediction in Driving Scenes: A Survey, arXiv preprint arXiv:2211.00385, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.00385.pdf)]\n- A Survey on Motion Prediction of Pedestrians and Vehicles for Autonomous Driving, IEEE Access, 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9559998)]\n- Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches, arXiv preprint arXiv:2111.06740, 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.06740.pdf)]\n- A Survey on Trajectory Data Management, Analytics, and Learning, CSUR 2021. [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3440207)]\n- Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features, IEEE T-ITS, 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9660784)]\n- A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction, Sensors, 2021. [[paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F21\u002F22\u002F7543\u002Fpdf)]\n- A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving, ROBIO 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.10436.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FHenry1iu\u002FTNT-Trajectory-Predition)]\n- A Survey of Deep Learning Techniques for Autonomous Driving, Journal of Field Robotics, 2020. [[paper](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1002\u002Frob.21918?saml_referrer)]\n- Human Motion Trajectory Prediction: A Survey, International Journal of Robotics Research, 2020. [[paper](http:\u002F\u002Fsage.cnpereading.com\u002Fparagraph\u002Fdownload\u002F?doi=10.1177\u002F0278364920917446)]\n- Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies, arXiv preprint arXiv:2006.06091, 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2006\u002F2006.06091.pdf)]\n- A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving, Computer Graphics Forum 2020. [[paper](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1111\u002Fcgf.13803?saml_referrer)]\n- Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review, IEEE T-ITS 2020. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9158529)]\n- Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles, IEEE T-ITS 2020. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9210154)]\n- Vehicle Trajectory Similarity: Models, Methods, and Applications, ACM Computing Surveys (CSUR 2020). [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3406096)]\n- Modeling and Prediction of Human Driver Behavior: A Survey, 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08832)]\n- A literature review on the prediction of pedestrian behavior in urban scenarios, ITSC 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8569415)\\]\n- Survey on Vision-Based Path Prediction. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-91131-1_4)\\]\n- Autonomous vehicles that interact with pedestrians: A survey of theory and practice. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11773)\\]\n- Trajectory data mining: an overview. \\[[paper](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2743025)\\]\n- A survey on motion prediction and risk assessment for intelligent vehicles. \\[[paper](https:\u002F\u002Frobomechjournal.springeropen.com\u002Farticles\u002F10.1186\u002Fs40648-014-0001-z)\\]\n\n### Physics Systems with Interaction\n\n- Learning Physical Dynamics with Subequivariant Graph Neural Networks, NeurIPS 2022. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.06876)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fhanjq17\u002FSGNN)\\]\n- EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NeurIPS 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- Interaction Templates for Multi-Robot Systems, IROS 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8737744\u002F)\\]\n- Factorised Neural Relational  Inference for Multi-Interaction Systems, ICML workshop 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08721v1)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fekwebb\u002FfNRI)\\]\n- Physics-as-Inverse-Graphics: Joint Unsupervised Learning of Objects and Physics from Video, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.11169v1.pdf)\\]\n- Neural Relational Inference for Interacting Systems, ICML 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04687v2)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fethanfetaya\u002FNRI)\\]\n- Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks, UAI 2018. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1807.09244v2)\\]\n- Relational inductive biases, deep learning, and graph networks, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01261v3)\\]\n- Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions, ICLR 2018. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10353v1)\\]\n- Graph networks as learnable physics engines for inference and control, ICML 2018. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01242v1)\\]\n- Flexible Neural Representation for Physics Prediction, 2018. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1806.08047v2)\\]\n- A simple neural network module for relational reasoning, 2017. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01427v1)\\]\n- VAIN: Attentional Multi-agent Predictive Modeling, NeurIPS 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.06122.pdf)\\]\n- Visual Interaction Networks, 2017. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01433v1)\\]\n- A Compositional Object-Based Approach to Learning Physical Dynamics, ICLR 2017. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00341v2)\\]\n- Interaction Networks for Learning about Objects, Relations and Physics, 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00222)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fhiggsfield\u002Finteraction_network_pytorch)\\]\n\n### Intelligent Vehicles & Traffic & Pedestrians\n\n- Diffusion-Based Environment-Aware Trajectory Prediction, arXiv preprint arXiv:2403.11643, 2024. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.11643)]\n- MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs, IEEE T-IV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00735)] [[code](https:\u002F\u002Fgithub.com\u002Fwestny\u002Fmtp-go)]\n- MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FJiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.pdf)]\n- Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction, CVPR 2023. [[paper](http:\u002F\u002Fxxx.itp.ac.cn\u002Fpdf\u002F2303.16005.pdf)]\n- Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Fchengy12.github.io\u002Ffiles\u002FBosampler.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fviewsetting\u002FUnsupervised_sampling_promoting)]\n- Planning-oriented Autonomous Driving, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FHu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FOpenDriveLab\u002FUniAD)]\n- IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.00575.pdf)]\n- Stimulus Verification is a Universal and Effective Sampler in Multi-modal Human Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FSun_Stimulus_Verification_Is_a_Universal_and_Effective_Sampler_in_Multi-Modal_CVPR_2023_paper.pdf)]\n- Query-Centric Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FZhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FZikangZhou\u002FQCNet)] [[QCNeXt](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.10508.pdf)]\n- FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.16574.pdf)]\n- Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion, CVPR 2023. [[paper](https:\u002F\u002Fnv-tlabs.github.io\u002Ftrace-pace\u002Fdocs\u002Ftrace_and_pace.pdf)] [[website](https:\u002F\u002Fnv-tlabs.github.io\u002Ftrace-pace\u002F)]\n- FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.16197.pdf)] [[website](https:\u002F\u002Frluke22.github.io\u002FFJMP\u002F)]\n- Leapfrog Diffusion Model for Stochastic Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.10895.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FLED)]\n- ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries, CVPR 2023. [[paper](http:\u002F\u002Fxxx.itp.ac.cn\u002Fpdf\u002F2208.01582.pdf)] [[website](https:\u002F\u002Ftsinghua-mars-lab.github.io\u002FViP3D\u002F)]\n- EqMotion: Equivariant Multi-Agent Motion Prediction with Invariant Interaction Reasoning, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.10876.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FEqMotion)]\n- V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FYu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FAIR-THU\u002FDAIR-V2X-Seq)]\n- Weakly Supervised Class-agnostic Motion Prediction for Autonomous Driving, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FLi_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.pdf)]\n- Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FGao_Decompose_More_and_Aggregate_Better_Two_Closer_Looks_at_Frequency_CVPR_2023_paper.pdf)]\n- HumanMAC: Masked Motion Completion for Human Motion Prediction, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03665)] [[code](https:\u002F\u002Fgithub.com\u002FLinghaoChan\u002FHumanMAC)]\n- BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14304)] [[code](https:\u002F\u002Fgithub.com\u002FBarqueroGerman\u002FBeLFusion)]\n- EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09306)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FEigenTrajectory)]\n- ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.14187.pdf)] [[code](https:\u002F\u002Fkuis-ai.github.io\u002Fadapt\u002F)]\n- PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View, IJCAI 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.10761.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FEdwardLeeLPZ\u002FPowerBEV)]\n- Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction, AAAI 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.05976.pdf)]\n- Multi-stream Representation Learning for Pedestrian Trajectory Prediction, AAAI 2023. [[paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25389)]\n- Continuous Trajectory Generation Based on Two-Stage GAN, AAAI 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.07103.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FWenMellors\u002FTS-TrajGen)]\n- A Set of Control Points Conditioned Pedestrian Trajectory Prediction, AAAI 2023. [[paper](https:\u002F\u002Fassets.underline.io\u002Flecture\u002F67747\u002Fpaper\u002F82988b653861eb7a0d5cdc91c4b26f8c.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FGraphTERN)]\n- Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction, ICLR 2023. [[paper](https:\u002F\u002Fopenreview.net\u002Fforum?id=CGBCTp2M6lA)]\n- TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios, ICRA 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.06609.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fmetadriverse\u002Ftrafficgen)]\n- GANet: Goal Area Network for Motion Forecasting, ICRA 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.09723.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fkingwmk\u002FGANet)]\n- TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving, ICRA 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.20068.pdf)]\n- SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving, CoRL 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.14116.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FAutoVision-cloud\u002FSSL-Lanes)]\n- LimSim: A Long-term Interactive Multi-scenario Traffic Simulator, ITSC 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.06648.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FPJLab-ADG\u002FLimSim)]\n- MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10056303)]\n- Adaptive and Simultaneous Trajectory Prediction for Heterogeneous Agents via Transferable Hierarchical Transformer Network, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10149109)]\n- SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction, TNNLS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10063206)] [[code](https:\u002F\u002Fgithub.com\u002FWW-Tong\u002Fssagcn_for_path_prediction)]\n- Disentangling Crowd Interactions for Pedestrians Trajectory Prediction, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10083225)]\n- VNAGT: Variational Non-Autoregressive Graph Transformer Network for Multi-Agent Trajectory Prediction, IEEE Transactions on Vehicular Technology. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10121688)]\n- Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction, TIV. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10149368)]\n- Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323002935)]\n- Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323004703)]\n- Multimodal Vehicular Trajectory Prediction With Inverse Reinforcement Learning and Risk Aversion at Urban Unsignalized Intersections, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10164651)]\n- Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph, IET Intelligent Transport Systems. [[paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1049\u002Fitr2.12265)]\n- Social Self-Attention Generative Adversarial Networks for Human Trajectory Prediction, IEEE Transactions on Artificial Intelligence. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10197467)]\n- CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10215313)]\n- Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10207845)]\n- A physics-informed Transformer model for vehicle trajectory prediction on highways, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X23002619)] [[code](https:\u002F\u002Fgithub.com\u002FGengmaosi\u002FPIT-IDM)]\n- MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction, RAL. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2308.10280.pdf)]\n- MRGTraj: A Novel Non-Autoregressive Approach for Human Trajectory Prediction, TCSVT. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10226250)] [[code](https:\u002F\u002Fgithub.com\u002Fwisionpeng\u002FMRGTraj)]\n- Planning-inspired Hierarchical Trajectory Prediction via Lateral-Longitudinal Decomposition for Autonomous Driving, TIV. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10226224)]\n- Traj-MAE: Masked Autoencoders for Trajectory Prediction, arXiv preprint arXiv:2303.06697, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06697.pdf)]\n- Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion, arXiv preprint arXiv:2303.08367, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.08367.pdf)]\n- Diffusion Model for GPS Trajectory Generation, arXiv preprint arXiv:2304.11582, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.11582.pdf)]\n- Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.11868.pdf)] [[website](https:\u002F\u002Fmultiverse-transformer.github.io\u002Fsim-agents\u002F)]\n- Joint-Multipath++ for Simulation Agents: 2nd Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https:\u002F\u002Fstorage.googleapis.com\u002Fwaymo-uploads\u002Ffiles\u002Fresearch\u002F2023%20Technical%20Reports\u002FSA_hm_jointMP.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fwangwenxi-handsome\u002FJoint-Multipathpp)]\n- MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying, 1st Place Solution for Waymo Open Motion Prediction Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.17770.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fsshaoshuai\u002FMTR)]\n- GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving, arXiv preprint arXiv:2303.05760, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.05760.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMCZhi\u002FGameFormer)] [[website](https:\u002F\u002Fmczhi.github.io\u002FGameFormer\u002F)]\n- GameFormer Planner: A Learning-enabled Interactive Prediction and Planning Framework for Autonomous Vehicles, the nuPlan Planning Challenge at the CVPR 2023 End-to-End Autonomous Driving Workshop. [[paper](https:\u002F\u002Fopendrivelab.com\u002Fe2ead\u002FAD23Challenge\u002FTrack_4_AID.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMCZhi\u002FGameFormer-Planner\u002F)]\n- trajdata: A Unified Interface to Multiple Human Trajectory Datasets, arXiv preprint arXiv:2307.13924, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.13924.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FNVlabs\u002Ftrajdata)]\n- Remember Intentions: Retrospective-Memory-based Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.11474.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FMemoNet)]\n- STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.01026.pdf)] [[code](https:\u002F\u002Fgithub.com\u002F4DVLab\u002FSTCrowd.git)]\n- Vehicle trajectory prediction works, but not everywhere, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.03909.pdf)] [[code](https:\u002F\u002Fs-attack.github.io\u002F)]\n- Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.13777.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fgutianpei\u002FMID)]\n- Non-Probability Sampling Network for Stochastic Human Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.13471.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Finhwanbae\u002FNPSN)]\n- On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05057.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fzqzqz\u002FAdvTrajectoryPrediction)]\n- Adaptive Trajectory Prediction via Transferable GNN, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.05046.pdf)]\n- Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.14820.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fcausalmotion), [code](https:\u002F\u002Fgithub.com\u002Fsherwinbahmani\u002Fynet_adaptive)]\n- How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.04781.pdf)]\n- Learning from All Vehicles, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.11934.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fdotchen\u002FLAV)]\n- Forecasting from LiDAR via Future Object Detection, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.16297.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fneeharperi\u002FFutureDet)]\n- End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.16910.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FKguo-cs\u002FTDOR)]\n- M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11884.pdf)] [[code](https:\u002F\u002Ftsinghua-mars-lab.github.io\u002FM2I\u002F)]\n- GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08770.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FGroupNet)]\n- Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-Based Prediction, CVPR 2022. [[paper](https:\u002F\u002Fxinshuoweng.com\u002Fpapers\u002FAffinipred\u002Fcamera_ready.pdf)]\n- ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FChen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FScePT)]\n- Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FLi_Graph-Based_Spatial_Transformer_With_Memory_Replay_for_Multi-Future_Pedestrian_Trajectory_CVPR_2022_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FJacobieee\u002FST-MR)]\n- MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FLee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.pdf)]\n- LTP: Lane-based Trajectory Prediction for Autonomous Driving, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FWang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.pdf)]\n- ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FWang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.pdf)]\n- Human Trajectory Prediction with Momentary Observation, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FSun_Human_Trajectory_Prediction_With_Momentary_Observation_CVPR_2022_paper.pdf)]\n- HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FZhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FZikangZhou\u002FHiVT)]\n- Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.09953.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FGPGraph)]\n- Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.03057.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fabduallahmohamed\u002FSocial-Implicit)] [[website](https:\u002F\u002Fwww.abduallahmohamed.com\u002Fsocial-implicit-amdamv-adefde-demo)] \n- Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.04624.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fd1024choi\u002FHLSTrajForecast)]\n- SocialVAE: Human Trajectory Prediction using Timewise Latents, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.08207.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fxupei0610\u002FSocialVAE)]\n- View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07288.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fcocoon2wong\u002FVertical)]\n- Entry-Flipped Transformer for Inference and Prediction of Participant Behavior, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.06235.pdf)]\n- D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.10398.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FVTP-TL\u002FD2-TPred)]\n- Human Trajectory Prediction via Neural Social Physics, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.10435.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Frealcrane\u002FHuman-Trajectory-Prediction-via-Neural-Social-Physics)]\n- Social-SSL: Self-Supervised Cross-Sequence Representation Learning Based on Transformers for Multi-Agent Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Fbasiclab.lab.nycu.edu.tw\u002Fassets\u002FSocial-SSL.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FSigta678\u002FSocial-SSL)]\n- Aware of the History: Trajectory Forecasting with the Local Behavior Data, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.09646.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FKay1794\u002FAware-of-the-history)]\n- Action-based Contrastive Learning for Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08664.pdf)]\n- AdvDO: Realistic Adversarial Attacks for Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.08744.pdf)]\n- ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.07601.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FOpenPerceptionX\u002FST-P3)]\n- Social ODE: Multi-Agent Trajectory Forecasting with Neural Ordinary Differential Equations, ECCV 2022. [[paper](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136820211.pdf)]\n- Forecasting Human Trajectory from Scene History, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.08732.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMaKaRuiNah\u002FSHENet)]\n- Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.08129)] [[code](https:\u002F\u002Fgithub.com\u002FOpenPerceptionX\u002FTCP)]\n- Motion Transformer with Global Intention Localization and Local Movement Refinement, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.13508.pdf)] [[website](https:\u002F\u002Fvas.mpi-inf.mpg.de\u002Fmotion-transformer-with-global-intention-localization-and-local-movement-refinement\u002F)]\n- Interaction Modeling with Multiplex Attention, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.10660.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Ffanyun-sun\u002FIMMA)]\n- Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models, Conference on Learning for Dynamics and Control (L4DC). [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.02392.pdf)] [[website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdeep-interactive-predict-plan)]\n- Social Interpretable Tree for Pedestrian Trajectory Prediction, AAAI 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.13296.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Flssiair\u002FSIT)]\n- Complementary Attention Gated Network for Pedestrian Trajectory Prediction, AAAI 2022. [[paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-1963.DuanJ.