Awesome Autonomous Driving (2026 Refresh)

Maintainers - Daehyun Ji (Samsung Electronics), Vertical AI 2 Team, AI Center Members in Samsung Electronics

I am looking for a maintainer! Let me know (captainzone@gmail.com) if interested.

Contributing

Please feel free to open pull requests to add papers, codebases, datasets, benchmarks, and courses.

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Table of Contents

• Papers
  • Overall Surveys
  • Foundation Models, VLMs, LLMs, and World Models
  • Classification / Representation Learning
  • 2D Object Detection
  • 3D Object Detection and BEV Perception
  • Object Tracking
  • Semantic Segmentation
  • Depth Estimation
  • Occupancy Prediction and Scene Representation
  • Localization and Mapping
  • Visual Odometry
  • Lane Detection and HD Map Learning
  • Motion Forecasting and Behavior Prediction
  • Decision Making
  • Planning
  • Control
  • End-to-End Driving
  • Reinforcement Learning in Autonomous Driving
• Datasets and Benchmarks
• Courses
• Books
• Videos
• Software
  • ROS and Autonomous Driving Stacks
  • Frameworks and Toolboxes
  • Simulation and Evaluation
• Conference and Workshop Channels
• Maintenance Notes

Papers

Overall Surveys

• Self-Driving Cars: A Survey [Paper]
  • Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius Brito Cardoso, Avelino Forechi, Luan Ferreira Reis Jesus, Rodrigo Ferreira Berriel, Thiago Meireles Paixão, Filipe Mutz, Thiago Oliveira-Santos, Alberto Ferreira De Souza
• Planning and Decision-Making for Autonomous Vehicles [Paper]
  • Wilko Schwarting, Javier Alonso-Mora, Daniela Rus
• A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles [Paper]
  • Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, Emilio Frazzoli
• A Survey for Foundation Models in Autonomous Driving [Paper]
  • Haoxiang Gao, Zhongruo Wang, Yaqian Li, Kaiwen Long, Ming Yang, Yiqing Shen
• A Survey of World Models for Autonomous Driving [Paper]
  • Tuo Feng, Wenguan Wang, Yi Yang
• Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis [Paper]
  • Mingyang Zhang, Haotian Wang, Yiduo Wang, et al.
• End-to-End Autonomous Driving: Challenges and Frontiers (TPAMI 2024) [Paper] [Repo]
  • Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, Hongyang Li
• A Survey on Vision-Language-Action Models for Autonomous Driving (ICCVW 2025) [Paper]
  • Sicong Jiang, Zilin Huang, Kangan Qian, et al.

Foundation Models, VLMs, LLMs, and World Models

• DriveLM: Driving with Graph Visual Question Answering [Paper] [Code]
  • Chonghao Sima, Katrin Renz, Kashyap Chitta, Li Chen, Hanxue Zhang, Chengen Xie, Jens Beißwenger, Ping Luo, Andreas Geiger, Hongyang Li
• Planning-Oriented Autonomous Driving (UniAD) (CVPR 2023) [Paper] [Code]
  • Yihan Hu, Jiazhi Yang, Li Chen, et al.
• TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [Paper] [Code]
  • Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, Andreas Geiger
• DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models (CoRL 2024) [Paper] [Project]
  • Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao
• EMMA: End-to-End Multimodal Model for Autonomous Driving (Waymo) [Paper]
  • Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, et al.
• Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving [Paper] [Code]
  • Bo Jiang, Shaoyu Chen, Bencheng Liao, Xingyu Zhang, Wei Yin, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang
• OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model [Paper]
  • Xingcheng Zhou, Xuyuan Han, Feng Yang, Yunpu Ma, Volker Tresp, Alois Knoll
• ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation [Paper]
  • Haoyu Fu, Diankun Zhang, Zongchuang Zhao, et al.
• AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning (NeurIPS 2025) [Paper] [Code]
  • Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, Jiaqi Ma
• SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment (CVPR 2025) [Paper]
  • Katrin Renz, Long Chen, Elahe Arani, Oleg Sinavski
• GAIA-1: A Generative World Model for Autonomous Driving [Paper]
  • Wayve
• GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving [Paper]
  • Wayve
• Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability (NeurIPS 2024) [Paper] [Code]
  • Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, Hongyang Li
• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving (ECCV 2024) [Paper] [Code]
  • Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jiagang Zhu, Jiwen Lu
• MagicDrive: Street View Generation with Diverse 3D Geometry Control (ICLR 2024) [Paper] [Code]
  • Ruiyuan Gao, Kai Chen, Enze Xie, et al.
• DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers [Paper]
  • Yuntao Chen, Yuqi Wang, Zhaoxiang Zhang
• Cosmos World Foundation Model Platform for Physical AI (NVIDIA) [Paper] [Code]
  • NVIDIA
• World Models for Autonomous Driving: An Initial Survey [Paper]
  • Chenhan Jiang, et al.
• Curated list: World Models for Autonomous Driving [Repo]

