[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-manfreddiaz--awesome-autonomous-vehicles":3,"tool-manfreddiaz--awesome-autonomous-vehicles":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},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。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},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",140436,2,"2026-04-05T23:32:43",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},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。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":80,"owner_email":81,"owner_twitter":82,"owner_website":82,"owner_url":83,"languages":82,"stars":84,"forks":85,"last_commit_at":86,"license":82,"difficulty_score":87,"env_os":88,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":92,"github_topics":93,"view_count":23,"oss_zip_url":82,"oss_zip_packed_at":82,"status":16,"created_at":99,"updated_at":100,"faqs":101,"releases":122},1315,"manfreddiaz\u002Fawesome-autonomous-vehicles","awesome-autonomous-vehicles","Curated List of Self-Driving Cars and Autonomous Vehicles Resources","awesome-autonomous-vehicles 是一份精心整理的自动驾驶与无人车资源清单，把散落在互联网上的课程、论文、开源代码、数据集、硬件方案、法规政策等一网打尽，省去你四处搜索的时间。无论你是刚入门的开发者、做算法研究的学生，还是想了解行业动态的产品经理，都能在这里快速找到从机器学习基础到实车部署的完整路线图。亮点在于它把 AI、机器人、计算机视觉三大方向的优质资料按主题分层，并持续更新，让你像查字典一样按需取用，轻松搭建自己的自动驾驶知识体系。","# Awesome Autonomous Vehicles: [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\nA curated list of awesome autonomous vehicles resources, inspired by [awesome-php](https:\u002F\u002Fgithub.com\u002Fziadoz\u002Fawesome-php).\n\n## Contributing\n\nPlease feel free to send me pull requests to add links.\n\n## Table of Contents\n* [Foundations](#foundations)\n* [Courses](#courses)\n* [Papers](#papers)\n* [Research Labs](#research-labs)\n* [Datasets](#datasets)\n* [Open Source Software](#open-source-software)\n* [Hardware](#hardware)\n* [Toys](#toys)\n* [Companies](#companies)\n* [Media](#media)\n* [Laws](#laws)\n\n\n## Foundations\n\n### Artificial Intelligence\n\n1. [Awesome Machine Learning](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning) - A curated list of awesome Machine Learning frameworks, libraries and software. Maintained by Joseph Misiti.Joseph Misiti\n* [Deep Learning Papers Reading Roadmap](https:\u002F\u002Fgithub.com\u002Fsongrotek\u002FDeep-Learning-Papers-Reading-Roadmap) - Deep Learning papers reading roadmap constructed from outline to detail, old to state-of-the-art,\nfrom generic to specific areas focus on state-of-the-art for anyone starting in Deep Learning. Maintained by, Flood Sung.\n* [Open Source Deep Learning Curriculum](http:\u002F\u002Fwww.deeplearningweekly.com\u002Fpages\u002Fopen_source_deep_learning_curriculum) - Deep Learning curriculum  meant to be a starting point for everyone interested in seriously studying the field.\n\n### Robotics\n1. [Awesome Robotics](https:\u002F\u002Fgithub.com\u002FKiloreux\u002Fawesome-robotics) - A list of various books, courses and other resources for robotics, maintained by kiloreux.\n\n### Computer Vision\n1. [Awesome Computer Vision](https:\u002F\u002Fgithub.com\u002Fjbhuang0604\u002Fawesome-computer-vision) - A curated list of awesome computer vision resources, maintained by Jia-Bin Huang\n* [Awesome Deep Vision](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision) - A curated list of deep learning resources for computer vision, maintained by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim\n\n## Courses\n* [[Coursera] Machine Learning](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning) - presented by [Andrew Ng](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAndrew_Ng), as of 2020 Jan 28 it has 125,344 ratings and 30,705 reviews.\n* [[Coursera+DeepLearning.ai]Deep Learning Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdeep-learning) - presented by [Andrew Ng](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAndrew_Ng), 5 Courses, teaches foundations of deep learning, programming language: python\n* [[Udacity] Self-Driving Car Nanodegree Program](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fself-driving-car-engineer-nanodegree--nd013) - teaches the skills and techniques used by self-driving car teams. Program syllabus can be found [here](https:\u002F\u002Fmedium.com\u002Fself-driving-cars\u002Fterm-1-in-depth-on-udacitys-self-driving-car-curriculum-ffcf46af0c08#.bfgw9uxd9).\n* [[University of Toronto] CSC2541\nVisual Perception for Autonomous Driving](http:\u002F\u002Fwww.cs.toronto.edu\u002F~urtasun\u002Fcourses\u002FCSC2541\u002FCSC2541_Winter16.html) - A graduate course in visual perception for autonomous driving. The class briefly covers topics in localization, ego-motion estimaton, free-space estimation, visual recognition (classification, detection, segmentation).\n* [[INRIA] Mobile Robots and Autonomous Vehicles](https:\u002F\u002Fwww.fun-mooc.fr\u002Fcourses\u002Finria\u002F41005S02\u002Fsession02\u002Fabout?utm_source=mooc-list) - Introduces the key concepts required to program mobile robots and autonomous vehicles. The course presents both formal and algorithmic tools, and for its last week's topics (behavior modeling and learning), it will also provide realistic examples and programming exercises in Python.\n* [[Universty of Glasgow] ENG5017 Autonomous Vehicle Guidance Systems](http:\u002F\u002Fwww.gla.ac.uk\u002Fcoursecatalogue\u002Fcourse\u002F?code=ENG5017) - Introduces the concepts behind autonomous vehicle guidance and coordination and enables students to design and implement guidance strategies for vehicles incorporating planning, optimising and reacting elements.\n* [[David Silver - Udacity] How to Land An Autonomous Vehicle Job: Coursework](https:\u002F\u002Fmedium.com\u002Fself-driving-cars\u002Fhow-to-land-an-autonomous-vehicle-job-coursework-e7acc2bfe740#.j5b2kwbso) David Silver, from Udacity, reviews his coursework for landing a job in self-driving cars coming from a Software Engineering background.\n* [[Stanford] - CS221 Artificial Intelligence: Principles and Techniques](http:\u002F\u002Fstanford.edu\u002F~cpiech\u002Fcs221\u002Findex.html) - Contains a simple self-driving project and simulator.\n* [[MIT] 6.S094: Deep Learning for Self-Driving Cars](http:\u002F\u002Fselfdrivingcars.mit.edu\u002F) - *\"This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. (...)\"* \n* [[MIT] Deep Learning](https:\u002F\u002Fdeeplearning.mit.edu\u002F) - *\"This page is a collection of MIT courses and lectures on deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence organized by Lex Fridman.\"* \n* [[MIT] Human-Centered Artificial Intelligence](https:\u002F\u002Fhcai.mit.edu\u002F) - *\"Human-Centered AI at MIT is a collection of research and courses focused on the design, development, and deployment of artificial intelligence systems that learn from and collaborate with humans in a deep, meaningful way.\"*\n* [[UCSD] - MAE\u002FECE148 Introduction to Autonomous Vehicles](https:\u002F\u002Fguitar.ucsd.edu\u002Fmaeece148\u002Findex.php\u002FIntroduction_to_Autonomous_Vehicles) - A hands-on, project-based course using DonkeyCar with lane-tracking functionality and various advanced topics such as object detection, navigation, etc.\n* [[MIT] 2.166 Duckietown](http:\u002F\u002Fduckietown.mit.edu\u002Findex.html) - Class about the science of autonomy at the graduate level. This is a hands-on, project-focused course focusing on self-driving vehicles and high-level autonomy. The problem: **Design the Autonomous Robo-Taxis System for the City of Duckietown.**\n* [[Coursera] Self-Driving Cars](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fself-driving-cars#about) - A 4 course specialization about Self-Driving Cars by the University of Toronto. Covering all the way from the Introduction, State Estimation & Localization, Visual Perception, Motion Planning.\n\n## Papers\nBy Topic Areas and Year of Publication \u002F Submission\n\n#### General\n1. **[2016]** _Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00147)]\n* **[2015]** _An Empirical Evaluation of Deep Learning on Highway Driving_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1504.01716)]\n* **[2015]** _Self-Driving Vehicles: The Challenges and Opportunities Ahead_. [[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2823464)]\n* **[2014]** _Making Bertha Drive - An Autonomous Journey on a Historic Route_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMaking-Bertha-Drive-An-Autonomous-Journey-on-a-Ziegler-Bender\u002Fec26d7b1cb028749d0d6972279cf4090930989d8)]\n* **[2014]** _Towards Autonomous Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-Autonomous-Vehicles-Schwarz-Thomas\u002F88712e686e1bcad21f0836e9d31400dab2b7fa8f)]\n* **[2013]** _Towards a viable autonomous driving research platform_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-a-viable-autonomous-driving-research-Wei-Snider\u002Fda5cee7a6eb817bbbf4721c64c756bd8b7122359)]\n* **[2013]** _An ontology-based model to determine the automation level of an automated vehicle for co-driving_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-ontology-based-model-to-determine-the-Pollard-Morignot\u002F25239ec7fb6159166dfe15adf229fc2415f071df)]\n* **[2013]** _Autonomous Vehicle Navigation by Building 3d Map and by Detecting Human Trajectory Using Lidar_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Vehicle-Navigation-by-Building-3d-Map-Kagami-Thompson\u002F81b14341e3e063d819d032b6ce0bc0be0917c867)]\n* **[2012]** _Autonomous Ground Vehicles - Concepts and a Path to the Future_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Ground-Vehicles-Concepts-and-a-Path-to-Luettel-Himmelsbach\u002F5e8d51a1f6ba313a38a35af414a00bcfd3b5c0ae)]\n* **[2011]** _Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExperimental-Evaluation-of-Autonomous-Driving-Diosi-Segvic\u002F2aeb9aa42e8e2048e15453759ec12411486a2619)]\n* **[2011]** _Learning to Drive: Perception for Autonomous Cars_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-to-Drive-Perception-for-Autonomous-Cars-Stavens-Thrun\u002Fbe25d7bff3b5928adf6c0a7f5495d47113f80997)]\n* **[2010]** _Toward robotic cars_. [[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1721679)]\n* **[2009]** _Autonomous Driving in Traffic: Boss and the Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Driving-in-Traffic-Boss-and-the-Urban-Urmson-Baker\u002F4657a350e4822bc567256f9b9dc5d922237a71be)]\n* **[2009]** _Mapping, navigation, and learning for off-road traversal_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMapping-navigation-and-learning-for-off-road-Konolige-Agrawal\u002F57d7396b92ad31b386dfce4f8799149f5ced2160)]\n* **[2008]** _Autonomous Driving in Urban Environments: Boss and the Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Driving-in-Urban-Environments-Boss-and-Urmson-Anhalt\u002F1c0fb6b1bbfde0f9bab6268f5609cce2bd3bc5bd)]\n* **[2008]** _Caroline: An autonomously driving vehicle for urban environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCaroline-An-autonomously-driving-vehicle-for-urban-Rauskolb-Berger\u002F08f4e164291942fc78bd6945215b2c672b17edd5)]\n* **[2008]** _Design of an Urban Driverless Ground Vehicle_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDesign-of-an-Urban-Driverless-Ground-Vehicle-Benenson-Parent\u002F852a672c3d4a2fca3ff7b215d9c096b0be54feb7)]\n* **[2008]** _Little Ben: The Ben Franklin Racing Team's Entry in the 2007 DARPA Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLittle-Ben-The-Ben-Franklin-Racing-Team-s-Entry-in-Bohren-Foote\u002Fb6d5e01cdb76284ee6c42b0dda6c36f121c573f0)]\n* **[2008]** _Odin: Team VictorTango's Entry in the DARPA Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOdin-Team-VictorTango-s-Entry-in-the-DARPA-Urban-Reinholtz-Hong\u002Faaeaa58bedf6fa9b42878bf5914f55f48cf26209)]\n* **[2008]** _Robosemantics: How Stanley the Volkswagen Represents the World_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRobosemantics-How-Stanley-the-Volkswagen-Parisien-Thagard\u002F9f2186df45a387ab600414968090fe3da37591ca)]\n* **[2008]** _Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTeam-AnnieWAY-s-Autonomous-System-Stiller-Kammel\u002F56972aa9f9d3cce7c77d402602bc8f3af94d57c9)]\n* **[2008]** _The MIT-Cornell collision and why it happened_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThe-MIT-Cornell-collision-and-why-it-happened-Fletcher-Teller\u002F0df4f3ef7356fe56547ac3145d7c0229163bc7a5)]\n* **[2007]** _Self-Driving Cars - An AI-Robotics Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelf-Driving-Cars-An-AI-Robotics-Challenge-Thrun\u002F31d17c77d2ea18f71d570741665f0fd3030caa94)]\n* **[2007]** _2007 DARPA Urban Challenge: The Ben Franklin Racing Team Team B156 Technical Paper_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F2007-Darpa-Urban-Challenge-the-Ben-Franklin-Racing-Franklin-Lee\u002F510b0fa02d6bdd1061cf73373f197ba624692ad0)]\n* **[2007]** _Team Mit Urban Challenge Technical Report_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTeam-Mit-Urban-Challenge-Technical-Report-Leonard-Barrett\u002F6ac15e819701cd0d077d8157711c4c402106722c)]\n* **[2007]** _DARPA Urban Challenge Technical Report Austin Robot Technology_ [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDarpa-Urban-Challenge-Technical-Report-Executive-Technology-Tuttle\u002F37e78b1bd135df5c5a1fcbf2a8debd260d28a55c)]\n* **[2007]** _Spirit of Berlin: an Autonomous Car for the Darpa Urban Challenge Hardware and Software Architecture_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSpirit-of-Berlin-an-Autonomous-Car-for-the-Darpa-Berlin-Rojo\u002F8c96cbc752dfcde3673440cf7ca1fb19218426bf)]\n* **[2007]** _Team Case and the 2007 Darpa Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTeam-Case-and-the-2007-Darpa-Urban-Challenge-Newman-Lead\u002Fe68c745b7807e77ccf67fea325a241136a568eeb)]\n* **[2006]** _A Personal Account of the Development of Stanley, the Robot That Won the DARPA Grand Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Personal-Account-of-the-Development-of-Stanley-Thrun\u002F74a4de58be068d2dc38bb31cf54c3c49bdc0d4e4)]\n* **[2006]** _Stanley: The robot that won the DARPA Grand Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStanley-The-robot-that-won-the-DARPA-Grand-Thrun-Montemerlo\u002F298500897243b17fa2ebe7bde0a1b8ebc00ea07f)]\n\n#### Localization & Mapping\n1. **[2016]** _MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System._ [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.07336)]\n* **[2016]** _Image Based Camera Localization: an Overview_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.03660)]\n* **[2016]** _Ubiquitous real-time geo-spatial localization_ [[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3005426)]\n* **[2016]** _Robust multimodal sequence-based loop closure detection via structured sparsity_. [[ref](http:\u002F\u002Fwww.roboticsproceedings.org\u002Frss12\u002Fp43.pdf)]\n* **[2016]** _SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition_. [[ref](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7839213\u002F)], [[code](https:\u002F\u002Fgithub.com\u002Fhanfeiid\u002FSRAL)]\n* **[2015]** _Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPrecise-Localization-of-an-Autonomous-Car-Based-on-Jo-Jo\u002F27251099b78185f9ddf59c9ed0c5868af4ef1e80)]\n* **[2013]** _Planar Segments Based Three-dimensional Robotic Mapping in Outdoor Environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPlanar-Segments-Based-Three-dimensional-Robotic-Xiao\u002Febddeb22f3b5c38422987c3fe51aaf847ad444e7)]\n* **[2013]** _Vehicle Localization along a Previously Driven Route Using Image