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FjinghaiD\u002FCAGN)]\n- Scene Transformer: A unified architecture for predicting future trajectories of multiple agents, ICLR 2022. [[paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Wm3EA5OlHsG)]\n- You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction, ICLR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.05304.pdf)]\n- Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction, ICLR 2022. [[paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Dup_dDqkZC5)] [[code](https:\u002F\u002Ffgolemo.github.io\u002Fautobots\u002F)]\n- THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling, ICLR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.06607)]\n- Path-Aware Graph Attention for HD Maps in Motion Prediction, ICRA 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.13772.pdf)]\n- Trajectory Prediction with Linguistic Representations, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9811928)]\n- Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9811718)] [[website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fsmoothness-attention)]\n- KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812337)]\n- Domain Generalization for Vision-based Driving Trajectory Generation, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812070)] [[website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdg-traj-gen)]\n- A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811567)]\n- Conditioned Human Trajectory Prediction using Iterative Attention Blocks, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812404)]\n- StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811830)]\n- Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811632)] [[website](https:\u002F\u002Fsites.google.com\u002Fillinois.edu\u002Fmesrnn\u002Fhome)]\n- Propagating State Uncertainty Through Trajectory Forecasting, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811776)] [[code](https:\u002F\u002Fgithub.com\u002FStanfordASL\u002FPSU-TF)]\n- HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812254)]\n- Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811585)]\n- Crossmodal Transformer Based Generative Framework for Pedestrian Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812226)]\n- Trajectory Prediction for Autonomous Driving with Topometric Map, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811712)] [[code](https:\u002F\u002Fgithub.com\u002FJiaolong\u002Ftrajectory-prediction)]\n- CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9811637)] [[code](https:\u002F\u002Fgithub.com\u002Fschmidt-ju\u002Fcrat-pred)]\n- MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812107)]\n- Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812060\u002F)]\n- GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation, ICRA 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.01827.pdf)]\n- TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=9811591)]\n- Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty, IROS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.12446.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FTRI-ML\u002FHAICU)] [[trajdata](https:\u002F\u002Fgithub.com\u002Fnvr-avg\u002Ftrajdata)]\n- Trajectory Prediction with Graph-based Dual-scale Context Fusion, IROS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.01592.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FHKUST-Aerial-Robotics\u002FDSP)]\n- Robust Trajectory Prediction against Adversarial Attacks, CoRL 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.00094.pdf)] [[code](https:\u002F\u002Frobustav.github.io\u002FRobustTraj\u002F)]\n- Planning with Diffusion for Flexible Behavior Synthesis, ICML 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.09991)] [[website](https:\u002F\u002Fdiffusion-planning.github.io\u002F)]\n- Synchronous Bi-Directional Pedestrian Trajectory Prediction with Error Compensation, ACCV 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2022\u002Fpapers\u002FXie_Synchronous_Bi-Directional_Pedestrian_Trajectory_Prediction_with_Error_Compensation_ACCV_2022_paper.pdf)]\n- AI-TP: Attention-based Interaction-aware Trajectory Prediction for Autonomous Driving, IEEE T-IV, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9723649)] [[code](https:\u002F\u002Fgithub.com\u002FKP-Zhang\u002FAI-TP)]\n- MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction, Computational Intelligence and Neuroscience. [[paper](https:\u002F\u002Fdownloads.hindawi.com\u002Fjournals\u002Fcin\u002F2022\u002F4192367.pdf)]\n- Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9737058)]\n- Multi-Agent Trajectory Prediction with Heterogeneous Edge-Enhanced Graph Attention Network, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fdspace.lib.cranfield.ac.uk\u002Fbitstream\u002Fhandle\u002F1826\u002F17541\u002FMulti-agent_trajectory_prediction-2022.pdf?sequence=1&isAllowed=y)]\n- Fully Convolutional Encoder-Decoder With an Attention Mechanism for Practical Pedestrian Trajectory Prediction, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9768201)]\n- STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal Features, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9743363)]\n- Trajectory Prediction for Autonomous Driving Using Spatial-Temporal Graph Attention Transformer, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9768029)]\n- Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9767719)] [[code](https:\u002F\u002Fxbchen82.github.io\u002Fresource\u002F)]\n- Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9686621&tag=1)]\n- DeepTrack: Lightweight Deep Learning for Vehicle Trajectory Prediction in Highways, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9770480)]\n- Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9745461&tag=1)]\n- Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional Networks, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9781338)]\n- Trajectory Prediction Neural Network and Model Interpretation Based on Temporal Pattern Attention, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9945660)]\n- Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction, IEEE RA-L, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9664278)] [[code](https:\u002F\u002Fgithub.com\u002Ftedhuang96\u002Fgst)]\n- GAMMA: A General Agent Motion Prediction Model for Autonomous Driving, RAL. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01566.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FAdaCompNUS\u002Fgamma)]\n- Stepwise Goal-Driven Networks for Trajectory Prediction, RAL. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.14107v3.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FChuhuaW\u002FSGNet.pytorch)]\n- GA-STT: Human Trajectory Prediction with Group Aware Spatial-Temporal Transformer, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9779572)]\n- Long-term 4D trajectory prediction using generative adversarial networks, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X22000031)]\n- A context-aware pedestrian trajectory prediction framework for automated vehicles, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X21004423)]\n- Explainable multimodal trajectory prediction using attention models, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X22002509)]\n- CSCNet: Contextual semantic consistency network for trajectory prediction in crowded spaces, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320322000334)]\n- CSR: Cascade Conditional Variational AutoEncoder with Social-aware Regression for Pedestrian Trajectory Prediction, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320322005106)]\n- Step Attention: Sequential Pedestrian Trajectory Prediction, IEEE Sensors Journal. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9732437)]\n- Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism, IEEE Transactions on Instrumentation and Measurement. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9749176)]\n- Spatio-temporal Interaction Aware and Trajectory Distribution Aware Graph Convolution Network for Pedestrian Multimodal Trajectory Prediction, IEEE Transactions on Instrumentation and Measurement. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9997233)]\n- Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model, Physica A: Statistical Mechanics and its Applications. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378437122000139)]\n- PTPGC: Pedestrian trajectory prediction by graph attention network with ConvLSTM, Robotics and Autonomous Systems. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0921889021002165)]\n- GCHGAT: pedestrian trajectory prediction using group constrained hierarchical graph attention networks, Applied Intelligence. [[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-021-02997-w)]\n- Vehicles Trajectory Prediction Using Recurrent VAE Network, IEEE Access. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9740177)] [[code](https:\u002F\u002Fgithub.com\u002Fmidemig\u002Ftraj_pred_vae)]\n- SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction, TPAMI. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9699076)]\n- PTP-STGCN: Pedestrian Trajectory Prediction Based on a Spatio-temporal Graph Convolutional Neural Network, Applied Intelligence. [[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-022-03524-1)]\n- Trajectory distributions: A new description of movement for trajectory prediction, Computational Visual Media. [[paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs41095-021-0236-6.pdf)]\n- Trajectory prediction for autonomous driving based on multiscale spatial-temporal graph, IET Intelligent Transport Systems. [[paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1049\u002Fitr2.12265)]\n- Continual learning-based trajectory prediction with memory augmented networks, Knowledge-Based Systems. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705122011157)]\n- Atten-GAN: Pedestrian Trajectory Prediction with GAN Based on Attention Mechanism, Cognitive Computation. [[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-022-10029-z#Abs1)]\n- EvoSTGAT: Evolving spatiotemporal graph attention networks for pedestrian trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0925231222003460?ref=pdf_download&fr=RR-2&rr=7da0ead45e800fcc)]\n- Raising context awareness in motion forecasting, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.08048.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FCAB)]\n- Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.11561.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fluigifilippochiara\u002FGoal-SAR)]\n- Importance Is in Your Attention: Agent Importance Prediction for Autonomous Driving, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.09121.pdf)]\n- MPA: MultiPath++ Based Architecture for Motion Prediction, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.10041.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fstepankonev\u002Fwaymo-motion-prediction-challenge-2022-multipath-plus-plus)]\n- TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model, arXiv:2201.02941, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.02941v1.pdf)]\n- Wayformer: Motion Forecasting via Simple & Efficient Attention Networks, arXiv preprint arXiv:2207.05844, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.05844.pdf)]\n- PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer, arXiv preprint arXiv:2203.09293, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.09293.pdf)]\n- LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction, arXiv preprint arXiv:2203.01880, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01880.pdf)]\n- Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss, arXiv preprint arXiv:2206.08641, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.08641.pdf)]\n- Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction, arXiv preprint arXiv:2205.14230, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.14230.pdf)]\n- Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning, arXiv preprint arXiv:2211.00848, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.00848.pdf)]\n- GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model, arXiv preprint arXiv:2209.07857, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.07857.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fmengmengliu1998\u002FGATraj)]\n- Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning, arXiv preprint arXiv:2206.13114, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.13114.pdf)]\n- Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting, arXiv preprint arXiv:2207.05195, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.05195)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FCollaborative-Uncertainty)]\n- Guided Conditional Diffusion for Controllable Traffic Simulation, arXiv preprint arXiv:2210.17366, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.17366.pdf)] [[website](https:\u002F\u002Faiasd.github.io\u002Fctg.github.io\u002F)]\n- PhysDiff: Physics-Guided Human Motion Diffusion Model, arXiv preprint arXiv:2212.02500, 2022. [[paper](http:\u002F\u002Fxxx.itp.ac.cn\u002Fpdf\u002F2212.02500.pdf)]\n- MPA: MultiPath++ Based Architecture for Motion Prediction, CVPR Workshop on Autonomous Driving 2022. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10041)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fstepankonev\u002Fwaymo-motion-prediction-challenge-2022-multipath-plus-plus)\\]\n- Collaborative Uncertainty in Multi-Agent Trajectory Forecasting, NeurIPS 2021. [[paper](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002F31ca0ca71184bbdb3de7b20a51e88e90-Paper.pdf)]\n- GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction, NeurIPS 2021. [[paper](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002Fe3670ce0c315396e4836d7024abcf3dd-Paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Flongyuanli\u002FGRIN_NeurIPS21)]\n- LibCity: An Open Library for Traffic Prediction, SIGSPATIAL 2021. [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3474717.3483923)] [[code](https:\u002F\u002Fgithub.com\u002FLibCity\u002FBigscity-LibCity)]\n- Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9575242)]\n- Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast, ICRA 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.04853.pdf)]\n- AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention, ICRA 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.05682.pdf)]\n- Exploring Dynamic Context for Multi-path Trajectory Prediction, ICRA 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9562034)] [[code](https:\u002F\u002Fgithub.com\u002Fwtliao\u002FDCENet)]\n- Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks, ICRA 2021. [[paper](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F346614349_Pedestrian_Trajectory_Prediction_using_Context-Augmented_Transformer_Networks)] [[code](https:\u002F\u002Fgithub.com\u002FKhaledSaleh\u002FContext-Transformer-PedTraj)]\n- Spectral Temporal Graph Neural Network for Trajectory Prediction, ICRA 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.02930.pdf)]\n- Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance, ICRA 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9560994)] [[code](https:\u002F\u002Fgithub.com\u002Fxuxie1031\u002FCollisionFreeMultiAgentTrajectoryPrediciton)]\n- Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements, ICRA 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9561022)]\n- AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FYuan_AgentFormer_Agent-Aware_Transformers_for_Socio-Temporal_Multi-Agent_Forecasting_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FKhrylx\u002FAgentFormer)] [[website](https:\u002F\u002Fye-yuan.com\u002Fagentformer\u002F)]\n- Likelihood-Based Diverse Sampling for Trajectory Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FJason_Likelihood-Based_Diverse_Sampling_for_Trajectory_Forecasting_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FJasonMa2016\u002FLDS)]\n- MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction, ICCV 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09274.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fselflein\u002FMG-GAN)]\n- Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FLi_Spatial-Temporal_Consistency_Network_for_Low-Latency_Trajectory_Forecasting_ICCV_2021_paper.pdf)]\n- Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FSun_Three_Steps_to_Multimodal_Trajectory_Prediction_Modality_Clustering_Classification_and_ICCV_2021_paper.pdf)]\n- From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FMangalam_From_Goals_Waypoints__Paths_to_Long_Term_Human_Trajectory_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fkarttikeya.github.io\u002Fpublication\u002Fynet\u002F)]\n- Where are you heading? Dynamic Trajectory Prediction with Expert Goal Examples, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FZhao_Where_Are_You_Heading_Dynamic_Trajectory_Prediction_With_Expert_Goal_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FJoeHEZHAO\u002Fexpert_traj)]\n- DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FGu_DenseTNT_End-to-End_Trajectory_Prediction_From_Dense_Goal_Sets_ICCV_2021_paper.pdf)]\n- Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FRen_Safety-Aware_Motion_Prediction_With_Unseen_Vehicles_for_Autonomous_Driving_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fxrenaa\u002FSafety-Aware-Motion-Prediction)]\n- LOKI: Long Term and Key Intentions for Trajectory Prediction, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FGirase_LOKI_Long_Term_and_Key_Intentions_for_Trajectory_Prediction_ICCV_2021_paper.pdf)] [[dataset](https:\u002F\u002Fusa.honda-ri.com\u002Floki)]\n- Human Trajectory Prediction via Counterfactual Analysis, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FChen_Human_Trajectory_Prediction_via_Counterfactual_Analysis_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FCHENGY12\u002FCausalHTP)]\n- Personalized Trajectory Prediction via Distribution Discrimination, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FChen_Personalized_Trajectory_Prediction_via_Distribution_Discrimination_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FCHENGY12\u002FDisDis)]\n- Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FZheng_Unlimited_Neighborhood_Interaction_for_Heterogeneous_Trajectory_Prediction_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fzhengfang1997\u002FUnlimited-Neighborhood-Interaction-for-Heterogeneous-Trajectory-Prediction)]\n- Social NCE: Contrastive Learning of Socially-aware Motion Representations, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FLiu_Social_NCE_Contrastive_Learning_of_Socially-Aware_Motion_Representations_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fsocial-nce)]\n- RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FLi_RAIN_Reinforced_Hybrid_Attention_Inference_Network_for_Motion_Forecasting_ICCV_2021_paper.pdf)]\n- Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision, AAAI 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.01884.pdf)]\n- SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction, AAAI 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.00109.pdf)]\n- Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction, AAAI 2021. [[paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI21Papers\u002FAAAI-1677.BaeI.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FDMRGCN)]\n- MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FWu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.pdf)]\n- Multimodal Motion Prediction with Stacked Transformers, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FLiu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fdecisionforce\u002FmmTransformer)] [[website](https:\u002F\u002Fdecisionforce.github.io\u002FmmTransformer\u002F?utm_source=catalyzex.com)]\n- SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fshuaishiliu\u002FSGCN)]\n- LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FKim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.pdf)]\n- Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction, CVPR 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08277.pdf)]\n- Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FBhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.pdf)] [[dataset](https:\u002F\u002Fwww.mpi-inf.mpg.de\u002Fdepartments\u002Fcomputer-vision-and-machine-learning\u002Fresearch\u002Feuro-pvi-dataset)]\n- Trajectory Prediction with Latent Belief Energy-Based Model, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FPang_Trajectory_Prediction_With_Latent_Belief_Energy-Based_Model_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fbpucla\u002Flbebm)]\n- Shared Cross-Modal Trajectory Prediction for Autonomous Driving, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FChoi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.pdf)]\n- Pedestrian and Ego-vehicle Trajectory Prediction from Monocular camera, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FNeumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_camera_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgitlab.com\u002FlukeN86\u002FpedFutureTracking)]\n- Interpretable Social Anchors for Human Trajectory Forecasting in Crowds, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FKothari_Interpretable_Social_Anchors_for_Human_Trajectory_Forecasting_in_Crowds_CVPR_2021_paper.pdf)]\n- Introvert: Human Trajectory Prediction via Conditional 3D Attention, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.pdf)]\n- MP3: A Unified Model to Map, Perceive, Predict and Plan, CVPR 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.06806.pdf)]\n- TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FSuo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.pdf)]\n- Multimodal Transformer Network for Pedestrian Trajectory Prediction, IJCAI 2021. [[paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0174.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fericyinyzy\u002FMTN_trajectory)]\n- Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction, IROS 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.05814.pdf)]\n- Joint Intention and Trajectory Prediction Based on Transformer, IROS 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9636241)]\n- Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks, IROS 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9636875)]\n- Multiple Contextual Cues Integrated Trajectory Prediction for Autonomous Driving, IROS 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9476975)]\n- MultiXNet: Multiclass Multistage Multimodal Motion Prediction, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9575718)]\n- Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9576054)]\n- Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios, IV 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9575958)]\n- Generating Scenarios with Diverse Pedestrian Behaviors for Autonomous Vehicle Testing, Conference on Robot Learning (CoRL 2021). [[paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HTfApPeT4DZ)] [[code](https:\u002F\u002Fgithub.com\u002FMariaPriisalu\u002Fspl)]\n- Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals, CoRL 2021. [[paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv164\u002Fdeo22a.html)] [[code](https:\u002F\u002Fgithub.com\u002Fnachiket92\u002FPGP)]\n- Learning to Predict Vehicle Trajectories with Model-based Planning, CoRL 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.04027.pdf)]\n- Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks, International Conference on Pattern Recognition (ICPR 2021). [[paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002F978-3-030-68763-2_5.pdf)]\n- GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction, WACV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2021\u002Fpapers\u002FWang_GraphTCN_Spatio-Temporal_Interaction_Modeling_for_Human_Trajectory_Prediction_WACV_2021_paper.pdf)]\n- Goal-driven Long-Term Trajectory Prediction, WACV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2021\u002Fpapers\u002FTran_Goal-Driven_Long-Term_Trajectory_Prediction_WACV_2021_paper.pdf)]\n- Multimodal Trajectory Predictions for Autonomous Driving without a Detailed Prior Map, WACV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2021\u002Fpapers\u002FKawasaki_Multimodal_Trajectory_Predictions_for_Autonomous_Driving_Without_a_Detailed_Prior_WACV_2021_paper.pdf)]\n- Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction, IEEE International Conference on Image Processing (ICIP 2021). [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.06320v2.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fserenetech90\u002FAOL_ovsc)]\n- S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving, Asian Conference on Machine Learning 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.10902.