令人惊叹的交互感知行为与轨迹预测

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）

维护者: 李嘉辰（斯坦福大学）；马恒博、李锦宁（加州大学伯克利分校）

邮箱: jiachen_li@stanford.edu; {hengbo_ma, jinning_li}@berkeley.edu

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。

注: 这里 也是强化学习、决策制定和运动规划相关资料的集合。

如果您觉得本仓库有用，请考虑引用我们的工作：

@inproceedings{li2020evolvegraph,
  title={EvolveGraph: 多智能体动态关系推理的轨迹预测},
  author={李嘉辰、杨帆、富冢昌义、崔致浩},
  booktitle={2020年神经信息处理系统大会 (NeurIPS)},
  year={2020}
}

@inproceedings{li2019conditional,
  title={用于概率轨迹预测的条件生成神经网络系统},
  author={李嘉辰、马恒博、富冢昌义},
  booktitle={2019年IEEE/RSJ国际智能机器人与系统会议 (IROS)},
  pages={6150--6156},
  year={2019},
  organization={IEEE}
}

目录

• 数据集
  • 车辆与交通
  • 行人
  • 体育运动员
• 文献与代码
  • 综述论文
  • 具有交互作用的物理系统
  • 智能车辆与行人
  • 移动机器人
  • 体育运动员
  • 基准测试与评估指标
  • 其他

数据集

车辆与交通

数据集	智能体	场景	传感器
Waymo开放数据集	车辆 / 自行车骑行者 / 行人	城市 / 高速公路	LiDAR / 摄像头 / 雷达
Argoverse	车辆 / 自行车骑行者 / 行人	城市 / 高速公路	LiDAR / 摄像头 / 雷达
nuScenes	车辆	城市	摄像头 / LiDAR / 雷达
highD	车辆	高速公路	摄像头
inD	车辆	高速公路	摄像头
roundD	车辆	高速公路	摄像头
BDD100k	车辆 / 自行车骑行者 / 行人	高速公路 / 城市	摄像头
KITTI	车辆 / 自行车骑行者 / 行人	高速公路 / 农村地区	摄像头 / LiDAR
NGSIM	车辆	高速公路	摄像头
INTERACTION	车辆 / 自行车骑行者 / 行人	环岛 / 十字路口	摄像头
自行车骑行者数据集	自行车骑行者	城市	摄像头
Apolloscapes	车辆 / 自行车骑行者 / 行人	城市	摄像头
Udacity	车辆	城市	摄像头
Cityscapes	车辆 / 行人	城市	摄像头
斯坦福无人机数据集	车辆 / 自行车骑行者 / 行人	城市	摄像头
Argoverse	车辆 / 行人	城市	摄像头 / LiDAR
TRAF	车辆 / 公交车 / 自行车骑行者 / 摩托车骑行者 / 行人 / 动物	城市	摄像头
阿沙芬堡姿态数据集	自行车骑行者 / 行人	城市	摄像头

行人

数据集	代理	场景	传感器
UCY	人	zara / 学生	摄像机
ETH (ICCV09)	人	城市	摄像机
VIRAT	人 / 车辆	城市	摄像机
KITTI	车辆 / 自行车手 / 人	高速公路 / 农村地区	摄像机 / LiDAR
ATC	人	购物中心	测距传感器
Daimler	人	来自行驶的车辆	摄像机
中央车站	人	车站内部	摄像机
市中心	人	城市街道	摄像机
爱丁堡	人	城市	摄像机
Cityscapes	车辆 / 人	城市	摄像机
Argoverse	车辆 / 人	城市	摄像机 / LiDAR
斯坦福无人机	车辆 / 自行车手 / 人	城市	摄像机
TrajNet	人	城市	摄像机
PIE	人	城市	摄像机
ForkingPaths	人	城市 / 模拟	摄像机
TrajNet++	人	城市	摄像机
阿沙芬堡姿态数据集	自行车手 / 人	城市	摄像机
自行车手俯视数据集 (CTV)	自行车手 / 人	城市	摄像机