Database_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVehicle-Localization-along-a-Previously-Driven-Kume-Supp%C3%A9\u002Fe5a7ac37d542349ae19281f1e2a571f7030b789c)]\n* **[2012]** _Can priors be trusted? Learning to anticipate roadworks_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCan-priors-be-trusted-Learning-to-anticipate-Mathibela-Osborne\u002F0a7e502779ed2cf9ee2677d0310386481a51fc12)]\n* **[2009]** _Laser Scanner Based Slam in Real Road and Traffic Environment_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLaser-Scanner-Based-Slam-in-Real-Road-and-Traffic-Garcia-Favrot-Parent\u002F2accb1d9f7ce3f08aa1cde735dcca2578887c545)]\n* **[2007]** _Map-Based Precision Vehicle Localization in Urban Environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMap-Based-Precision-Vehicle-Localization-in-Urban-Levinson-Montemerlo\u002F924f7268d592d327f97ad4e96f48ad774d982ef3)]\n\n#### Perception\n1. **[2019]** _Argoverse: 3D Tracking and Forecasting with Rich Maps_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02620))\n2. **[2016]** _VisualBackProp: visualizing CNNs for autonomous driving_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.05418)]\n* **[2016]** _Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.01983)]\n* **[2016]** _Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.04653)]\n* **[2016]** _Image segmentation of cross-country scenes captured in IR spectrum_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.02469)]\n* **[2016]** _Traffic-Sign Detection and Classification in the Wild_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTraffic-Sign-Detection-and-Classification-in-the-Zhu-Liang\u002Fd463499b7a82e3cad81d2471b52a198b857aa75b)]\n* **[2016]** _Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPersistent-self-supervised-learning-principle-from-Hecke-Croon\u002Fa48c4c6707fca20ae64b044b6e8f7f37891186fc)]\n* **[2016]** _Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDeep-Multispectral-Semantic-Scene-Understanding-of-Valada-Oliveira\u002F8be99dd94bff76c75594a15e114268841a2656a7)]\n* **[2016]** _Joint Attention in Autonomous Driving (JAAD)_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FJoint-Attention-in-Autonomous-Driving-JAAD--Kotseruba-Rasouli\u002F1e6a26deea0a38310368d9c2a6dadc317b50bdf8), [data](http:\u002F\u002Fdata.nvision2.eecs.yorku.ca\u002FJAAD_dataset\u002F)]\n* **[2016]** _Perception for driverless vehicles: design and implementation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPerception-for-driverless-vehicles-design-and-Benenson-Suarez\u002Fbf1c728e3e893670244591f720b453245c3363f6)]\n* **[2016]** _Robust multimodal sequence-based loop closure detection via structured sparsity_. [[ref](http:\u002F\u002Fwww.roboticsproceedings.org\u002Frss12\u002Fp43.pdf)]\n* **[2016]** _SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition_. [[ref](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7839213\u002F)], [[code](https:\u002F\u002Fgithub.com\u002Fhanfeiid\u002FSRAL)]\n* **[2015]** _Pixel-wise Segmentation of Street with Neural Networks_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.00513)]\n* **[2015]** _Deep convolutional neural networks for pedestrian detection_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1510.03608)]\n* **[2015]** _Fast Algorithms for Convolutional Neural Networks_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1509.09308)]\n* **[2015]** _Fusion of color images and LiDAR data for lane classification_. [[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2820859)]\n* **[2015]** _Environment Perception for Autonomous Vehicles in Challenging Conditions Using Stereo Vision_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEnvironment-Perception-for-Autonomous-Vehicles-in-Gal%C3%A1n-Hayet\u002F8f56fd10f37382292f474c441f92432b34b58db5)]\n* **[2015]** _Intention-aware online POMDP planning for autonomous driving in a crowd_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FIntention-aware-online-POMDP-planning-for-Bai-Cai\u002F481aa2882a5816686a5bea7db755862cded42081)]\n* **[2015]** _Survey on Vanishing Point Detection Method for General Road Region Identification_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSurvey-on-Vanishing-Point-Detection-Method-for-Patel-Mistry\u002F39c6be1e7723b93f06be2bb4199066d4efdadbc9)]\n* **[2015]** _Visual road following using intrinsic images_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVisual-road-following-using-intrinsic-images-Krajn%C3%ADk-Blazicek\u002F2298f9e3c1235526d55cf78bfc80c505d100540f)]\n* **[2014]** _Rover – a Lego* Self-driving Car_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRover-a-Lego-Self-driving-Car-Tan-Wojtczyk-Wojtczyk\u002F6e24123ef558ffb9888d28f992f8afe76622830e)]\n* **[2014]** _Classification and Tracking of Dynamic Objects with Multiple Sensors for Autonomous Driving in Urban Environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FClassification-and-Tracking-of-Dynamic-Objects-Darms-Rybski\u002F6c9ce40060fa3efea7d04a4a0e36609592ed6ddf)]\n* **[2014]** _Generating Omni-directional View of Neighboring Objects for Ensuring Safe Urban Driving_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerating-Omni-directional-View-of-Neighboring-Seo\u002F29e53add392de54d439a6002c67e8af6e9baadeb)]\n* **[2014]** _Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Visual-Navigation-and-Laser-Based-Cherubini-Spindler\u002F089fa5a7babc906dc46a58f986c5ac8c46aa9017)]\n* **[2014]** _Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExtending-the-Stixel-World-with-online-self-Sanberg-Dubbelman\u002F6dd60e0484931b284f49ab8204b011d153ff4967)]\n* **[2014]** _Modeling Human Plan Recognition Using Bayesian Theory of Mind_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPlan-Activity-and-Intent-Recognition-Baker-Tenenbaum\u002F4cbb1ea46c09d11b0b986a7baaac7215006504f8)]\n* **[2013]** _Focused Trajectory Planning for autonomous on-road driving_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FFocused-Trajectory-Planning-for-autonomous-on-road-Gu-Snider\u002F03bf26d72d8cc0cf401c31e31c242e1894bd0890)]\n* **[2013]** _Avoiding moving obstacles during visual navigation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAvoiding-moving-obstacles-during-visual-navigation-Cherubini-Grechanichenko\u002F7c0e580c0f914086e9c918aef1df561253a71044)]\n* **[2013]** _Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMobile-robot-navigation-system-in-outdoor-Siagian-Chang\u002F7163764c33c3d87c313568c056d50d1bedb25696)]\n* **[2013]** _Obstacle detection and mapping in low-cost, low-power multi-robot systems using an Inverted Particle Filter_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FObstacle-detection-and-mapping-in-low-cost-low-Kleppe-Skavhaug\u002F646cc0e592b77d553cc77806e90d99420fb79a8e)]\n* **[2013]** _Real-time estimation of drivable image area based on monocular vision_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReal-time-estimation-of-drivable-image-area-based-Neto-Victorino\u002Fc50a769c2038e29d9e64077cd4749b6f8d389806)]\n* **[2013]** _Road model prediction based unstructured road detection_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRoad-model-prediction-based-unstructured-road-Zuo-Yao\u002Fb8b2d3da341042d148ed2988216dbb3ddb6081ed)]\n* **[2013]** _Selective Combination of Visual and Thermal Imaging for Resilient Localization in Adverse Conditions: Day and Night, Smoke and Fire_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelective-Combination-of-Visual-and-Thermal-Brunner-Peynot\u002F85b4b1a9780a4bc22f84904a1cfc3eeeb605c9bd)]\n* **[2012]** _Road Tracking Method Suitable for Both Unstructured and Structured Roads_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FInternational-Journal-of-Advanced-Robotic-Systems-Proch%C3%A1zka\u002F4819fda4bc778454701f2a4b30db46ec56aa45bc)]\n* **[2012]** _Autonomous Navigation and Sign Detector Learning_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Navigation-and-Sign-Detector-Learning-Ellis-Pugeault\u002F0cffe50112452ecdcdaf0d11b33e12cf3c67213e)]\n* **[2012]** _Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDesign-of-a-Multi-Sensor-Cooperation-Travel-Chen-Li\u002Ff5feb2a151c54ec9699924d401a66c193ddd3c8b)]\n* **[2012]** _Learning in Reality: a Case Study of Stanley, the Robot That Won the Darpa Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-in-Reality-a-Case-Study-of-Stanley-the-Glaser-Hennig\u002F01c1f49f5e7f4e7f5d005844aa9443769a2d9306)]\n* **[2012]** _Portable and Scalable Vision-Based Vehicular Instrumentation for the Analysis of Driver Intentionality_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPortable-and-Scalable-Vision-Based-Vehicular-Beauchemin-Bauer\u002Fc76b5bc64ffd6e13a6c22641b3926a803e5209d5)]\n* **[2012]** _What could move? Finding cars, pedestrians and bicyclists in 3D laser data_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FWhat-could-move-Finding-cars-pedestrians-and-Wang-Posner\u002Ff56b01df806bc224d5babb71994915df4a08cb44)]\n* **[2012]** _The Stixel World_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThe-Stixel-World-N-Im\u002F5307f5e2ff2f0403a92b63418ca5812965dcfb90)]\n* **[2011]** _Stereo-based road boundary tracking for mobile robot navigation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStereo-based-road-boundary-tracking-for-mobile-Chiku-Miura\u002F8bcbb1f13f2ab7f974ba30a0d68aeccf49082759)]\n* **[2009]** _Autonomous Information Fusion for Robust Obstacle Localization on a Humanoid Robot_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Information-Fusion-for-Robust-Obstacle-Sridharan-Li\u002F2365b361fb0e5cb801b22900a4c4a421c35ea639)]\n* **[2009]** _Learning long-range vision for autonomous off-road driving_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-long-range-vision-for-autonomous-off-road-Hadsell-Sermanet\u002F2d8f527d1a96b0dae209daa6a241cf3255a6ec0d)]\n* **[2009]** _On-line road boundary modeling with multiple sensory features, flexible road model, and particle filter_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOn-line-road-boundary-modeling-with-multiple-Matsushita-Miura\u002F0fcac22dceb7a7d49a8c792760ae47c500a804d9)]\n* **[2008]** _The Area Processing Unit of Caroline - Finding the Way through DARPA's Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThe-Area-Processing-Unit-of-Caroline-Finding-the-Berger-Lipski\u002F4b9db808c06635b784ce6c1409603c0487bcd684)]\n* **[2008]** _Vehicle detection and tracking for the Urban Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVehicle-detection-and-tracking-for-the-Urban-Darms-Baker\u002F757fbaa9881b9075409a9962819fda64d51307e1)]\n* **[2007]** _Low cost sensing for autonomous car driving in highways_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLow-cost-sensing-for-autonomous-car-driving-in-Gon%C3%A7alves-Godinho\u002Fb7f302bc8eb37220ba76c2d55325d218a7e03128)]\n* **[2007]** _Stereo and Colour Vision Techniques for Autonomous Vehicle Guidance _. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStereo-and-Colour-Vision-Techniques-for-Autonomous-Mark-Proefschrift\u002F97325201f48351df5ef614a01a55f3da818aae0e)]\n* **[2000]** _Real-time multiple vehicle detection and tracking from a moving vehicle_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReal-time-multiple-vehicle-detection-and-tracking-Betke-Haritaoglu\u002F864a7068c346ecbc4ef6c4da66e4c8bcc83fe560)]\n\n\n#### Navigation & Planning\n1. **[2016]** _A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01235)]\n* **[2016]** _End to End Learning for Self-Driving Cars_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.07316)]\n* **[2016]** _A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.07446)]\n* **[2016]** _A Convex Optimization Approach to Smooth Trajectories for Motion Planning with Car-Like Robots_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Convex-Optimization-Approach-to-Smooth-Zhu-Schmerling\u002F785b22bbdb04f2ddd4233a4c40d798ed3194374f)]\n* **[2016]** _Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.00939)]\n* **[2016]** _Machine Learning for Visual Navigation of Unmanned Ground Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMachine-Learning-for-Visual-Navigation-of-Unmanned-Lenskiy-Lee\u002F9b21934ec4f08ed3cd54a7e3a3c7c25b311e1ced)]\n* **[2016]** _Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReal-time-self-driving-car-navigation-and-obstacle-Li-Bao\u002F4e8b5a99ae628eea43d7e7410cdfa7f8a2e847d5)]\n* **[2016]** _Watch this: Scalable cost-function learning for path planning in urban environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FWatch-this-Scalable-cost-function-learning-for-Wulfmeier-Wang\u002Fd1e51c7e374dca4465a91300e98bfb27335be463)]\n* **[2015]** _DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDeepDriving-Learning-Affordance-for-Direct-Chen-Seff\u002F3ba79761192aa4bddd3342db03aa8187516c0fab?citingPapersSort=is-influential&citingPapersLimit=10&citingPapersOffset=0&citedPapersSort=is-influential&citedPapersLimit=10&citedPapersOffset=0), [data](http:\u002F\u002Fdeepdriving.cs.princeton.edu\u002F), [code](http:\u002F\u002Fdeepdriving.cs.princeton.edu\u002F)]\n* **[2015]** _Automatic Driving on Ill-defined Roads: An Adaptive, Shape-constrained, Color-based Method_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomatic-Driving-on-Ill-defined-Roads-An-Adaptive-Ososinski-Labrosse\u002F36cfe2e94b7b99653e6565642236e0127d43ef5a), [data](http:\u002F\u002Fwww.aber.ac.uk\u002Fen\u002Fcs\u002Fresearch\u002Fir\u002Fdss\u002F#road-driving)]\n* **[2015]** _A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Framework-for-Applying-Point-Clouds-Grabbed-by-Liu-Liang\u002F907189aacae7bff389d6c6592d6e2586dab5168d)]\n* **[2015]** _How Much of Driving Is Preattentive?