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fchenghuang66\u002Fs2tnet)]\n- Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes, IEEE Robotics and Automation Letters 2021 \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9309332)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Ftdavchev\u002Fstructured-trajectory-prediction)\\]\n- Trajectory Prediction using Equivariant Continuous Convolution, ICLR 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.11344.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FRose-STL-Lab\u002FECCO)]\n- TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation, International Conference on Intelligent Autonomous Systems 2021. [[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-95892-3_31#Abs1)]\n- HOME: Heatmap Output for future Motion Estimation, ITSC 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.10968.pdf)]\n- Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving, ITSC 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564929)]\n- SCSG Attention: A Self-Centered Star Graph with Attention for Pedestrian Trajectory Prediction, International Conference on Database Systems for Advanced Applications (DASFAA 2021). [[paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002F978-3-030-73194-6_29.pdf)]\n- Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection, IEEE Symposium Series on Computational Intelligence (SSCI 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9660004)] [[code](https:\u002F\u002Fgithub.com\u002Fakanuasiegbu\u002FLeveraging-Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection)]\n- Are socially-aware trajectory prediction models really socially-aware?, Transportation Research: Part C. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.10879.pdf), [paper](https:\u002F\u002Ficcv21-adv-workshop.github.io\u002Fshort_paper\u002Fs-attack-arow2021.pdf)] [[code](https:\u002F\u002Fs-attack.github.io\u002F)]\n- Injecting knowledge in data-driven vehicle trajectory predictors, Transportation Research: Part C. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0968090X21000425?token=F03D20769BFB255F56662C10348A81F3D07A42C6B4AB9BA19E3F7B2A5F1DA7D99B96B783616BDA86C12866AFCF4C5671&originRegion=eu-west-1&originCreation=20220506090622)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002FRRB)]\n- Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning, Transportation Research: Part C. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X2030855X)]\n- Human Trajectory Forecasting in Crowds: A Deep Learning Perspective,  IEEE Transactions on Intelligent Transportation Systems. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9408398)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Ftrajnetplusplusbaselines)]\n- NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9629362)]\n- Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9491972)]\n- A Hierarchical Framework for Interactive Behaviour Prediction of Heterogeneous Traffic Participants Based on Graph Neural Network, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9468360&tag=1)]\n- TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning, Transportation Research Part C. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0968090X21001121?token=3DEACAF2AD919E99B3331E74F747B61A0EAC2741E79B6F99F4F806155EB394F163D74F2F83806358BBD65911E107EF01&originRegion=us-east-1&originCreation=20220416040814)] [[code](https:\u002F\u002Fgithub.com\u002Fbenchoi93\u002FTrajGAIL)]\n- Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, IEEE ROBOTICS AND AUTOMATION LETTERS. [[paper](https:\u002F\u002Fwww.gilitschenski.org\u002Figor\u002Fpublications\u002F202104-ral-logic_gan\u002Fral21-logic_gan.pdf)]\n- Vehicle Trajectory Prediction Using LSTMs with Spatial-Temporal Attention Mechanisms, IEEE Intelligent Transportation Systems Magazine. [[paper](http:\u002F\u002Furdata.net\u002Ffiles\u002F2020_VTP.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fleilin-research\u002FVTP)]\n- Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment, Transportation Research Record. [[paper](http:\u002F\u002Fsage.cnpereading.com\u002Fparagraph\u002Fdownload\u002F?doi=10.1177\u002F0361198121993471)]\n- Temporal Pyramid Network with Spatial-Temporal Attention for Pedestrian Trajectory Prediction, IEEE Transactions on Network Science and Engineering. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9373939)]\n- An efficient Spatial–Temporal model based on gated linear units for trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0925231221018907?token=C894F657732BB6078B77AEC9BD3858338C1A7F1254CCC0BBC34ADA1421A95CF9A4F68BDCA8812457DE27FB37EEB8F198&originRegion=us-east-1&originCreation=20220420144432)]\n- SRAI-LSTM: A Social Relation Attention-based Interaction-aware LSTM for human trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0925231221018014?token=BB22DAAC41E3BF453C326A9D72A0CC900C2DFFD0D8AE07B7DEED51C7F2250B9CB40CC89B6812CA20DBFA6A7EDD32AAD6&originRegion=us-east-1&originCreation=20220512100647)]\n- AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS092523122100388X)]\n- Multi-PPTP: Multiple Probabilistic Pedestrian Trajectory Prediction in the Complex Junction Scene, IEEE Transactions on Intelligent Transportation Systems. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9619864)]\n- A Novel Graph-Based Trajectory Predictor With Pseudo-Oracle, TNNLS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9447207)]\n- Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN, Journal of Data Science. [[paper](https:\u002F\u002Fwww.jds-online.com\u002Ffiles\u002FJDS202001-08.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FXingruiWang\u002FTwo-Stage-Gan-in-trajectory-generation)]\n- Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories, IEEE Transactions on Intelligent Vehicles. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9707640)]\n- STI-GAN: Multimodal Pedestrian Trajectory Prediction Using Spatiotemporal Interactions and a Generative Adversarial Network, IEEE Access. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=9387292)]\n- Holistic LSTM for Pedestrian Trajectory Prediction, TIP. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9361440)]\n- Pedestrian trajectory prediction with convolutional neural networks, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320321004325)]\n- LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320320306038)]\n- Human trajectory prediction and generation using LSTM models and GANs, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS003132032100323X)]\n- Vehicle trajectory prediction and generation using LSTM models and GANs, Plos one. [[paper](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0253868)]\n- BiTraP: Bi-Directional Pedestrian Trajectory Prediction With Multi-Modal Goal Estimation, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9345445)] [[code](https:\u002F\u002Fgithub.com\u002Fumautobots\u002Fbidireaction-trajectory-prediction)]\n- A Kinematic Model for Trajectory Prediction in General Highway Scenarios, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9472993)] [[code](https:\u002F\u002Fgithub.com\u002Fumautobots\u002Fkinematic_highway)]\n- Trajectory Prediction in Autonomous Driving With a Lane Heading Auxiliary Loss, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9387075)]\n- Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9366373)]\n- Tra2Tra: Trajectory-to-Trajectory Prediction With a Global Social Spatial-Temporal Attentive Neural Network, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9347678)]\n- Social graph convolutional LSTM for pedestrian trajectory prediction, IET Intelligent Transport Systems. [[paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1049\u002Fitr2.12033)]\n- HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction, IEEE Transactions on Vehicular Technology (TVT). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9548801)]\n- Environment-Attention Network for Vehicle Trajectory Prediction, TVT. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9534487)]\n- Where Are They Going? Predicting Human Behaviors in Crowded Scenes, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3449359)]\n- Multi-Agent Trajectory Prediction with Spatio-Temporal Sequence Fusion, IEEE Transactions on Multimedia (TMM). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9580659)]\n- EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NeurIPS 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- V2VNet- Vehicle-to-Vehicle Communication for Joint Perception and Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.07519)]\n- SMART- Simultaneous Multi-Agent Recurrent Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.13078)]\n- SimAug- Learning Robust Representations from Simulation for Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.02022)]\n- Learning Lane Graph Representations for Motion Forecasting, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.13732)]\n- Implicit Latent Variable Model for Scene-Consistent Motion Forecasting, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.12036)]\n- Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03212)]\n- Semantic Synthesis of Pedestrian Locomotion, ACCV 2020. [[Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2020\u002Fhtml\u002FPriisalu_Semantic_Synthesis_of_Pedestrian_Locomotion_ACCV_2020_paper.html)]\n- Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction, CoRL 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.05127)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fwzhi\u002FKernelTrajectoryMaps)\\]\n- Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network, 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.06241)\\]\n- Social NCE: Contrastive Learning of Socially-aware Motion Representations. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11717)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fsocial-nce)\\]\n- Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks, ICPR International Workshops and Challenges 2020. \\[[paper](https:\u002F\u002Fwww.springerprofessional.de\u002Fpose-based-trajectory-forecast-of-vulnerable-road-users-using-re\u002F18885576)\\]\n- EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NeurIPS 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.08514)]\n- It is not the Journey but the Destination- Endpoint Conditioned Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.02025)]\n- How Can I See My Future? FvTraj: Using First-person View for Pedestrian Trajectory Prediction, ECCV 2020. [[paper](http:\u002F\u002Fgraphics.cs.uh.edu\u002Fwp-content\u002Fpapers\u002F2020\u002F2020-ECCV-PedestrianTrajPrediction.pdf)]\n- Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.00777)]\n- Human Trajectory Forecasting in Crowds: A Deep Learning Perspective, 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.03639.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Ftrajnetplusplusbaselines)\\]\n- SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen cameras, ECCV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.02022)\\], \\[[code](https:\u002F\u002Fgithub.com\u002FJunweiLiang\u002FMultiverse)\\]\n- DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting, ICPR 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12661)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Falexmonti19\u002Fdagnet)\\]\n- Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision, WACV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.01138)\\]\n- Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network, 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.06241)\\]\n- Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction, CVPR 2020. \\[[Paper](\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.11927.pdf>)\\], \\[[Code](\u003Chttps:\u002F\u002Fgithub.com\u002Fabduallahmohamed\u002FSocial-STGCNN\u002F>)\\]\n- The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction, CVPR 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.06445.pdf)\\], \\[[code\u002Fdataset](https:\u002F\u002Fnext.cs.cmu.edu\u002Fmultiverse\u002Findex.html)\\]\n- Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision, WACV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.01138)\\]\n- Pose Based Trajectory Forecast of Vulnerable Road Users, SSCI 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9003023)\\]\n- The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FIvanovic_The_Trajectron_Probabilistic_Multi-Agent_Trajectory_Modeling_With_Dynamic_Spatiotemporal_Graphs_ICCV_2019_paper.pdf)\\] \\[[code](https:\u002F\u002Fgithub.com\u002FStanfordASL\u002FTrajectron)\\]\n- STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FHuang_STGAT_Modeling_Spatial-Temporal_Interactions_for_Human_Trajectory_Prediction_ICCV_2019_paper.pdf)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fhuang-xx\u002FSTGAT)\\]\n- Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FKim_Instance-Level_Future_Motion_Estimation_in_a_Single_Image_Based_on_ICCV_2019_paper.pdf)\\]\n- Social and Scene-Aware Trajectory Prediction in Crowded Spaces, ICCV workshop 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08840.pdf)\\] \\[[code](https:\u002F\u002Fgithub.com\u002FOghma\u002Fsns-lstm\u002F)\\]\n- Stochastic Sampling Simulation for Pedestrian Trajectory Prediction, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.01860)\\]\n- Long-Term Prediction of Motion Trajectories Using Path Homology Clusters, IROS 2019. \\[[paper](http:\u002F\u002Fwww.csc.kth.se\u002F~fpokorny\u002Fstatic\u002Fpublications\u002Fcarvalho2019a.pdf)\\]\n- StarNet: Pedestrian Trajectory Prediction Using Deep Neural Network in Star Topology, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01797.pdf)\\]\n- Learning Generative Socially-Aware Models of Pedestrian Motion, IROS 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8760356\u002F)\\]\n- Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model, CVWW 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.05437.pdf)\\]\n- Path predictions using object attributes and semantic environment, VISIGRAPP 2019. \\[[paper](http:\u002F\u002Fmprg.jp\u002Fdata\u002FMPRG\u002FC_group\u002FC20190225_minoura.pdf)\\]\n- Probabilistic Path Planning using Obstacle Trajectory Prediction, CoDS-COMAD 2019. \\[[paper](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3297006)\\]\n- Human Trajectory Prediction using Adversarial Loss, hEART 2019. \\[[paper](http:\u002F\u002Fwww.strc.ch\u002F2019\u002FKothari_Alahi.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002FAdversarialLoss-SGAN)\\]\n- Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs, CVPR 2019. \\[[*Precognition Workshop*](https:\u002F\u002Fsites.google.com\u002Fview\u002Fieeecvf-cvpr2019-precognition)\\], \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FPrecognition\u002FAmirian_Social_Ways_Learning_Multi-Modal_Distributions_of_Pedestrian_Trajectories_With_GANs_CVPRW_2019_paper.pdf)\\], \\[[code](\u003Chttps:\u002F\u002Fgithub.com\u002Famiryanj\u002Fsocialways>)\\]\n- Peeking into the Future: Predicting Future Person Activities and Locations in Videos, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FLiang_Peeking_Into_the_Future_Predicting_Future_Person_Activities_and_Locations_CVPR_2019_paper.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fnext-prediction)\\]\n- Learning to Infer Relations for Future Trajectory Forecast, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FPrecognition\u002FChoi_Learning_to_Infer_Relations_for_Future_Trajectory_Forecast_CVPRW_2019_paper.pdf)\\]\n- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FChandra_TraPHic_Trajectory_Prediction_in_Dense_and_Heterogeneous_Traffic_Using_Weighted_CVPR_2019_paper.pdf>)\\]\n- Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FLi_Which_Way_Are_You_Going_Imitative_Decision_Learning_for_Path_CVPR_2019_paper.pdf>)\\]\n- Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FMakansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.pdf>)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Flmb-freiburg\u002FMultimodal-Future-Prediction)\\]\n- Sophie: An attentive gan for predicting paths compliant to social and physical constraints, CVPR 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01482)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002FREADME.md)\\]\n- Pedestrian path, pose, and intention prediction through gaussian process dynamical models and pedestrian activity recognition, 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8370119\u002F)\\]\n- Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06887)\\]\n- The simpler the better: Constant velocity for pedestrian motion prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.07933)\\]\n- Pedestrian trajectory prediction in extremely crowded scenarios, 2019. \\[[paper](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpubmed\u002F30862018)\\]\n- Srlstm: State refinement for lstm towards pedestrian trajectory prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.02793)\\]\n- Location-velocity attention for pedestrian trajectory prediction, WACV 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8659060)\\]\n- Pedestrian Trajectory Prediction in Extremely Crowded Scenarios, Sensors, 2019. \\[[paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F19\u002F5\u002F1223\u002Fpdf)\\]\n- Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.01118.pdf)\\] \\[[code](https:\u002F\u002Fgamma.umd.edu\u002Fresearchdirections\u002Fautonomousdriving\u002Fspectralcows\u002F)\\]\n- Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FBi_Joint_Prediction_for_Kinematic_Trajectories_in_Vehicle-Pedestrian-Mixed_Scenes_ICCV_2019_paper.pdf)\\]\n- Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FThiede_Analyzing_the_Variety_Loss_in_the_Context_of_Probabilistic_Trajectory_ICCV_2019_paper.pdf)\\]\n- Looking to Relations for Future Trajectory Forecast, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FChoi_Looking_to_Relations_for_Future_Trajectory_Forecast_ICCV_2019_paper.pdf)\\]\n- Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.04586)\\]\n- Sharing Is Caring: Socially-Compliant Autonomous Intersection Negotiation, IROS 2019. \\[[paper](https:\u002F\u002Fpdfs.semanticscholar.org\u002Ff4b2\u002F021353bba52224eb33923b3b98956e2c9821.pdf)\\]\n- INFER: INtermediate Representations for FuturE PRediction, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.10641)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Ftalsperre\u002FINFER)\\]\n- Deep Predictive Autonomous Driving Using Multi-Agent Joint Trajectory Prediction and Traffic Rules, IROS 2019. \\[[paper](http:\u002F\u002Frllab.snu.ac.kr\u002Fpublications\u002Fpapers\u002F2019_iros_predstl.pdf)\\]\n- NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.10971)\\]\n- Urban Street Trajectory Prediction with Multi-Class LSTM Networks, IROS 2019. \\[N\u002FA\\]\n- Spatiotemporal Learning of Directional Uncertainty in Urban Environments with Kernel Recurrent Mixture Density Networks, IROS 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8772158)\\]\n- Conditional generative neural system for probabilistic trajectory prediction, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.01631)\\]\n- Interaction-aware multi-agent tracking and probabilistic behavior prediction via adversarial learning, ICRA 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02390)\\]\n- Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving, IEEE Trans. Intell. Transport. Systems, 2019. \\[[paper](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F334560415_Generic_Tracking_and_Probabilistic_Prediction_Framework_and_Its_Application_in_Autonomous_Driving)\\]\n- Coordination and trajectory prediction for vehicle interactions via bayesian generative modeling, IV 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.00587)\\]\n- Wasserstein generative learning with kinematic constraints for probabilistic interactive driving behavior prediction, IV 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8813783)\\]\n- GRIP: Graph-based Interaction-aware Trajectory Prediction, ITSC 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.07792)\\]\n- AGen: Adaptable Generative Prediction Networks for Autonomous Driving, IV 2019. \\[[paper](http:\u002F\u002Fwww.cs.cmu.edu\u002F~cliu6\u002Ffiles\u002Fiv19-1.pdf)\\]\n- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FChandra_TraPHic_Trajectory_Prediction_in_Dense_and_Heterogeneous_Traffic_Using_Weighted_CVPR_2019_paper.pdf>)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Frohanchandra30\u002FTrackNPred)\\]\n- Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks, CVPR 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.09395.pdf)\\]\n- Argoverse: 3D Tracking and Forecasting With Rich Maps, CVPR 2019 \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FChang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf)\\]\n- Robust Aleatoric Modeling for Future Vehicle Localization, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FPrecognition\u002FHudnell_Robust_Aleatoric_Modeling_for_Future_Vehicle_Localization_CVPRW_2019_paper.pdf)\\]\n- Pedestrian occupancy prediction for autonomous vehicles, IRC 2019. \\[paper\\]\n- Context-based path prediction for targets with switching dynamics, 2019.\\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-018-1104-4)\\]\n- Deep Imitative Models for Flexible Inference, Planning, and Control, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.06544)\\]\n- Infer: Intermediate representations for future prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.10641)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Ftalsperre\u002FINFER)\\]\n- Multi-agent tensor fusion for contextual trajectory prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.04776)\\]\n- Context-Aware Pedestrian Motion Prediction In Urban Intersections, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.09453)\\]\n- Generic probabilistic interactive situation recognition and prediction: From virtual to real, ITSC 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8569780)\\]\n- Generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter, IV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8500626)\\]\n- Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.05499)\\]\n- Sequence-to-sequence prediction of vehicle trajectory via lstm encoder-decoder architecture, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06338)\\]\n- R2P2: A ReparameteRized Pushforward Policy for diverse, precise generative path forecasting, ECCV 2018. \\[[paper](https:\u002F\u002Fwww.cs.cmu.edu\u002F~nrhineha\u002FR2P2.html)\\]\n- Predicting trajectories of vehicles using large-scale motion priors, IV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8500604)\\]\n- Vehicle trajectory prediction by integrating physics-and maneuver based approaches using interactive multiple models, 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8186191)\\]\n- Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05819v1)\\]\n- Generative multi-agent behavioral cloning, 2018. \\[[paper](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerative-Multi-Agent-Behavioral-Cloning-Zhan-Zheng\u002Fccc196ada6ec9cad1e418d7321b0cd6813d9b261)\\]\n- Deep Sequence Learning with Auxiliary Information for Traffic Prediction, KDD 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.07380.