Classification / Representation Learning

• ImageNet Classification with Deep Convolutional Neural Networks [Paper]
  • Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
• Very Deep Convolutional Networks for Large-Scale Image Recognition [Paper]
  • Karen Simonyan, Andrew Zisserman
• Going Deeper with Convolutions [Paper]
  • Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
• Deep Residual Learning for Image Recognition [Paper]
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
• Densely Connected Convolutional Networks [Paper]
  • Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [Paper]
  • Alexey Dosovitskiy, et al.
• A ConvNet for the 2020s [Paper]
  • Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie

2D Object Detection

• Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation [Paper]
  • Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
• Fast R-CNN [Paper]
  • Ross Girshick
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [Paper]
  • Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
• You Only Look Once: Unified, Real-Time Object Detection [Paper]
  • Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
• SSD: Single Shot MultiBox Detector [Paper]
  • Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
• End-to-End Object Detection with Transformers [Paper]
  • Nicolas Carion, et al.
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection [Paper]
  • Hao Zhang, et al.

3D Object Detection and BEV Perception

• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [Paper]
  • Yin Zhou, Oncel Tuzel
• PointPillars: Fast Encoders for Object Detection from Point Clouds [Paper]
  • Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom
• SECOND: Sparsely Embedded Convolutional Detection [Paper]
  • Yan Yan, Yuxing Mao, Bo Li
• CenterPoint: Tracking Objects as Points [Paper]
  • Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
• DETR3D: 3D Object Detection from Multi-View Images via 3D-to-2D Queries [Paper]
  • Yue Wang, et al.
• PETR: Position Embedding Transformation for Multi-View 3D Object Detection [Paper]
  • Yilun Liu, et al.
• BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers [Paper] [Code]
  • Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, Jifeng Dai
• BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [Paper] [Code]
  • Zhijian Liu, Haotian Tang, Alexander Amini, Hanrui Wang, Song Han
• StreamPETR: Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection (ICCV 2023) [Paper] [Code]
  • Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, Xiangyu Zhang
• SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos (ICCV 2023) [Paper] [Code]
  • Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, Limin Wang
• Occupancy and BEV methods from 2023-2026
  • See also occupancy and end-to-end sections below, because the field increasingly merges 3D detection, map perception, forecasting, and planning.

Object Tracking

• Simple Online and Realtime Tracking [Paper]
  • Alex Bewley, et al.
• Simple Online and Realtime Tracking with a Deep Association Metric [Paper]
  • Nicolai Wojke, Alex Bewley, Dietrich Paulus
• AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics [Paper] [Code]
  • Xinshuo Weng, Jianren Wang, David Held, Kris Kitani
• CenterTrack: Tracking Objects as Points [Paper]
  • Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl

Semantic Segmentation

• Fully Convolutional Networks for Semantic Segmentation [Paper]
  • Jonathan Long, Evan Shelhamer, Trevor Darrell
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs [Paper]
  • Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [Paper]
  • Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [Paper]
  • Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam
• Pyramid Scene Parsing Network [Paper]
  • Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers [Paper]
  • Enze Xie, et al.
• Masked-attention Mask Transformer for Universal Image Segmentation [Paper]
  • Bowen Cheng, et al.