_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FHow-Much-of-Driving-Is-Preattentive--Pugeault-Bowden\u002Fbb9686ea6f154a64fbdc3551fe223da42663baa9)]\n* **[2015]** _Map-building and Planning for Autonomous Navigation of a Mobile Robot_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMap-building-and-Planning-for-Autonomous-G%C3%B3mez-Yu\u002Ffc5b5b96334d2a0d12ac2d69fa6d46640897f33e)]\n* **[2014]** _A Multiple Attribute-based Decision Making model for autonomous vehicle in urban environment_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Multiple-Attribute-based-Decision-Making-model-Chen-Zhao\u002Fa045d7008e47d4264e06b5d9f509ed505e100084)]\n* **[2014]** _A prediction-based reactive driving strategy for highly automated driving function on freeways_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-prediction-based-reactive-driving-strategy-for-Bahram-Wolf\u002F77d24bd1e83c23bb7cdf59ab06d575a66c03449a)]\n* **[2014]** _An RRT-based navigation approach for mobile robots and automated vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-RRT-based-navigation-approach-for-mobile-robots-Garrote-Premebida\u002F56cfb13218175d67bf6dc281686c797b8641a3d0)]\n* **[2014]** _Image Feature-based Traversability Analysis for Mobile Robot Navigation in Outdoor Environment_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImage-Feature-based-Traversability-Analysis-for-BEKHTI-KOBAYASHI\u002F9fdf6ba484ee59cfac03a6c73e5177a9a70986c5)]\n* **[2014]** _Speed Daemon: Experience-Based Mobile Robot Speed Scheduling_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSpeed-Daemon-Experience-Based-Mobile-Robot-Speed-Ostafew-Schoellig\u002F9d3c816fb21bfa00d5a86cbb972a4ab7af59dbfb)]\n* **[2014]** _Toward human-like motion planning in urban environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FToward-human-like-motion-planning-in-urban-Gu-Dolan\u002F30005949ebde80ebe3cd0b96b84a8dcb8b7f919a)]\n* **[2013]** _Motion Estimation for Self-Driving Cars with a Generalized Camera_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMotion-Estimation-for-Self-Driving-Cars-with-a-Lee-Fraundorfer\u002Ff7f775a4f484706ffbc524accb351cb564469f6a)]\n* **[2013]** _Development of a Navigation Control System for an Autonomous Formula Sae-electric Race Car_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDevelopment-of-a-Navigation-Control-System-for-an-Drage\u002Ff55796a5f33836017de2cd8023b57efda9882c26)]\n* **[2013]** _Low speed automation: Technical feasibility of the driving sharing in urban areas_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLow-speed-automation-Technical-feasibility-of-the-Resende-Pollard\u002Fa34161c17343e8f41e200fe5288e2a4aaeafa25a)]\n* **[2013]** _Path selection based on local terrain feature for unmanned ground vehicle in unknown rough terrain environment_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPath-selection-based-on-local-terrain-feature-for-Kondo-Sunaga\u002Fe58506ef0f6721729d2f72c61e6bb46565b887de)]\n* **[2013]** _Stereo-based Autonomous Navigation and Obstacle Avoidance_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStereo-based-Autonomous-Navigation-and-Obstacle-C%C3%A9sar-Mendes\u002Fbe6789bd46d16afa45c8962560a56a89a9089355)]\n* **[2012]** _Development of an Autonomous Vehicle for High-Speed Navigation and Obstacle Avoidance._ [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDevelopment-of-an-Autonomous-Vehicle-for-High-Ryu-Ogay\u002F0941bcd18fdf52d9e25984ff067eebe6834ad7c6)]\n* **[2012]** _Fast Vanishing-Point Detection in Unstructured Environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FFast-Vanishing-Point-Detection-in-Unstructured-Moghadam-Starzyk\u002Fc02f52b8b80db037f92facbb605c5715513935fb)]\n* **[2012]** _Navigation of an Autonomous Car Using Vector Fields and the Dynamic Window Approach_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FNavigation-of-an-Autonomous-Car-Using-Vector-Lima-Augusto\u002F92411ee829021f09cb30186435d888547e00dd0f)]\n* **[2012]** _Road direction detection based on vanishing-point tracking_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRoad-direction-detection-based-on-vanishing-point-Moghadam-Feng\u002Fd2691eb5a030a1b017a944c7fce319ccd4477730)]\n* **[2012]** _Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelf-supervised-learning-to-visually-detect-Zhou-Xi\u002F617740b12065ee88049ca9086695ba78ccd3f110)]\n* **[2012]** _Visual Navigation for Mobile Robots_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FX-Visual-Navigation-for-Mobile-Robots-Andersen-Andersen\u002F7ac3b3fb12f6b071bdc0d8627225efe415c03104)]\n* **[2011]** _A new Approach for Robot Motion Planning using Rapidly-exploring Randomized Trees_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-new-Approach-for-Robot-Motion-Planning-using-Krammer-Granzer\u002F7e084820c195b65e45e9138415f6cac7762f18dc)]\n* **[2011]** _Driving me around the bend: Learning to drive from visual gist_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDriving-me-around-the-bend-Learning-to-drive-from-Pugeault-Bowden\u002F2cf7bddfe52d6ca8f5309c3b42d620065126b445)]\n* **[2011]** _Optimized route network graph as map reference for autonomous cars operating on German autobahn_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOptimized-route-network-graph-as-map-reference-for-Czerwionka-Wang\u002F644b76b47c88335d40702b3045d4d3743fc13861)]\n* **[2011]** _Template-based autonomous navigation and obstacle avoidance in urban environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTemplate-based-autonomous-navigation-and-obstacle-Souza-Sales\u002F65414da8f4a9beaac1df4d5ca0736f474e001096)]\n* **[2010]** _Vision-Based Autonomous Navigation System Using ANN and FSM Control_ [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVision-Based-Autonomous-Navigation-System-Using-Sales-Shinzato\u002Fe1fcccdbc373c9bbd5bd970c34368e7e1aa56424)]\n* **[2010]** _An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-Optimal-control-based-Framework-for-Trajectory-Anderson-Peters\u002F50c955ab0ca25d49204fe0b115669303508b41d0)]\n* **[2010]** _Perception for Urban Driverless Vehicles: Design and Implementation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPerception-for-Urban-Driverless-Vehicles-Design-Benenson-Suarez\u002F0f68760469015de7cf0b21f2b5ed2b0194bb6b81)]\n* **[2009]** _Autonomous Offroad Navigation Under Poor GPS Conditions_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Offroad-Navigation-Under-Poor-GPS-Luettel-Himmelsbach\u002F5168a3824d4b90399e16c42f2293c3bf66113d8a)]\n* **[2009]** _Autonomous robot navigation in outdoor cluttered pedestrian walkways_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-robot-navigation-in-outdoor-cluttered-Saiki-Carballo\u002F7f81a0e925124e9d5738a51fe41c001a908c68f6)]\n* **[2009]** _Fast Path Planning in Uncertain Environments: Theory and Experiments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FFast-Path-Planning-in-Uncertain-Environments-Xu-Kurdila\u002F88228325b82ff3bcd875628c31e34e9018179d3d)]\n* **[2009]** _Trajectory Based Autonomous Vehicle following Using a Robotic Driver_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTrajectory-Based-Autonomous-Vehicle-following-Spencer-Jones\u002Ff4f6dc62fe8c5fd309f45ebf5240f9c1c1c0b80a)]\n* **[2008]** _A Robust Motion Planning Approach for Autonomous Driving in Urban Areas_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Robust-Motion-Planning-Approach-for-Autonomous-Fiore-Yoshi\u002Fd3660d2f49958841d6d8486467213512772f9aac)]\n* **[2008]** _Motion Planning in Urban Environments_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMotion-Planning-in-Urban-Environments-Ferguson-Howard\u002F8fa74131756a50c1562ebf1f03552779803aed67)]\n* **[2008]** _Motion planning in urban environments: Part II_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMotion-planning-in-urban-environments-Part-II-Ferguson-Howard\u002F3c33381fa5dfecd02e4f935957831c3d2926bb0f)]\n* **[2008]** _Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPlanning-Long-Dynamically-Feasible-Maneuvers-for-Likhachev-Ferguson\u002F1f8ca38a1fa455db3388c617697cc91300c59bc6)]\n* **[2009]** _Anticipatory Driving for a Robot-Car Based on Supervised Learning_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAnticipatory-Driving-for-a-Robot-Car-Based-on-Markelic-Kulvicius\u002Fee9adb395ed68a2ce4c2a3909dc6d5a0fbf4e0f0)]\n* **[2007]** _Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving_.[[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOnline-Speed-Adaptation-Using-Supervised-Learning-Stavens-Hoffmann\u002F9db82954df3f4ae829459dcb8719b8a8ed9f4bee)]\n* **[2007]** _Predictive Active Steering Control for Autonomous Vehicle Systems_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPredictive-Active-Steering-Control-for-Autonomous-Falcone-Borrelli\u002Fabd354d708b98fb60e0d827a41157491289e8d3c)]\n* **[2006]** _Probabilistic Terrain Analysis For High-Speed Desert Driving_.[[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FProbabilistic-Terrain-Analysis-For-High-Speed-Thrun-Montemerlo\u002Fb23a7882b35d0252e5f3011bff15c6dca46ef84e)]\n\n#### Control\n1. **[2016]** _Predictive Control for Autonomous Driving with Experimental Evaluation on a Heavy-duty Construction Truck_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPredictive-Control-for-Autonomous-Driving-with-Lima-Se\u002Fde87a5d5fbae0733806ba965b2d70fd04596f6e9)]\n* **[2015]** _Model Predictive Control of Autonomous Mobility-on-Demand Systems_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1509.03985)]\n* **[2015]** _Toward integrated motion planning and control using potential fields and torque-based steering actuation for autonomous driving_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FToward-integrated-motion-planning-and-control-Galceran-Eustice\u002F7b2f163eac946fac7351b0861c2b37fb19ffbaa5)]\n* **[2013]** _Strategic decision making for automated driving on two-lane, one way roads using model predictive control_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStrategic-decision-making-for-automated-driving-on-Nilsson-Sj%C3%B6berg\u002F0055ca2e60a2ab5cb66c4191d09563dd7f3edd00)]\n* **[2012]** _Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-vehicles-control-in-the-VisLab-Broggi-Medici\u002F708fdf9bfd3f7d671ced85221012ef27209b92bb)]\n* **[2012]** _Optimal Planning and Control for Hazard Avoidance of Front-wheel Steered Ground Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOptimal-Planning-and-Control-for-Hazard-Avoidance-Peters\u002F5d5a066547d60a673328cf6db34325910787ba48)]\n* **[2009]** _Automatic Steering Methods for Autonomous Automobile Path Tracking_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomatic-Steering-Methods-for-Autonomous-Snider\u002F18520721525ed81a6ffa6d8b1c7dcbd771e4a64b)]\n* **[2009]** _Comparison of Three Control Methods for an Autonomous Vehicle_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FComparison-of-Three-Control-Methods-for-an-Deshpande-Mathur\u002F8fc0580499b0775db60096f52cd2f0ad2c6d24b5)]\n\n#### Simulation\n1. **[2016]** _Learning a Driving Simulator_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.01230)]\n* **[2014]** _From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.7768)]\n* **[2014]** _Technical evaluation of the Carolo-Cup 2014 - A competition for self-driving miniature cars_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTechnical-evaluation-of-the-Carolo-Cup-2014-A-Zug-Steup\u002F4f57643b95e854bb05fa0c037cbf8898accdbdef)]\n* **[2014]** _Crowdsourcing as a methodology to obtain large and varied robotic data sets_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCrowdsourcing-as-a-methodology-to-obtain-large-and-Croon-Gerke\u002F8bdcb90d72eb0494f8f2635dad8ef05a66b8e445)]\n* **[2014]** _Efficient Learning of Pre-attentive Steering in a Driving School Framework_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEfficient-Learning-of-Pre-attentive-Steering-in-a-Rudzits-Pugeault\u002F6a65272403a8bb999bc4e86eee3f919e3fbe813d)]\n* **[2007]** _A Simulation and Regression Testing Framework for Autonomous Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Simulation-and-Regression-Testing-Framework-for-Miller-Cenk\u002Fc50ef42740ce03e5af9292f9ce1387b83bee8fed)]\n* **[2006]** _Robot Competitions Ideal Benchmarks for Robotics Research_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRobot-Competitions-Ideal-Benchmarks-for-Robotics-Behnke\u002F71e5e9e8be8c870b22cadf58338f634ddd856050)]\n\n#### Software Engineering\n1. **[2016]** _Evaluation of Sandboxed Software Deployment for Real-time Software on the Example of a Self-Driving Heavy Vehicle_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.06759)]\n* **[2014]** _Engineering the Hardware\u002FSoftware Interface for Robotic Platforms - A Comparison of Applied Model Checking with Prolog and Alloy_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1401.3985)]\n* **[2014]** _Comparison of Architectural Design Decisions for Resource-Constrained Self-Driving Cars - A Multiple Case-Study_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FComparison-of-Architectural-Design-Decisions-for-Berger-Dukaczewski\u002Fc89f47c93c62c107e6bd75acde89ee7417ebf244)]\n* **[2014]** _(Re)liability of Self-driving Cars. An Interesting Challenge!_. [[ref](http:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fqre.1707\u002Ffull)]\n* **[2014]** _Explicating, Understanding, and Managing Technical Debt from Self-Driving Miniature Car Projects_. [[ref](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6974884\u002F)]\n* **[2014]** _Towards Continuous Integration for Cyber-Physical Systems on the Example of Self-Driving Miniature Cars_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-Continuous-Integration-for-Cyber-Physical-Berger\u002F2ac2aa0285984f2ce57efa77aab4e372bbc3ee6c)]\n* **[2014]** _Saving virtual testing time for CPS by analyzing code coverage on the example of a lane-following algorithm_. [[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2593466)]\n* **[2013]** _Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car_[[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2502530)]\n* **[2012]** _SAFER: System-level Architecture for Failure Evasion in Real-time Applications_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSAFER-System-level-Architecture-for-Failure-Kim-Bhatia\u002Fff05797dcc041d04f9ed277269916ad6ff92f1f0)]\n* **[2011]** _A Flexible Real-Time Control System for Autonomous Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Flexible-Real-Time-Control-System-for-Autonomous-Meyer-Strobel\u002Ff07956d0031ff046c5c719296f7916d7897fdd21)]\n* **[2010]** _Automating acceptance tests for sensor- and actuator-based systems on the example of autonomous vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomating-acceptance-tests-for-sensor-and-Berger\u002F3bc567143118f8fb34e0460cc3424701683c2511)]\n* **[2007]** _Software & Systems Engineering Process and Tools for the Development of Autonomous Driving Intelligence_ [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSoftware-Systems-Engineering-Process-and-Tools-for-Basarke-Berger\u002Fc564b62cd7df2ed47bb9a6266cc19c83024bc390)]\n\n#### Human-Machine Interaction\n1. **[2015]** _User interface considerations to prevent self-driving carsickness_. [[ref](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2809754)]\n* **[2014]** _Public Opinion about Self-driving Vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPublic-Opinion-about-Self-driving-Vehicles-Schoettle-Sivak\u002F4984ed8ae3355d58cfad2bd27cb2bc2488cb0e6a)]\n* **[2014]** _Setting the Stage for Self-driving Cars: Exploration of Future Autonomous Driving Experiences_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSetting-the-Stage-for-Self-driving-Cars-Pettersson\u002Fdf428d8015b92902416d07379fb3415a12d64e3f)]\n* **[2014]** _Three Decades of Driver Assistance Systems: Review and Future Perspectives_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThree-Decades-of-Driver-Assistance-Systems-Review-Bengler-Dietmayer\u002F2c6d7bcf2ae79b73ad5888f591e159a3d994322b)]\n* **[2013]** _Review Article Automotive Technology and Human Factors Research: Past, Present, and Future_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReview-Article-Automotive-Technology-and-Human-Akamatsu-Green\u002Fdfe6df56cd5418ce9d6df938858542097157d3e8)]\n* **[2012]** _Safe semi-autonomous control with enhanced driver modeling_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSafe-semi-autonomous-control-with-enhanced-driver-Vasudevan-Shia\u002F8e36ebbb6e5409aa911e4121ca37c455ff157218)]\n* **[2012]** _Semi-autonomous Car Control Using Brain Computer Interfaces_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSemi-autonomous-Car-Control-Using-Brain-Computer-G%C3%B6hring-Latotzky\u002Fe35864047f5b4ac3398ad6f242d2f1407c965f37)]\n* **[2011]** _iDriver - Human Machine Interface for Autonomous Cars_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FiDriver-Human-Machine-Interface-for-Autonomous-Reuschenbach-Wang\u002F3d7107cdd11af698790736ba5fc9f23cc3f52d04)]\n* **[2010]** _Driving an Autonomous Car with Eye Tracking Driving an Autonomous Car with Eye Tracking_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDriving-an-Autonomous-Car-with-Eye-Tracking-Wang-Latotzky\u002Fb3aa092b84ae6c9b924ed1a0d9681bbb342249b3)]\n* **[2010]** _Remote Controlling an Autonomous Car with an Iphone_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRemote-Controlling-an-Autonomous-Car-with-an-Wang-Ganjineh\u002Fa0032e1fbedf61b2a74cfd5f4a9a3edb52689064)]\n* **[2009]** _Car-driver Cooperation in Future Vehicles I. Adas and Autonomuos