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002FJingqingZ\u002FBaiduTraffic)\\]\n- A data-driven model for interaction-aware pedestrian motion prediction in object cluttered environments, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.08528)\\]\n- Move, Attend and Predict: An attention-based neural model for people’s movement prediction, Pattern Recognition Letters 2018. \\[[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS016786551830182X?token=1EF2B664B70D2B0C3ECDD07B6D8B664F5113AEA7533CE5F0B564EF9F4EE90D3CC228CDEB348F79FEB4E8CDCD74D4BA31)\\]\n- GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds, ACCV 2018, \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.07667.pdf)\\], \\[[demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7cCIC_JIfms)\\]\n- Ss-lstm: a hierarchical lstm model for pedestrian trajectory prediction, WACV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8354239)\\]\n- Social Attention: Modeling Attention in Human Crowds, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.04689)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FTNTant\u002Fsocial_lstm)\\]\n- Pedestrian prediction by planning using deep neural networks, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05904)\\]\n- Joint long-term prediction of human motion using a planning-based social force approach, ICRA 2018. \\[[paper](https:\u002F\u002Filiad-project.eu\u002Fpublications\u002F2018-2\u002Fjoint-long-term-prediction-of-human-motion-using-a-planning-based-social-force-approach\u002F)\\]\n- Human motion prediction under social grouping constraints, IROS 2018. \\[[paper](http:\u002F\u002Filiad-project.eu\u002Fpublications\u002F2018-2\u002Fhuman-motion-prediction-under-social-grouping-constraints\u002F)\\]\n- Future Person Localization in First-Person Videos, CVPR 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FYagi_Future_Person_Localization_CVPR_2018_paper.pdf)\\]\n- Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks, CVPR 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10892)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fagrimgupta92\u002Fsgan)\\]\n- Group LSTM: Group Trajectory Prediction in Crowded Scenarios, ECCV 2018. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-11015-4_18)\\]\n- Mx-lstm: mixing tracklets and vislets to jointly forecast trajectories and head poses, CVPR 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00652)\\]\n- Intent prediction of pedestrians via motion trajectories using stacked recurrent neural networks, 2018. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8481390\u002F)\\]\n- Transferable pedestrian motion prediction models at intersections, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00495)\\]\n- Probabilistic map-based pedestrian motion prediction taking traffic participants into consideration, 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8500562)\\]\n- A Computationally Efficient Model for Pedestrian Motion Prediction, ECC 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.04702)\\]\n- Context-aware trajectory prediction, ICPR 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02503)\\]\n- Set-based prediction of pedestrians in urban environments considering formalized traffic rules, ITSC 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8569434)\\]\n- Building prior knowledge: A markov based pedestrian prediction model using urban environmental data, ICARCV 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.06045)\\]\n- Depth Information Guided Crowd Counting for Complex Crowd Scenes, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.02256)\\]\n- Tracking by Prediction: A Deep Generative Model for Mutli-Person Localisation and Tracking, WACV 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.03347)\\]\n- “Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention, WACV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8354238)\\]\n- Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty, CVPR 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FBhattacharyya_Long-Term_On-Board_Prediction_CVPR_2018_paper.pdf)\\], \\[[code+data](https:\u002F\u002Fgithub.com\u002Fapratimbhattacharyya18\u002Fonboard_long_term_prediction)\\]\n- Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction, CVPR 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FXu_Encoding_Crowd_Interaction_CVPR_2018_paper.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002FShanghaiTechCVDL\u002FCIDNN)\\]\n- Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction, 2017. \\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10514-017-9619-z)\\]\n- Probabilistic long-term prediction for autonomous vehicles, IV 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7995726)\\]\n- Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network, ITSC 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6632960)\\]\n- Desire: Distant future prediction in dynamic scenes with interacting agents, CVPR 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04394)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fyadrimz\u002FDESIRE)\\]\n- Imitating driver behavior with generative adversarial networks, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.06699)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fsisl\u002Fgail-driver)\\]\n- Infogail: Interpretable imitation learning from visual demonstrations, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.08840)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FYunzhuLi\u002FInfoGAIL)\\]\n- Long-term planning by short-term prediction, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.01580)\\]\n- Long-term path prediction in urban scenarios using circular distributions, 2017. \\[[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0262885617301853)\\]\n- Deep learning driven visual path prediction from a single image, 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.07265)\\]\n- Walking Ahead: The Headed Social Force Model, 2017. \\[[paper](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0169734)\\]\n- Real-time certified probabilistic pedestrian forecasting, 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7959047)\\]\n- A multiple-predictor approach to human motion prediction, ICRA 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7989265)\\]\n- Forecasting interactive dynamics of pedestrians with fictitious play, CVPR 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.01431)\\]\n- Forecast the plausible paths in crowd scenes, IJCAI 2017. \\[[paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2017\u002F386)\\]\n- Bi-prediction: pedestrian trajectory prediction based on bidirectional lstm classification, DICTA 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8227412\u002F)\\]\n- Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos, IJCAI 2017. \\[[paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2017\u002F17)\\]\n- Natural vision based method for predicting pedestrian behaviour in urban environments, ITSC 2017. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8317848\u002F)\\]\n- Human Trajectory Prediction using Spatially aware Deep Attention Models, 2017. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.09436.pdf)\\]\n- Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1702.05552.pdf)\\]\n- Forecasting Interactive Dynamics of Pedestrians with Fictitious Play, CVPR 2017. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FMa_Forecasting_Interactive_Dynamics_CVPR_2017_paper.pdf)\\]\n- Social LSTM: Human trajectory prediction in crowded spaces, CVPR 2016. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2016\u002Fhtml\u002FAlahi_Social_LSTM_Human_CVPR_2016_paper.html)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Ftrajnetplusplusbaselines)\\]\n- Comparison and evaluation of pedestrian motion models for vehicle safety systems, ITSC 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7795912)\\]\n- Age and Group-driven Pedestrian Behaviour: from Observations to Simulations, 2016. \\[[paper](https:\u002F\u002Fcollective-dynamics.eu\u002Findex.php\u002Fcod\u002Farticle\u002Fview\u002FA3)\\]\n- Structural-RNN: Deep learning on spatio-temporal graphs, CVPR 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.05298)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fasheshjain399\u002FRNNexp)\\]\n- Intent-aware long-term prediction of pedestrian motion, ICRA 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487409)\\]\n- Context-based detection of pedestrian crossing intention for autonomous driving in urban environments, IROS 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7759351\u002F)\\]\n- Novel planning-based algorithms for human motion prediction, ICRA 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487505)\\]\n- Learning social etiquette: Human trajectory understanding in crowded scenes, ECCV 2016. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46484-8_33)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FSajjadMzf\u002FPedestrian_Datasets_VIS)\\]\n- GLMP-realtime pedestrian path prediction using global and local movement patterns, ICRA 2016. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487768\u002F)\\]\n- Knowledge transfer for scene-specific motion prediction, ECCV 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.06987)\\]\n- STF-RNN: Space Time Features-based Recurrent Neural Network for predicting People Next Location, SSCI 2016. \\[[code](https:\u002F\u002Fgithub.com\u002Fmhjabreel\u002FSTF-RNN)\\]\n- Goal-directed pedestrian prediction, ICCV 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7406377)\\]\n- Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations, 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7225707)\\]\n- Predicting and recognizing human interactions in public spaces, 2015. \\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11554-014-0428-8)\\]\n- Learning collective crowd behaviors with dynamic pedestrian-agents, 2015. \\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-014-0735-3)\\]\n- Modeling spatial-temporal dynamics of human movements for predicting future trajectories, AAAI 2015. \\[[paper](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FWS\u002FAAAIW15\u002Fpaper\u002Fview\u002F10126)\\]\n- Unsupervised robot learning to predict person motion, ICRA 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7139254)\\]\n- A controlled interactive multiple model filter for combined pedestrian intention recognition and path prediction, ITSC 2015. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7313129\u002F)\\]\n- Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions, 2014. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1405.5581)\\]\n- Behavior estimation for a complete framework for human motion prediction in crowded environments, ICRA 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6907734)\\]\n- Pedestrian’s trajectory forecast in public traffic with artificial neural network, ICPR 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6977417)\\]\n- Will the pedestrian cross? A study on pedestrian path prediction, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6632960)\\]\n- BRVO: Predicting pedestrian trajectories using velocity-space reasoning, 2014. \\[[paper](https:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002Fabs\u002F10.1177\u002F0278364914555543)\\]\n- Context-based pedestrian path prediction, ECCV 2014. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-10599-4_40)\\]\n- Pedestrian path prediction using body language traits, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856498\u002F)\\]\n- Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856480)\\]\n- Learning intentions for improved human motion prediction, 2013. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6766565)\\]\n- Understanding interactions between traffic participants based on learned behaviors, 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7535554)\\]\n- Visual path prediction in complex scenes with crowded moving objects, CVPR 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7780661\u002F)\\]\n- A game-theoretic approach to replanning-aware interactive scene prediction and planning, 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7353203)\\]\n- Intention-aware online pomdp planning for autonomous driving in a crowd, ICRA 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7139219)\\]\n- Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856480)\\]\n- Patch to the future: Unsupervised visual prediction, CVPR 2014. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6909818\u002F)\\]\n- Mobile agent trajectory prediction using bayesian nonparametric reachability trees, 2011. \\[[paper](https:\u002F\u002Fdspace.mit.edu\u002Fhandle\u002F1721.1\u002F114899)\\]\n\n\n### Mobile Robots\n- Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements, ICRA 2021. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.06235)\\]\n- Social NCE: Contrastive Learning of Socially-aware Motion Representations. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11717)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fsocial-nce)\\]\n- Multimodal probabilistic model-based planning for human-robot interaction, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09483)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FStanfordASL\u002FTrafficWeavingCVAE)\\]\n- Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning, ICRA 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.07845)\\]\n- Augmented dictionary learning for motion prediction, ICRA 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487407)\\]\n- Predicting future agent motions for dynamic environments, ICMLA 2016. \\[[paper](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPredicting-Future-Agent-Motions-for-Dynamic-Previtali-Bordallo\u002F2df8179ac7b819bad556b6d185fc2030c40f98fa)\\]\n- Bayesian intention inference for trajectory prediction with an unknown goal destination, IROS 2015. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7354203\u002F)\\]\n- Learning to predict trajectories of cooperatively navigating agents, ICRA 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6907442)\\]\n\n\n### Sport Players\n\n- EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NeurIPS 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation, CVPR 2020. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FQi_Imitative_Non-Autoregressive_Modeling_for_Trajectory_Forecasting_and_Imputation_CVPR_2020_paper.html)]\n- DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting, ICPR 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12661)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Falexmonti19\u002Fdagnet)\\]\n- Diverse Generation for Multi-Agent Sports Games, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fhtml\u002FYeh_Diverse_Generation_for_Multi-Agent_Sports_Games_CVPR_2019_paper.html)\\]\n- Stochastic Prediction of Multi-Agent Interactions from Partial Observations, ICLR 2019. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1902.09641v1)\\]\n- Generating Multi-Agent Trajectories using Programmatic Weak Supervision, ICLR 2019. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1803.07612v6)\\]\n- Generative Multi-Agent Behavioral Cloning, ICML 2018. \\[[paper](http:\u002F\u002Fwww.stephanzheng.com\u002Fpdf\u002FZhan_Zheng_Lucey_Yue_Generative_Multi_Agent_Behavioral_Cloning.pdf)\\]\n- Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders, ECCV 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ECCV_2018\u002Fpapers\u002FPanna_Felsen_Where_Will_They_ECCV_2018_paper.pdf)\\]\n- Coordinated Multi-Agent Imitation Learning, ICML 2017. \\[[paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1703.03121v2)\\]\n- Generating long-term trajectories using deep hierarchical networks, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.07138)\\]\n- Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction, ICDM 2014. \\[[paper](http:\u002F\u002Fwww.yisongyue.com\u002Fpublications\u002Ficdm2014_bball_predict.pdf)]\n- Generative Modeling of Multimodal Multi-Human Behavior, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.02015.pdf)\\]\n- What will Happen Next? Forecasting Player Moves in Sports Videos, ICCV 2017, \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2017\u002Fpapers\u002FFelsen_What_Will_Happen_ICCV_2017_paper.pdf)\\]\n\n### Benchmark and Evaluation Metrics\n- A Preprocessing and Evaluation Toolbox for Trajectory Prediction Research on the Drone Datasets, arXiv preprint arXiv:2405.00604, 2024. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.00604)] [[code](https:\u002F\u002Fgithub.com\u002Fwestny\u002Fdronalize)]\n- Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation, ECCV 2022. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03057)] \\[[code](https:\u002F\u002Fgithub.com\u002Fabduallahmohamed\u002FSocial-Implicit)]\n- OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets, ACCV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00890)] \\[[code](https:\u002F\u002Fgithub.com\u002Fcrowdbotp\u002FOpenTraj)]\n- Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06020)]\n- PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FRasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.pdf)\\]\n- Towards a fatality-aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios, ITSC 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.03478)\\]\n- How good is my prediction? Finding a similarity measure for trajectory prediction evaluation, ITSC 2017. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8317825\u002F)\\]\n- Trajnet: Towards a benchmark for human trajectory prediction. \\[[website](http:\u002F\u002Ftrajnet.epfl.ch\u002F)\\]\n\n### Others\n\n- Pose Based Start Intention Detection of Cyclists, ITSC 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917215)\\]\n- Cyclist trajectory prediction using bidirectional recurrent neural networks, AI 2018. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-03991-2_28)\\]\n- Road infrastructure indicators for trajectory prediction, 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8500678)\\]\n- Using road topology to improve cyclist path prediction, 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7995734\u002F)\\]\n- Trajectory prediction of cyclists using a physical model and an artificial neural network, 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7535484\u002F)\\]\n","# 令人惊叹的交互感知行为与轨迹预测\n![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg) ![版本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FVersion-2.0-ff69b4.svg) ![最后更新](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLastUpdated-2023.09-lightgrey.svg) ![主题](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTopic-trajectory--prediction-yellow.svg?logo=github)\n\n这是一个关于轨迹预测领域最前沿研究资料（数据集、博客、论文及公开代码）的清单。希望对学术界和工业界都能有所帮助。（仍在持续更新中）\n\n**维护者**: [**李嘉辰**](https:\u002F\u002Fjiachenli94.github.io)（斯坦福大学）；[**马恒博**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fhengboma\u002F)、[**李锦宁**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjinningli\u002F)（加州大学伯克利分校）\n\n**邮箱**: jiachen_li@stanford.edu; {hengbo_ma, jinning_li}@berkeley.edu\n\n欢迎随时提交 Pull Request 添加新资源，或发送邮件与我们交流、讨论及合作。\n\n**注**: [**这里**](https:\u002F\u002Fgithub.com\u002Fjiachenli94\u002FAwesome-Decision-Making-Reinforcement-Learning) 也是强化学习、决策制定和运动规划相关资料的集合。\n\n\n\n如果您觉得本仓库有用，请考虑引用我们的工作：\n\n```\n@inproceedings{li2020evolvegraph,\n  title={EvolveGraph: 多智能体动态关系推理的轨迹预测},\n  author={李嘉辰、杨帆、富冢昌义、崔致浩},\n  booktitle={2020年神经信息处理系统大会 (NeurIPS)},\n  year={2020}\n}\n\n@inproceedings{li2019conditional,\n  title={用于概率轨迹预测的条件生成神经网络系统},\n  author={李嘉辰、马恒博、富冢昌义},\n  booktitle={2019年IEEE\u002FRSJ国际智能机器人与系统会议 (IROS)},\n  pages={6150--6156},\n  year={2019},\n  organization={IEEE}\n}\n```\n\n### 目录\n\n\u003C!-- TOC depthFrom:1 depthTo:6 withLinks:1 updateOnSave:1 orderedList:0 -->\n- [**数据集**](#datasets)\n\t- [车辆与交通](#vehicles-and-traffic)\n\t- [行人](#pedestrians)\n\t- [体育运动员](#sport-players)\n- [**文献与代码**](#literature-and-codes)\n\t- [综述论文](#survey-papers)\n\t- [具有交互作用的物理系统](#physics-systems-with-interaction)\n\t- [智能车辆与行人](#intelligent-vehicles-and-pedestrians)\n\t- [移动机器人](#mobile-robots)\n\t- [体育运动员](#sport-players)\n\t- [基准测试与评估指标](#benchmark-and-evaluation-metrics)\n\t- [其他](#others)\n\t\u003C!-- \u002FTOC -->\n\n## **数据集**\n### 车辆与交通\n\n|                           数据集                            |            智能体            |         场景         |        传感器         |\n| :----------------------------------------------------------: | :--------------------------: | :-----------------------: | :--------------------: |\n|      [Waymo开放数据集](https:\u002F\u002Fwaymo.com\u002Fopen\u002F)           | 车辆 \u002F 自行车骑行者 \u002F 行人 |\t\t\t\t城市 \u002F 高速公路     |\tLiDAR \u002F 摄像头 \u002F 雷达 |\n|      [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F)           | 车辆 \u002F 自行车骑行者 \u002F 行人 |\t\t\t\t城市 \u002F 高速公路     |\tLiDAR \u002F 摄像头 \u002F 雷达 |\n|            [nuScenes](https:\u002F\u002Fwww.nuscenes.org\u002F)             |           车辆           |           城市           | 摄像头 \u002F LiDAR \u002F 雷达 |\n|           [highD](https:\u002F\u002Fwww.highd-dataset.com\u002F)            |           车辆           |          高速公路          |         摄像头         |\n|           [inD](https:\u002F\u002Fwww.ind-dataset.com\u002F)            |           车辆           |          高速公路          |         摄像头         |\n|           [roundD](https:\u002F\u002Fwww.round-dataset.com\u002F)            |           车辆           |          高速公路          |         摄像头         |\n|          [BDD100k](https:\u002F\u002Fbdd-data.berkeley.edu\u002F)           | 车辆 \u002F 自行车骑行者 \u002F 行人 |      高速公路 \u002F 城市      |         摄像头         |\n|        [KITTI](http:\u002F\u002Fwww.cvlibs.net\u002Fdatasets\u002Fkitti\u002F)        | 车辆 \u002F 自行车骑行者 \u002F 行人 |   高速公路 \u002F 农村地区   |     摄像头 \u002F LiDAR     |\n| [NGSIM](https:\u002F\u002Fops.fhwa.dot.gov\u002Ftrafficanalysistools\u002Fngsim.htm) |           车辆           |          高速公路          |         摄像头         |\n|      [INTERACTION](http:\u002F\u002Fwww.interaction-dataset.com\u002F)      | 车辆 \u002F 自行车骑行者 \u002F 行人 | 环岛 \u002F 十字路口 |     摄像头     |\n| [自行车骑行者数据集](http:\u002F\u002Fwww.gavrila.net\u002FDatasets\u002FDaimler_Pedestrian_Benchmark_D\u002FTsinghua-Daimler_Cyclist_Detec\u002Ftsinghua-daimler_cyclist_detec.html) |           自行车骑行者           |           城市           |         摄像头         |\n| [Apolloscapes](http:\u002F\u002Fapolloscape.auto\u002F?source=post_page---------------------------) | 车辆 \u002F 自行车骑行者 \u002F 行人 |           城市           |         摄像头         |\n| [Udacity](https:\u002F\u002Fgithub.com\u002Fudacity\u002Fself-driving-car\u002Ftree\u002Fmaster\u002Fdatasets) |           车辆           |           城市           |         摄像头         |\n|      [Cityscapes](https:\u002F\u002Fwww.cityscapes-dataset.com\u002F)       |       车辆 \u002F 行人       |           城市           |         摄像头         |\n| [斯坦福无人机数据集](http:\u002F\u002Fcvgl.stanford.edu\u002Fprojects\u002Fuav_data\u002F) | 车辆 \u002F 自行车骑行者 \u002F 行人 |           城市           |         摄像头         |\n|           [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F)            |      车辆 \u002F 行人       |           城市           |     摄像头 \u002F LiDAR     |\n| [TRAF](https:\u002F\u002Fgamma.umd.edu\u002Fresearchdirections\u002Fautonomousdriving\u002Ftrafdataset)            |      车辆 \u002F 公交车 \u002F 自行车骑行者 \u002F 摩托车骑行者 \u002F 行人 \u002F 动物       |           城市           |     摄像头      |\n|[阿沙芬堡姿态数据集](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.5724486)               |    自行车骑行者 \u002F 行人     |           城市           |         摄像头         |\n\n### 行人\n\n|                           数据集                            |           代理            |       场景       |    传感器     |\n| :----------------------------------------------------------: | :-------------------------: | :-------------------: | :------------: |\n| [UCY](https:\u002F\u002Fgraphics.cs.ucy.ac.cy\u002Fresearch\u002Fdownloads\u002Fcrowd-data) |           人           |    zara \u002F 学生    |     摄像机     |\n|       [ETH (ICCV09)](https:\u002F\u002Ficu.ee.ethz.ch\u002Fresearch\u002Fdatsets.html)       |           人           |         城市         |     摄像机     |\n|              [VIRAT](http:\u002F\u002Fwww.viratdata.org\u002F)              |      人 \u002F 车辆      |         城市         |     摄像机     |\n|        [KITTI](http:\u002F\u002Fwww.cvlibs.net\u002Fdatasets\u002Fkitti\u002F)        | 车辆 \u002F 自行车手 \u002F 人 | 高速公路 \u002F 农村地区 | 摄像机 \u002F LiDAR |\n|     [ATC](https:\u002F\u002Firc.atr.jp\u002Fcrest2010_HRI\u002FATC_dataset\u002F)     |           人           |    购物中心    | 测距传感器  |\n| [Daimler](http:\u002F\u002Fwww.gavrila.net\u002FDatasets\u002FDaimler_Pedestrian_Benchmark_D\u002Fdaimler_pedestrian_benchmark_d.html) |           人           |  来自行驶的车辆  |     摄像机     |\n| [中央车站](http:\u002F\u002Fwww.ee.cuhk.edu.hk\u002F~xgwang\u002Fgrandcentral.html) |           人           |    车站内部    |     摄像机     |\n| [市中心](http:\u002F\u002Fwww.robots.ox.ac.uk\u002FActiveVision\u002FResearch\u002FProjects\u002F2009bbenfold_headpose\u002Fproject.html#datasets) |           人           |     城市街道     |     摄像机     |\n| [爱丁堡](http:\u002F\u002Fhomepages.inf.ed.ac.uk\u002Frbf\u002FFORUMTRACKING\u002F) |           人           |         城市         |     摄像机     |\n|   [Cityscapes](https:\u002F\u002Fwww.cityscapes-dataset.com\u002Flogin\u002F)    |      车辆 \u002F 人      |         城市         |     摄像机     |\n|           [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F)            |      车辆 \u002F 人      |         城市         | 摄像机 \u002F LiDAR |\n| [斯坦福无人机](http:\u002F\u002Fcvgl.stanford.edu\u002Fprojects\u002Fuav_data\u002F) | 车辆 \u002F 自行车手 \u002F 人 |         城市         |     摄像机     |\n|           [TrajNet](http:\u002F\u002Ftrajnet.stanford.edu\u002F)            |           人           |         城市         |     摄像机     |\n|           [PIE](http:\u002F\u002Fdata.nvision2.eecs.yorku.ca\u002FPIE_dataset\u002F)            |           人           |         城市         |     摄像机     |\n|           [ForkingPaths](https:\u002F\u002Fnext.cs.cmu.edu\u002Fmultiverse\u002Findex.html)            |           人           |         城市 \u002F 模拟         |     摄像机     |\n|           [TrajNet++](https:\u002F\u002Fwww.aicrowd.com\u002Fchallenges\u002Ftrajnet-a-trajectory-forecasting-challenge)            |           人           |         城市         |     摄像机     |\n|[阿沙芬堡姿态数据集](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.5724486)               |    自行车手 \u002F 人    |           城市           |         摄像机         |\n|[自行车手俯视数据集 (CTV)](https:\u002F\u002Fwww.ifi-mec.tu-clausthal.de\u002Fctv-dataset)               |    自行车手 \u002F 人    |           城市           |         摄像机         |\n\n### 运动员\n\n|                           数据集                            | 代理 |     场景     | 传感器 |\n| :----------------------------------------------------------: | :----: | :---------------: | :-----: |\n|     [足球](https:\u002F\u002Fdatahub.io\u002Fcollections\u002Ffootball)      | 人 |  足球场   | 摄像机  |\n| [NBA SportVU](https:\u002F\u002Fgithub.com\u002Flinouk23\u002FNBA-Player-Movements) | 人 |  篮球馆  | 摄像机  |\n|      [NFL](https:\u002F\u002Fgithub.com\u002Fa-vhadgar\u002FBig-Data-Bowl)       | 人 |  美式橄榄球 | 摄像机  |\n\n\n## **文献与代码**\n\n### 综述论文\n\n- 面向自动驾驶车辆轨迹预测的机器学习：综合综述、挑战与未来研究方向，arXiv预印本 arXiv:2307.07527，2023年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.07527.pdf)]\n- 在基于深度学习的车辆轨迹预测中融入驾驶知识：综述，IEEE T-IV，2023年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10100881)]\n- 行人与车辆混杂环境下的行人轨迹预测：系统性综述，IEEE T-ITS，2023年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10181234)]\n- 自动驾驶轨迹预测方法综述，IEEE T-IV 2022年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9756903)]\n- 基于深度学习模型的车辆轨迹预测综述，可持续专家系统国际会议，ICSES 2022年。