运动员

数据集	代理	场景	传感器
足球	人	足球场	摄像机
NBA SportVU	人	篮球馆	摄像机
NFL	人	美式橄榄球	摄像机

文献与代码

综述论文

• 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]
• 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]
• 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]
• 自动驾驶轨迹预测方法综述，IEEE T-IV 2022年。[论文]
• 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]
• 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]
• 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]
• 自动驾驶中的深度强化学习：综述，IEEE T-ITS，2022年。[论文]
• 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]
• 面向时空数据的生成对抗网络：综述，ACM T-IST，2022年。[论文]
• 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]
• 自动驾驶中行人与车辆运动预测综述，IEEE Access，2021年。[论文]
• 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]
• 轨迹数据管理、分析与学习综述，CSUR 2021年。[论文]
• 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]
• 基于深度学习的行人轨迹预测方法综述，Sensors，2021年。[论文]
• 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] [代码]
• 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]
• 人类运动轨迹预测：综述，国际机器人研究期刊，2020年。[论文]
• 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]
• 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]
• 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]
• 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]
• 车辆轨迹相似性：模型、方法与应用，ACM计算综述（CSUR 2020）。[论文]
• 人类驾驶员行为建模与预测：综述，2020年。[论文]
• 城市场景下行人行为预测的文献综述，ITSC 2018年。[论文]
• 基于视觉的路径预测综述。[论文]
• 与行人交互的自动驾驶车辆：理论与实践综述。[论文]
• 轨迹数据挖掘：概述。[论文]
• 智能车辆的运动预测与风险评估综述。[论文]

具有交互作用的物理系统

• 使用次等变图神经网络学习物理动力学，NeurIPS 2022。[论文] [代码]
• EvolveGraph：基于动态关系推理的多智能体轨迹预测，NeurIPS 2020。[论文]
• 多机器人系统的交互模板，IROS 2019。[论文]
• 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] [代码]
• 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]
• 用于交互系统的神经关系推理，ICML 2018。[论文] [代码]
• 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]
• 关系归纳偏置、深度学习与图网络，2018年。[论文]
• 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]
• 图网络作为可学习的物理引擎用于推理和控制，ICML 2018。[论文]
• 用于物理预测的灵活神经表示，2018年。[论文]
• 一种用于关系推理的简单神经网络模块，2017年。[论文]
• VAIN：基于注意力的多智能体预测建模，NeurIPS 2017。[论文]
• 视觉交互网络，2017年。[论文]
• 基于组合性对象的学习物理动力学方法，ICLR 2017。[论文]
• 用于学习物体、关系与物理规律的交互网络，2016年。[论文] [代码]