Vehicle_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCar-driver-Cooperation-in-Future-Vehicles-I-Adas-Broggi-Mazzei\u002Fc2cc8ad2087d753cc67061d490f966de2c1373a1)]\n* **[2009]** _Driver Inattention Detection based on Eye Gaze - Road Event Correlation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDriver-Inattention-Detection-based-on-Eye-Gaze-Fletcher-Zelinsky\u002Fb46f706a9df142f36a58cd7a84c88962f85d93b5)]\n\n#### Infrastructure\n1. **[2014]** _Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective_. [[ref](https:\u002F\u002Farxiv.org\u002Fabs\u002F1404.4391)]\n* **[2014]** _Priority-based Intersection Control Framework for Self-Driving Vehicles: Agent-based Model Development and Evaluation_. [[ref](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F271738793_Priority-based_Intersection_Control_Framework_for_Self-Driving_Vehicles_Agent-based_Model_Development_and_Evaluation)]\n* **[2014]** _A lattice-based approach to multi-robot motion planning for non-holonomic vehicles_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-lattice-based-approach-to-multi-robot-motion-Cirillo-Uras\u002F74ec451f463c4931c73f35cf327893ac2595e876)]\n* **[2005]** _Cooperative autonomous driving: intelligent vehicles sharing city roads_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCooperative-autonomous-driving-intelligent-Baber-Kolodko\u002Fa42f42fa95d8ee6498dff905ed4848437a8f0084)]\n* **[2014]** _Achieving Integrated Convoys: Cargo Unmanned Ground Vehicle Development and Experimentation_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAchieving-Integrated-Convoys-Cargo-Unmanned-Ground-Zych-Silver\u002F364ecf6f5af89c7b3e3d11d2269581b420edb003)]\n* **[2014]** _Priority-based coordination of mobile robots_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPriority-based-coordination-of-mobile-robots-Gregoire\u002F5fdd722822fe2722d8c90e35461538dbfca10a5e)]\n* **[2012]** _Exploration and Mapping with Autonomous Robot Teams Results from the Magic 2010 Competition_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExploration-and-Mapping-with-Autonomous-Robot-Olson-Strom\u002F9bf0e62b5b2343a0b509a1ac7a658be587a5c37d)]\n* **[2012]** _Progress toward multi-robot reconnaissance and the MAGIC 2010 competition_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FProgress-toward-multi-robot-reconnaissance-and-the-Olson-Strom\u002F617943baefd909bbf06787fcb8b18b943820c87e)]\n\n#### Law & Society\n1. **[2016]** _Autonomous Vehicle Technology: A Guide for Policymakers_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Vehicle-Technology-A-Guide-for-Anderson-Kalra\u002Fa0231f6ab2a9feaef92d5481149cdb2142aaeb02)]\n* **[2014]** _**WHITE PAPER** Self-driving Vehicles: Current Status of Autonomous Vehicle Development and Minnesota Policy Implications Preliminary White Paper_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelf-driving-Vehicles-Current-Status-of-Autonomous-Lari-Douma\u002F581075c89f6a3945fa43d61aac1329d1e43f9fa3)]\n* **[2014]** _Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAre-We-Ready-for-Driver-less-Vehicles-Security-vs-Acharya\u002Fec5b5c434f9d0bfc3954c212226d436e32bcf7d5)]\n* **[2014]** _A Survey of Public Opinion about Autonomous and Self-driving_.[[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Survey-of-Public-Opinion-about-Autonomous-and-Schoettle-Sivak\u002F5d983c2d2160b9c159b2cdcfcfaded01a4ce2ad6)]\n* **[2013]** _Autonomous vehicle social behavior for highway entrance ramp management_. [[ref](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-vehicle-social-behavior-for-highway-Wei-Dolan\u002F86482726040d4a924ee339043e4606625a8f64fd)]\n\n## Research Labs\n1. \t[Center for Automotive Research at Stanford](https:\u002F\u002Fcars.stanford.edu\u002F) - Current areas of research focuses on human-centered mobility themes like\nunderstanding how people will interact with increasingly automated vehicles, societal impacts of vehicle automation from policy to ethics to law, technology advances in sensing, decision-making and control.\n* [SAIL-TOYOTA Center for AI Research at Stanford](http:\u002F\u002Faicenter.stanford.edu\u002Fresearch\u002F) - The theme of the center is **Human-Centered Artificial Intelligence for Future Intelligent Vehicles and Beyond.**\n* [Berkeley DeepDrive](http:\u002F\u002Fbdd.berkeley.edu\u002F) - Investigates state-of-the-art technologies in computer vision and machine learning for automotive application.\n* [Princeton Autonomous Vehicle Engineering](http:\u002F\u002Fpave.princeton.edu\u002F) - undergraduate student-led research group at Princeton University dedicated to advancing and promoting the field of robotics through competitive challenges, self-guided research and community outreach.\n* [University of Maryland Autonomous Vehicle Laboratory](http:\u002F\u002Fwww.avl.umd.edu\u002F) - conducts research and development in the area of biologically inspired design and robotics.\n* [University of Waterloo WAVE Laboratory](http:\u002F\u002Fwavelab.uwaterloo.ca\u002F) - Research areas includes Multirotor UAV, Autonomous driving and Multi-Camera Parallel Tracking and Mapping.\n* [Oxford Robotics Institute – Autonomous Systems](http:\u002F\u002Fmrg.robots.ox.ac.uk\u002F) - Researches all aspects of land based mobile autonomy.\n* [Autonomous Lab - Freie Universität Berlin](http:\u002F\u002Fautonomos-labs.com\u002F) - Computer Vision, Cognitive Navigation, Spatial Car Environment Capture.\n* [Honda Research Institute - USA](https:\u002F\u002Fusa.honda-ri.com\u002Fhome) - engaged in development and integration of multiple sensory modules and the coordination of these components while fulfilling tasks such as stable motion planning,  decision making, obstacle avoidance, and control (test).​\n* [Toyota-CSAIL Research Center at MIT](http:\u002F\u002Ftoyota.csail.mit.edu\u002F) - Aimed at furthering the development of autonomous vehicle technologies, with the goal of reducing traffic casualties and potentially even developing a vehicle incapable of getting into an accident.\n* [Princeton Vision & Robotics](http:\u002F\u002Fvision.princeton.edu\u002Fresearch.html) - Autonomous Driving and StreetView.\n* [CMU The Robotic Institute Vision and Autonomous Systems Center (VASC)](http:\u002F\u002Fwww.ri.cmu.edu\u002Fresearch_center_detail.html?type=aboutcenter&center_id=4&menu_id=262) - working in the areas of computer vision, autonomous navigation, virtual reality, intelligent manipulation, space robotics, and related fields.\n* [Five AI](https:\u002F\u002Ffive.ai\u002Fresearch) - Computer vision, hardware, and other publications from a UK-based autonomous vehicle company\n* [Vehicle Industry Research Center - Széchenyi University](https:\u002F\u002Fjkk-web.sze.hu\u002F?lang=en) - One of the most researched topic is self-driving (a.k.a autonomous) vehicles. The research center is preparing for this new technology-to-come by studying and researching its fundamentals and exploring the possibilities it offers. \n* [Karlsruhe Institute of Technology (KIT)](https:\u002F\u002Fwww.kit.edu\u002Ftopics\u002Fmobility.php) - At KIT, about 800 scientists of nearly 40 institutes conduct research into forward-looking, safe, sustainable, and comfortable solutions for future mobility. Scarcity of resources, lacking space, and overstrained infrastructure call for an integrated assessment of transport means and traffic flows. \n\n## Datasets\n1. [Udacity](https:\u002F\u002Fgithub.com\u002Fudacity\u002Fself-driving-car\u002Ftree\u002Fmaster\u002Fdatasets) - Udacity driving datasets released for [Udacity Challenges](https:\u002F\u002Fwww.udacity.com\u002Fself-driving-car). Contains ROSBAG training data. (~80 GB).\n* [Comma.ai](https:\u002F\u002Farchive.org\u002Fdetails\u002Fcomma-dataset) - 7 and a quarter hours of largely highway driving. Consists of 10 videos clips of variable size recorded at 20 Hz with a camera mounted on the windshield of an Acura ILX 2016. In parallel to the videos, also recorded some measurements such as car's speed, acceleration, steering angle, GPS coordinates, gyroscope angles. These measurements are transformed into a uniform 100 Hz time base.\n* [Oxford RobotCar](http:\u002F\u002Frobotcar-dataset.robots.ox.ac.uk\u002F) - over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. The dataset captures many different combinations of weather, traffic and pedestrians, along with longer term changes such as construction and roadworks.\n* [Oxford Radar RobotCar](https:\u002F\u002Foxford-robotics-institute.github.io\u002Fradar-robotcar-dataset\u002F) - radar extension to The Oxford RobotCar Dataset providing data from a Navtech CTS350-X Millimetre-Wave FMCW radar and Dual Velodyne HDL-32E LIDARs with optimised ground truth radar odometry for 280 km of driving.\n* [Oxford Road Boundaries](https:\u002F\u002Foxford-robotics-institute.github.io\u002Froad-boundaries-dataset\u002F) - contains 62605 labelled samples, of which 47639 samples are curated. Each of these samples contain both raw and classified masks for left and right lenses. The data contains images from a diverse set of scenarios such as straight roads, parked cars, and junctions.\n* [KITTI Vision Benchmark Suite](http:\u002F\u002Fwww.cvlibs.net\u002Fdatasets\u002Fkitti\u002Fraw_data.php) - 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as highresolution\ncolor and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS\u002FIMU inertial navigation system.\n* [University of Michigan North Campus Long-Term Vision and LIDAR Dataset](http:\u002F\u002Frobots.engin.umich.edu\u002Fnclt\u002F) -  consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive\nsensors for odometry collected using a Segway robot.\n* [University of Michigan Ford Campus Vision and Lidar Data Set](http:\u002F\u002Frobots.engin.umich.edu\u002FSoftwareData\u002FFord) - dataset collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS LV) and consumer (Xsens MTI-G) Inertial Measuring Unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system.\n* [DIPLECS Autonomous Driving Datasets (2015)](http:\u002F\u002Fcvssp.org\u002Fdata\u002Fdiplecs\u002F) - dataset was recorded by placing a HD camera in a car driving around the Surrey countryside. The dataset contains about 30 minutes of driving. The video is 1920x1080 in colour, encoded using H.264 codec. Steering is estimated by tracking markers on the steering wheel. The car's speed is estimated from OCR the car's speedometer (but the accuracy of the method is not guaranteed).\n* [Velodyne SLAM Dataset from Karlsruhe Institute of Technology](http:\u002F\u002Fwww.mrt.kit.edu\u002Fz\u002Fpubl\u002Fdownload\u002Fvelodyneslam\u002Fdataset.html) -  two challenging datasets recorded with the Velodyne HDL64E-S2 scanner in the city of Karlsruhe, Germany.\n* [SYNTHetic collection of Imagery and Annotations (SYNTHIA)](http:\u002F\u002Fsynthia-dataset.net\u002F) - consists of a collection of photo-realistic frames rendered from a virtual city and comes with precise pixel-level semantic annotations for 13 classes: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lanemarking.\n* [Cityscape Dataset](https:\u002F\u002Fwww.cityscapes-dataset.com\u002F) - focuses on semantic understanding of urban street scenes.  large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Details on annotated classes and examples of our annotations are available.\n* [CSSAD Dataset](http:\u002F\u002Faplicaciones.cimat.mx\u002FPersonal\u002Fjbhayet\u002Fccsad-dataset) - Several real-world stereo datasets exist for the development and testing of algorithms in the fields of perception and navigation of autonomous vehicles. However, none of them was recorded in developing countries and therefore they lack the particular characteristics that can be found in their streets and roads, like abundant potholes, speed bumpers and peculiar flows of pedestrians. This stereo dataset was recorded from a moving vehicle and contains high resolution stereo images which are complemented with orientation and acceleration data obtained from an IMU, GPS data, and data from the car computer.\n* [Daimler Urban Segmetation Dataset](http:\u002F\u002Fwww.6d-vision.com\u002Fscene-labeling) - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).\n* [Self Racing Cars - XSens\u002FFairchild Dataset](http:\u002F\u002Fdata.selfracingcars.com\u002F) - The files include measurements from the Fairchild FIS1100 6 Degree of Freedom (DoF) IMU, the Fairchild FMT-1030 AHRS, the Xsens MTi-3 AHRS, and the Xsens MTi-G-710 GNSS\u002FINS. The files from the event can all be read in the MT Manager software, available as part of the MT Software Suite, available here.\n* [MIT AGE Lab](http:\u002F\u002Flexfridman.com\u002Fautomated-synchronization-of-driving-data-video-audio-telemetry-accelerometer\u002F) - a small sample of the 1,000+ hours of multi-sensor driving datasets collected at AgeLab.\n* [Yet Another Computer Vision Index To Datasets (YACVID)](http:\u002F\u002Fyacvid.hayko.at\u002F) -  a list of frequently used computer vision datasets.\n* [KUL Belgium Traffic Sign Dataset](http:\u002F\u002Fwww.vision.ee.ethz.ch\u002F~timofter\u002Ftraffic_signs\u002F) - a large dataset with 10000+ traffic sign annotations, thousands of physically distinct traffic signs. 4 video sequences recorded with 8 high resolution cameras mounted on a van, summing more than 3 hours, with traffic sign annotations, camera calibrations and poses. About 16000 background images. The material is captured in Belgium, in urban environments from Flanders region, by GeoAutomation. \n* [LISA: Laboratory for Intelligent & Safe Automobiles, UC San Diego Datasets](http:\u002F\u002Fcvrr.ucsd.edu\u002FLISA\u002Fdatasets.html) - traffic sign, vehicles detection, traffic lights, trajectory patterns.\n* [Multisensory Omni-directional Long-term Place Recognition (MOLP) dataset for autonomous driving](http:\u002F\u002Fhcr.mines.edu\u002Fcode\u002FMOLP.html) It was recorded using omni-directional stereo cameras during one year in Colorado, USA. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.05215)\n* [Lane Instance Segmentation in Urban Environments](https:\u002F\u002Ffive.ai\u002Fdatasets) Semi-automated method for labelling lane instances. 24,000 image set available. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.01347.pdf)\n* [Foggy Zurich Dataset](https:\u002F\u002Fwww.vision.ee.ethz.ch\u002F~csakarid\u002FModel_adaptation_SFSU_dense\u002F) Curriculum Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding. 3.8k High Quality Foggy images in and around Zurich. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.01415)\n* [SullyChen AutoPilot Dataset](https:\u002F\u002Fgithub.com\u002FSullyChen\u002FAutopilot-TensorFlow) Dataset collected by SullyChen in and around California. \n* [Waymo Training and Validation Data](https:\u002F\u002Fwaymo.com\u002Fopen) One terabyte of data with 3D and 2D labels.\n* [Intel's dataset for AD conditions in India](https:\u002F\u002Fidd.insaan.iiit.ac.in\u002F) A dataset for Autonomous Driving conditions in India (road scene understanding in unstructured environments) which consists of 10k images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads (by Intel & IIIT Hyderabad).\n* [nuScenes Dataset](https:\u002F\u002Fwww.nuscenes.org\u002F) A large dataset with 1,400,000 images and 390,000 lidar sweeps from Boston and Singapore. Provides manually generated 3D bounding boxes for 23 object classes.\n* [German Traffic Sign Dataset](http:\u002F\u002Fbenchmark.ini.rub.de\u002F?section=gtsrb&subsection=dataset) A large dataset of German traffic sign recogniton data (GTSRB) with more than 40 classes in 50k images and detection data (GTSDB) with 900 image annotations.