[[论文](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-19-7874-6_48)]\n- 自动驾驶车辆的情境理解与运动预测——综述与比较，IEEE T-ITS，2022年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9733973)]\n- 基于车辆信息的多模态融合技术：综述，arXiv预印本 arXiv:2211.06080，2022年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.06080.pdf)]\n- 自动驾驶中的深度强化学习：综述，IEEE T-ITS，2022年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9351818)]\n- 自动驾驶中的社会交互：回顾与展望，arXiv预印本 arXiv:2208.07541，2022年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.07541.pdf)]\n- 面向时空数据的生成对抗网络：综述，ACM T-IST，2022年。[[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3474838)]\n- 驾驶场景中的行为意图预测：综述，arXiv预印本 arXiv:2211.00385，2022年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.00385.pdf)]\n- 自动驾驶中行人与车辆运动预测综述，IEEE Access，2021年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9559998)]\n- 行人轨迹预测方法综述：深度学习与基于知识的方法对比，arXiv预印本 arXiv:2111.06740，2021年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.06740.pdf)]\n- 轨迹数据管理、分析与学习综述，CSUR 2021年。[[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3440207)]\n- 自动驾驶中的行人行为预测：需求、指标与相关特征，IEEE T-ITS，2021年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9660784)]\n- 基于深度学习的行人轨迹预测方法综述，Sensors，2021年。[[论文](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F21\u002F22\u002F7543\u002Fpdf)]\n- 自动驾驶中车辆轨迹预测的深度学习方法综述，ROBIO 2021年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.10436.pdf)] [[代码](https:\u002F\u002Fgithub.com\u002FHenry1iu\u002FTNT-Trajectory-Predition)]\n- 自动驾驶中的深度学习技术综述，野外机器人学杂志，2020年。[[论文](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1002\u002Frob.21918?saml_referrer)]\n- 人类运动轨迹预测：综述，国际机器人研究期刊，2020年。[[论文](http:\u002F\u002Fsage.cnpereading.com\u002Fparagraph\u002Fdownload\u002F?doi=10.1177\u002F0278364920917446)]\n- 深度学习在自动驾驶中的应用：最新技术综述，arXiv预印本 arXiv:2006.06091，2020年。[[论文](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2006\u002F2006.06091.pdf)]\n- 视觉交通仿真综述：模型、评估及在自动驾驶中的应用，计算机图形学论坛，2020年。[[论文](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1111\u002Fcgf.13803?saml_referrer)]\n- 基于深度学习的自动驾驶车辆行为预测：综述，IEEE T-ITS 2020年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9158529)]\n- 自动驾驶车辆运动规划中的深度强化学习综述，IEEE T-ITS 2020年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9210154)]\n- 车辆轨迹相似性：模型、方法与应用，ACM计算综述（CSUR 2020）。[[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3406096)]\n- 人类驾驶员行为建模与预测：综述，2020年。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08832)]\n- 城市场景下行人行为预测的文献综述，ITSC 2018年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8569415)]\n- 基于视觉的路径预测综述。[[论文](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-91131-1_4)]\n- 与行人交互的自动驾驶车辆：理论与实践综述。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11773)]\n- 轨迹数据挖掘：概述。[[论文](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2743025)]\n- 智能车辆的运动预测与风险评估综述。[[论文](https:\u002F\u002Frobomechjournal.springeropen.com\u002Farticles\u002F10.1186\u002Fs40648-014-0001-z)]\n\n### 具有交互作用的物理系统\n\n- 使用次等变图神经网络学习物理动力学，NeurIPS 2022。\\[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.06876)\\] \\[[代码](https:\u002F\u002Fgithub.com\u002Fhanjq17\u002FSGNN)\\]\n- EvolveGraph：基于动态关系推理的多智能体轨迹预测，NeurIPS 2020。\\[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- 多机器人系统的交互模板，IROS 2019。\\[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8737744\u002F)\\]\n- 面向多交互系统的因子化神经关系推理，ICML 2019年研讨会。\\[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08721v1)\\] \\[[代码](https:\u002F\u002Fgithub.com\u002Fekwebb\u002FfNRI)\\]\n- 物理即逆向图形：从视频中联合无监督地学习物体与物理规律，2019年。\\[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.11169v1.pdf)\\]\n- 用于交互系统的神经关系推理，ICML 2018。\\[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04687v2)\\] \\[[代码](https:\u002F\u002Fgithub.com\u002Fethanfetaya\u002FNRI)\\]\n- 利用感知—预测网络进行潜在物理属性的无监督学习，UAI 2018。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1807.09244v2)\\]\n- 关系归纳偏置、深度学习与图网络，2018年。\\[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01261v3)\\]\n- 关系神经期望最大化：无监督发现物体及其相互作用，ICLR 2018。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10353v1)\\]\n- 图网络作为可学习的物理引擎用于推理和控制，ICML 2018。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01242v1)\\]\n- 用于物理预测的灵活神经表示，2018年。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1806.08047v2)\\]\n- 一种用于关系推理的简单神经网络模块，2017年。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01427v1)\\]\n- VAIN：基于注意力的多智能体预测建模，NeurIPS 2017。\\[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.06122.pdf)\\]\n- 视觉交互网络，2017年。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01433v1)\\]\n- 基于组合性对象的学习物理动力学方法，ICLR 2017。\\[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00341v2)\\]\n- 用于学习物体、关系与物理规律的交互网络，2016年。\\[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00222)\\] \\[[代码](https:\u002F\u002Fgithub.com\u002Fhiggsfield\u002Finteraction_network_pytorch)\\]\n\n### 智能车辆、交通与行人\n\n- Diffusion-Based Environment-Aware Trajectory Prediction, arXiv preprint arXiv:2403.11643, 2024. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.11643)]\n- MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs, IEEE T-IV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00735)] [[code](https:\u002F\u002Fgithub.com\u002Fwestny\u002Fmtp-go)]\n- MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FJiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.pdf)]\n- Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction, CVPR 2023. [[paper](http:\u002F\u002Fxxx.itp.ac.cn\u002Fpdf\u002F2303.16005.pdf)]\n- Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Fchengy12.github.io\u002Ffiles\u002FBosampler.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fviewsetting\u002FUnsupervised_sampling_promoting)]\n- Planning-oriented Autonomous Driving, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FHu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FOpenDriveLab\u002FUniAD)]\n- IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.00575.pdf)]\n- Stimulus Verification is a Universal and Effective Sampler in Multi-modal Human Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FSun_Stimulus_Verification_Is_a_Universal_and_Effective_Sampler_in_Multi-Modal_CVPR_2023_paper.pdf)]\n- Query-Centric Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FZhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FZikangZhou\u002FQCNet)] [[QCNeXt](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.10508.pdf)]\n- FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.16574.pdf)]\n- Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion, CVPR 2023. [[paper](https:\u002F\u002Fnv-tlabs.github.io\u002Ftrace-pace\u002Fdocs\u002Ftrace_and_pace.pdf)] [[website](https:\u002F\u002Fnv-tlabs.github.io\u002Ftrace-pace\u002F)]\n- FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.16197.pdf)] [[website](https:\u002F\u002Frluke22.github.io\u002FFJMP\u002F)]\n- Leapfrog Diffusion Model for Stochastic Trajectory Prediction, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.10895.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FLED)]\n- ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries, CVPR 2023. [[paper](http:\u002F\u002Fxxx.itp.ac.cn\u002Fpdf\u002F2208.01582.pdf)] [[website](https:\u002F\u002Ftsinghua-mars-lab.github.io\u002FViP3D\u002F)]\n- EqMotion: Equivariant Multi-Agent Motion Prediction with Invariant Interaction Reasoning, CVPR 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.10876.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FEqMotion)]\n- V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FYu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FAIR-THU\u002FDAIR-V2X-Seq)]\n- Weakly Supervised Class-agnostic Motion Prediction for Autonomous Driving, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FLi_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.pdf)]\n- Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction, CVPR 2023. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FGao_Decompose_More_and_Aggregate_Better_Two_Closer_Looks_at_Frequency_CVPR_2023_paper.pdf)]\n- HumanMAC: Masked Motion Completion for Human Motion Prediction, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03665)] [[code](https:\u002F\u002Fgithub.com\u002FLinghaoChan\u002FHumanMAC)]\n- BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.14304)] [[code](https:\u002F\u002Fgithub.com\u002FBarqueroGerman\u002FBeLFusion)]\n- EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09306)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FEigenTrajectory)]\n- ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation, ICCV 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.14187.pdf)] [[code](https:\u002F\u002Fkuis-ai.github.io\u002Fadapt\u002F)]\n- PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View, IJCAI 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.10761.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FEdwardLeeLPZ\u002FPowerBEV)]\n- Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction, AAAI 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.05976.pdf)]\n- Multi-stream Representation Learning for Pedestrian Trajectory Prediction, AAAI 2023. [[paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F25389)]\n- Continuous Trajectory Generation Based on Two-Stage GAN, AAAI 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.07103.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FWenMellors\u002FTS-TrajGen)]\n- A Set of Control Points Conditioned Pedestrian Trajectory Prediction, AAAI 2023. [[paper](https:\u002F\u002Fassets.underline.io\u002Flecture\u002F67747\u002Fpaper\u002F82988b653861eb7a0d5cdc91c4b26f8c.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FGraphTERN)]\n- Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction, ICLR 2023. [[paper](https:\u002F\u002Fopenreview.net\u002Fforum?id=CGBCTp2M6lA)]\n- TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios, ICRA 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.06609.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fmetadriverse\u002Ftrafficgen)]\n- GANet: Goal Area Network for Motion Forecasting, ICRA 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.09723.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fkingwmk\u002FGANet)]\n- TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving, ICRA 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.20068.pdf)]\n- SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving, CoRL 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.14116.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FAutoVision-cloud\u002FSSL-Lanes)]\n- LimSim: A Long-term Interactive Multi-scenario Traffic Simulator, ITSC 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.06648.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FPJLab-ADG\u002FLimSim)]\n- MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10056303)]\n- Adaptive and Simultaneous Trajectory Prediction for Heterogeneous Agents via Transferable Hierarchical Transformer Network, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10149109)]\n- SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction, TNNLS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10063206)] [[code](https:\u002F\u002Fgithub.com\u002FWW-Tong\u002Fssagcn_for_path_prediction)]\n- Disentangling Crowd Interactions for Pedestrians Trajectory Prediction, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10083225)]\n- VNAGT: Variational Non-Autoregressive Graph Transformer Network for Multi-Agent Trajectory Prediction, IEEE Transactions on Vehicular Technology. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10121688)]\n- Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction, TIV. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10149368)]\n- Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323002935)]\n- Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323004703)]\n- Multimodal Vehicular Trajectory Prediction With Inverse Reinforcement Learning and Risk Aversion at Urban Unsignalized Intersections, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10164651)]\n- Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph, IET Intelligent Transport Systems. [[paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1049\u002Fitr2.12265)]\n- Social Self-Attention Generative Adversarial Networks for Human Trajectory Prediction, IEEE Transactions on Artificial Intelligence. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10197467)]\n- CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10215313)]\n- Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10207845)]\n- A physics-informed Transformer model for vehicle trajectory prediction on highways, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X23002619)] [[code](https:\u002F\u002Fgithub.com\u002FGengmaosi\u002FPIT-IDM)]\n- MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction, RAL. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2308.10280.pdf)]\n- MRGTraj: A Novel Non-Autoregressive Approach for Human Trajectory Prediction, TCSVT. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10226250)] [[code](https:\u002F\u002Fgithub.com\u002Fwisionpeng\u002FMRGTraj)]\n- Planning-inspired Hierarchical Trajectory Prediction via Lateral-Longitudinal Decomposition for Autonomous Driving, TIV. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10226224)]\n- Traj-MAE: Masked Autoencoders for Trajectory Prediction, arXiv preprint arXiv:2303.06697, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06697.pdf)]\n- Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion, arXiv preprint arXiv:2303.08367, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.08367.pdf)]\n- Diffusion Model for GPS Trajectory Generation, arXiv preprint arXiv:2304.11582, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.11582.pdf)]\n- Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.11868.pdf)] [[website](https:\u002F\u002Fmultiverse-transformer.github.io\u002Fsim-agents\u002F)]\n- Joint-Multipath++ for Simulation Agents: 2nd Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https:\u002F\u002Fstorage.googleapis.com\u002Fwaymo-uploads\u002Ffiles\u002Fresearch\u002F2023%20Technical%20Reports\u002FSA_hm_jointMP.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fwangwenxi-handsome\u002FJoint-Multipathpp)]\n- MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying, 1st Place Solution for Waymo Open Motion Prediction Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.17770.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fsshaoshuai\u002FMTR)]\n- GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving, arXiv preprint arXiv:2303.05760, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.05760.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMCZhi\u002FGameFormer)] [[website](https:\u002F\u002Fmczhi.github.io\u002FGameFormer\u002F)]\n- GameFormer Planner: A Learning-enabled Interactive Prediction and Planning Framework for Autonomous Vehicles, the nuPlan Planning Challenge at the CVPR 2023 End-to-End Autonomous Driving Workshop. [[paper](https:\u002F\u002Fopendrivelab.com\u002Fe2ead\u002FAD23Challenge\u002FTrack_4_AID.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMCZhi\u002FGameFormer-Planner\u002F)]\n- trajdata: A Unified Interface to Multiple Human Trajectory Datasets, arXiv preprint arXiv:2307.13924, 2023. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.13924.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FNVlabs\u002Ftrajdata)]\n- Remember Intentions: Retrospective-Memory-based Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.11474.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FMemoNet)]\n- STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.01026.pdf)] [[code](https:\u002F\u002Fgithub.com\u002F4DVLab\u002FSTCrowd.git)]\n- Vehicle trajectory prediction works, but not everywhere, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.03909.pdf)] [[code](https:\u002F\u002Fs-attack.github.io\u002F)]\n- Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.13777.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fgutianpei\u002FMID)]\n- Non-Probability Sampling Network for Stochastic Human Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.13471.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Finhwanbae\u002FNPSN)]\n- On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05057.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fzqzqz\u002FAdvTrajectoryPrediction)]\n- Adaptive Trajectory Prediction via Transferable GNN, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.05046.pdf)]\n- Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.14820.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fcausalmotion), [code](https:\u002F\u002Fgithub.com\u002Fsherwinbahmani\u002Fynet_adaptive)]\n- How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.04781.pdf)]\n- Learning from All Vehicles, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.11934.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fdotchen\u002FLAV)]\n- Forecasting from LiDAR via Future Object Detection, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.16297.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fneeharperi\u002FFutureDet)]\n- End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.16910.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FKguo-cs\u002FTDOR)]\n- M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11884.pdf)] [[code](https:\u002F\u002Ftsinghua-mars-lab.github.io\u002FM2I\u002F)]\n- GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning, CVPR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08770.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FGroupNet)]\n- Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-Based Prediction, CVPR 2022. [[paper](https:\u002F\u002Fxinshuoweng.com\u002Fpapers\u002FAffinipred\u002Fcamera_ready.pdf)]\n- ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FChen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FNVlabs\u002FScePT)]\n- Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FLi_Graph-Based_Spatial_Transformer_With_Memory_Replay_for_Multi-Future_Pedestrian_Trajectory_CVPR_2022_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FJacobieee\u002FST-MR)]\n- MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FLee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.pdf)]\n- LTP: Lane-based Trajectory Prediction for Autonomous Driving, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FWang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.pdf)]\n- ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FWang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.pdf)]\n- Human Trajectory Prediction with Momentary Observation, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FSun_Human_Trajectory_Prediction_With_Momentary_Observation_CVPR_2022_paper.pdf)]\n- HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction, CVPR 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FZhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FZikangZhou\u002FHiVT)]\n- Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.09953.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FGPGraph)]\n- Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.03057.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fabduallahmohamed\u002FSocial-Implicit)] [[website](https:\u002F\u002Fwww.abduallahmohamed.com\u002Fsocial-implicit-amdamv-adefde-demo)] \n- Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.04624.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fd1024choi\u002FHLSTrajForecast)]\n- SocialVAE: Human Trajectory Prediction using Timewise Latents, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.08207.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fxupei0610\u002FSocialVAE)]\n- View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07288.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fcocoon2wong\u002FVertical)]\n- Entry-Flipped Transformer for Inference and Prediction of Participant Behavior, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.06235.pdf)]\n- D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.10398.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FVTP-TL\u002FD2-TPred)]\n- Human Trajectory Prediction via Neural Social Physics, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.10435.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Frealcrane\u002FHuman-Trajectory-Prediction-via-Neural-Social-Physics)]\n- Social-SSL: Self-Supervised Cross-Sequence Representation Learning Based on Transformers for Multi-Agent Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Fbasiclab.lab.nycu.edu.tw\u002Fassets\u002FSocial-SSL.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FSigta678\u002FSocial-SSL)]\n- Aware of the History: Trajectory Forecasting with the Local Behavior Data, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.09646.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FKay1794\u002FAware-of-the-history)]\n- Action-based Contrastive Learning for Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08664.pdf)]\n- AdvDO: Realistic Adversarial Attacks for Trajectory Prediction, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.08744.pdf)]\n- ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning, ECCV 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.07601.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FOpenPerceptionX\u002FST-P3)]\n- Social ODE: Multi-Agent Trajectory Forecasting with Neural Ordinary Differential Equations, ECCV 2022. [[paper](https:\u002F\u002Fwww.ecva.net\u002Fpapers\u002Feccv_2022\u002Fpapers_ECCV\u002Fpapers\u002F136820211.pdf)]\n- Forecasting Human Trajectory from Scene History, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.08732.