Depth Estimation

• Unsupervised Monocular Depth Estimation with Left-Right Consistency [Paper] [Code]
  • Clement Godard, Oisin Mac Aodha, Gabriel J. Brostow
• Digging into Self-Supervised Monocular Depth Estimation [Paper]
  • Clément Godard, Oisin Mac Aodha, Michael Firman, Gabriel J. Brostow
• PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation [Paper]
  • Vitor Guizilini, et al.
• DORN: Deep Ordinal Regression Network for Monocular Depth Estimation [Paper]
  • Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Dacheng Tao
• Depth Anything [Paper] [Code]
  • Lihe Yang, et al.
• Depth Anything V2 [Paper] [Code]
  • Lihe Yang, et al.

Occupancy Prediction and Scene Representation

• SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving [Paper] [Code]
  • Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
• Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving [Paper]
  • Yiming Ge, et al.
• TPVFormer: Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction [Paper]
  • Yiming Huang, et al.
• OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving (ECCV 2024) [Paper] [Code]
  • Wenzhao Zheng, Weiliang Chen, Yuanhui Huang, Borui Zhang, Yueqi Duan, Jiwen Lu
• DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes (ICLR 2025 Spotlight) [Paper] [Code] [Project]
  • Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, Ziwei Liu
• Occupancy Network / occupancy-based scene modeling papers (2023-2026)
  • This is now a core subfield linking perception, forecasting, and world modeling.

Localization and Mapping

• Visual SLAM Algorithms: A Survey from 2010 to 2016 [Paper]

  • Takafumi Taketomi, Hideaki Uchiyama, Sei Ikeda

• Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age [Paper]

  • César Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jose Neira, Ian Reid, John J. Leonard

• LOAM: Lidar Odometry and Mapping in Real-time [Paper]

  • Ji Zhang, Sanjiv Singh

• LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping [Paper] [Code]

  • Tianyue Shan, Brendan Englot, et al.

• ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial, and Multi-Map SLAM [Paper] [Code]

  • Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, Juan D. Tardós

• FAST-LIO2: Fast Direct LiDAR-Inertial Odometry [Paper] [Code]

  • Wei Xu, et al.

• FusionCore: A ROS 2 UKF Sensor Fusion Library for Outdoor Mobile Robots [Paper] [Code]

  • Manan Kharwar

Visual Odometry

• Review of Visual Odometry: Types, Approaches, Challenges, and Applications [Paper]
  • Mohammad O. A. Aqel, Mohammad H. Marhaban, M. Iqbal Saripan, Napsiah Bt. Ismail
• ORB-SLAM: A Versatile and Accurate Monocular SLAM System [Paper]
  • Raúl Mur-Artal, J. M. M. Montiel, Juan D. Tardós
• DF-VO: What Should Be Learnt for Visual Odometry? [Paper]
  • Zhaoyang Lv, et al.
• DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras [Paper] [Code]
  • Zachary Teed, Jia Deng

Lane Detection and HD Map Learning

• Towards End-to-End Lane Detection: An Instance Segmentation Approach [Paper]
  • Davy Neven, Bert De Brabandere, Stamatios Georgoulis, Marc Proesmans, Luc Van Gool
• Ultra Fast Structure-Aware Deep Lane Detection [Paper]
  • Zequn Qin, et al.
• LaneATT: Robust Multi-Lane Detection from Stereo or Monocular Input [Paper] [Code]
  • Lucas Tabelini, Rodrigo Berriel, et al.
• CLRNet: Cross Layer Refinement Network for Lane Detection [Paper] [Code]
  • Tianheng Cheng, et al.
• MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction [Paper] [Code]
  • Bencheng Liao, et al.
• StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction [Paper]
  • Jiahao He, et al.

Motion Forecasting and Behavior Prediction

• VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation [Paper]
  • Li Liang, et al.
• LaneGCN: Motion Forecasting with Lane Graph Convolutions [Paper]
  • Ming Liang, Bin Yang, Rui Hu, Yun Chen, Raquel Urtasun
• MTR: Motion Transformer for Motion Prediction [Paper]
  • Shaoshuai Shi, et al.
• Wayformer: Motion Forecasting via Simple and Efficient Attention Networks [Paper]
  • Yixiao Wei, et al.
• Scene Transformer: A Unified Architecture for Predicting Multiple Agent Trajectories [Paper]
  • Junru Gu, et al.