智能车辆、交通与行人

• Diffusion-Based Environment-Aware Trajectory Prediction, arXiv preprint arXiv:2403.11643, 2024. [paper]
• MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs, IEEE T-IV 2023. [paper] [code]
• MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion, CVPR 2023. [paper]
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction, CVPR 2023. [paper]
• Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction, CVPR 2023. [paper] [code]
• Planning-oriented Autonomous Driving, CVPR 2023. [paper] [code]
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction, CVPR 2023. [paper]
• Stimulus Verification is a Universal and Effective Sampler in Multi-modal Human Trajectory Prediction, CVPR 2023. [paper]
• Query-Centric Trajectory Prediction, CVPR 2023. [paper] [code] [QCNeXt]
• FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction, CVPR 2023. [paper]
• Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion, CVPR 2023. [paper] [website]
• FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs, CVPR 2023. [paper] [website]
• Leapfrog Diffusion Model for Stochastic Trajectory Prediction, CVPR 2023. [paper] [code]
• ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries, CVPR 2023. [paper] [website]
• EqMotion: Equivariant Multi-Agent Motion Prediction with Invariant Interaction Reasoning, CVPR 2023. [paper] [code]
• V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting, CVPR 2023. [paper] [code]
• Weakly Supervised Class-agnostic Motion Prediction for Autonomous Driving, CVPR 2023. [paper]
• Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction, CVPR 2023. [paper]
• HumanMAC: Masked Motion Completion for Human Motion Prediction, ICCV 2023. [paper] [code]
• BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction, ICCV 2023. [paper] [code]
• EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting, ICCV 2023. [paper] [code]
• ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation, ICCV 2023. [paper] [code]
• PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View, IJCAI 2023. [paper] [code]
• Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction, AAAI 2023. [paper]
• Multi-stream Representation Learning for Pedestrian Trajectory Prediction, AAAI 2023. [paper]
• Continuous Trajectory Generation Based on Two-Stage GAN, AAAI 2023. [paper] [code]
• A Set of Control Points Conditioned Pedestrian Trajectory Prediction, AAAI 2023. [paper] [code]
• Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction, ICLR 2023. [paper]
• TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios, ICRA 2023. [paper] [code]
• GANet: Goal Area Network for Motion Forecasting, ICRA 2023. [paper] [code]
• TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving, ICRA 2023. [paper]
• SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving, CoRL 2023. [paper] [code]
• LimSim: A Long-term Interactive Multi-scenario Traffic Simulator, ITSC 2023. [paper] [code]
• MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents, TITS. [paper]
• Adaptive and Simultaneous Trajectory Prediction for Heterogeneous Agents via Transferable Hierarchical Transformer Network, TITS. [paper]
• SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction, TNNLS. [paper] [code]
• Disentangling Crowd Interactions for Pedestrians Trajectory Prediction, RAL. [paper]
• VNAGT: Variational Non-Autoregressive Graph Transformer Network for Multi-Agent Trajectory Prediction, IEEE Transactions on Vehicular Technology. [paper]
• Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction, TIV. [paper]
• Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles, PR. [paper]
• Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction, PR. [paper]
• Multimodal Vehicular Trajectory Prediction With Inverse Reinforcement Learning and Risk Aversion at Urban Unsignalized Intersections, TITS. [paper]
• Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph, IET Intelligent Transport Systems. [paper]
• Social Self-Attention Generative Adversarial Networks for Human Trajectory Prediction, IEEE Transactions on Artificial Intelligence. [paper]
• CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction, TITS. [paper]
• Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks, RAL. [paper]
• A physics-informed Transformer model for vehicle trajectory prediction on highways, Transportation Research Part C: Emerging Technologies. [paper] [code]
• MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction, RAL. [paper]
• MRGTraj: A Novel Non-Autoregressive Approach for Human Trajectory Prediction, TCSVT. [paper] [code]
• Planning-inspired Hierarchical Trajectory Prediction via Lateral-Longitudinal Decomposition for Autonomous Driving, TIV. [paper]
• Traj-MAE: Masked Autoencoders for Trajectory Prediction, arXiv preprint arXiv:2303.06697, 2023. [paper]
• Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion, arXiv preprint arXiv:2303.08367, 2023. [paper]
• Diffusion Model for GPS Trajectory Generation, arXiv preprint arXiv:2304.11582, 2023. [paper]
• Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [paper] [website]
• Joint-Multipath++ for Simulation Agents: 2nd Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [paper] [code]
• 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] [code]
• GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving, arXiv preprint arXiv:2303.05760, 2023. [paper] [code] [website]
• 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] [code]
• trajdata: A Unified Interface to Multiple Human Trajectory Datasets, arXiv preprint arXiv:2307.13924, 2023. [paper] [code]
• Remember Intentions: Retrospective-Memory-based Trajectory Prediction, CVPR 2022. [paper] [code]
• STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes, CVPR 2022. [paper] [code]
• Vehicle trajectory prediction works, but not everywhere, CVPR 2022. [paper] [code]
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion, CVPR 2022. [paper] [code]
• Non-Probability Sampling Network for Stochastic Human Trajectory Prediction, CVPR 2022. [paper] [code]
• On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles, CVPR 2022. [paper] [code]
• Adaptive Trajectory Prediction via Transferable GNN, CVPR 2022. [paper]
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective, CVPR 2022. [paper] [code, code]
• How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting, CVPR 2022. [paper]
• Learning from All Vehicles, CVPR 2022. [paper] [code]
• Forecasting from LiDAR via Future Object Detection, CVPR 2022. [paper] [code]
• End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps, CVPR 2022. [paper] [code]
• M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction, CVPR 2022. [paper] [code]
• GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning, CVPR 2022. [paper] [code]
• Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-Based Prediction, CVPR 2022. [paper]
• ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning, CVPR 2022. [paper] [code]
• Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction, CVPR 2022. [paper] [code]
• MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction, CVPR 2022. [paper]
• LTP: Lane-based Trajectory Prediction for Autonomous Driving, CVPR 2022. [paper]
• ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework, CVPR 2022. [paper]
• Human Trajectory Prediction with Momentary Observation, CVPR 2022. [paper]
• HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction, CVPR 2022. [paper] [code]
• Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction, ECCV 2022. [paper] [code]
• Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation, ECCV 2022. [paper] [code] [website]
• Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting, ECCV 2022. [paper] [code]
• SocialVAE: Human Trajectory Prediction using Timewise Latents, ECCV 2022. [paper] [code]
• View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums, ECCV 2022. [paper] [code]
• Entry-Flipped Transformer for Inference and Prediction of Participant Behavior, ECCV 2022. [paper]
• D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights, ECCV 2022. [paper] [code]
• Human Trajectory Prediction via Neural Social Physics, ECCV 2022. [paper] [code]
• Social-SSL: Self-Supervised Cross-Sequence Representation Learning Based on Transformers for Multi-Agent Trajectory Prediction, ECCV 2022. [paper] [code]
• Aware of the History: Trajectory Forecasting with the Local Behavior Data, ECCV 2022. [paper] [code]
• Action-based Contrastive Learning for Trajectory Prediction, ECCV 2022. [paper]
• AdvDO: Realistic Adversarial Attacks for Trajectory Prediction, ECCV 2022. [paper]
• ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning, ECCV 2022. [paper] [code]
• Social ODE: Multi-Agent Trajectory Forecasting with Neural Ordinary Differential Equations, ECCV 2022. [paper]
• Forecasting Human Trajectory from Scene History, NeurIPS 2022. [paper] [code]
• Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline, NeurIPS 2022. [paper] [code]
• Motion Transformer with Global Intention Localization and Local Movement Refinement, NeurIPS 2022. [paper] [website]
• Interaction Modeling with Multiplex Attention, NeurIPS 2022. [paper] [code]
• Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models, Conference on Learning for Dynamics and Control (L4DC). [paper] [website]
• Social Interpretable Tree for Pedestrian Trajectory Prediction, AAAI 2022. [paper] [code]
• Complementary Attention Gated Network for Pedestrian Trajectory Prediction, AAAI 2022. [paper] [code]
• Scene Transformer: A unified architecture for predicting future trajectories of multiple agents, ICLR 2022. [paper]
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction, ICLR 2022. [paper]
• Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction, ICLR 2022. [paper] [code]
• THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling, ICLR 2022. [paper]
• Path-Aware Graph Attention for HD Maps in Motion Prediction, ICRA 2022. [paper]
• Trajectory Prediction with Linguistic Representations, ICRA 2022. [paper]
• Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction, ICRA 2022. [paper] [website]
• KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction, ICRA 2022. [paper]
• Domain Generalization for Vision-based Driving Trajectory Generation, ICRA 2022. [paper] [website]
• A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction, ICRA 2022. [paper]
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks, ICRA 2022. [paper]
• StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving, ICRA 2022. [paper]
• Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction, ICRA 2022. [paper] [website]
• Propagating State Uncertainty Through Trajectory Forecasting, ICRA 2022. [paper] [code]
• HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling, ICRA 2022. [paper]
• Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting, ICRA 2022. [paper]
• Crossmodal Transformer Based Generative Framework for Pedestrian Trajectory Prediction, ICRA 2022. [paper]
• Trajectory Prediction for Autonomous Driving with Topometric Map, ICRA 2022. [paper] [code]
• CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention, ICRA 2022. [paper] [code]
• MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction, ICRA 2022. [paper]
• Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving, ICRA 2022. [paper]
• GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation, ICRA 2022. [paper]
• TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation, ICRA 2022. [paper]
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• Set-based prediction of pedestrians in urban environments considering formalized traffic rules, ITSC 2018. [paper]
• Building prior knowledge: A markov based pedestrian prediction model using urban environmental data, ICARCV 2018. [paper]
• Depth Information Guided Crowd Counting for Complex Crowd Scenes, 2018. [paper]
• Tracking by Prediction: A Deep Generative Model for Mutli-Person Localisation and Tracking, WACV 2018. [paper]
• “Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention, WACV 2018. [paper]
• Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty, CVPR 2018. [paper], [code+data]
• Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction, CVPR 2018. [paper], [code]
• Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction, 2017. [paper]
• Probabilistic long-term prediction for autonomous vehicles, IV 2017. [paper]
• Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network, ITSC 2017. [paper]
• Desire: Distant future prediction in dynamic scenes with interacting agents, CVPR 2017. [paper][code]
• Imitating driver behavior with generative adversarial networks, 2017. [paper][code]
• Infogail: Interpretable imitation learning from visual demonstrations, 2017. [paper][code]
• Long-term planning by short-term prediction, 2017. [paper]
• Long-term path prediction in urban scenarios using circular distributions, 2017. [paper]
• Deep learning driven visual path prediction from a single image, 2016. [paper]
• Walking Ahead: The Headed Social Force Model, 2017. [paper]
• Real-time certified probabilistic pedestrian forecasting, 2017. [paper]
• A multiple-predictor approach to human motion prediction, ICRA 2017. [paper]
• Forecasting interactive dynamics of pedestrians with fictitious play, CVPR 2017. [paper]
• Forecast the plausible paths in crowd scenes, IJCAI 2017. [paper]
• Bi-prediction: pedestrian trajectory prediction based on bidirectional lstm classification, DICTA 2017. [paper]
• Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos, IJCAI 2017. [paper]
• Natural vision based method for predicting pedestrian behaviour in urban environments, ITSC 2017. [paper]
• Human Trajectory Prediction using Spatially aware Deep Attention Models, 2017. [paper]
• Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection, 2017. [paper]
• Forecasting Interactive Dynamics of Pedestrians with Fictitious Play, CVPR 2017. [paper]
• Social LSTM: Human trajectory prediction in crowded spaces, CVPR 2016. [paper][code]
• Comparison and evaluation of pedestrian motion models for vehicle safety systems, ITSC 2016. [paper]
• Age and Group-driven Pedestrian Behaviour: from Observations to Simulations, 2016. [paper]
• Structural-RNN: Deep learning on spatio-temporal graphs, CVPR 2016. [paper][code]
• Intent-aware long-term prediction of pedestrian motion, ICRA 2016. [paper]
• Context-based detection of pedestrian crossing intention for autonomous driving in urban environments, IROS 2016. [paper]
• Novel planning-based algorithms for human motion prediction, ICRA 2016. [paper]
• Learning social etiquette: Human trajectory understanding in crowded scenes, ECCV 2016. [paper][code]
• GLMP-realtime pedestrian path prediction using global and local movement patterns, ICRA 2016. [paper]
• Knowledge transfer for scene-specific motion prediction, ECCV 2016. [paper]
• STF-RNN: Space Time Features-based Recurrent Neural Network for predicting People Next Location, SSCI 2016. [code]
• Goal-directed pedestrian prediction, ICCV 2015. [paper]
• Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations, 2015. [paper]
• Predicting and recognizing human interactions in public spaces, 2015. [paper]
• Learning collective crowd behaviors with dynamic pedestrian-agents, 2015. [paper]
• Modeling spatial-temporal dynamics of human movements for predicting future trajectories, AAAI 2015. [paper]
• Unsupervised robot learning to predict person motion, ICRA 2015. [paper]
• A controlled interactive multiple model filter for combined pedestrian intention recognition and path prediction, ITSC 2015. [paper]
• Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions, 2014. [paper]
• Behavior estimation for a complete framework for human motion prediction in crowded environments, ICRA 2014. [paper]
• Pedestrian’s trajectory forecast in public traffic with artificial neural network, ICPR 2014. [paper]
• Will the pedestrian cross? A study on pedestrian path prediction, 2014. [paper]
• BRVO: Predicting pedestrian trajectories using velocity-space reasoning, 2014. [paper]
• Context-based pedestrian path prediction, ECCV 2014. [paper]
• Pedestrian path prediction using body language traits, 2014. [paper]
• Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014. [paper]
• Learning intentions for improved human motion prediction, 2013. [paper]
• Understanding interactions between traffic participants based on learned behaviors, 2016. [paper]
• Visual path prediction in complex scenes with crowded moving objects, CVPR 2016. [paper]
• A game-theoretic approach to replanning-aware interactive scene prediction and planning, 2016. [paper]
• Intention-aware online pomdp planning for autonomous driving in a crowd, ICRA 2015. [paper]
• Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014. [paper]
• Patch to the future: Unsupervised visual prediction, CVPR 2014. [paper]
• Mobile agent trajectory prediction using bayesian nonparametric reachability trees, 2011. [paper]