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FMaKaRuiNah\u002FSHENet)]\n- Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.08129)] [[code](https:\u002F\u002Fgithub.com\u002FOpenPerceptionX\u002FTCP)]\n- Motion Transformer with Global Intention Localization and Local Movement Refinement, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.13508.pdf)] [[website](https:\u002F\u002Fvas.mpi-inf.mpg.de\u002Fmotion-transformer-with-global-intention-localization-and-local-movement-refinement\u002F)]\n- Interaction Modeling with Multiplex Attention, NeurIPS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.10660.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Ffanyun-sun\u002FIMMA)]\n- Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models, Conference on Learning for Dynamics and Control (L4DC). [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.02392.pdf)] [[website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdeep-interactive-predict-plan)]\n- Social Interpretable Tree for Pedestrian Trajectory Prediction, AAAI 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.13296.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Flssiair\u002FSIT)]\n- Complementary Attention Gated Network for Pedestrian Trajectory Prediction, AAAI 2022. [[paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-1963.DuanJ.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FjinghaiD\u002FCAGN)]\n- Scene Transformer: A unified architecture for predicting future trajectories of multiple agents, ICLR 2022. [[paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Wm3EA5OlHsG)]\n- You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction, ICLR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.05304.pdf)]\n- Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction, ICLR 2022. [[paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Dup_dDqkZC5)] [[code](https:\u002F\u002Ffgolemo.github.io\u002Fautobots\u002F)]\n- THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling, ICLR 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.06607)]\n- Path-Aware Graph Attention for HD Maps in Motion Prediction, ICRA 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.13772.pdf)]\n- Trajectory Prediction with Linguistic Representations, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9811928)]\n- Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9811718)] [[website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fsmoothness-attention)]\n- KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812337)]\n- Domain Generalization for Vision-based Driving Trajectory Generation, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812070)] [[website](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdg-traj-gen)]\n- A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811567)]\n- Conditioned Human Trajectory Prediction using Iterative Attention Blocks, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812404)]\n- StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811830)]\n- Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811632)] [[website](https:\u002F\u002Fsites.google.com\u002Fillinois.edu\u002Fmesrnn\u002Fhome)]\n- Propagating State Uncertainty Through Trajectory Forecasting, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811776)] [[code](https:\u002F\u002Fgithub.com\u002FStanfordASL\u002FPSU-TF)]\n- HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812254)]\n- Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811585)]\n- Crossmodal Transformer Based Generative Framework for Pedestrian Trajectory Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812226)]\n- Trajectory Prediction for Autonomous Driving with Topometric Map, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9811712)] [[code](https:\u002F\u002Fgithub.com\u002FJiaolong\u002Ftrajectory-prediction)]\n- CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9811637)] [[code](https:\u002F\u002Fgithub.com\u002Fschmidt-ju\u002Fcrat-pred)]\n- MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812107)]\n- Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9812060\u002F)]\n- GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation, ICRA 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.01827.pdf)]\n- TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation, ICRA 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=9811591)]\n- Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty, IROS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.12446.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FTRI-ML\u002FHAICU)] [[trajdata](https:\u002F\u002Fgithub.com\u002Fnvr-avg\u002Ftrajdata)]\n- Trajectory Prediction with Graph-based Dual-scale Context Fusion, IROS 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.01592.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FHKUST-Aerial-Robotics\u002FDSP)]\n- Robust Trajectory Prediction against Adversarial Attacks, CoRL 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.00094.pdf)] [[code](https:\u002F\u002Frobustav.github.io\u002FRobustTraj\u002F)]\n- Planning with Diffusion for Flexible Behavior Synthesis, ICML 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.09991)] [[website](https:\u002F\u002Fdiffusion-planning.github.io\u002F)]\n- Synchronous Bi-Directional Pedestrian Trajectory Prediction with Error Compensation, ACCV 2022. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2022\u002Fpapers\u002FXie_Synchronous_Bi-Directional_Pedestrian_Trajectory_Prediction_with_Error_Compensation_ACCV_2022_paper.pdf)]\n- AI-TP: Attention-based Interaction-aware Trajectory Prediction for Autonomous Driving, IEEE T-IV, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9723649)] [[code](https:\u002F\u002Fgithub.com\u002FKP-Zhang\u002FAI-TP)]\n- MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction, Computational Intelligence and Neuroscience. [[paper](https:\u002F\u002Fdownloads.hindawi.com\u002Fjournals\u002Fcin\u002F2022\u002F4192367.pdf)]\n- Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9737058)]\n- Multi-Agent Trajectory Prediction with Heterogeneous Edge-Enhanced Graph Attention Network, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fdspace.lib.cranfield.ac.uk\u002Fbitstream\u002Fhandle\u002F1826\u002F17541\u002FMulti-agent_trajectory_prediction-2022.pdf?sequence=1&isAllowed=y)]\n- Fully Convolutional Encoder-Decoder With an Attention Mechanism for Practical Pedestrian Trajectory Prediction, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9768201)]\n- STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal Features, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9743363)]\n- Trajectory Prediction for Autonomous Driving Using Spatial-Temporal Graph Attention Transformer, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9768029)]\n- Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9767719)] [[code](https:\u002F\u002Fxbchen82.github.io\u002Fresource\u002F)]\n- Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9686621&tag=1)]\n- DeepTrack: Lightweight Deep Learning for Vehicle Trajectory Prediction in Highways, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9770480)]\n- Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9745461&tag=1)]\n- Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional Networks, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9781338)]\n- Trajectory Prediction Neural Network and Model Interpretation Based on Temporal Pattern Attention, IEEE T-ITS, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9945660)]\n- Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction, IEEE RA-L, 2022. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9664278)] [[code](https:\u002F\u002Fgithub.com\u002Ftedhuang96\u002Fgst)]\n- GAMMA: A General Agent Motion Prediction Model for Autonomous Driving, RAL. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01566.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FAdaCompNUS\u002Fgamma)]\n- Stepwise Goal-Driven Networks for Trajectory Prediction, RAL. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.14107v3.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FChuhuaW\u002FSGNet.pytorch)]\n- GA-STT: Human Trajectory Prediction with Group Aware Spatial-Temporal Transformer, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9779572)]\n- Long-term 4D trajectory prediction using generative adversarial networks, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X22000031)]\n- A context-aware pedestrian trajectory prediction framework for automated vehicles, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X21004423)]\n- Explainable multimodal trajectory prediction using attention models, Transportation Research Part C: Emerging Technologies. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X22002509)]\n- CSCNet: Contextual semantic consistency network for trajectory prediction in crowded spaces, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320322000334)]\n- CSR: Cascade Conditional Variational AutoEncoder with Social-aware Regression for Pedestrian Trajectory Prediction, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320322005106)]\n- Step Attention: Sequential Pedestrian Trajectory Prediction, IEEE Sensors Journal. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9732437)]\n- Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism, IEEE Transactions on Instrumentation and Measurement. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9749176)]\n- Spatio-temporal Interaction Aware and Trajectory Distribution Aware Graph Convolution Network for Pedestrian Multimodal Trajectory Prediction, IEEE Transactions on Instrumentation and Measurement. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9997233)]\n- Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model, Physica A: Statistical Mechanics and its Applications. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378437122000139)]\n- PTPGC: Pedestrian trajectory prediction by graph attention network with ConvLSTM, Robotics and Autonomous Systems. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0921889021002165)]\n- GCHGAT: pedestrian trajectory prediction using group constrained hierarchical graph attention networks, Applied Intelligence. [[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-021-02997-w)]\n- Vehicles Trajectory Prediction Using Recurrent VAE Network, IEEE Access. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9740177)] [[code](https:\u002F\u002Fgithub.com\u002Fmidemig\u002Ftraj_pred_vae)]\n- SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction, TPAMI. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9699076)]\n- PTP-STGCN: Pedestrian Trajectory Prediction Based on a Spatio-temporal Graph Convolutional Neural Network, Applied Intelligence. [[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-022-03524-1)]\n- Trajectory distributions: A new description of movement for trajectory prediction, Computational Visual Media. [[paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs41095-021-0236-6.pdf)]\n- Trajectory prediction for autonomous driving based on multiscale spatial-temporal graph, IET Intelligent Transport Systems. [[paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1049\u002Fitr2.12265)]\n- Continual learning-based trajectory prediction with memory augmented networks, Knowledge-Based Systems. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705122011157)]\n- Atten-GAN: Pedestrian Trajectory Prediction with GAN Based on Attention Mechanism, Cognitive Computation. [[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-022-10029-z#Abs1)]\n- EvoSTGAT: Evolving spatiotemporal graph attention networks for pedestrian trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0925231222003460?ref=pdf_download&fr=RR-2&rr=7da0ead45e800fcc)]\n- Raising context awareness in motion forecasting, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.08048.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fvaleoai\u002FCAB)]\n- Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.11561.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fluigifilippochiara\u002FGoal-SAR)]\n- Importance Is in Your Attention: Agent Importance Prediction for Autonomous Driving, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.09121.pdf)]\n- MPA: MultiPath++ Based Architecture for Motion Prediction, CVPR Workshops 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.10041.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fstepankonev\u002Fwaymo-motion-prediction-challenge-2022-multipath-plus-plus)]\n- TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model, arXiv:2201.02941, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.02941v1.pdf)]\n- Wayformer: Motion Forecasting via Simple & Efficient Attention Networks, arXiv preprint arXiv:2207.05844, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.05844.pdf)]\n- PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer, arXiv preprint arXiv:2203.09293, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.09293.pdf)]\n- LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction, arXiv preprint arXiv:2203.01880, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01880.pdf)]\n- Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss, arXiv preprint arXiv:2206.08641, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.08641.pdf)]\n- Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction, arXiv preprint arXiv:2205.14230, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.14230.pdf)]\n- Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning, arXiv preprint arXiv:2211.00848, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.00848.pdf)]\n- GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model, arXiv preprint arXiv:2209.07857, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.07857.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fmengmengliu1998\u002FGATraj)]\n- Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning, arXiv preprint arXiv:2206.13114, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.13114.pdf)]\n- Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting, arXiv preprint arXiv:2207.05195, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.05195)] [[code](https:\u002F\u002Fgithub.com\u002FMediaBrain-SJTU\u002FCollaborative-Uncertainty)]\n- Guided Conditional Diffusion for Controllable Traffic Simulation, arXiv preprint arXiv:2210.17366, 2022. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.17366.pdf)] [[website](https:\u002F\u002Faiasd.github.io\u002Fctg.github.io\u002F)]\n- PhysDiff: Physics-Guided Human Motion Diffusion Model, arXiv preprint arXiv:2212.02500, 2022. [[paper](http:\u002F\u002Fxxx.itp.ac.cn\u002Fpdf\u002F2212.02500.pdf)]\n- MPA: MultiPath++ Based Architecture for Motion Prediction, CVPR Workshop on Autonomous Driving 2022. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.10041)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fstepankonev\u002Fwaymo-motion-prediction-challenge-2022-multipath-plus-plus)\\]\n- Collaborative Uncertainty in Multi-Agent Trajectory Forecasting, NeurIPS 2021. [[paper](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002F31ca0ca71184bbdb3de7b20a51e88e90-Paper.pdf)]\n- GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction, NeurIPS 2021. [[paper](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002Fe3670ce0c315396e4836d7024abcf3dd-Paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Flongyuanli\u002FGRIN_NeurIPS21)]\n- LibCity: An Open Library for Traffic Prediction, SIGSPATIAL 2021. [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3474717.3483923)] [[code](https:\u002F\u002Fgithub.com\u002FLibCity\u002FBigscity-LibCity)]\n- Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9575242)]\n- Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast, ICRA 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.04853.pdf)]\n- AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention, ICRA 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.05682.pdf)]\n- Exploring Dynamic Context for Multi-path Trajectory Prediction, ICRA 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9562034)] [[code](https:\u002F\u002Fgithub.com\u002Fwtliao\u002FDCENet)]\n- Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks, ICRA 2021. [[paper](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F346614349_Pedestrian_Trajectory_Prediction_using_Context-Augmented_Transformer_Networks)] [[code](https:\u002F\u002Fgithub.com\u002FKhaledSaleh\u002FContext-Transformer-PedTraj)]\n- Spectral Temporal Graph Neural Network for Trajectory Prediction, ICRA 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.02930.pdf)]\n- Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance, ICRA 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9560994)] [[code](https:\u002F\u002Fgithub.com\u002Fxuxie1031\u002FCollisionFreeMultiAgentTrajectoryPrediciton)]\n- Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements, ICRA 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9561022)]\n- AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FYuan_AgentFormer_Agent-Aware_Transformers_for_Socio-Temporal_Multi-Agent_Forecasting_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FKhrylx\u002FAgentFormer)] [[website](https:\u002F\u002Fye-yuan.com\u002Fagentformer\u002F)]\n- Likelihood-Based Diverse Sampling for Trajectory Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FJason_Likelihood-Based_Diverse_Sampling_for_Trajectory_Forecasting_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FJasonMa2016\u002FLDS)]\n- MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction, ICCV 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09274.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fselflein\u002FMG-GAN)]\n- Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FLi_Spatial-Temporal_Consistency_Network_for_Low-Latency_Trajectory_Forecasting_ICCV_2021_paper.pdf)]\n- Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FSun_Three_Steps_to_Multimodal_Trajectory_Prediction_Modality_Clustering_Classification_and_ICCV_2021_paper.pdf)]\n- From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FMangalam_From_Goals_Waypoints__Paths_to_Long_Term_Human_Trajectory_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fkarttikeya.github.io\u002Fpublication\u002Fynet\u002F)]\n- Where are you heading? Dynamic Trajectory Prediction with Expert Goal Examples, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FZhao_Where_Are_You_Heading_Dynamic_Trajectory_Prediction_With_Expert_Goal_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FJoeHEZHAO\u002Fexpert_traj)]\n- DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FGu_DenseTNT_End-to-End_Trajectory_Prediction_From_Dense_Goal_Sets_ICCV_2021_paper.pdf)]\n- Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FRen_Safety-Aware_Motion_Prediction_With_Unseen_Vehicles_for_Autonomous_Driving_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fxrenaa\u002FSafety-Aware-Motion-Prediction)]\n- LOKI: Long Term and Key Intentions for Trajectory Prediction, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FGirase_LOKI_Long_Term_and_Key_Intentions_for_Trajectory_Prediction_ICCV_2021_paper.pdf)] [[dataset](https:\u002F\u002Fusa.honda-ri.com\u002Floki)]\n- Human Trajectory Prediction via Counterfactual Analysis, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FChen_Human_Trajectory_Prediction_via_Counterfactual_Analysis_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FCHENGY12\u002FCausalHTP)]\n- Personalized Trajectory Prediction via Distribution Discrimination, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FChen_Personalized_Trajectory_Prediction_via_Distribution_Discrimination_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FCHENGY12\u002FDisDis)]\n- Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FZheng_Unlimited_Neighborhood_Interaction_for_Heterogeneous_Trajectory_Prediction_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fzhengfang1997\u002FUnlimited-Neighborhood-Interaction-for-Heterogeneous-Trajectory-Prediction)]\n- Social NCE: Contrastive Learning of Socially-aware Motion Representations, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FLiu_Social_NCE_Contrastive_Learning_of_Socially-Aware_Motion_Representations_ICCV_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fsocial-nce)]\n- RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting, ICCV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021\u002Fpapers\u002FLi_RAIN_Reinforced_Hybrid_Attention_Inference_Network_for_Motion_Forecasting_ICCV_2021_paper.pdf)]\n- Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision, AAAI 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.01884.pdf)]\n- SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction, AAAI 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.00109.pdf)]\n- Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction, AAAI 2021. [[paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI21Papers\u002FAAAI-1677.BaeI.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FInhwanBae\u002FDMRGCN)]\n- MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FWu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.pdf)]\n- Multimodal Motion Prediction with Stacked Transformers, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FLiu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fdecisionforce\u002FmmTransformer)] [[website](https:\u002F\u002Fdecisionforce.github.io\u002FmmTransformer\u002F?utm_source=catalyzex.com)]\n- SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fshuaishiliu\u002FSGCN)]\n- LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FKim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.pdf)]\n- Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction, CVPR 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08277.pdf)]\n- Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FBhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.pdf)] [[dataset](https:\u002F\u002Fwww.mpi-inf.mpg.de\u002Fdepartments\u002Fcomputer-vision-and-machine-learning\u002Fresearch\u002Feuro-pvi-dataset)]\n- Trajectory Prediction with Latent Belief Energy-Based Model, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FPang_Trajectory_Prediction_With_Latent_Belief_Energy-Based_Model_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fbpucla\u002Flbebm)]\n- Shared Cross-Modal Trajectory Prediction for Autonomous Driving, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FChoi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.pdf)]\n- Pedestrian and Ego-vehicle Trajectory Prediction from Monocular camera, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FNeumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_camera_CVPR_2021_paper.pdf)] [[code](https:\u002F\u002Fgitlab.com\u002FlukeN86\u002FpedFutureTracking)]\n- Interpretable Social Anchors for Human Trajectory Forecasting in Crowds, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FKothari_Interpretable_Social_Anchors_for_Human_Trajectory_Forecasting_in_Crowds_CVPR_2021_paper.pdf)]\n- Introvert: Human Trajectory Prediction via Conditional 3D Attention, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.pdf)]\n- MP3: A Unified Model to Map, Perceive, Predict and Plan, CVPR 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.06806.pdf)]\n- TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors, CVPR 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FSuo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.pdf)]\n- Multimodal Transformer Network for Pedestrian Trajectory Prediction, IJCAI 2021. [[paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0174.