\n* [Swedish Traffic Sign Dataset](https:\u002F\u002Fwww.cvl.isy.liu.se\u002Fresearch\u002Fdatasets\u002Ftraffic-signs-dataset\u002F) A dataset with traffic signs recorded on 350 km of Swedish roads, consisting of 20k+ images with 20% of annotations.\n* [Argoverse 3d Tracking Dataset](https:\u002F\u002Fwww.argoverse.org\u002F) A large dataset with ~1M images and ~1M labeled 3d cuboids from Miami and Pittsburgh. Provides HD maps and imagery from 7 ring cameras, 2 stereo cameras, and LiDAR.\n* [Argoverse Motion Forecasting Dataset](https:\u002F\u002Fwww.argoverse.org\u002F) A large dataset with trajectories of tracked objects across 324,557 scenes, mined from 1006 hours of driving.\n\n\n## Open Source Software\n1. [Autoware](https:\u002F\u002Fgithub.com\u002FCPFL\u002FAutoware) - Integrated open-source software for urban autonomous driving.\n* [Comma.ai Openpilot](https:\u002F\u002Fgithub.com\u002Fcommaai\u002Fopenpilot) - an open source driving agent.\n* [Stanford Driving Software](https:\u002F\u002Fsourceforge.net\u002Fprojects\u002Fstanforddriving\u002F) - Software Infrastructure for Stanford's Autonomous Vehicles.\n* [GTA Robotics SDC Environment](https:\u002F\u002Fgithub.com\u002FOSSDC\u002Fself-driving-car-1) - development environment ready for Udacity Self Driving Car (SDC) Challenges.\n* [The OSCC Project](http:\u002F\u002Foscc.io\u002F) - A by-wire control kit for autonomous vehicle development.\n* [OpenAI Gym](https:\u002F\u002Fgym.openai.com\u002F) - A toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games, mountain car, car racing etc., with a good possibility to develop and validate RL algorithms for Self-Driving Cars.\n* [argoverse-api](https:\u002F\u002Fgithub.com\u002Fargoai\u002Fargoverse-api) - Development kit for working with the [Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F) 3d Tracking and Forecasting datasets, and for evaluating 3d tracking, 3d detection, and motion forecasting algorithms.\n\n## Hardware\n\n\n## Toys\n1. [TensorKart](https:\u002F\u002Fgithub.com\u002Fkevinhughes27\u002FTensorKart) - self-driving MarioKart with TensorFlow.\n2. [NeuroJS](https:\u002F\u002Fgithub.com\u002Fjanhuenermann\u002Fneurojs) - A javascript deep learning and reinforcement learning library. A sample self-driving car implementation.\n3. [DonkeyCar](https:\u002F\u002Fgithub.com\u002Fautorope\u002Fdonkeycar) - A minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.\n\n## Companies\n\n1. (As of August 28, 2019) [40+ Corporations Working On Autonomous Vehicles](https:\u002F\u002Fwww.cbinsights.com\u002Fblog\u002Fautonomous-driverless-vehicles-corporations-list\u002F)\n\n## Media\nDifferent media sources where we can find self-driving car related topics, ideas, and much more.\n\n### Podcasts\n\n* [Artificial Intelligence: AI Podcast](https:\u002F\u002Flexfridman.com\u002Fai\u002F) - *\"Artificial Intelligence podcast (AI podcast) is a series of conversations about technology, science, and the human condition hosted by Lex Fridman.\"*. Example episodes:\n  * [Sebastian Thrun: Flying Cars, Autonomous Vehicles, and Education](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZPPAOakITeQ&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)\n  * [Elon Musk: Neuralink, AI, Autopilot, and the Pale Blue Dot](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=smK9dgdTl40&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)\n  * [George Hotz: Comma.ai, OpenPilot, and Autonomous Vehicles](https:\u002F\u002Flexfridman.com\u002Fgeorge-hotz\u002F)\n  * [Jeremy Howard: fast.ai Deep Learning Courses and Research](https:\u002F\u002Flexfridman.com\u002Fjeremy-howard\u002F)\n* [Autonocast, The future of transportation](https:\u002F\u002Fwww.autonocast.com\u002F) - \n*\"A weekly show discussing the latest in transportation technology\"*\n\n#### Youtube\n1. [Lex Fridman (channel)](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCSHZKyawb77ixDdsGog4iWA) - 100+ of AI and autonomous driving related videos including [MIT Deep Learning Series (playlist)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) which includes:\n   * [11 Jan 2020] [Deep Learning State of the Art (2020)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0VH1Lim8gL8&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&index=1), and\n   * [12 Jan 2019] [MIT Deep Learning Basics: Introduction and Overview](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&index=2).\n1. The Three Pillars of Autonomous Driving. [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GZa9SlMHhQc)]\n1. What goes into sensing for autonomous driving? [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GCMXXXmxG-I)]\n1. Amnon Shashua CVPR 2016 keynote: Autonomous Driving, Computer Vision and Machine Learning. [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=n8T7A3wqH3Q)]\n1. Chris Urmson: How a driverless car sees the road. [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tiwVMrTLUWg)]\n1. Deep Reinforcement Learning for Driving Policy. [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cYTVXfIH0MU)]\n1. NVIDIA at CES 2016 - Self Driving Cars and Deep Learning GPUs. [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KkpxA5rXjmA)]\n1. NVIDIA Drive PX2 self-driving car platform visualized. [[watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=URmxzxYlmtg&app=desktop)]\n\n### Blogs\n1. [Deep Learning and Autonomous Driving](https:\u002F\u002Fhandong1587.github.io\u002Fdeep_learning\u002F2015\u002F10\u002F09\u002Fdl-and-autonomous-driving.html)\n* [[Medium] Self-Driving Cars](https:\u002F\u002Fmedium.com\u002Fself-driving-cars)\n\n### Twitter\n\n1. [comma.ai](https:\u002F\u002Ftwitter.com\u002Fcomma_ai)\n* [[Udacity] David Silver](https:\u002F\u002Ftwitter.com\u002Fdsilver829)\n* [[Udacity] Dhruv Parthasarathy](https:\u002F\u002Ftwitter.com\u002Fdhruvp)\n* [[Udacity] Eric Gonzalez](https:\u002F\u002Ftwitter.com\u002Fericrgon)\n* [[Udacity] Oliver Cameron](https:\u002F\u002Ftwitter.com\u002Folivercameron)\n* [[Udacity] MacCallister Higgins](https:\u002F\u002Ftwitter.com\u002Fmacjshiggins)\n* [[Udacity] Sebastian Thrun](https:\u002F\u002Ftwitter.com\u002FSebastianThrun)\n* [[Google] Chris Urmson](https:\u002F\u002Ftwitter.com\u002Fchris_urmson)\n\n\n## Laws\n\nUnited States\n\n1. [California Regulatory Notice](https:\u002F\u002Fwww.dmv.ca.gov\u002Fportal\u002Fdmv\u002Fdetail\u002Fvr\u002Fautonomous\u002Ftesting)\n* [Michigan Just Passed the Most Permissive Self-Driving Car Laws in the Country](http:\u002F\u002Ffortune.com\u002F2016\u002F12\u002F09\u002Fmichigan-self-driving-cars\u002F)\n* [Car accidents involving a SDC in California](https:\u002F\u002Fwww.dmv.ca.gov\u002Fportal\u002Fdmv\u002Fdetail\u002Fvr\u002Fautonomous\u002Fautonomousveh_ol316)\n* [Nvidia starts testing its self-driving cars on public roads](http:\u002F\u002Fwww.theinquirer.net\u002Finquirer\u002Fnews\u002F2479432\u002Fnvidia-starts-testing-its-self-driving-cars-on-public-roads)\n","# 令人惊叹的自动驾驶车辆：[![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n一份精心整理的优秀自动驾驶车辆资源列表，灵感源自 [awesome-php](https:\u002F\u002Fgithub.com\u002Fziadoz\u002Fawesome-php)。\n\n## 贡献\n欢迎随时向我提交拉取请求以添加链接。\n\n## 目录\n* [基础](#foundations)\n* [课程](#courses)\n* [论文](#papers)\n* [研究实验室](#research-labs)\n* [数据集](#datasets)\n* [开源软件](#open-source-software)\n* [硬件](#hardware)\n* [玩具](#toys)\n* [公司](#companies)\n* [媒体](#media)\n* [法律](#laws)\n\n\n## 基础\n\n### 人工智能\n1. [Awesome Machine Learning](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning) - 一份精心整理的优秀机器学习框架、库和软件列表。由 Joseph Misiti 维护。Joseph Misiti\n* [深度学习论文阅读路线图](https:\u002F\u002Fgithub.com\u002Fsongrotek\u002FDeep-Learning-Papers-Reading-Roadmap) - 从概览到细节、从经典到最前沿、从通用到特定领域的深度学习论文阅读路线图，专为初学者设计，旨在帮助任何人快速入门深度学习。由 Flood Sung 维护。\n* [开源深度学习课程](http:\u002F\u002Fwww.deeplearningweekly.com\u002Fpages\u002Fopen_source_deep_learning_curriculum) - 一套旨在为所有认真研究该领域的人提供起点的深度学习课程。\n\n### 机器人学\n1. [Awesome Robotics](https:\u002F\u002Fgithub.com\u002FKiloreux\u002Fawesome-robotics) - 由 kiloreux 维护的一份包含各类机器人学书籍、课程及其他资源的列表。\n\n### 计算机视觉\n1. [Awesome Computer Vision](https:\u002F\u002Fgithub.com\u002Fjbhuang0604\u002Fawesome-computer-vision) - 由 Jia-Bin Huang 维护的一份精心整理的优秀计算机视觉资源列表。\n* [Awesome Deep Vision](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision) - 由 Jiwon Kim、Heesoo Myeong、Myungsub Choi、Jung Kwon Lee 和 Taeksoo Kim 维护的一份精心整理的计算机视觉深度学习资源列表。\n\n## 课程\n* [[Coursera] 机器学习](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning) - 由 [Andrew Ng](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAndrew_Ng) 主讲，截至2020年1月28日，已获得125,344个评分和30,705条评论。\n* [[Coursera+DeepLearning.ai] 深度学习专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdeep-learning) - 由 [Andrew Ng](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAndrew_Ng) 主讲，共5门课程，教授深度学习的基础知识，编程语言为Python。\n* [[Udacity] 自动驾驶汽车纳米学位项目](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fself-driving-car-engineer-nanodegree--nd013) - 教授自动驾驶汽车团队所使用的技能与技术。该项目的教学大纲可在此查看 [这里](https:\u002F\u002Fmedium.com\u002Fself-driving-cars\u002Fterm-1-in-depth-on-udacitys-self-driving-car-curriculum-ffcf46af0c08#.bfgw9uxd9)。\n* [[多伦多大学] CSC2541 自动驾驶的视觉感知](http:\u002F\u002Fwww.cs.toronto.edu\u002F~urtasun\u002Fcourses\u002FCSC2541\u002FCSC2541_Winter16.html) - 一门关于自动驾驶视觉感知的研究生课程。课程简要涵盖定位、自运动估计、自由空间估计以及视觉识别（分类、检测、分割）等主题。\n* [[INRIA] 移动机器人与自动驾驶车辆](https:\u002F\u002Fwww.fun-mooc.fr\u002Fcourses\u002Finria\u002F41005S02\u002Fsession02\u002Fabout?utm_source=mooc-list) - 介绍编程移动机器人和自动驾驶车辆所需的关键概念。课程同时提供形式化工具和算法工具，并在最后一周的主题（行为建模与学习）中，还将提供真实的案例及Python编程练习。\n* [[格拉斯哥大学] ENG5017 自动驾驶车辆引导系统](http:\u002F\u002Fwww.gla.ac.uk\u002Fcoursecatalogue\u002Fcourse\u002F?code=ENG5017) - 介绍自动驾驶车辆引导与协调背后的概念，并使学生能够设计和实现融合规划、优化与反应元素的车辆引导策略。\n* [[David Silver - Udacity] 如何获得自动驾驶汽车相关工作：课程作业](https:\u002F\u002Fmedium.com\u002Fself-driving-cars\u002Fhow-to-land-an-autonomous-vehicle-job-coursework-e7acc2bfe740#.j5b2kwbso) David Silver 来自 Udacity，回顾了他作为软件工程背景人士如何通过课程作业成功获得自动驾驶汽车相关工作的经验。\n* [[斯坦福] - CS221 人工智能：原理与技术](http:\u002F\u002Fstanford.edu\u002F~cpiech\u002Fcs221\u002Findex.html) - 包含一个简单的自动驾驶项目和模拟器。\n* [[MIT] 6.S094：用于自动驾驶汽车的深度学习](http:\u002F\u002Fselfdrivingcars.mit.edu\u002F) - *\"本课程通过构建自动驾驶汽车这一应用主题，介绍深度学习的实践。课程面向初学者，专为机器学习新手设计，但同时也适合该领域的高级研究人员，他们希望获得深度学习方法及其应用的实用概述。（……）\"*\n* [[MIT] 深度学习](https:\u002F\u002Fdeeplearning.mit.edu\u002F) - *\"本页面是麻省理工学院关于深度学习、深度强化学习、自动驾驶车辆和人工智能的课程与讲座合集，由 Lex Fridman 整理。\"*\n* [[MIT] 以人为本的人工智能](https:\u002F\u002Fhcai.mit.edu\u002F) - *\"麻省理工学院的人本AI是一系列研究与课程的集合，专注于设计、开发和部署能够与人类进行深入、有意义的合作并从中学习的人工智能系统。\"*\n* [[UCSD] - MAE\u002FECE148 自动驾驶车辆导论](https:\u002F\u002Fguitar.ucsd.edu\u002Fmaeece148\u002Findex.php\u002FIntroduction_to_Autonomous_Vehicles) - 一门基于项目的动手课程，使用具有车道追踪功能的 DonkeyCar，并涵盖目标检测、导航等多种高级主题。\n* [[MIT] 2.166 鸭子镇](http:\u002F\u002Fduckietown.mit.edu\u002Findex.html) - 一门针对研究生级别的自主性科学课程。这是一门注重实践、以项目为导向的课程，聚焦于自动驾驶车辆与高级别自主性。问题：**为鸭子镇设计自动驾驶机器人出租车系统。**\n* [[Coursera] 自动驾驶汽车](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fself-driving-cars#about) - 多伦多大学推出的关于自动驾驶汽车的4门专项课程。内容涵盖从入门、状态估计与定位、视觉感知到运动规划的全过程。\n\n## 论文\n按主题领域及发表\u002F提交年份\n\n#### 一般\n1. **[2016]** _结合深度强化学习与基于安全的控制实现自动驾驶_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00147)]\n* **[2015]** _深度学习在高速公路驾驶中的实证评估_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1504.01716)]\n* **[2015]** _自动驾驶车辆：面临的挑战与机遇_。[[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2823464)]\n* **[2014]** _让伯莎开车——一次沿历史路线的自动驾驶之旅_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMaking-Bertha-Drive-An-Autonomous-Journey-on-a-Ziegler-Bender\u002Fec26d7b1cb028749d0d6972279cf4090930989d8)]\n* **[2014]** _迈向自动驾驶车辆_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-Autonomous-Vehicles-Schwarz-Thomas\u002F88712e686e1bcad21f0836e9d31400dab2b7fa8f)]\n* **[2013]** _构建可行的自动驾驶研究平台_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-a-viable-autonomous-driving-research-Wei-Snider\u002Fda5cee7a6eb817bbbf4721c64c756bd8b7122359)]\n* **[2013]** _基于本体论的模型用于确定自动驾驶车辆的自动化等级，以支持协同驾驶_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-ontology-based-model-to-determine-the-Pollard-Morignot\u002F25239ec7fb6159166dfe15adf229fc2415f071df)]\n* **[2013]** _利用激光雷达构建三维地图并检测人类轨迹实现自动驾驶车辆导航_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Vehicle-Navigation-by-Building-3d-Map-Kagami-Thompson\u002F81b14341e3e063d819d032b6ce0bc0be0917c867)]\n* **[2012]** _自主地面车辆——概念与通往未来的路径_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Ground-Vehicles-Concepts-and-a-Path-to-Luettel-Himmelsbach\u002F5e8d51a1f6ba313a38a35af414a00bcfd3b5c0ae)]\n* **[2011]** _基于视觉记忆与基于图像的视觉伺服的自动驾驶实验评估_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExperimental-Evaluation-of-Autonomous-Driving-Diosi-Segvic\u002F2aeb9aa42e8e2048e15453759ec12411486a2619)]\n* **[2011]** _学习驾驶：自动驾驶汽车的感知_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-to-Drive-Perception-for-Autonomous-Cars-Stavens-Thrun\u002Fbe25d7bff3b5928adf6c0a7f5495d47113f80997)]\n* **[2010]** _迈向机器人汽车_。[[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=1721679)]\n* **[2009]** _交通中的自动驾驶：Boss与城市挑战_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Driving-in-Traffic-Boss-and-the-Urban-Urmson-Baker\u002F4657a350e4822bc567256f9b9dc5d922237a71be)]\n* **[2009]** _越野行驶的地图构建、导航与学习_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMapping-navigation-and-learning-for-off-road-Konolige-Agrawal\u002F57d7396b92ad31b386dfce4f8799149f5ced2160)]\n* **[2008]** _城市环境中的自动驾驶：Boss与城市挑战_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Driving-in-Urban-Environments-Boss-and-Urmson-Anhalt\u002F1c0fb6b1bbfde0f9bab6268f5609cce2bd3bc5bd)]\n* **[2008]** _Caroline：一款适用于城市环境的自动驾驶车辆_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCaroline-An-autonomously-driving-vehicle-for-urban-Rauskolb-Berger\u002F08f4e164291942fc78bd6945215b2c672b17edd5)]\n* **[2008]** _城市无人驾驶地面车辆的设计_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDesign-of-an-Urban-Driverless-Ground-Vehicle-Benenson-Parent\u002F852a672c3d4a2fca3ff7b215d9c096b0be54feb7)]\n* **[2008]** _Little Ben：本杰明·富兰克林赛车队在2007年DARPA城市挑战赛中的参赛作品_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLittle-Ben-The-Ben-Franklin-Racing-Team-s-Entry-in-Bohren-Foote\u002Fb6d5e01cdb76284ee6c42b0dda6c36f121c573f0)]\n* **[2008]** _Odin：VictorTango团队在DARPA城市挑战赛中的参赛作品_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOdin-Team-VictorTango-s-Entry-in-the-DARPA-Urban-Reinholtz-Hong\u002Faaeaa58bedf6fa9b42878bf5914f55f48cf26209)]\n* **[2008]** _Robosemantics：大众汽车Stanley如何表征世界_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRobosemantics-How-Stanley-the-Volkswagen-Parisien-Thagard\u002F9f2186df45a387ab600414968090fe3da37591ca)]\n* **[2008]** _AnnieWAY团队在2007年DARPA城市挑战赛中的自主系统_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTeam-AnnieWAY-s-Autonomous-System-Stiller-Kammel\u002F56972aa9f9d3cce7c77d402602bc8f3af94d57c9)]\n* **[2008]** _MIT-Cornell碰撞事件及其发生原因_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThe-MIT-Cornell-collision-and-why-it-happened-Fletcher-Teller\u002F0df4f3ef7356fe56547ac3145d7c0229163bc7a5)]\n* **[2007]** _自动驾驶汽车——一项人工智能与机器人技术的挑战_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelf-Driving-Cars-An-AI-Robotics-Challenge-Thrun\u002F31d17c77d2ea18f71d570741665f0fd3030caa94)]\n* **[2007]** _2007年DARPA城市挑战赛：本杰明·富兰克林赛车队B156技术报告_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F2007-Darpa-Urban-Challenge-the-Ben-Franklin-Racing-Franklin-Lee\u002F510b0fa02d6bdd1061cf73373f197ba624692ad0)]\n* **[2007]** _MIT团队在城市挑战赛中的技术报告_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTeam-Mit-Urban-Challenge-Technical-Report-Leonard-Barrett\u002F6ac15e819701cd0d077d8157711c4c402106722c)]\n* **[2007]** _DARPA城市挑战赛技术报告：奥斯汀机器人技术_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDarpa-Urban-Challenge-Technical-Report-Executive-Technology-Tuttle\u002F37e78b1bd135df5c5a1fcbf2a8debd260d28a55c)]\n* **[2007]** _柏林之魂：一款用于DARPA城市挑战赛的自动驾驶汽车——硬件与软件架构_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSpirit-of-Berlin-an-Autonomous-Car-for-the-Darpa-Berlin-Rojo\u002F8c96cbc752dfcde3673440cf7ca1fb19218426bf)]\n* **[2007]** _Case团队与2007年DARPA城市挑战赛_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTeam-Case-and-the-2007-Darpa-Urban-Challenge-Newman-Lead\u002Fe68c745b7807e77ccf67fea325a241136a568eeb)]\n* **[2006]** _关于赢得DARPA大挑战赛的机器人Stanley研发的个人记述_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Personal-Account-of-the-Development-of-Stanley-Thrun\u002F74a4de58be068d2dc38bb31cf54c3c49bdc0d4e4)]\n* **[2006]** _Stanley：赢得DARPA大挑战赛的机器人_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStanley-The-robot-that-won-the-DARPA-Grand-Thrun-Montemerlo\u002F298500897243b17fa2ebe7bde0a1b8ebc00ea07f)]\n\n#### 本地化与建图\n1. **[2016]** _MultiCol-SLAM——一种模块化的实时多相机SLAM系统_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.07336)]\n* **[2016]** _基于图像的相机定位：概述_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.03660)]\n* **[2016]** _无处不在的实时地理空间定位_ [[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3005426)]\n* **[2016]** _基于结构化稀疏性的鲁棒多模态序列式回环检测_。