移动机器人

• 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]
• Social NCE：社会感知运动表示的对比学习。[论文]，[代码]
• 面向人机交互的多模态概率模型规划，ICRA 2018。[论文] [代码]
• 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]
• 用于运动预测的增强字典学习，ICRA 2016。[论文]
• 针对动态环境的未来智能体运动预测，ICMLA 2016。[论文]
• 基于贝叶斯意图推理的未知目标轨迹预测，IROS 2015。[论文]
• 学习预测协同导航智能体的轨迹，ICRA 2014。[论文]

体育运动员

• EvolveGraph：基于动态关系推理的多智能体轨迹预测，NeurIPS 2020。[论文]
• 用于轨迹预测与插补的模仿式非自回归建模，CVPR 2020。[论文]
• DAG-Net：用于轨迹预测的双重注意力图神经网络，ICPR 2020。[论文] [代码]
• 多智能体体育比赛中的多样化生成，CVPR 2019。[论文]
• 基于部分观测的多智能体交互随机预测，ICLR 2019。[论文]
• 利用程序化弱监督生成多智能体轨迹，ICLR 2019。[论文]
• 生成式多智能体行为克隆，ICML 2018。[论文]
• 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]
• 协调式多智能体模仿学习，ICML 2017。[论文]
• 使用深度层次网络生成长期轨迹，2017年。[论文]
• 学习用于动态体育比赛预测的精细空间模型，ICDM 2014。[论文]
• 多模态多人行为的生成式建模，2018年。[论文]
• 接下来会发生什么？体育视频中球员动作的预测，ICCV 2017，[论文]

基准与评估指标

• 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] [代码]
• 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] [代码]
• OpenTraj：评估人类轨迹数据集中预测的复杂性，ACCV 2020。[论文] [代码]
• 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]
• 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]
• 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]
• 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]
• Trajnet：迈向人类轨迹预测的基准。[网站]

其他

• 基于姿态的骑行者出发意图检测，ITSC 2019。[论文]
• 使用双向循环神经网络进行骑行者轨迹预测，AI 2018。[论文]
• 用于轨迹预测的道路基础设施指标，2018年。[论文]
• 利用道路拓扑改善骑行者路径预测，2017年。[论文]
• 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]

Awesome-Interaction-Aware-Trajectory-Prediction 快速上手指南

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。

环境准备

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：

• 操作系统: Linux (推荐 Ubuntu 18.04/20.04) 或 macOS。
• 编程语言: Python 3.7+ (大多数深度学习项目的主流版本)。
• 核心依赖:
  • git: 用于克隆仓库和子模块。
  • conda 或 venv: 强烈建议使用虚拟环境管理不同项目的依赖。
  • 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）。
• 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。

安装步骤

1. 克隆资源清单仓库

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。

git clone https://github.com/jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction.git
cd Awesome-Interaction-Aware-Trajectory-Prediction

2. 获取具体项目代码

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。

示例：假设您选择了列表中的某个基于 PyTorch 的项目：

# 进入您选定的具体项目目录 (此处为示例路径)
git clone <具体项目的 GitHub 地址>
cd <具体项目文件夹>

# 创建虚拟环境 (推荐)
conda create -n traj_pred python=3.8
conda activate traj_pred

# 安装该项目特定的依赖 (通常包含在 requirements.txt 中)
pip install -r requirements.txt

3. 下载数据集

在 Datasets 部分选择适合您场景的数据集（如车辆、行人或运动员）。

• 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。
• 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。

基本使用

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：

步骤 1: 选择基准模型

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。

步骤 2: 准备数据

下载清单中推荐的标准数据集（例如 ETH/UCY 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。

# 示例：将下载的数据集移动到项目指定的 data 目录
mkdir -p data/eth
mv <下载的数据文件> data/eth/

步骤 3: 运行训练与评估

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：

# 示例命令：启动训练过程
python train.py --data_dir data/eth --model_name evolvegraph --epochs 50

# 示例命令：在测试集上评估模型
python evaluate.py --checkpoint checkpoints/best_model.pth --dataset eth

步骤 4: 结果分析

查看生成的指标（如 ADE/FDE - 平均位移误差/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。

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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。