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fericyinyzy\u002FMTN_trajectory)]\n- Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction, IROS 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.05814.pdf)]\n- Joint Intention and Trajectory Prediction Based on Transformer, IROS 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9636241)]\n- Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks, IROS 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9636875)]\n- Multiple Contextual Cues Integrated Trajectory Prediction for Autonomous Driving, IROS 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9476975)]\n- MultiXNet: Multiclass Multistage Multimodal Motion Prediction, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9575718)]\n- Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9576054)]\n- Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios, IV 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9575958)]\n- Generating Scenarios with Diverse Pedestrian Behaviors for Autonomous Vehicle Testing, Conference on Robot Learning (CoRL 2021). [[paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HTfApPeT4DZ)] [[code](https:\u002F\u002Fgithub.com\u002FMariaPriisalu\u002Fspl)]\n- Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals, CoRL 2021. [[paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv164\u002Fdeo22a.html)] [[code](https:\u002F\u002Fgithub.com\u002Fnachiket92\u002FPGP)]\n- Learning to Predict Vehicle Trajectories with Model-based Planning, CoRL 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.04027.pdf)]\n- Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks, International Conference on Pattern Recognition (ICPR 2021). [[paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002F978-3-030-68763-2_5.pdf)]\n- GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction, WACV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2021\u002Fpapers\u002FWang_GraphTCN_Spatio-Temporal_Interaction_Modeling_for_Human_Trajectory_Prediction_WACV_2021_paper.pdf)]\n- Goal-driven Long-Term Trajectory Prediction, WACV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2021\u002Fpapers\u002FTran_Goal-Driven_Long-Term_Trajectory_Prediction_WACV_2021_paper.pdf)]\n- Multimodal Trajectory Predictions for Autonomous Driving without a Detailed Prior Map, WACV 2021. [[paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FWACV2021\u002Fpapers\u002FKawasaki_Multimodal_Trajectory_Predictions_for_Autonomous_Driving_Without_a_Detailed_Prior_WACV_2021_paper.pdf)]\n- Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction, IEEE International Conference on Image Processing (ICIP 2021). [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.06320v2.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fserenetech90\u002FAOL_ovsc)]\n- S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving, Asian Conference on Machine Learning 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.10902.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fchenghuang66\u002Fs2tnet)]\n- Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes, IEEE Robotics and Automation Letters 2021 \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9309332)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Ftdavchev\u002Fstructured-trajectory-prediction)\\]\n- Trajectory Prediction using Equivariant Continuous Convolution, ICLR 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.11344.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FRose-STL-Lab\u002FECCO)]\n- TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation, International Conference on Intelligent Autonomous Systems 2021. [[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-95892-3_31#Abs1)]\n- HOME: Heatmap Output for future Motion Estimation, ITSC 2021. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.10968.pdf)]\n- Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving, ITSC 2021. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9564929)]\n- SCSG Attention: A Self-Centered Star Graph with Attention for Pedestrian Trajectory Prediction, International Conference on Database Systems for Advanced Applications (DASFAA 2021). [[paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002F978-3-030-73194-6_29.pdf)]\n- Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection, IEEE Symposium Series on Computational Intelligence (SSCI 2021). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9660004)] [[code](https:\u002F\u002Fgithub.com\u002Fakanuasiegbu\u002FLeveraging-Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection)]\n- Are socially-aware trajectory prediction models really socially-aware?, Transportation Research: Part C. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.10879.pdf), [paper](https:\u002F\u002Ficcv21-adv-workshop.github.io\u002Fshort_paper\u002Fs-attack-arow2021.pdf)] [[code](https:\u002F\u002Fs-attack.github.io\u002F)]\n- Injecting knowledge in data-driven vehicle trajectory predictors, Transportation Research: Part C. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0968090X21000425?token=F03D20769BFB255F56662C10348A81F3D07A42C6B4AB9BA19E3F7B2A5F1DA7D99B96B783616BDA86C12866AFCF4C5671&originRegion=eu-west-1&originCreation=20220506090622)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002FRRB)]\n- Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning, Transportation Research: Part C. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0968090X2030855X)]\n- Human Trajectory Forecasting in Crowds: A Deep Learning Perspective,  IEEE Transactions on Intelligent Transportation Systems. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9408398)] [[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Ftrajnetplusplusbaselines)]\n- NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9629362)]\n- Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9491972)]\n- A Hierarchical Framework for Interactive Behaviour Prediction of Heterogeneous Traffic Participants Based on Graph Neural Network, TITS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9468360&tag=1)]\n- TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning, Transportation Research Part C. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0968090X21001121?token=3DEACAF2AD919E99B3331E74F747B61A0EAC2741E79B6F99F4F806155EB394F163D74F2F83806358BBD65911E107EF01&originRegion=us-east-1&originCreation=20220416040814)] [[code](https:\u002F\u002Fgithub.com\u002Fbenchoi93\u002FTrajGAIL)]\n- Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, IEEE ROBOTICS AND AUTOMATION LETTERS. [[paper](https:\u002F\u002Fwww.gilitschenski.org\u002Figor\u002Fpublications\u002F202104-ral-logic_gan\u002Fral21-logic_gan.pdf)]\n- Vehicle Trajectory Prediction Using LSTMs with Spatial-Temporal Attention Mechanisms, IEEE Intelligent Transportation Systems Magazine. [[paper](http:\u002F\u002Furdata.net\u002Ffiles\u002F2020_VTP.pdf)] [[code](https:\u002F\u002Fgithub.com\u002Fleilin-research\u002FVTP)]\n- Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment, Transportation Research Record. [[paper](http:\u002F\u002Fsage.cnpereading.com\u002Fparagraph\u002Fdownload\u002F?doi=10.1177\u002F0361198121993471)]\n- Temporal Pyramid Network with Spatial-Temporal Attention for Pedestrian Trajectory Prediction, IEEE Transactions on Network Science and Engineering. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9373939)]\n- An efficient Spatial–Temporal model based on gated linear units for trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0925231221018907?token=C894F657732BB6078B77AEC9BD3858338C1A7F1254CCC0BBC34ADA1421A95CF9A4F68BDCA8812457DE27FB37EEB8F198&originRegion=us-east-1&originCreation=20220420144432)]\n- SRAI-LSTM: A Social Relation Attention-based Interaction-aware LSTM for human trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS0925231221018014?token=BB22DAAC41E3BF453C326A9D72A0CC900C2DFFD0D8AE07B7DEED51C7F2250B9CB40CC89B6812CA20DBFA6A7EDD32AAD6&originRegion=us-east-1&originCreation=20220512100647)]\n- AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction, Neurocomputing. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS092523122100388X)]\n- Multi-PPTP: Multiple Probabilistic Pedestrian Trajectory Prediction in the Complex Junction Scene, IEEE Transactions on Intelligent Transportation Systems. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9619864)]\n- A Novel Graph-Based Trajectory Predictor With Pseudo-Oracle, TNNLS. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9447207)]\n- Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN, Journal of Data Science. [[paper](https:\u002F\u002Fwww.jds-online.com\u002Ffiles\u002FJDS202001-08.pdf)] [[code](https:\u002F\u002Fgithub.com\u002FXingruiWang\u002FTwo-Stage-Gan-in-trajectory-generation)]\n- Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories, IEEE Transactions on Intelligent Vehicles. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9707640)]\n- STI-GAN: Multimodal Pedestrian Trajectory Prediction Using Spatiotemporal Interactions and a Generative Adversarial Network, IEEE Access. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=9387292)]\n- Holistic LSTM for Pedestrian Trajectory Prediction, TIP. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9361440)]\n- Pedestrian trajectory prediction with convolutional neural networks, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320321004325)]\n- LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320320306038)]\n- Human trajectory prediction and generation using LSTM models and GANs, PR. [[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS003132032100323X)]\n- Vehicle trajectory prediction and generation using LSTM models and GANs, Plos one. [[paper](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0253868)]\n- BiTraP: Bi-Directional Pedestrian Trajectory Prediction With Multi-Modal Goal Estimation, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9345445)] [[code](https:\u002F\u002Fgithub.com\u002Fumautobots\u002Fbidireaction-trajectory-prediction)]\n- A Kinematic Model for Trajectory Prediction in General Highway Scenarios, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9472993)] [[code](https:\u002F\u002Fgithub.com\u002Fumautobots\u002Fkinematic_highway)]\n- Trajectory Prediction in Autonomous Driving With a Lane Heading Auxiliary Loss, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9387075)]\n- Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9366373)]\n- Tra2Tra: Trajectory-to-Trajectory Prediction With a Global Social Spatial-Temporal Attentive Neural Network, RAL. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9347678)]\n- Social graph convolutional LSTM for pedestrian trajectory prediction, IET Intelligent Transport Systems. [[paper](https:\u002F\u002Fietresearch.onlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1049\u002Fitr2.12033)]\n- HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction, IEEE Transactions on Vehicular Technology (TVT). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9548801)]\n- Environment-Attention Network for Vehicle Trajectory Prediction, TVT. [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9534487)]\n- Where Are They Going? Predicting Human Behaviors in Crowded Scenes, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). [[paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3449359)]\n- Multi-Agent Trajectory Prediction with Spatio-Temporal Sequence Fusion, IEEE Transactions on Multimedia (TMM). [[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9580659)]\n- EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NeurIPS 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- V2VNet- Vehicle-to-Vehicle Communication for Joint Perception and Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.07519)]\n- SMART- Simultaneous Multi-Agent Recurrent Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.13078)]\n- SimAug- Learning Robust Representations from Simulation for Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.02022)]\n- Learning Lane Graph Representations for Motion Forecasting, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.13732)]\n- Implicit Latent Variable Model for Scene-Consistent Motion Forecasting, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.12036)]\n- Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03212)]\n- Semantic Synthesis of Pedestrian Locomotion, ACCV 2020. [[Paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FACCV2020\u002Fhtml\u002FPriisalu_Semantic_Synthesis_of_Pedestrian_Locomotion_ACCV_2020_paper.html)]\n- Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction, CoRL 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.05127)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fwzhi\u002FKernelTrajectoryMaps)\\]\n- Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network, 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.06241)\\]\n- Social NCE: Contrastive Learning of Socially-aware Motion Representations. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11717)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fsocial-nce)\\]\n- Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks, ICPR International Workshops and Challenges 2020. \\[[paper](https:\u002F\u002Fwww.springerprofessional.de\u002Fpose-based-trajectory-forecast-of-vulnerable-road-users-using-re\u002F18885576)\\]\n- EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NeurIPS 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)\\]\n- Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.08514)]\n- It is not the Journey but the Destination- Endpoint Conditioned Trajectory Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.02025)]\n- How Can I See My Future? FvTraj: Using First-person View for Pedestrian Trajectory Prediction, ECCV 2020. [[paper](http:\u002F\u002Fgraphics.cs.uh.edu\u002Fwp-content\u002Fpapers\u002F2020\u002F2020-ECCV-PedestrianTrajPrediction.pdf)]\n- Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction, ECCV 2020. [[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.00777)]\n- Human Trajectory Forecasting in Crowds: A Deep Learning Perspective, 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.03639.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Ftrajnetplusplusbaselines)\\]\n- SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen cameras, ECCV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.02022)\\], \\[[code](https:\u002F\u002Fgithub.com\u002FJunweiLiang\u002FMultiverse)\\]\n- DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting, ICPR 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12661)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Falexmonti19\u002Fdagnet)\\]\n- Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision, WACV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.01138)\\]\n- Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network, 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.06241)\\]\n- Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction, CVPR 2020. \\[[Paper](\u003Chttps:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.11927.pdf>)\\], \\[[Code](\u003Chttps:\u002F\u002Fgithub.com\u002Fabduallahmohamed\u002FSocial-STGCNN\u002F>)\\]\n- The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction, CVPR 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.06445.pdf)\\], \\[[code\u002Fdataset](https:\u002F\u002Fnext.cs.cmu.edu\u002Fmultiverse\u002Findex.html)\\]\n- Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision, WACV 2020. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.01138)\\]\n- Pose Based Trajectory Forecast of Vulnerable Road Users, SSCI 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9003023)\\]\n- The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FIvanovic_The_Trajectron_Probabilistic_Multi-Agent_Trajectory_Modeling_With_Dynamic_Spatiotemporal_Graphs_ICCV_2019_paper.pdf)\\] \\[[code](https:\u002F\u002Fgithub.com\u002FStanfordASL\u002FTrajectron)\\]\n- STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FHuang_STGAT_Modeling_Spatial-Temporal_Interactions_for_Human_Trajectory_Prediction_ICCV_2019_paper.pdf)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Fhuang-xx\u002FSTGAT)\\]\n- Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FKim_Instance-Level_Future_Motion_Estimation_in_a_Single_Image_Based_on_ICCV_2019_paper.pdf)\\]\n- Social and Scene-Aware Trajectory Prediction in Crowded Spaces, ICCV workshop 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08840.pdf)\\] \\[[code](https:\u002F\u002Fgithub.com\u002FOghma\u002Fsns-lstm\u002F)\\]\n- Stochastic Sampling Simulation for Pedestrian Trajectory Prediction, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.01860)\\]\n- Long-Term Prediction of Motion Trajectories Using Path Homology Clusters, IROS 2019. \\[[paper](http:\u002F\u002Fwww.csc.kth.se\u002F~fpokorny\u002Fstatic\u002Fpublications\u002Fcarvalho2019a.pdf)\\]\n- StarNet: Pedestrian Trajectory Prediction Using Deep Neural Network in Star Topology, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.01797.pdf)\\]\n- Learning Generative Socially-Aware Models of Pedestrian Motion, IROS 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8760356\u002F)\\]\n- Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model, CVWW 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.05437.pdf)\\]\n- Path predictions using object attributes and semantic environment, VISIGRAPP 2019. \\[[paper](http:\u002F\u002Fmprg.jp\u002Fdata\u002FMPRG\u002FC_group\u002FC20190225_minoura.pdf)\\]\n- Probabilistic Path Planning using Obstacle Trajectory Prediction, CoDS-COMAD 2019. \\[[paper](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3297006)\\]\n- Human Trajectory Prediction using Adversarial Loss, hEART 2019. \\[[paper](http:\u002F\u002Fwww.strc.ch\u002F2019\u002FKothari_Alahi.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002FAdversarialLoss-SGAN)\\]\n- Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs, CVPR 2019. \\[[*Precognition Workshop*](https:\u002F\u002Fsites.google.com\u002Fview\u002Fieeecvf-cvpr2019-precognition)\\], \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FPrecognition\u002FAmirian_Social_Ways_Learning_Multi-Modal_Distributions_of_Pedestrian_Trajectories_With_GANs_CVPRW_2019_paper.pdf)\\], \\[[code](\u003Chttps:\u002F\u002Fgithub.com\u002Famiryanj\u002Fsocialways>)\\]\n- Peeking into the Future: Predicting Future Person Activities and Locations in Videos, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FLiang_Peeking_Into_the_Future_Predicting_Future_Person_Activities_and_Locations_CVPR_2019_paper.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fnext-prediction)\\]\n- Learning to Infer Relations for Future Trajectory Forecast, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FPrecognition\u002FChoi_Learning_to_Infer_Relations_for_Future_Trajectory_Forecast_CVPRW_2019_paper.pdf)\\]\n- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FChandra_TraPHic_Trajectory_Prediction_in_Dense_and_Heterogeneous_Traffic_Using_Weighted_CVPR_2019_paper.pdf>)\\]\n- Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FLi_Which_Way_Are_You_Going_Imitative_Decision_Learning_for_Path_CVPR_2019_paper.pdf>)\\]\n- Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FMakansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.pdf>)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Flmb-freiburg\u002FMultimodal-Future-Prediction)\\]\n- Sophie: An attentive gan for predicting paths compliant to social and physical constraints, CVPR 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01482)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002FREADME.md)\\]\n- Pedestrian path, pose, and intention prediction through gaussian process dynamical models and pedestrian activity recognition, 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8370119\u002F)\\]\n- Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06887)\\]\n- The simpler the better: Constant velocity for pedestrian motion prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.07933)\\]\n- Pedestrian trajectory prediction in extremely crowded scenarios, 2019. \\[[paper](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpubmed\u002F30862018)\\]\n- Srlstm: State refinement for lstm towards pedestrian trajectory prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.02793)\\]\n- Location-velocity attention for pedestrian trajectory prediction, WACV 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8659060)\\]\n- Pedestrian Trajectory Prediction in Extremely Crowded Scenarios, Sensors, 2019. \\[[paper](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F19\u002F5\u002F1223\u002Fpdf)\\]\n- Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.01118.pdf)\\] \\[[code](https:\u002F\u002Fgamma.umd.edu\u002Fresearchdirections\u002Fautonomousdriving\u002Fspectralcows\u002F)\\]\n- Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FBi_Joint_Prediction_for_Kinematic_Trajectories_in_Vehicle-Pedestrian-Mixed_Scenes_ICCV_2019_paper.pdf)\\]\n- Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FThiede_Analyzing_the_Variety_Loss_in_the_Context_of_Probabilistic_Trajectory_ICCV_2019_paper.pdf)\\]\n- Looking to Relations for Future Trajectory Forecast, ICCV 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FChoi_Looking_to_Relations_for_Future_Trajectory_Forecast_ICCV_2019_paper.pdf)\\]\n- Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.04586)\\]\n- Sharing Is Caring: Socially-Compliant Autonomous Intersection Negotiation, IROS 2019. \\[[paper](https:\u002F\u002Fpdfs.semanticscholar.org\u002Ff4b2\u002F021353bba52224eb33923b3b98956e2c9821.pdf)\\]\n- INFER: INtermediate Representations for FuturE PRediction, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.10641)\\] \\[[code](https:\u002F\u002Fgithub.com\u002Ftalsperre\u002FINFER)\\]\n- Deep Predictive Autonomous Driving Using Multi-Agent Joint Trajectory Prediction and Traffic Rules, IROS 2019. \\[[paper](http:\u002F\u002Frllab.snu.ac.kr\u002Fpublications\u002Fpapers\u002F2019_iros_predstl.pdf)\\]\n- NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.10971)\\]\n- Urban Street Trajectory Prediction with Multi-Class LSTM Networks, IROS 2019. \\[N\u002FA\\]\n- Spatiotemporal Learning of Directional Uncertainty in Urban Environments with Kernel Recurrent Mixture Density Networks, IROS 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8772158)\\]\n- Conditional generative neural system for probabilistic trajectory prediction, IROS 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.01631)\\]\n- Interaction-aware multi-agent tracking and probabilistic behavior prediction via adversarial learning, ICRA 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02390)\\]\n- Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving, IEEE Trans. Intell. Transport. Systems, 2019. \\[[paper](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F334560415_Generic_Tracking_and_Probabilistic_Prediction_Framework_and_Its_Application_in_Autonomous_Driving)\\]\n- Coordination and trajectory prediction for vehicle interactions via bayesian generative modeling, IV 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.00587)\\]\n- Wasserstein generative learning with kinematic constraints for probabilistic interactive driving behavior prediction, IV 2019. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8813783)\\]\n- GRIP: Graph-based Interaction-aware Trajectory Prediction, ITSC 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.07792)\\]\n- AGen: Adaptable Generative Prediction Networks for Autonomous Driving, IV 2019. \\[[paper](http:\u002F\u002Fwww.cs.cmu.edu\u002F~cliu6\u002Ffiles\u002Fiv19-1.pdf)\\]\n- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, CVPR 2019.  \\[[paper](\u003Chttp:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FChandra_TraPHic_Trajectory_Prediction_in_Dense_and_Heterogeneous_Traffic_Using_Weighted_CVPR_2019_paper.pdf>)\\], \\[[code](https:\u002F\u002Fgithub.com\u002Frohanchandra30\u002FTrackNPred)\\]\n- Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks, CVPR 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.09395.pdf)\\]\n- Argoverse: 3D Tracking and Forecasting With Rich Maps, CVPR 2019 \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FChang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf)\\]\n- Robust Aleatoric Modeling for Future Vehicle Localization, CVPR 2019. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPRW_2019\u002Fpapers\u002FPrecognition\u002FHudnell_Robust_Aleatoric_Modeling_for_Future_Vehicle_Localization_CVPRW_2019_paper.pdf)\\]\n- Pedestrian occupancy prediction for autonomous vehicles, IRC 2019. \\[paper\\]\n- Context-based path prediction for targets with switching dynamics, 2019.