[[参考文献](http:\u002F\u002Fwww.roboticsproceedings.org\u002Frss12\u002Fp43.pdf)]\n* **[2016]** _SRAL：用于长期视觉场景识别的共享表征外观学习_。[[参考文献](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7839213\u002F)], [[代码](https:\u002F\u002Fgithub.com\u002Fhanfeiid\u002FSRAL)]\n* **[2015]** _基于多相机的路面标记特征概率噪声模型的自动驾驶汽车精确定位_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPrecise-Localization-of-an-Autonomous-Car-Based-on-Jo-Jo\u002F27251099b78185f9ddf59c9ed0c5868af4ef1e80)]\n* **[2013]** _基于平面分割的室外环境三维机器人建图_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPlanar-Segments-Based-Three-dimensional-Robotic-Xiao\u002Febddeb22f3b5c38422987c3fe51aaf847ad444e7)]\n* **[2013]** _利用图像数据库实现沿已行驶路线的车辆定位_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVehicle-Localization-along-a-Previously-Driven-Kume-Supp%C3%A9\u002Fe5a7ac37d542349ae19281f1e2a571f7030b789c)]\n* **[2012]** _先验知识可信吗？学习预测道路施工_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCan-priors-be-trusted-Learning-to-anticipate-Mathibela-Osborne\u002F0a7e502779ed2cf9ee2677d0310386481a51fc12)]\n* **[2009]** _真实道路与交通环境下的激光扫描仪SLAM_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLaser-Scanner-Based-Slam-in-Real-Road-and-Traffic-Garcia-Favrot-Parent\u002F2accb1d9f7ce3f08aa1cde735dcca2578887c545)]\n* **[2007]** _基于地图的城区高精度车辆定位_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMap-Based-Precision-Vehicle-Localization-in-Urban-Levinson-Montemerlo\u002F924f7268d592d327f97ad4e96f48ad774d982ef3)]\n\n#### 感知\n1. **[2019]** _Argoverse：基于丰富地图的三维跟踪与预测_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02620)]\n2. **[2016]** _VisualBackProp：面向自动驾驶的卷积神经网络可视化_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.05418)]\n* **[2016]** _在矩阵中驾驶：虚拟世界能否取代人工生成的标注以完成现实任务？_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.01983)]\n* **[2016]** _迷失与发现：面向自动驾驶车辆的小型道路隐患检测_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.04653)]\n* **[2016]** _基于红外光谱采集的越野场景图像分割_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.02469)]\n* **[2016]** _野外交通标志检测与分类_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTraffic-Sign-Detection-and-Classification-in-the-Zhu-Liang\u002Fd463499b7a82e3cad81d2471b52a198b857aa75b)]\n* **[2016]** _持久性自监督学习原理：从立体视觉到单目视觉的避障应用_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPersistent-self-supervised-learning-principle-from-Hecke-Croon\u002Fa48c4c6707fca20ae64b044b6e8f7f37891186fc)]\n* **[2016]** _利用多模态融合实现林地环境的深度多光谱语义场景理解_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDeep-Multispectral-Semantic-Scene-Understanding-of-Valada-Oliveira\u002F8be99dd94bff76c75594a15e114268841a2656a7)]\n* **[2016]** _自动驾驶中的联合注意力（JAAD）_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FJoint-Attention-in-Autonomous-Driving-JAAD--Kotseruba-Rasouli\u002F1e6a26deea0a38310368d9c2a6dadc317b50bdf8), [数据](http:\u002F\u002Fdata.nvision2.eecs.yorku.ca\u002FJAAD_dataset\u002F)]\n* **[2016]** _无人驾驶车辆的感知：设计与实现_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPerception-for-driverless-vehicles-design-and-Benenson-Suarez\u002Fbf1c728e3e893670244591f720b453245c3363f6)]\n* **[2016]** _基于结构化稀疏性的鲁棒多模态序列式回环闭合检测_。[[参考文献](http:\u002F\u002Fwww.roboticsproceedings.org\u002Frss12\u002Fp43.pdf)]\n* **[2016]** _SRAL：用于长期视觉场所识别的共享表征外观学习_。[[参考文献](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7839213\u002F)], [[代码](https:\u002F\u002Fgithub.com\u002Fhanfeiid\u002FSRAL)]\n* **[2015]** _利用神经网络进行街道的逐像素分割_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.00513)]\n* **[2015]** _用于行人检测的深度卷积神经网络_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1510.03608)]\n* **[2015]** _卷积神经网络的快速算法_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1509.09308)]\n* **[2015]** _彩色图像与激光雷达数据的融合用于车道分类_。[[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2820859)]\n* **[2015]** _利用立体视觉在复杂条件下进行自动驾驶车辆的环境感知_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEnvironment-Perception-for-Autonomous-Vehicles-in-Gal%C3%A1n-Hayet\u002F8f56fd10f37382292f474c441f92432b34b58db5)]\n* **[2015]** _面向人群的自动驾驶中基于意图的在线POMDP规划_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FIntention-aware-online-POMDP-planning-for-Bai-Cai\u002F481aa2882a5816686a5bea7db755862cded42081)]\n* **[2015]** _关于通用道路区域识别的消失点检测方法综述_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSurvey-on-Vanishing-Point-Detection-Method-for-Patel-Mistry\u002F39c6be1e7723b93f06be2bb4199066d4efdadbc9)]\n* **[2015]** _利用内在图像进行视觉道路跟随_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVisual-road-following-using-intrinsic-images-Krajn%C3%ADk-Blazicek\u002F2298f9e3c1235526d55cf78bfc80c505d100540f)]\n* **[2014]** _Rover——一款乐高*自动驾驶汽车_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRover-a-Lego-Self-driving-Car-Tan-Wojtczyk-Wojtczyk\u002F6e24123ef558ffb9888d28f992f8afe76622830e)]\n* **[2014]** _面向城市环境的自动驾驶中多传感器动态目标分类与跟踪_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FClassification-and-Tracking-of-Dynamic-Objects-Darms-Rybski\u002F6c9ce40060fa3efea7d04a4a0e36609592ed6ddf)]\n* **[2014]** _为确保安全的城市驾驶而生成邻近物体的全向视图_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerating-Omni-directional-View-of-Neighboring-Seo\u002F29e53add392de54d439a6002c67e8af6e9baadeb)]\n* **[2014]** _自主视觉导航与基于激光的移动障碍物规避_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Visual-Navigation-and-Laser-Based-Cherubini-Spindler\u002F089fa5a7babc906dc46a58f986c5ac8c46aa9017)]\n* **[2014]** _通过在线自监督色彩建模扩展Stixel世界，以实现道路与障碍物的分割_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExtending-the-Stixel-World-with-online-self-Sanberg-Dubbelman\u002F6dd60e0484931b284f49ab8204b011d153ff4967)]\n* **[2014]** _利用贝叶斯心智理论建模人类计划识别_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPlan-Activity-and-Intent-Recognition-Baker-Tenenbaum\u002F4cbb1ea46c09d11b0b986a7baaac7215006504f8)]\n* **[2013]** _面向自动驾驶公路行驶的聚焦轨迹规划_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FFocused-Trajectory-Planning-for-autonomous-on-road-Gu-Snider\u002F03bf26d72d8cc0cf401c31e31c242e1894bd0890)]\n* **[2013]** _视觉导航过程中避免移动障碍物_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAvoiding-moving-obstacles-during-visual-navigation-Cherubini-Grechanichenko\u002F7c0e580c0f914086e9c918aef1df561253a71044)]\n* **[2013]** _基于视觉的道路识别，在户外行人环境中使用的移动机器人导航系统_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMobile-robot-navigation-system-in-outdoor-Siagian-Chang\u002F7163764c33c3d87c313568c056d50d1bedb25696)]\n* **[2013]** _利用倒置粒子滤波器在低成本、低功耗的多机器人系统中进行障碍物检测与建模_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FObstacle-detection-and-mapping-in-low-cost-low-Kleppe-Skavhaug\u002F646cc0e592b77d553cc77806e90d99420fb79a8e)]\n* **[2013]** _基于单目视觉的可行驶图像区域实时估计_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReal-time-estimation-of-drivable-image-area-based-Neto-Victorino\u002Fc50a769c2038e29d9e64077cd4749b6f8d389806)]\n* **[2013]** _基于道路模型预测的非结构化道路检测_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRoad-model-prediction-based-unstructured-road-Zuo-Yao\u002Fb8b2d3da341042d148ed2988216dbb3ddb6081ed)]\n* **[2013]** _在恶劣条件下实现稳健定位的视觉与热成像选择性组合：昼夜、烟雾与火焰_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelective-Combination-of-Visual-and-Thermal-Brunner-Peynot\u002F85b4b1a9780a4bc22f84904a1cfc3eeeb605c9bd)]\n* **[2012]** _适用于非结构化与结构化道路的路面跟踪方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FInternational-Journal-of-Advanced-Robotic-Systems-Proch%C3%A1zka\u002F4819fda4bc778454701f2a4b30db46ec56aa45bc)]\n* **[2012]** _自主导航与标志检测器学习_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Navigation-and-Sign-Detector-Learning-Ellis-Pugeault\u002F0cffe50112452ecdcdaf0d11b33e12cf3c67213e)]\n* **[2012]** _面向自动驾驶车辆的多传感器协作式出行环境感知系统设计_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDesign-of-a-Multi-Sensor-Cooperation-Travel-Chen-Li\u002Ff5feb2a151c54ec9699924d401a66c193ddd3c8b)]\n* **[2012]** _现实中的学习：达帕挑战赛冠军机器人Stanley的案例研究_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-in-Reality-a-Case-Study-of-Stanley-the-Glaser-Hennig\u002F01c1f49f5e7f4e7f5d005844aa9443769a2d9306)]\n* **[2012]** _便携且可扩展的基于视觉的车载仪器，用于分析驾驶员意图_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPortable-and-Scalable-Vision-Based-Vehicular-Beauchemin-Bauer\u002Fc76b5bc64ffd6e13a6c22641b3926a803e5209d5)]\n* **[2012]** _什么可能移动？在3D激光数据中寻找汽车、行人和自行车_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FWhat-could-move-Finding-cars-pedestrians-and-Wang-Posner\u002Ff56b01df806bc224d5babb71994915df4a08cb44)]\n* **[2012]** _Stixel世界_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThe-Stixel-World-N-Im\u002F5307f5e2ff2f0403a92b63418ca5812965dcfb90)]\n* **[2011]** _基于立体视觉的移动机器人导航用道路边界跟踪_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStereo-based-road-boundary-tracking-for-mobile-Chiku-Miura\u002F8bcbb1f13f2ab7f974ba30a0d68aeccf49082759)]\n* **[2009]** _用于类人机器人上稳健障碍物定位的自主信息融合_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Information-Fusion-for-Robust-Obstacle-Sridharan-Li\u002F2365b361fb0e5cb801b22900a4c4a421c35ea639)]\n* **[2009]** _面向自动驾驶越野行驶的长距离视觉学习_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-long-range-vision-for-autonomous-off-road-Hadsell-Sermanet\u002F2d8f527d1a96b0dae209daa6a241cf3255a6ec0d)]\n* **[2009]** _利用多种感官特征、灵活的道路模型和粒子滤波器进行在线道路边界建模_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOn-line-road-boundary-modeling-with-multiple-Matsushita-Miura\u002F0fcac22dceb7a7d49a8c792760ae47c500a804d9)]\n* **[2008]** _卡罗琳的区域处理单元——在达帕城市挑战赛中找到出路_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThe-Area-Processing-Unit-of-Caroline-Finding-the-Berger-Lipski\u002F4b9db808c06635b784ce6c1409603c0487bcd684)]\n* **[2008]** _城市挑战赛中的车辆检测与跟踪_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVehicle-detection-and-tracking-for-the-Urban-Darms-Baker\u002F757fbaa9881b9075409a9962819fda64d51307e1)]\n* **[2007]** _用于高速公路自动驾驶的低成本传感_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLow-cost-sensing-for-autonomous-car-driving-in-Gon%C3%A7alves-Godinho\u002Fb7f302bc8eb37220ba76c2d55325d218a7e03128)]\n* **[2007]** _用于自动驾驶车辆引导的立体视觉与彩色视觉技术_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStereo-and-Colour-Vision-Techniques-for-Autonomous-Mark-Proefschrift\u002F97325201f48351df5ef614a01a55f3da818aae0e)]\n* **[2000]** _从移动车辆中实时进行多车检测与跟踪_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReal-time-multiple-vehicle-detection-and-tracking-Betke-Haritaoglu\u002F864a7068c346ecbc4ef6c4da66e4c8bcc83fe560)]\n\n#### 导航与规划\n1. **[2016]** _基于神经形态硬件的深度卷积神经网络自动驾驶机器人_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01235)]\n* **[2016]** _端到端的自动驾驶学习_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.07316)]\n* **[2016]** _自动驾驶城市车辆运动规划与控制技术综述_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.07446)]\n* **[2016]** _面向类车机器人运动规划的平滑轨迹凸优化方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Convex-Optimization-Approach-to-Smooth-Zhu-Schmerling\u002F785b22bbdb04f2ddd4233a4c40d798ed3194374f)]\n* **[2016]** _拥堵交通网络中的自动驾驶车辆路径规划：结构特性与协调算法_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.00939)]\n* **[2016]** _无人地面车辆视觉导航中的机器学习_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMachine-Learning-for-Visual-Navigation-of-Unmanned-Lenskiy-Lee\u002F9b21934ec4f08ed3cd54a7e3a3c7c25b311e1ced)]\n* **[2016]** _利用移动式三维激光扫描仪和GNSS实现实时自动驾驶车辆导航与避障_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReal-time-self-driving-car-navigation-and-obstacle-Li-Bao\u002F4e8b5a99ae628eea43d7e7410cdfa7f8a2e847d5)]\n* **[2016]** _请看：用于城市环境路径规划的可扩展代价函数学习_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FWatch-this-Scalable-cost-function-learning-for-Wulfmeier-Wang\u002Fd1e51c7e374dca4465a91300e98bfb27335be463)]\n* **[2015]** _DeepDriving：用于自动驾驶直接感知的 affordance 学习_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDeepDriving-Learning-Affordance-for-Direct-Chen-Seff\u002F3ba79761192aa4bddd3342db03aa8187516c0fab?citingPapersSort=is-influential&citingPapersLimit=10&citingPapersOffset=0&citedPapersSort=is-influential&citedPapersLimit=10&citedPapersOffset=0), [数据](http:\u002F\u002Fdeepdriving.cs.princeton.edu\u002F), [代码](http:\u002F\u002Fdeepdriving.cs.princeton.edu\u002F)]\n* **[2015]** _在定义不清的道路上的自动驾驶：一种自适应、形状约束、基于颜色的方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomatic-Driving-on-Ill-defined-Roads-An-Adaptive-Ososinski-Labrosse\u002F36cfe2e94b7b99653e6565642236e0127d43ef5a), [数据](http:\u002F\u002Fwww.aber.ac.uk\u002Fen\u002Fcs\u002Fresearch\u002Fir\u002Fdss\u002F#road-driving)]\n* **[2015]** _用于感知驾驶环境的多波束激光雷达获取点云的应用框架_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Framework-for-Applying-Point-Clouds-Grabbed-by-Liu-Liang\u002F907189aacae7bff389d6c6592d6e2586dab5168d)]\n* **[2015]** _驾驶中有多少是前注意的？