\\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-018-1104-4)\\]\n- Deep Imitative Models for Flexible Inference, Planning, and Control, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.06544)\\]\n- Infer: Intermediate representations for future prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.10641)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Ftalsperre\u002FINFER)\\]\n- Multi-agent tensor fusion for contextual trajectory prediction, 2019. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.04776)\\]\n- Context-Aware Pedestrian Motion Prediction In Urban Intersections, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.09453)\\]\n- Generic probabilistic interactive situation recognition and prediction: From virtual to real, ITSC 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8569780)\\]\n- Generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter, IV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8500626)\\]\n- Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.05499)\\]\n- Sequence-to-sequence prediction of vehicle trajectory via lstm encoder-decoder architecture, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06338)\\]\n- R2P2: A ReparameteRized Pushforward Policy for diverse, precise generative path forecasting, ECCV 2018. \\[[paper](https:\u002F\u002Fwww.cs.cmu.edu\u002F~nrhineha\u002FR2P2.html)\\]\n- Predicting trajectories of vehicles using large-scale motion priors, IV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8500604)\\]\n- Vehicle trajectory prediction by integrating physics-and maneuver based approaches using interactive multiple models, 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8186191)\\]\n- Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05819v1)\\]\n- Generative multi-agent behavioral cloning, 2018. \\[[paper](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerative-Multi-Agent-Behavioral-Cloning-Zhan-Zheng\u002Fccc196ada6ec9cad1e418d7321b0cd6813d9b261)\\]\n- Deep Sequence Learning with Auxiliary Information for Traffic Prediction, KDD 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.07380.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002FJingqingZ\u002FBaiduTraffic)\\]\n- A data-driven model for interaction-aware pedestrian motion prediction in object cluttered environments, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.08528)\\]\n- Move, Attend and Predict: An attention-based neural model for people’s movement prediction, Pattern Recognition Letters 2018. \\[[paper](https:\u002F\u002Freader.elsevier.com\u002Freader\u002Fsd\u002Fpii\u002FS016786551830182X?token=1EF2B664B70D2B0C3ECDD07B6D8B664F5113AEA7533CE5F0B564EF9F4EE90D3CC228CDEB348F79FEB4E8CDCD74D4BA31)\\]\n- GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds, ACCV 2018, \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.07667.pdf)\\], \\[[demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7cCIC_JIfms)\\]\n- Ss-lstm: a hierarchical lstm model for pedestrian trajectory prediction, WACV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8354239)\\]\n- Social Attention: Modeling Attention in Human Crowds, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.04689)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FTNTant\u002Fsocial_lstm)\\]\n- Pedestrian prediction by planning using deep neural networks, ICRA 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05904)\\]\n- Joint long-term prediction of human motion using a planning-based social force approach, ICRA 2018. \\[[paper](https:\u002F\u002Filiad-project.eu\u002Fpublications\u002F2018-2\u002Fjoint-long-term-prediction-of-human-motion-using-a-planning-based-social-force-approach\u002F)\\]\n- Human motion prediction under social grouping constraints, IROS 2018. \\[[paper](http:\u002F\u002Filiad-project.eu\u002Fpublications\u002F2018-2\u002Fhuman-motion-prediction-under-social-grouping-constraints\u002F)\\]\n- Future Person Localization in First-Person Videos, CVPR 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FYagi_Future_Person_Localization_CVPR_2018_paper.pdf)\\]\n- Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks, CVPR 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10892)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fagrimgupta92\u002Fsgan)\\]\n- Group LSTM: Group Trajectory Prediction in Crowded Scenarios, ECCV 2018. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-11015-4_18)\\]\n- Mx-lstm: mixing tracklets and vislets to jointly forecast trajectories and head poses, CVPR 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00652)\\]\n- Intent prediction of pedestrians via motion trajectories using stacked recurrent neural networks, 2018. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8481390\u002F)\\]\n- Transferable pedestrian motion prediction models at intersections, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00495)\\]\n- Probabilistic map-based pedestrian motion prediction taking traffic participants into consideration, 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8500562)\\]\n- A Computationally Efficient Model for Pedestrian Motion Prediction, ECC 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.04702)\\]\n- Context-aware trajectory prediction, ICPR 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02503)\\]\n- Set-based prediction of pedestrians in urban environments considering formalized traffic rules, ITSC 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8569434)\\]\n- Building prior knowledge: A markov based pedestrian prediction model using urban environmental data, ICARCV 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.06045)\\]\n- Depth Information Guided Crowd Counting for Complex Crowd Scenes, 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.02256)\\]\n- Tracking by Prediction: A Deep Generative Model for Mutli-Person Localisation and Tracking, WACV 2018. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.03347)\\]\n- “Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention, WACV 2018. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8354238)\\]\n- Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty, CVPR 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FBhattacharyya_Long-Term_On-Board_Prediction_CVPR_2018_paper.pdf)\\], \\[[code+data](https:\u002F\u002Fgithub.com\u002Fapratimbhattacharyya18\u002Fonboard_long_term_prediction)\\]\n- Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction, CVPR 2018. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FXu_Encoding_Crowd_Interaction_CVPR_2018_paper.pdf)\\], \\[[code](https:\u002F\u002Fgithub.com\u002FShanghaiTechCVDL\u002FCIDNN)\\]\n- Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction, 2017. \\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10514-017-9619-z)\\]\n- Probabilistic long-term prediction for autonomous vehicles, IV 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7995726)\\]\n- Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network, ITSC 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6632960)\\]\n- Desire: Distant future prediction in dynamic scenes with interacting agents, CVPR 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04394)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fyadrimz\u002FDESIRE)\\]\n- Imitating driver behavior with generative adversarial networks, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.06699)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fsisl\u002Fgail-driver)\\]\n- Infogail: Interpretable imitation learning from visual demonstrations, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.08840)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FYunzhuLi\u002FInfoGAIL)\\]\n- Long-term planning by short-term prediction, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.01580)\\]\n- Long-term path prediction in urban scenarios using circular distributions, 2017. \\[[paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0262885617301853)\\]\n- Deep learning driven visual path prediction from a single image, 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.07265)\\]\n- Walking Ahead: The Headed Social Force Model, 2017. \\[[paper](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0169734)\\]\n- Real-time certified probabilistic pedestrian forecasting, 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7959047)\\]\n- A multiple-predictor approach to human motion prediction, ICRA 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7989265)\\]\n- Forecasting interactive dynamics of pedestrians with fictitious play, CVPR 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.01431)\\]\n- Forecast the plausible paths in crowd scenes, IJCAI 2017. \\[[paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2017\u002F386)\\]\n- Bi-prediction: pedestrian trajectory prediction based on bidirectional lstm classification, DICTA 2017. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8227412\u002F)\\]\n- Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos, IJCAI 2017. \\[[paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2017\u002F17)\\]\n- Natural vision based method for predicting pedestrian behaviour in urban environments, ITSC 2017. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8317848\u002F)\\]\n- Human Trajectory Prediction using Spatially aware Deep Attention Models, 2017. [[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.09436.pdf)\\]\n- Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection, 2017. \\[[paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1702.05552.pdf)\\]\n- Forecasting Interactive Dynamics of Pedestrians with Fictitious Play, CVPR 2017. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FMa_Forecasting_Interactive_Dynamics_CVPR_2017_paper.pdf)\\]\n- Social LSTM: Human trajectory prediction in crowded spaces, CVPR 2016. \\[[paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2016\u002Fhtml\u002FAlahi_Social_LSTM_Human_CVPR_2016_paper.html)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Ftrajnetplusplusbaselines)\\]\n- Comparison and evaluation of pedestrian motion models for vehicle safety systems, ITSC 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7795912)\\]\n- Age and Group-driven Pedestrian Behaviour: from Observations to Simulations, 2016. \\[[paper](https:\u002F\u002Fcollective-dynamics.eu\u002Findex.php\u002Fcod\u002Farticle\u002Fview\u002FA3)\\]\n- Structural-RNN: Deep learning on spatio-temporal graphs, CVPR 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.05298)\\]\\[[code](https:\u002F\u002Fgithub.com\u002Fasheshjain399\u002FRNNexp)\\]\n- Intent-aware long-term prediction of pedestrian motion, ICRA 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487409)\\]\n- Context-based detection of pedestrian crossing intention for autonomous driving in urban environments, IROS 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7759351\u002F)\\]\n- Novel planning-based algorithms for human motion prediction, ICRA 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487505)\\]\n- Learning social etiquette: Human trajectory understanding in crowded scenes, ECCV 2016. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-46484-8_33)\\]\\[[code](https:\u002F\u002Fgithub.com\u002FSajjadMzf\u002FPedestrian_Datasets_VIS)\\]\n- GLMP-realtime pedestrian path prediction using global and local movement patterns, ICRA 2016. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487768\u002F)\\]\n- Knowledge transfer for scene-specific motion prediction, ECCV 2016. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.06987)\\]\n- STF-RNN: Space Time Features-based Recurrent Neural Network for predicting People Next Location, SSCI 2016. \\[[code](https:\u002F\u002Fgithub.com\u002Fmhjabreel\u002FSTF-RNN)\\]\n- Goal-directed pedestrian prediction, ICCV 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7406377)\\]\n- Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations, 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7225707)\\]\n- Predicting and recognizing human interactions in public spaces, 2015. \\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11554-014-0428-8)\\]\n- Learning collective crowd behaviors with dynamic pedestrian-agents, 2015. \\[[paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11263-014-0735-3)\\]\n- Modeling spatial-temporal dynamics of human movements for predicting future trajectories, AAAI 2015. \\[[paper](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FWS\u002FAAAIW15\u002Fpaper\u002Fview\u002F10126)\\]\n- Unsupervised robot learning to predict person motion, ICRA 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7139254)\\]\n- A controlled interactive multiple model filter for combined pedestrian intention recognition and path prediction, ITSC 2015. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7313129\u002F)\\]\n- Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions, 2014. \\[[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1405.5581)\\]\n- Behavior estimation for a complete framework for human motion prediction in crowded environments, ICRA 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6907734)\\]\n- Pedestrian’s trajectory forecast in public traffic with artificial neural network, ICPR 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6977417)\\]\n- Will the pedestrian cross? A study on pedestrian path prediction, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6632960)\\]\n- BRVO: Predicting pedestrian trajectories using velocity-space reasoning, 2014. \\[[paper](https:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002Fabs\u002F10.1177\u002F0278364914555543)\\]\n- Context-based pedestrian path prediction, ECCV 2014. \\[[paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-10599-4_40)\\]\n- Pedestrian path prediction using body language traits, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856498\u002F)\\]\n- Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856480)\\]\n- Learning intentions for improved human motion prediction, 2013. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6766565)\\]\n- Understanding interactions between traffic participants based on learned behaviors, 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7535554)\\]\n- Visual path prediction in complex scenes with crowded moving objects, CVPR 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7780661\u002F)\\]\n- A game-theoretic approach to replanning-aware interactive scene prediction and planning, 2016. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7353203)\\]\n- Intention-aware online pomdp planning for autonomous driving in a crowd, ICRA 2015. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7139219)\\]\n- Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014. \\[[paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6856480)\\]\n- Patch to the future: Unsupervised visual prediction, CVPR 2014. \\[[paper](http:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6909818\u002F)\\]\n- Mobile agent trajectory prediction using bayesian nonparametric reachability trees, 2011. \\[[paper](https:\u002F\u002Fdspace.mit.edu\u002Fhandle\u002F1721.1\u002F114899)\\]\n\n### 移动机器人\n- 基于行人未来运动概率预测的拥挤人群预见性导航，ICRA 2021。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.06235)]\n- Social NCE：社会感知运动表示的对比学习。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11717)]，[[代码](https:\u002F\u002Fgithub.com\u002Fvita-epfl\u002Fsocial-nce)]\n- 面向人机交互的多模态概率模型规划，ICRA 2018。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09483)] [[代码](https:\u002F\u002Fgithub.com\u002FStanfordASL\u002FTrafficWeavingCVAE)]\n- 基于深度强化学习的去中心化无通信多智能体避障，ICRA 2017。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.07845)]\n- 用于运动预测的增强字典学习，ICRA 2016。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7487407)]\n- 针对动态环境的未来智能体运动预测，ICMLA 2016。[[论文](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPredicting-Future-Agent-Motions-for-Dynamic-Previtali-Bordallo\u002F2df8179ac7b819bad556b6d185fc2030c40f98fa)]\n- 基于贝叶斯意图推理的未知目标轨迹预测，IROS 2015。[[论文](http:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7354203\u002F)]\n- 学习预测协同导航智能体的轨迹，ICRA 2014。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6907442)]\n\n\n### 体育运动员\n\n- EvolveGraph：基于动态关系推理的多智能体轨迹预测，NeurIPS 2020。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.13924)]\n- 用于轨迹预测与插补的模仿式非自回归建模，CVPR 2020。[[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2020\u002Fhtml\u002FQi_Imitative_Non-Autoregressive_Modeling_for_Trajectory_Forecasting_and_Imputation_CVPR_2020_paper.html)]\n- DAG-Net：用于轨迹预测的双重注意力图神经网络，ICPR 2020。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12661)] [[代码](https:\u002F\u002Fgithub.com\u002Falexmonti19\u002Fdagnet)]\n- 多智能体体育比赛中的多样化生成，CVPR 2019。[[论文](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fhtml\u002FYeh_Diverse_Generation_for_Multi-Agent_Sports_Games_CVPR_2019_paper.html)]\n- 基于部分观测的多智能体交互随机预测，ICLR 2019。[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1902.09641v1)]\n- 利用程序化弱监督生成多智能体轨迹，ICLR 2019。[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1803.07612v6)]\n- 生成式多智能体行为克隆，ICML 2018。[[论文](http:\u002F\u002Fwww.stephanzheng.com\u002Fpdf\u002FZhan_Zheng_Lucey_Yue_Generative_Multi_Agent_Behavioral_Cloning.pdf)]\n- 他们将去往何处？利用条件变分自编码器预测精细粒度的对抗性多智能体运动，ECCV 2018。[[论文](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ECCV_2018\u002Fpapers\u002FPanna_Felsen_Where_Will_They_ECCV_2018_paper.pdf)]\n- 协调式多智能体模仿学习，ICML 2017。[[论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1703.03121v2)]\n- 使用深度层次网络生成长期轨迹，2017年。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.07138)]\n- 学习用于动态体育比赛预测的精细空间模型，ICDM 2014。[[论文](http:\u002F\u002Fwww.yisongyue.com\u002Fpublications\u002Ficdm2014_bball_predict.pdf)]\n- 多模态多人行为的生成式建模，2018年。[[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.02015.pdf)]\n- 接下来会发生什么？体育视频中球员动作的预测，ICCV 2017，[[论文](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2017\u002Fpapers\u002FFelsen_What_Will_Happen_ICCV_2017_paper.pdf)]\n\n### 基准与评估指标\n- 无人机数据集轨迹预测研究的预处理与评估工具箱，arXiv预印本arXiv:2405.00604，2024年。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.00604)] [[代码](https:\u002F\u002Fgithub.com\u002Fwestny\u002Fdronalize)]\n- Social-Implicit：重新思考轨迹预测评估及隐式最大似然估计的有效性，ECCV 2022。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03057)] [[代码](https:\u002F\u002Fgithub.com\u002Fabduallahmohamed\u002FSocial-Implicit)]\n- OpenTraj：评估人类轨迹数据集中预测的复杂性，ACCV 2020。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00890)] [[代码](https:\u002F\u002Fgithub.com\u002Fcrowdbotp\u002FOpenTraj)]\n- 通过模拟感知与预测测试自动驾驶车辆的安全性，ECCV 2020。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.06020)]\n- PIE：用于行人意图估计和轨迹预测的大规模数据集及模型，ICCV 2019。[[论文](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FRasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.pdf)]\n- 面向高度交互驾驶场景的概率性反应预测的致死率敏感型基准，ITSC 2018。[[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.03478)]\n- 我的预测有多好？寻找轨迹预测评估的相似性度量，ITSC 2017。[[论文](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8317825\u002F)]\n- Trajnet：迈向人类轨迹预测的基准。[[网站](http:\u002F\u002Ftrajnet.epfl.ch\u002F)]\n\n### 其他\n- 基于姿态的骑行者出发意图检测，ITSC 2019。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8917215)]\n- 使用双向循环神经网络进行骑行者轨迹预测，AI 2018。[[论文](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-03991-2_28)]\n- 用于轨迹预测的道路基础设施指标，2018年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8500678)]\n- 利用道路拓扑改善骑行者路径预测，2017年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7995734\u002F)]\n- 基于物理模型和人工神经网络的骑行者轨迹预测，2016年。[[论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7535484\u002F)]","# Awesome-Interaction-Aware-Trajectory-Prediction 快速上手指南\n\n本项目并非一个单一的代码库，而是一个**精选资源清单**，汇集了状态最先进的（SOTA）轨迹预测相关研究材料，包括数据集、博客、论文和开源代码。本指南将帮助您快速利用该清单获取所需资源并开展研究。\n\n## 环境准备\n\n由于本项目整合了多个不同的开源项目和数据集，具体的系统要求和依赖项取决于您选择使用的具体算法或数据集。以下是通用的基础环境建议：\n\n*   **操作系统**: Linux (推荐 Ubuntu 18.04\u002F20.04) 或 macOS。\n*   **编程语言**: Python 3.7+ (大多数深度学习项目的主流版本)。\n*   **核心依赖**:\n    *   `git`: 用于克隆仓库和子模块。\n    *   `conda` 或 `venv`: 强烈建议使用虚拟环境管理不同项目的依赖。\n    *   `PyTorch` 或 `TensorFlow`: 根据您选择的具体论文代码而定（目前学术界 PyTorch 更为普遍）。\n*   **硬件要求**: 若需复现深度学习模型，建议配备 NVIDIA GPU (支持 CUDA)；若仅用于查阅文献或处理小型数据集，CPU 即可。\n\n## 安装步骤\n\n### 1. 克隆资源清单仓库\n首先，将该清单仓库克隆到本地，以便查阅最新的论文链接和代码库地址。\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fjiachenli94\u002FAwesome-Interaction-Aware-Trajectory-Prediction.git\ncd Awesome-Interaction-Aware-Trajectory-Prediction\n```\n\n### 2. 获取具体项目代码\n浏览仓库中的 `README.md` 文件，在 **Literature and Codes** 部分找到您感兴趣的论文或项目链接。点击链接进入对应的 GitHub 仓库，然后按照该项目自身的说明进行安装。\n\n*示例：假设您选择了列表中的某个基于 PyTorch 的项目：*\n```bash\n# 进入您选定的具体项目目录 (此处为示例路径)\ngit clone \u003C具体项目的 GitHub 地址>\ncd \u003C具体项目文件夹>\n\n# 创建虚拟环境 (推荐)\nconda create -n traj_pred python=3.8\nconda activate traj_pred\n\n# 安装该项目特定的依赖 (通常包含在 requirements.txt 中)\npip install -r requirements.txt\n```\n\n### 3. 下载数据集\n在 **Datasets** 部分选择适合您场景的数据集（如车辆、行人或运动员）。\n*   **注意**: 大部分数据集（如 Waymo, Argoverse, nuScenes）需要在其官方网站注册并同意许可协议后下载。\n*   **国内加速建议**: 对于大型数据集，如果官方源下载缓慢，可尝试在开源社区（如 OpenDataLab、ModelScope 或 AI Studio）搜索是否有国内镜像托管。\n\n## 基本使用\n\n由于这是一个资源索引库，\"使用\"的核心在于**复现清单中列出的具体算法**。以下是一个通用的工作流示例，展示如何利用清单中的资源进行一个简单的轨迹预测实验：\n\n### 步骤 1: 选择基准模型\n在 `README.md` 的 **Intelligent Vehicles and Pedestrians** 或 **Survey Papers** 章节中，找到一篇高引用的论文（例如 `EvolveGraph` 或 `Social-LSTM`），并记录其代码仓库地址。\n\n### 步骤 2: 准备数据\n下载清单中推荐的标准数据集（例如 `ETH\u002FUCY` 行人数据集或 `Argoverse` 车辆数据集），并按照所选代码仓库的要求整理目录结构。\n\n```bash\n# 示例：将下载的数据集移动到项目指定的 data 目录\nmkdir -p data\u002Feth\nmv \u003C下载的数据文件> data\u002Feth\u002F\n```\n\n### 步骤 3: 运行训练与评估\n进入具体项目的代码目录，运行提供的训练脚本。大多数项目会提供类似的命令：\n\n```bash\n# 示例命令：启动训练过程\npython train.py --data_dir data\u002Feth --model_name evolvegraph --epochs 50\n\n# 示例命令：在测试集上评估模型\npython evaluate.py --checkpoint checkpoints\u002Fbest_model.pth --dataset eth\n```\n\n### 步骤 4: 结果分析\n查看生成的指标（如 ADE\u002FFDE - 平均位移误差\u002F最终位移误差），并与 `README.md` 中 **Benchmark and Evaluation Metrics** 部分提到的标准进行对比。\n\n---\n**提示**: 如果您发现了新的优质资源或代码，欢迎通过 Pull Request 贡献到此清单仓库，或与维护者（Jiachen Li 等）发送邮件交流。","某自动驾驶初创公司的算法团队正在开发城市复杂路口的预测模块，急需提升车辆对行人和其他车辆交互行为的预判能力。\n\n### 没有 Awesome-Interaction-Aware-Trajectory-Prediction 时\n- **数据筛选耗时巨大**：工程师需花费数周在海量文献中手动寻找适合“人车混行”场景的高质量数据集（如 nuScenes 或 Argoverse），效率极低。\n- **模型选型盲目**：缺乏对前沿交互感知算法（如动态关系推理）的系统梳理，团队只能复用过时的独立轨迹预测模型，导致路口博弈场景下事故率高。\n- **评估标准缺失**：没有统一的基准测试和评估指标参考，难以量化新模型在复杂交互下的真实性能，研发迭代方向模糊。\n- **代码复现困难**：找不到官方开源代码或相关技术博客，从零实现论文算法不仅周期长，还极易因细节缺失导致效果不达预期。\n\n### 使用 Awesome-Interaction-Aware-Trajectory-Prediction 后\n- **资源获取一站式完成**：团队直接利用清单中分类清晰的\"Vehicles and Traffic\"数据集板块，迅速锁定了包含激光雷达与摄像头融合数据的 Waymo Open Dataset。\n- **精准锁定 SOTA 方案**：通过\"Intelligent Vehicles and Pedestrians\"栏目，快速定位到处理多智能体动态关系的最新论文（如 EvolveGraph），显著提升了路口预测准确率。\n- **建立科学评估体系**：参考\"Benchmark and Evaluation Metrics\"部分提供的标准指标，团队建立了客观的模型对比框架，明确了优化路径。\n- **加速工程落地**：借助清单中附带的公开代码链接和技术综述，算法工程师在几天内就完成了基线模型的复现与微调，大幅缩短研发周期。\n\nAwesome-Interaction-Aware-Trajectory-Prediction 通过整合全球顶尖的交互感知轨迹预测资源，将研发团队从繁琐的信息搜集工作中解放出来，使其能专注于核心算法的创新与落地。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjiachenli94_Awesome-Interaction-Aware-Trajectory-Prediction_0887fa17.png","jiachenli94","Jiachen Li","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjiachenli94_322e5daf.jpg","Assistant Professor @ UC Riverside. Lead the Trustworthy Autonomous Systems Laboratory (TASL).","University of California, Riverside","Riverside, CA, USA",null,"JiachenLi8","https:\u002F\u002Fjiachenli94.github.io","https:\u002F\u002Fgithub.com\u002Fjiachenli94",[83],{"name":84,"color":85,"percentage":86},"TeX","#3D6117",100,1674,306,"2026-04-03T11:55:50","MIT",1,"","未说明",{"notes":95,"python":93,"dependencies":96},"该仓库是一个资源清单（Awesome List），汇集了轨迹预测领域的数据集、论文和开源代码链接，本身不是一个可独立运行的软件工具，因此 README 中未包含具体的操作系统、硬件配置或依赖库安装要求。用户需根据列表中引用的具体子项目（如 EvolveGraph 等）查阅其各自的文档以获取运行环境需求。",[],[14,15,60,13],[99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118],"trajectory-prediction","trajectory-generation","social-interactions","behavior-prediction","computer-vision","machine-learning","artificial-intelligence","deep-learning","autonomous-driving","autonomous-vehicles","multiagent-systems","multiagent-learning","motion-prediction","paper","path-predictions","vehicle-trajectory","traffic","pedestrian-trajectories","human-trajectory-prediction","behavior-analysis","2026-03-27T02:49:30.150509","2026-04-06T21:14:20.509404",[],[]]