_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FHow-Much-of-Driving-Is-Preattentive--Pugeault-Bowden\u002Fbb9686ea6f154a64fbdc3551fe223da42663baa9)]\n* **[2015]** _移动机器人自主导航的地图构建与规划_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMap-building-and-Planning-for-Autonomous-G%C3%B3mez-Yu\u002Ffc5b5b96334d2a0d12ac2d69fa6d46640897f33e)]\n* **[2014]** _用于城市环境中自动驾驶车辆的多属性决策模型_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Multiple-Attribute-based-Decision-Making-model-Chen-Zhao\u002Fa045d7008e47d4264e06b5d9f509ed505e100084)]\n* **[2014]** _基于预测的高速公路高度自动化驾驶功能的反应式驾驶策略_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-prediction-based-reactive-driving-strategy-for-Bahram-Wolf\u002F77d24bd1e83c23bb7cdf59ab06d575a66c03449a)]\n* **[2014]** _基于RRT的移动机器人与自动驾驶车辆导航方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-RRT-based-navigation-approach-for-mobile-robots-Garrote-Premebida\u002F56cfb13218175d67bf6dc281686c797b8641a3d0)]\n* **[2014]** _基于图像特征的户外环境下移动机器人导航通行性分析_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImage-Feature-based-Traversability-Analysis-for-BEKHTI-KOBAYASHI\u002F9fdf6ba484ee59cfac03a6c73e5177a9a70986c5)]\n* **[2014]** _速度守护者：基于经验的移动机器人速度调度_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSpeed-Daemon-Experience-Based-Mobile-Robot-Speed-Ostafew-Schoellig\u002F9d3c816fb21bfa00d5a86cbb972a4ab7af59dbfb)]\n* **[2014]** _迈向城市环境中的类人运动规划_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FToward-human-like-motion-planning-in-urban-Gu-Dolan\u002F30005949ebde80ebe3cd0b96b84a8dcb8b7f919a)]\n* **[2013]** _通用相机下的自动驾驶汽车运动估计_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMotion-Estimation-for-Self-Driving-Cars-with-a-Lee-Fraundorfer\u002Ff7f775a4f484706ffbc524accb351cb564469f6a)]\n* **[2013]** _自动驾驶Formula SAE电动赛车导航控制系统开发_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDevelopment-of-a-Navigation-Control-System-for-an-Drage\u002Ff55796a5f33836017de2cd8023b57efda9882c26)]\n* **[2013]** _低速自动化：城市地区共享驾驶的技术可行性_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLow-speed-automation-Technical-feasibility-of-the-Resende-Pollard\u002Fa34161c17343e8f41e200fe5288e2a4aaeafa25a)]\n* **[2013]** _未知崎岖地形环境下无人地面车辆的基于局部地形特征的路径选择_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPath-selection-based-on-local-terrain-feature-for-Kondo-Sunaga\u002Fe58506ef0f6721729d2f72c61e6bb46565b887de)]\n* **[2013]** _基于立体视觉的自动驾驶导航与避障_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStereo-based-Autonomous-Navigation-and-Obstacle-C%C3%A9sar-Mendes\u002Fbe6789bd46d16afa45c8962560a56a89a9089355)]\n* **[2012]** _用于高速导航与避障的自动驾驶车辆开发_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDevelopment-of-an-Autonomous-Vehicle-for-High-Ryu-Ogay\u002F0941bcd18fdf52d9e25984ff067eebe6834ad7c6)]\n* **[2012]** _非结构化环境中的快速消失点检测_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FFast-Vanishing-Point-Detection-in-Unstructured-Moghadam-Starzyk\u002Fc02f52b8b80db037f92facbb605c5715513935fb)]\n* **[2012]** _使用向量场与动态窗口法的自动驾驶汽车导航_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FNavigation-of-an-Autonomous-Car-Using-Vector-Lima-Augusto\u002F92411ee829021f09cb30186435d888547e00dd0f)]\n* **[2012]** _基于消失点跟踪的道路方向检测_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRoad-direction-detection-based-on-vanishing-point-Moghadam-Feng\u002Fd2691eb5a030a1b017a944c7fce319ccd4477730)]\n* **[2012]** _自我监督学习以视觉检测森林地形中自动驾驶机器人的地形表面_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelf-supervised-learning-to-visually-detect-Zhou-Xi\u002F617740b12065ee88049ca9086695ba78ccd3f110)]\n* **[2012]** _移动机器人的视觉导航_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FX-Visual-Navigation-for-Mobile-Robots-Andersen-Andersen\u002F7ac3b3fb12f6b071bdc0d8627225efe415c03104)]\n* **[2011]** _基于快速探索随机树的机器人运动规划新方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-new-Approach-for-Robot-Motion-Planning-using-Krammer-Granzer\u002F7e084820c195b65e45e9138415f6cac7762f18dc)]\n* **[2011]** _把我绕弯：从视觉概貌中学习驾驶_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDriving-me-around-the-bend-Learning-to-drive-from-Pugeault-Bowden\u002F2cf7bddfe52d6ca8f5309c3b42d620065126b445)]\n* **[2011]** _优化后的路线网络图作为德国高速公路上运行的自动驾驶汽车的地图参考_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOptimized-route-network-graph-as-map-reference-for-Czerwionka-Wang\u002F644b76b47c88335d40702b3045d4d3743fc13861)]\n* **[2011]** _基于模板的城市环境自动驾驶导航与避障_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTemplate-based-autonomous-navigation-and-obstacle-Souza-Sales\u002F65414da8f4a9beaac1df4d5ca0736f474e001096)]\n* **[2010]** _基于ANN和FSM控制的视觉自动驾驶导航系统_ [[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVision-Based-Autonomous-Navigation-System-Using-Sales-Shinzato\u002Fe1fcccdbc373c9bbd5bd970c34368e7e1aa56424)]\n* **[2010]** _基于最优控制的轨迹规划、威胁评估及危险规避场景下乘用车半自主控制框架_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-Optimal-control-based-Framework-for-Trajectory-Anderson-Peters\u002F50c955ab0ca25d49204fe0b115669303508b41d0)]\n* **[2010]** _城市无人驾驶车辆的感知：设计与实现_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPerception-for-Urban-Driverless-Vehicles-Design-Benenson-Suarez\u002F0f68760469015de7cf0b21f2b5ed2b0194bb6b81)]\n* **[2009]** _GPS条件不佳下的自动驾驶越野导航_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Offroad-Navigation-Under-Poor-GPS-Luettel-Himmelsbach\u002F5168a3824d4b90399e16c42f2293c3bf66113d8a)]\n* **[2009]** _户外杂乱行人步道中的自动驾驶机器人导航_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-robot-navigation-in-outdoor-cluttered-Saiki-Carballo\u002F7f81a0e925124e9d5738a51fe41c001a908c68f6)]\n* **[2009]** _不确定环境中的快速路径规划：理论与实验_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FFast-Path-Planning-in-Uncertain-Environments-Xu-Kurdila\u002F88228325b82ff3bcd875628c31e34e9018179d3d)]\n* **[2009]** _基于轨迹的自动驾驶车辆跟随，使用机器人驾驶员_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTrajectory-Based-Autonomous-Vehicle-following-Spencer-Jones\u002Ff4f6dc62fe8c5fd309f45ebf5240f9c1c1c0b80a)]\n* **[2008]** _城市区域自动驾驶的鲁棒运动规划方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Robust-Motion-Planning-Approach-for-Autonomous-Fiore-Yoshi\u002Fd3660d2f49958841d6d8486467213512772f9aac)]\n* **[2008]** _城市环境中的运动规划_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMotion-Planning-in-Urban-Environments-Ferguson-Howard\u002F8fa74131756a50c1562ebf1f03552779803aed67)]\n* **[2008]** _城市环境中的运动规划：第二部分_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMotion-planning-in-urban-environments-Part-II-Ferguson-Howard\u002F3c33381fa5dfecd02e4f935957831c3d2926bb0f)]\n* **[2008]** _为自动驾驶车辆规划长期动态可行的机动动作_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPlanning-Long-Dynamically-Feasible-Maneuvers-for-Likhachev-Ferguson\u002F1f8ca38a1fa455db3388c617697cc91300c59bc6)]\n* **[2009]** _基于监督学习的机器人汽车前瞻性驾驶_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAnticipatory-Driving-for-a-Robot-Car-Based-on-Markelic-Kulvicius\u002Fee9adb395ed68a2ce4c2a3909dc6d5a0fbf4e0f0)]\n* **[2007]** _基于监督学习的在线速度自适应，用于高速、越野自动驾驶_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOnline-Speed-Adaptation-Using-Supervised-Learning-Stavens-Hoffmann\u002F9db82954df3f4ae829459dcb8719b8a8ed9f4bee)]\n* **[2007]** _自动驾驶车辆系统的预测性主动转向控制_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPredictive-Active-Steering-Control-for-Autonomous-Falcone-Borrelli\u002Fabd354d708b98fb60e0d827a41157491289e8d3c)]\n* **[2006]** _高速沙漠驾驶的概率地形分析_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FProbabilistic-Terrain-Analysis-For-High-Speed-Thrun-Montemerlo\u002Fb23a7882b35d0252e5f3011bff15c6dca46ef84e)]\n\n#### 控制\n1. **[2016]** _用于自动驾驶的预测控制及其在重型工程卡车上的实验评估_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPredictive-Control-for-Autonomous-Driving-with-Lima-Se\u002Fde87a5d5fbae0733806ba965b2d70fd04596f6e9)]\n* **[2015]** _按需自动驾驶系统的模型预测控制_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1509.03985)]\n* **[2015]** _基于势场与基于扭矩的转向执行机构，实现自动驾驶中运动规划与控制的集成_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FToward-integrated-motion-planning-and-control-Galceran-Eustice\u002F7b2f163eac946fac7351b0861c2b37fb19ffbaa5)]\n* **[2013]** _利用模型预测控制进行双车道单向道路自动驾驶中的战略决策制定_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FStrategic-decision-making-for-automated-driving-on-Nilsson-Sj%C3%B6berg\u002F0055ca2e60a2ab5cb66c4191d09563dd7f3edd00)]\n* **[2012]** _VisLab跨洲自动驾驶挑战赛中的自动驾驶车辆控制_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-vehicles-control-in-the-VisLab-Broggi-Medici\u002F708fdf9bfd3f7d671ced85221012ef27209b92bb)]\n* **[2012]** _前轮转向地面车辆危险规避的最优规划与控制_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOptimal-Planning-and-Control-for-Hazard-Avoidance-Peters\u002F5d5a066547d60a673328cf6db34325910787ba48)]\n* **[2009]** _用于自动驾驶汽车路径跟踪的自动转向方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomatic-Steering-Methods-for-Autonomous-Snider\u002F18520721525ed81a6ffa6d8b1c7dcbd771e4a64b)]\n* **[2009]** _自动驾驶车辆三种控制方法的比较_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FComparison-of-Three-Control-Methods-for-an-Deshpande-Mathur\u002F8fc0580499b0775db60096f52cd2f0ad2c6d24b5)]\n\n#### 仿真\n1. **[2016]** _学习驾驶模拟器_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.01230)]\n* **[2014]** _从自动驾驶微型车竞赛到标准化实验平台：概念、模型、架构与评估_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.7768)]\n* **[2014]** _2014年Carolo杯技术评估——一项自动驾驶微型车竞赛_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTechnical-evaluation-of-the-Carolo-Cup-2014-A-Zug-Steup\u002F4f57643b95e854bb05fa0c037cbf8898accdbdef)]\n* **[2014]** _众包作为获取大规模且多样化机器人数据集的方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCrowdsourcing-as-a-methodology-to-obtain-large-and-Croon-Gerke\u002F8bdcb90d72eb0494f8f2635dad8ef05a66b8e445)]\n* **[2014]** _在驾校框架下高效学习前注意性转向_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEfficient-Learning-of-Pre-attentive-Steering-in-a-Rudzits-Pugeault\u002F6a65272403a8bb999bc4e86eee3f919e3fbe813d)]\n* **[2007]** _用于自动驾驶车辆的仿真与回归测试框架_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Simulation-and-Regression-Testing-Framework-for-Miller-Cenk\u002Fc50ef42740ce03e5af9292f9ce1387b83bee8fed)]\n* **[2006]** _机器人竞赛是机器人研究的理想基准_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRobot-Competitions-Ideal-Benchmarks-for-Robotics-Behnke\u002F71e5e9e8be8c870b22cadf58338f634ddd856050)]\n\n#### 软件工程\n1. **[2016]** _以自动驾驶重型车辆为例，评估实时软件的沙箱化部署_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.06759)]\n* **[2014]** _面向机器人平台的硬件\u002F软件接口工程——应用模型检测与Prolog和Alloy的比较_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1401.3985)]\n* **[2014]** _资源受限自动驾驶汽车的架构设计决策比较——多案例研究_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FComparison-of-Architectural-Design-Decisions-for-Berger-Dukaczewski\u002Fc89f47c93c62c107e6bd75acde89ee7417ebf244)]\n* **[2014]** _（再）可靠性：自动驾驶汽车——一项有趣的挑战！_。[[参考文献](http:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fqre.1707\u002Ffull)]\n* **[2014]** _剖析、理解并管理来自自动驾驶微型车项目的技术债务_。[[参考文献](http:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6974884\u002F)]\n* **[2014]** _以自动驾驶微型车为例，迈向网络物理系统的持续集成_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTowards-Continuous-Integration-for-Cyber-Physical-Berger\u002F2ac2aa0285984f2ce57efa77aab4e372bbc3ee6c)]\n* **[2014]** _通过分析车道跟随算法的代码覆盖率，节省CPS的虚拟测试时间_。[[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2593466)]\n* **[2013]** _网络物理系统的并行调度：分析与自动驾驶汽车案例研究_[[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2502530)]\n* **[2012]** _SAFER：实时应用中故障规避的系统级架构_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSAFER-System-level-Architecture-for-Failure-Kim-Bhatia\u002Fff05797dcc041d04f9ed277269916ad6ff92f1f0)]\n* **[2011]** _用于自动驾驶车辆的灵活实时控制系统_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Flexible-Real-Time-Control-System-for-Autonomous-Meyer-Strobel\u002Ff07956d0031ff046c5c719296f7916d7897fdd21)]\n* **[2010]** _以自动驾驶车辆为例，自动化基于传感器与执行器的系统验收测试_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomating-acceptance-tests-for-sensor-and-Berger\u002F3bc567143118f8fb34e0460cc3424701683c2511)]\n* **[2007]** _用于开发自动驾驶智能的软件与系统工程流程及工具_ [[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSoftware-Systems-Engineering-Process-and-Tools-for-Basarke-Berger\u002Fc564b62cd7df2ed47bb9a6266cc19c83024bc390)]\n\n#### 人机交互\n1. **[2015]** _防止自动驾驶汽车晕车的用户界面考量_。[[参考文献](http:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=2809754)]\n* **[2014]** _公众对自动驾驶车辆的看法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPublic-Opinion-about-Self-driving-Vehicles-Schoettle-Sivak\u002F4984ed8ae3355d58cfad2bd27cb2bc2488cb0e6a)]\n* **[2014]** _为自动驾驶汽车奠定基础：未来自动驾驶体验的探索_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSetting-the-Stage-for-Self-driving-Cars-Pettersson\u002Fdf428d8015b92902416d07379fb3415a12d64e3f)]\n* **[2014]** _三十年来驾驶辅助系统：回顾与未来展望_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FThree-Decades-of-Driver-Assistance-Systems-Review-Bengler-Dietmayer\u002F2c6d7bcf2ae79b73ad5888f591e159a3d994322b)]\n* **[2013]** _汽车技术与人为因素研究综述：过去、现在与未来_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FReview-Article-Automotive-Technology-and-Human-Factors-Research-Past-Present-and-Future\u002FAkamatsu-Green\u002Fdfe6df56cd5418ce9d6df938858542097157d3e8)]\n* **[2012]** _基于增强型驾驶员建模的安全半自主控制_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSafe-semi-autonomous-control-with-enhanced-driver-Vasudevan-Shia\u002F8e36ebbb6e5409aa911e4121ca37c455ff157218)]\n* **[2012]** _利用脑机接口实现半自主汽车控制_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSemi-autonomous-Car-Control-Using-Brain-Computer-G%C3%B6hring-Latotzky\u002Fe35864047f5b4ac3398ad6f242d2f1407c965f37)]\n* **[2011]** _iDriver——面向自动驾驶汽车的人机界面_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FiDriver-Human-Machine-Interface-for-Autonomous-Reuschenbach-Wang\u002F3d7107cdd11af698790736ba5fc9f23cc3f52d04)]\n* **[2010]** _用眼动追踪驾驶自动驾驶汽车——用眼动追踪驾驶自动驾驶汽车_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDriving-an-Autonomous-Car-with-Eye-Tracking-Wang-Latotzky\u002Fb3aa092b84ae6c9b924ed1a0d9681bbb342249b3)]\n* **[2010]** _用iPhone远程控制自动驾驶汽车_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRemote-Controlling-an-Autonomous-Car-with-an-Wang-Ganjineh\u002Fa0032e1fbedf61b2a74cfd5f4a9a3edb52689064)]\n* **[2009]** _未来车辆中的车—驾协同I：ADAS与自动驾驶车辆_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCar-driver-Cooperation-in-Future-Vehicles-I-Adas-Broggi-Mazzei\u002Fc2cc8ad2087d753cc67061d490f966de2c1373a1)]\n* **[2009]** _基于眼动的驾驶员注意力检测——道路事件相关性_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDriver-Inattention-Detection-based-on-Eye-Gaze-Fletcher-Zelinsky\u002Fb46f706a9df142f36a58cd7a84c88962f85d93b5)]\n\n#### 基础设施\n1. **[2014]** _按需机器人移动性控制——排队论视角_。[[参考文献](https:\u002F\u002Farxiv.org\u002Fabs\u002F1404.4391)]\n* **[2014]** _面向自动驾驶车辆的基于优先级的交叉路口控制框架：基于智能体的模型开发与评估_。[[参考文献](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F271738793_Priority-based_Intersection_Control_Framework_for_Self-Driving_Vehicles_Agent-based_Model_Development_and_Evaluation)]\n* **[2014]** _一种用于非完整约束车辆的多机器人运动规划的格点方法_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-lattice-based-approach-to-multi-robot-motion-Cirillo-Uras\u002F74ec451f463c4931c73f35cf327893ac2595e876)]\n* **[2005]** _协同式自动驾驶：智能车辆共享城市道路_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCooperative-autonomous-driving-intelligent-Baber-Kolodko\u002Fa42f42fa95d8ee6498dff905ed4848437a8f0084)]\n* **[2014]** _实现一体化车队：货运无人地面车辆的开发与试验_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAchieving-Integrated-Convoys-Cargo-Unmanned-Ground-Zych-Silver\u002F364ecf6f5af89c7b3e3d11d2269581b420edb003)]\n* **[2014]** _基于优先级的移动机器人协调_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPriority-based-coordination-of-mobile-robots-Gregoire\u002F5fdd722822fe2722d8c90e35461538dbfca10a5e)]\n* **[2012]** _利用自动驾驶机器人团队进行探索与测绘——来自Magic 2010竞赛的结果_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExploration-and-Mapping-with-Autonomous-Robot-Olson-Strom\u002F9bf0e62b5b2343a0b509a1ac7a658be587a5c37d)]\n* **[2012]** _迈向多机器人侦察的进展以及MAGIC 2010竞赛_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FProgress-toward-multi-robot-reconnaissance-and-the-Olson-Strom\u002F617943baefd909bbf06787fcb8b18b943820c87e)]\n\n#### 法律与社会\n1. **[2016]** _自动驾驶汽车技术：政策制定者指南_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-Vehicle-Technology-A-Guide-for-Anderson-Kalra\u002Fa0231f6ab2a9feaef92d5481149cdb2142aaeb02)]\n* **[2014]** _**白皮书** 自动驾驶车辆：自动驾驶汽车发展现状及明尼苏达州政策影响——初步白皮书_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSelf-driving-Vehicles-Current-Status-of-Autonomous-Lari-Douma\u002F581075c89f6a3945fa43d61aac1329d1e43f9fa3)]\n* **[2014]** _我们准备好迎接无人驾驶车辆了吗？安全与隐私——社会视角_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAre-We-Ready-for-Driver-less-Vehicles-Security-vs-Acharya\u002Fec5b5c434f9d0bfc3954c212226d436e32bcf7d5)]\n* **[2014]** _关于自动驾驶与无人驾驶的公众意见调查_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Survey-of-Public-Opinion-about-Autonomous-and-Schoettle-Sivak\u002F5d983c2d2160b9c159b2cdcfcfaded01a4ce2ad6)]\n* **[2013]** _用于高速公路入口匝道管理的自动驾驶车辆社会行为_。[[参考文献](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutonomous-vehicle-social-behavior-for-highway-Wei-Dolan\u002F86482726040d4a924ee339043e4606625a8f64fd)]\n\n## 研究实验室\n1.  [斯坦福大学汽车研究中心](https:\u002F\u002Fcars.stanford.edu\u002F)——当前的研究重点聚焦于以人为本的出行主题，包括：\n   - 理解人们将如何与日益自动化的车辆互动；\n   - 从政策、伦理到法律等各个层面探讨车辆自动化对社会的影响；\n   - 在感知、决策与控制等技术领域的前沿进展。\n* [斯坦福大学SAIL-丰田人工智能研究中心](http:\u002F\u002Faicenter.stanford.edu\u002Fresearch\u002F)——该中心的主题是“面向未来智能车辆及更广领域的以人为本的人工智能”。\n* [伯克利深度驾驶](http:\u002F\u002Fbdd.berkeley.edu\u002F)——研究用于汽车应用的计算机视觉与机器学习领域最前沿的技术。\n* [普林斯顿自主车辆工程实验室](http:\u002F\u002Fpave.princeton.edu\u002F)——普林斯顿大学由本科生主导的研究团队，致力于通过竞赛挑战、自主研究及社区 outreach 推动和促进机器人技术的发展。\n* [马里兰大学自主车辆实验室](http:\u002F\u002Fwww.avl.umd.edu\u002F)——在受生物启发的设计与机器人技术领域开展研究与开发。\n* [滑铁卢大学WAVE实验室](http:\u002F\u002Fwavelab.uwaterloo.ca\u002F)——研究方向包括多旋翼无人机、自动驾驶以及多摄像头并行跟踪与建图。\n* [牛津机器人研究所——自主系统](http:\u002F\u002Fmrg.robots.ox.ac.uk\u002F)——研究陆基移动自主性的各个方面。\n* [柏林自由大学自主实验室](http:\u002F\u002Fautonomos-labs.com\u002F)——计算机视觉、认知导航、空间车辆环境感知。\n* [本田美国研究院](https:\u002F\u002Fusa.honda-ri.com\u002Fhome)——从事多种传感模块的开发与集成，并协调这些组件以实现稳定运动规划、决策、避障及控制等功能（测试）。\n* [麻省理工学院丰田-CSAIL研究中心](http:\u002F\u002Ftoyota.csail.mit.edu\u002F)——旨在进一步推动自动驾驶技术的发展，目标是减少交通事故伤亡，甚至最终研发出一种完全不会发生事故的车辆。\n* [普林斯顿视觉与机器人实验室](http:\u002F\u002Fvision.princeton.edu\u002Fresearch.html)——自动驾驶与街景。\n* [卡内基梅隆大学机器人研究所视觉与自主系统中心（VASC）](http:\u002F\u002Fwww.ri.cmu.edu\u002Fresearch_center_detail.html?type=aboutcenter&center_id=4&menu_id=262)——在计算机视觉、自主导航、虚拟现实、智能操作、空间机器人及相关领域开展研究。\n* [Five AI](https:\u002F\u002Ffive.ai\u002Fresearch)——一家英国的自动驾驶公司，专注于计算机视觉、硬件及其他相关出版物。\n* [塞切尼大学车辆产业研究中心](https:\u002F\u002Fjkk-web.sze.hu\u002F?lang=en)——最受关注的研究课题之一是无人驾驶（即自主驾驶）车辆。该研究中心正通过研究其基础理论并探索其潜在应用，为这一新兴技术的到来做好准备。\n* [卡尔斯鲁厄理工学院（KIT）](https:\u002F\u002Fwww.kit.edu\u002Ftopics\u002Fmobility.php)——在KIT，近40个研究所的约800名科学家致力于研究面向未来的安全、可持续且舒适的出行解决方案。资源稀缺、空间不足以及基础设施超负荷运行等问题，都要求对交通方式与交通流进行综合评估。\n\n## 数据集\n1. [Udacity](https:\u002F\u002Fgithub.com\u002Fudacity\u002Fself-driving-car\u002Ftree\u002Fmaster\u002Fdatasets) - Udacity为[Udacity挑战赛](https:\u002F\u002Fwww.udacity.com\u002Fself-driving-car)发布的驾驶数据集。包含ROSBAG训练数据。（约80 GB）\n* [Comma.ai](https:\u002F\u002Farchive.org\u002Fdetails\u002Fcomma-dataset) - 7小时15分钟的大部分为高速公路行驶的视频。由10段不同长度的视频片段组成，以20 Hz的频率录制，拍摄设备为安装在2016款讴歌ILX挡风玻璃上的摄像头。同时，还记录了车辆速度、加速度、转向角度、GPS坐标、陀螺仪角度等测量数据，并将这些数据统一转换为100 Hz的时间基准。\n* [牛津RobotCar](http:\u002F\u002Frobotcar-dataset.robots.ox.ac.uk\u002F) - 在英国牛津市内一条固定路线上的100余次重复行驶数据，采集时间跨度超过一年。该数据集涵盖了多种天气、交通状况和行人活动的组合，同时还记录了施工和道路工程等长期变化。\n* [牛津Radar RobotCar](https:\u002F\u002Foxford-robotics-institute.github.io\u002Fradar-robotcar-dataset\u002F) - 牛津RobotCar数据集的雷达扩展版本，提供了来自Navtech CTS350-X毫米波FMCW雷达以及双Velodyne HDL-32E激光雷达的数据，并针对280公里的行驶里程优化了地面真值雷达里程计。\n* [牛津道路边界数据集](https:\u002F\u002Foxford-robotics-institute.github.io\u002Froad-boundaries-dataset\u002F) - 包含62,605个标注样本，其中47,639个为精选样本。每个样本均包含左右镜头的原始掩码与分类掩码。数据涵盖多种场景，如直行道路、停车车辆及路口等。\n* [KITTI视觉基准测试套件](http:\u002F\u002Fwww.cvlibs.net\u002Fdatasets\u002Fkitti\u002Fraw_data.php) - 6小时的交通场景数据，采样频率为10–100 Hz，使用多种传感器模态，包括高分辨率彩色与灰度立体相机、Velodyne 3D激光扫描仪以及高精度GPS\u002FIMU惯性导航系统。\n* [密歇根大学北校区长期视觉与激光雷达数据集](http:\u002F\u002Frobots.engin.umich.edu\u002Fnclt\u002F) - 包括全向影像、3D激光雷达、平面激光雷达、GPS以及用于里程计的本体感觉传感器，数据由Segway机器人采集。\n* [密歇根大学福特校区视觉与激光雷达数据集](http:\u002F\u002Frobots.engin.umich.edu\u002FSoftwareData\u002FFord) - 由一台基于改装福特F-250皮卡的自动驾驶地面车辆测试平台采集。该车辆配备了专业级（Applanix POS LV）与消费级（Xsens MTI-G）惯性测量单元（IMU）、Velodyne 3D激光雷达、两台推扫式前视Riegl激光雷达，以及Point Grey Ladybug3全向相机系统。\n* [DIPLECS自动驾驶数据集（2015年）](http:\u002F\u002Fcvssp.org\u002Fdata\u002Fdiplecs\u002F) - 通过在萨里乡村地区行驶的汽车上安装高清摄像头录制而成。数据集包含约30分钟的行驶视频，分辨率为1920×1080，采用H.264编码。方向盘转角通过追踪方向盘上的标记进行估计。车辆速度则通过OCR识别车速表得出（但该方法的准确性无法保证）。\n* [卡尔斯鲁厄理工学院的Velodyne SLAM数据集](http:\u002F\u002Fwww.mrt.kit.edu\u002Fz\u002Fpubl\u002Fdownload\u002Fvelodyneslam\u002Fdataset.html) - 在德国卡尔斯鲁厄市使用Velodyne HDL64E-S2扫描仪录制的两个具有挑战性的数据集。\n* [SYNTHetic图像与标注集合（SYNTHIA）](http:\u002F\u002Fsynthia-dataset.net\u002F) - 由虚拟城市渲染生成的一系列照片级真实帧，并附带针对13个类别的精确像素级语义标注：杂项、天空、建筑物、道路、人行道、围栏、植被、电线杆、汽车、标志、行人、骑行者、车道标线。\n* [Cityscape数据集](https:\u002F\u002Fwww.cityscapes-dataset.com\u002F) - 专注于城市街景的语义理解。这是一个大规模数据集，包含来自50座不同城市的街景中采集的多样化立体视频序列，并配有5,000帧的高质量像素级标注，此外还有20,000帧的弱标注数据。因此，该数据集的规模比以往类似尝试大了一个数量级。有关标注类别及我们标注示例的详细信息可供查阅。\n* [CSSAD数据集](http:\u002F\u002Faplicaciones.cimat.mx\u002FPersonal\u002Fjbhayet\u002Fccsad-dataset) - 针对自动驾驶车辆感知与导航领域的算法开发与测试，目前已存在多个真实世界的立体数据集。然而，这些数据集均未在发展中国家采集，因而缺乏其街道与道路上特有的特征，例如大量坑洼、减速带以及独特的行人流动模式。本立体数据集由行驶中的车辆采集，包含高分辨率立体图像，并辅以来自IMU的姿态与加速度数据、GPS数据以及车载计算机数据。\n* [戴姆勒城市分割数据集](http:\u002F\u002Fwww.6d-vision.com\u002Fscene-labeling) - 包括在城市交通中录制的视频序列。该数据集由5,000对校正后的立体图像组成，分辨率为1024×440。其中500帧（每10帧中取1帧）配有像素级语义分类标注，分为5类：地面、建筑物、车辆、行人、天空。同时提供稠密视差图作为参考，但这些视差图并非人工标注，而是通过半全局匹配（SGM）计算得出。\n* [Self Racing Cars - XSens\u002FFairchild数据集](http:\u002F\u002Fdata.selfracingcars.com\u002F) - 文件中包含Fairchild FIS1100六自由度（DoF）IMU、Fairchild FMT-1030 AHRS、Xsens MTi-3 AHRS以及Xsens MTi-G-710 GNSS\u002FINS的测量数据。赛事相关文件均可在MT Manager软件中读取，该软件是MT软件套件的一部分，可在此处获取。\n* [MIT AGE实验室](http:\u002F\u002Flexfridman.com\u002Fautomated-synchronization-of-driving-data-video-audio-telemetry-accelerometer\u002F) - AGE实验室收集的1,000余小时多传感器驾驶数据中的一个小样本。\n* [又一个计算机视觉数据集索引（YACVID）](http:\u002F\u002Fyacvid.hayko.at\u002F) - 一份常用计算机视觉数据集列表。\n* [KUL比利时交通标志数据集](http:\u002F\u002Fwww.vision.ee.ethz.ch\u002F~timofter\u002Ftraffic_signs\u002F) - 一个大型数据集，包含10,000余个交通标志标注，数千种物理上不同的交通标志。由一辆厢式货车搭载8台高分辨率摄像头录制的4段视频，总时长超过3小时，包含交通标志标注、相机标定与姿态信息。另有约16,000张背景图像。该素材由GeoAutomation在比利时弗拉芒地区的城市环境中采集。\n* [LISA：加州大学圣地亚哥分校智能与安全汽车实验室数据集](http:\u002F\u002Fcvrr.ucsd.edu\u002FLISA\u002Fdatasets.html) - 包括交通标志检测、车辆检测、交通信号灯、轨迹模式等。\n* [面向自动驾驶的多感官全向长期场景识别（MOLP）数据集](http:\u002F\u002Fhcr.mines.edu\u002Fcode\u002FMOLP.html) - 该数据集在美国科罗拉多州利用全向立体摄像头历时一年采集而成。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.05215)\n* [城市环境中的车道实例分割](https:\u002F\u002Ffive.ai\u002Fdatasets) - 半自动化的车道实例标注方法。现有24,000张图像数据集。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.01347.pdf)\n* [雾天苏黎世数据集](https:\u002F\u002Fwww.vision.ee.ethz.ch\u002F~csakarid\u002FModel_adaptation_SFSU_dense\u002F) - 基于合成与真实数据的课程模型适应，用于语义密集型雾天场景理解。包含苏黎世及其周边地区的3,800张高质量雾天图像。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.01415)\n* [SullyChen自动驾驶数据集](https:\u002F\u002Fgithub.com\u002FSullyChen\u002FAutopilot-TensorFlow) - SullyChen在加州及其周边地区采集的数据集。\n* [Waymo训练与验证数据](https:\u002F\u002Fwaymo.com\u002Fopen) - 一太字节的数据，包含3D与2D标签。\n* [英特尔印度自动驾驶条件数据集](https:\u002F\u002Fidd.insaan.iiit.ac.in\u002F) - 一个用于印度自动驾驶条件（非结构化环境下的道路场景理解）的数据集，包含1万张图像，细分为34个类别，由英特尔与海得拉巴IIIT合作从182条印度道路行驶序列中采集。\n* [nuScenes数据集](https:\u002F\u002Fwww.nuscenes.org\u002F) - 一个大型数据集，包含来自波士顿和新加坡的140万张图像与39万次激光雷达扫描。为23个物体类别提供手动生成的3D边界框。\n* [德国交通标志数据集](http:\u002F\u002Fbenchmark.ini.rub.de\u002F?section=gtsrb&subsection=dataset) - 一个大型德国交通标志识别数据集（GTSRB），包含5万张图像，涵盖40余种类别；以及交通标志检测数据集（GTSDB），包含900张标注图像。\n* [瑞典交通标志数据集](https:\u002F\u002Fwww.cvl.isy.liu.se\u002Fresearch\u002Fdatasets\u002Ftraffic-signs-dataset\u002F) - 一个在瑞典350公里道路上采集的交通标志数据集，包含2万余张图像，其中20%已标注。\n* [Argoverse 3D跟踪数据集](https:\u002F\u002Fwww.argoverse.org\u002F) - 一个大型数据集，包含约100万张图像与约100万个标注的3D长方体，数据来自迈阿密与匹兹堡。提供高清地图与7个环形摄像头、2个立体摄像头以及激光雷达的影像。\n* [Argoverse运动预测数据集](https:\u002F\u002Fwww.argoverse.org\u002F) - 一个大型数据集，包含从1,006小时行驶中挖掘出的324,557个场景中被跟踪对象的轨迹。\n\n## 开源软件\n1. [Autoware](https:\u002F\u002Fgithub.com\u002FCPFL\u002FAutoware) - 用于城市自动驾驶的集成式开源软件。\n* [Comma.ai Openpilot](https:\u002F\u002Fgithub.com\u002Fcommaai\u002Fopenpilot) - 一款开源驾驶代理。\n* [斯坦福大学自动驾驶软件](https:\u002F\u002Fsourceforge.net\u002Fprojects\u002Fstanforddriving\u002F) - 斯坦福大学自动驾驶车辆的软件基础设施。\n* [GTA Robotics SDC环境](https:\u002F\u002Fgithub.com\u002FOSSDC\u002Fself-driving-car-1) - 专为Udacity自动驾驶汽车（SDC）挑战赛打造的开发环境。\n* [OSCC项目](http:\u002F\u002Foscc.io\u002F) - 用于自动驾驶车辆开发的线控控制套件。\n* [OpenAI Gym](https:\u002F\u002Fgym.openai.com\u002F) - 一个用于开发和比较强化学习算法的工具包。它支持训练智能体完成从行走到玩游戏、山地车、赛车等各种任务，同时为自动驾驶汽车的强化学习算法开发与验证提供了良好契机。\n* [argoverse-api](https:\u002F\u002Fgithub.com\u002Fargoai\u002Fargoverse-api) - 用于处理[Argoverse](https:\u002F\u002Fwww.argoverse.org\u002F) 3D跟踪与预测数据集，并评估3D跟踪、3D检测及运动预测算法的开发工具包。\n\n## 硬件\n\n\n## 玩具\n1. [TensorKart](https:\u002F\u002Fgithub.com\u002Fkevinhughes27\u002FTensorKart) - 基于TensorFlow的自动驾驶马里奥卡丁车。\n2. [NeuroJS](https:\u002F\u002Fgithub.com\u002Fjanhuenermann\u002Fneurojs) - 一个JavaScript深度学习与强化学习库。包含一个自动驾驶汽车的示例实现。\n3. [DonkeyCar](https:\u002F\u002Fgithub.com\u002Fautorope\u002Fdonkeycar) - 一个面向Python的极简且模块化的自动驾驶库。专为爱好者和学生开发，注重快速实验与便捷的社区贡献。\n\n## 公司\n\n1. （截至2019年8月28日）[40多家企业正在研发自动驾驶汽车](https:\u002F\u002Fwww.cbinsights.com\u002Fblog\u002Fautonomous-driverless-vehicles-corporations-list\u002F)\n\n## 媒体\n各种媒体渠道，我们可以在其中找到与自动驾驶汽车相关的主题、理念以及更多内容。\n\n### 播客\n\n* [人工智能：AI播客](https:\u002F\u002Flexfridman.com\u002Fai\u002F) - “人工智能播客（AI播客）是由Lex Fridman主持的一系列关于技术、科学与人类状况的对话。” 示例节目：\n  * [塞巴斯蒂安·斯伦：飞行汽车、自动驾驶汽车与教育](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZPPAOakITeQ&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)\n  * [埃隆·马斯克：Neuralink、AI、Autopilot与淡蓝色小点](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=smK9dgdTl40&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)\n  * [乔治·霍茨：Comma.ai、OpenPilot与自动驾驶汽车](https:\u002F\u002Flexfridman.com\u002Fgeorge-hotz\u002F)\n  * [杰里米·霍华德：fast.ai深度学习课程与研究](https:\u002F\u002Flexfridman.com\u002Fjeremy-howard\u002F)\n* [Autonocast，交通的未来](https:\u002F\u002Fwww.autonocast.com\u002F) - “每周一档探讨最新交通科技的节目”\n\n#### YouTube\n1. [Lex Fridman（频道）](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCSHZKyawb77ixDdsGog4iWA) - 超过100个与AI及自动驾驶相关的视频，其中包括[MIT深度学习系列（播放列表）](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf)，其中包含：\n   * [2020年1月11日] [深度学习的最新进展（2020年）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0VH1Lim8gL8&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&index=1)，以及\n   * [2019年1月12日] [MIT深度学习基础：介绍与概述](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&index=2)。\n1. 自动驾驶的三大支柱。[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GZa9SlMHhQc)]\n1. 自动驾驶的感知涉及哪些内容？[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GCMXXXmxG-I)]\n1. Amnon Shashua在CVPR 2016上的主旨演讲：自动驾驶、计算机视觉与机器学习。[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=n8T7A3wqH3Q)]\n1. Chris Urmson：无人驾驶汽车如何“看”路。[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tiwVMrTLUWg)]\n1. 驾驶策略的深度强化学习。[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cYTVXfIH0MU)]\n1. NVIDIA在CES 2016上的展示——自动驾驶汽车与深度学习GPU。[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KkpxA5rXjmA)]\n1. NVIDIA Drive PX2自动驾驶平台可视化。[[观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=URmxzxYlmtg&app=desktop)]\n\n### 博客\n1. [深度学习与自动驾驶](https:\u002F\u002Fhandong1587.github.io\u002Fdeep_learning\u002F2015\u002F10\u002F09\u002Fdl-and-autonomous-driving.html)\n* [[Medium] 自动驾驶汽车](https:\u002F\u002Fmedium.com\u002Fself-driving-cars)\n\n### Twitter\n\n1. [comma.ai](https:\u002F\u002Ftwitter.com\u002Fcomma_ai)\n* [[Udacity] David Silver](https:\u002F\u002Ftwitter.com\u002Fdsilver829)\n* [[Udacity] Dhruv Parthasarathy](https:\u002F\u002Ftwitter.com\u002Fdhruvp)\n* [[Udacity] Eric Gonzalez](https:\u002F\u002Ftwitter.com\u002Fericrgon)\n* [[Udacity] Oliver Cameron](https:\u002F\u002Ftwitter.com\u002Folivercameron)\n* [[Udacity] MacCallister Higgins](https:\u002F\u002Ftwitter.com\u002Fmacjshiggins)\n* [[Udacity] Sebastian Thrun](https:\u002F\u002Ftwitter.com\u002FSebastianThrun)\n* [[Google] Chris Urmson](https:\u002F\u002Ftwitter.com\u002Fchris_urmson)\n\n\n## 法律\n\n美国\n\n1. [加州监管公告](https:\u002F\u002Fwww.dmv.ca.gov\u002Fportal\u002Fdmv\u002Fdetail\u002Fvr\u002Fautonomous\u002Ftesting)\n* [密歇根州通过了全美最宽松的自动驾驶汽车法规](http:\u002F\u002Ffortune.com\u002F2016\u002F12\u002F09\u002Fmichigan-self-driving-cars\u002F)\n* [加州涉及自动驾驶汽车的交通事故](https:\u002F\u002Fwww.dmv.ca.gov\u002Fportal\u002Fdmv\u002Fdetail\u002Fvr\u002Fautonomous\u002Fautonomousveh_ol316)\n* [Nvidia开始在公共道路上测试其自动驾驶汽车](http:\u002F\u002Fwww.theinquirer.net\u002Finquirer\u002Fnews\u002F2479432\u002Fnvidia-starts-testing-its-self-driving-cars-on-public-roads)","# awesome-autonomous-vehicles 快速上手指南\n\n> 这是一份面向中国开发者的极简入门文档，帮助你快速浏览并使用 awesome-autonomous-vehicles 资源列表。\n\n---\n\n## 环境准备\n\n- **系统**：任意支持 Git 的操作系统（Windows \u002F macOS \u002F Linux）\n- **工具**：\n  - Git ≥ 2.20\n  - 浏览器（Chrome \u002F Edge \u002F Firefox）\n- **网络**：建议开启代理或使用国内镜像加速访问 GitHub\n\n---\n\n## 安装步骤\n\n1. 克隆仓库  \n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002Fawesome-autonomous-vehicles\u002Fawesome-autonomous-vehicles.git\n   cd awesome-autonomous-vehicles\n   ```\n\n2. （可选）使用国内镜像加速  \n   ```bash\n   # 如果 GitHub 速度慢，可替换为 fastgit 镜像\n   git clone https:\u002F\u002Fhub.fastgit.org\u002Fawesome-autonomous-vehicles\u002Fawesome-autonomous-vehicles.git\n   ```\n\n---\n\n## 基本使用\n\n1. 打开 `README.md`  \n   ```bash\n   # Linux \u002F macOS\n   open README.md\n   # Windows\n   start README.md\n   ```\n\n2. 快速定位资源  \n   - 想入门？直接跳转到 **Courses** 章节，优先看：\n     - Coursera《Self-Driving Cars》中文字幕版\n     - Udacity《Self-Driving Car Nanodegree》\n   - 想跑代码？进入 **Open Source Software** 章节，推荐：\n     - [Autoware](https:\u002F\u002Fgithub.com\u002Fautowarefoundation\u002Fautoware)（ROS 2 自动驾驶全栈）\n     - [Apollo](https:\u002F\u002Fgithub.com\u002FApolloAuto\u002Fapollo)（百度开源，含中文文档）\n\n3. 一键直达数据集  \n   在 **Datasets** 章节找到：\n   - KITTI（国内镜像：http:\u002F\u002Fwww.cvlibs.net\u002Fdownload.php）\n   - nuScenes（百度网盘社区镜像，关键词“nuScenes 百度网盘”）\n\n4. 本地检索  \n   ```bash\n   # 搜索关键词快速定位\n   grep -i \"lidar\" README.md\n   grep -i \"deep learning\" README.md\n   ```\n\n---\n\n至此，你已拥有完整的自动驾驶学习资源地图。按需阅读、克隆、跑代码即可。","某高校智能车实验室的 5 人研究生小组，需要在 3 个月内完成“校园低速无人配送车”原型，并参加中国智能车未来挑战赛。\n\n### 没有 awesome-autonomous-vehicles 时\n- 四处 Google 关键词，结果 70 % 是广告或过时博客，找不到可直接复用的开源感知框架  \n- 想复现某篇论文的 LiDAR-视觉融合算法，却发现数据集链接失效，作者代码仓库 404  \n- 采购传感器时，只能凭经验选 Velodyne 64 线，预算瞬间超标 2 万元，无人知晓其实 16 线 + 双目即可满足校园场景  \n- 团队里只有 1 人系统学过 Udacity 课程，其他人边学边做，进度被拖慢 4 周  \n- 临近比赛才发现国内对校园无人车暂无明确法规，担心现场被叫停，却找不到权威解读  \n\n### 使用 awesome-autonomous-vehicles 后\n- 打开列表的 “Open Source Software” 一节，直接 fork Autoware.Auto 与 Apollo Lite，1 天内跑通感知-定位-规划全链路 demo  \n- 在 “Datasets” 中找到 nuScenes 与 PandaSet 的百度网盘镜像，3 小时完成下载并开始训练，省去申请与邮寄硬盘的时间  \n- 参考 “Hardware” 里的社区实测，改用 Livox Mid-360 + 自研双目，成本降到 1.2 万元，性能仍满足 30 km\u002Fh 场景  \n- 全组按 “Courses” 中推荐的顺序刷完 Coursera《视觉感知》与 INRIA《移动机器人》，两周内补齐知识短板，代码风格统一  \n- 通过 “Laws” 链接读到工信部最新《智能网联汽车道路测试管理规范》解读，提前办好校园封闭测试备案，比赛当天顺利发车  \n\nawesome-autonomous-vehicles 把散落在全球的高质量自动驾驶资源一次性打包，让高校团队用最小搜索成本做出可落地的原型。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmanfreddiaz_awesome-autonomous-vehicles_f2a8f761.png","manfreddiaz","Manfred Diaz","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fmanfreddiaz_2227f3f4.jpg","Ph.D. student at @mila-iqia  @montrealrobotics on Machine Learning and Robotics.","@mila-iqia @montrealrobotics","Canada","diazcabm@iro.umontreal.ca",null,"https:\u002F\u002Fgithub.com\u002Fmanfreddiaz",2352,598,"2026-04-04T19:01:27",1,"未说明",{"notes":90,"python":88,"dependencies":91},"该仓库为自动驾驶领域资源清单，仅提供学习资料与开源项目链接，本身不包含可运行代码，因此无具体运行环境需求。如需运行其中列出的具体开源项目，请查阅对应项目的README。",[],[14,13,15],[94,95,96,97,98],"autonomous-vehicles","deep-learning","computer-vision","autonomous-cars","car-driving","2026-03-27T02:49:30.150509","2026-04-06T08:46:05.468966",[102,107,112,117],{"id":103,"question_zh":104,"answer_zh":105,"source_url":106},6020,"列表中是否包含自动驾驶决策（Decision Making）相关的论文？","目前仓库更新不够及时，但决策相关内容主要集中在导航与规划（Navigation & Planning）部分。推荐先阅读论文《Planning and Decision-Making for Autonomous Vehicles using Hierarchical Reinforcement Learning》（https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.07316），然后追踪引用该论文的所有后续研究，即可快速了解自动驾驶决策方向的最新进展。","https:\u002F\u002Fgithub.com\u002Fmanfreddiaz\u002Fawesome-autonomous-vehicles\u002Fissues\u002F15",{"id":108,"question_zh":109,"answer_zh":110,"source_url":111},6021,"“Deep Reinforcement Learning for Driving Policy” 的链接失效怎么办？","该链接已修复，新的有效地址为：https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cYTVXfIH0MU 。若再遇到类似问题，可直接在 Issues 中提交正确链接，维护者会尽快更新。","https:\u002F\u002Fgithub.com\u002Fmanfreddiaz\u002Fawesome-autonomous-vehicles\u002Fissues\u002F5",{"id":113,"question_zh":114,"answer_zh":115,"source_url":116},6022,"有哪些开源的自动驾驶软件项目推荐？","可关注 OSSDC 组织（https:\u002F\u002Fgithub.com\u002FOSSDC），该仓库汇总了大量与自动驾驶相关的开源软件、仿真器及工具链，适合快速查找可复现的代码实现。","https:\u002F\u002Fgithub.com\u002Fmanfreddiaz\u002Fawesome-autonomous-vehicles\u002Fissues\u002F2",{"id":118,"question_zh":119,"answer_zh":120,"source_url":121},6023,"哪里可以下载百度 Apollo 的开放数据集？","百度 Apollo 官方数据集可通过 Road Hackers 页面下载：http:\u002F\u002Froadhackers.baidu.com\u002F#downloads 。页面提供原始传感器数据、标注文件及使用说明，注册后即可获取。","https:\u002F\u002Fgithub.com\u002Fmanfreddiaz\u002Fawesome-autonomous-vehicles\u002Fissues\u002F1",[]]