Decision Making

• Planning and Decision-Making for Autonomous Vehicles [Paper]
  • Wilko Schwarting, Javier Alonso-Mora, Daniela Rus
• Perception, Planning, Control, and Coordination for Autonomous Vehicles [Paper]
  • R. K. Satzoda, Mohan M. Trivedi
• A Behavioral Planning Framework for Autonomous Driving [Paper]
  • Junqing Wei, Jarrod M. Snider, Tianyu Gu, John M. Dolan, Bakhtiar Litkouhi
• Towards a Functional System Architecture for Automated Vehicles [Paper]
  • Simon Ulbrich, Andreas Reschka, Jens Rieken, Susanne Ernst, Gerrit Bagschik, Frank Dierkes, Marcus Nolte, Markus Maurer

Planning

• Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame [Paper]
  • Moritz Werling, Julius Ziegler, Sören Kammel, Sebastian Thrun
• Path Planning for Autonomous Vehicles in Unknown Semi-Structured Environments [Paper]
  • Dmitri Dolgov, et al.
• Trajectory Planning for Bertha — A Local, Continuous Method [Paper]
  • Julius Ziegler, Philipp Bender, Thao Dang, Christoph Stiller
• Real-Time Motion Planning Methods for Autonomous On-Road Driving: State-of-the-Art and Future Research Directions [Paper]
  • Christos Katrakazas, Mohammed Quddus, Wen-Hua Chen, Lipika Deka
• A Review of Motion Planning Techniques for Automated Vehicles [Paper]
  • David González, Joshué Pérez, Vicente Milanés, Fawzi Nashashibi
• Towards Learning-Based Planning: The nuPlan Benchmark for Real-World Autonomous Driving [Paper]
  • Holger Caesar, et al.

Control

• Stanley: The Robot that Won the DARPA Grand Challenge [Paper]
  • Sebastian Thrun, et al.
• Automatic Steering Methods for Autonomous Automobile Path Tracking [Paper]
  • Jarrod M. Snider
• A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles [Paper]
  • Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, Emilio Frazzoli

End-to-End Driving

• Learning by Cheating [Paper] [Code]
  • Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl
• TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [Paper] [Code]
  • Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, Andreas Geiger
• NEAT: Neural Attention Fields for End-to-End Autonomous Driving [Paper] [Code]
  • Bernhard Jaeger, et al.
• TCP: Trajectory-guided Control Prediction for End-to-End Autonomous Driving [Paper] [Code]
  • Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, Yu Qiao
• VAD: Vectorized Scene Representation for Efficient Autonomous Driving (ICCV 2023) [Paper] [Code]
  • Bo Jiang, Shaoyu Chen, Qing Xu, et al.
• SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation [Paper] [Code]
  • Wenchao Sun, Xuewu Lin, Yining Shi, Chuang Zhang, Haoran Wu, Sifa Zheng
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation [Paper] [Code]
  • Zhenxin Li, Kailin Li, Shihao Wang, et al.
• DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving (CVPR 2025) [Paper] [Code]
  • Bencheng Liao, Shaoyu Chen, Haoran Yin, et al.
• Planning-Oriented Autonomous Driving (UniAD) (CVPR 2023) [Paper] [Code]
  • Yihan Hu, Jiazhi Yang, Li Chen, et al.
• DriveLM: Driving with Graph Visual Question Answering [Paper] [Code]
  • Chonghao Sima, Katrin Renz, Kashyap Chitta, Li Chen, Hanxue Zhang, Chengen Xie, Jens Beißwenger, Ping Luo, Andreas Geiger, Hongyang Li

Reinforcement Learning in Autonomous Driving

• Playing for Data: Ground Truth from Computer Games [Paper]
  • Stephan R. Richter, Vibhav Vineet, Stefan Roth, Vladlen Koltun
• Deep Reinforcement Learning for Autonomous Driving: A Survey [Paper]
  • Kissan Tiwari, Bikash K. Dey, et al.
• Benchmarking Reinforcement Learning for Autonomous Driving in CARLA
  • Search terms: RL + CARLA + CoRL / NeurIPS / ICRA / IV for the latest policy-learning papers.

Datasets and Benchmarks

• KITTI Vision Benchmark Suite [Website]
  • Classical benchmark for stereo, optical flow, visual odometry, 3D object detection, and tracking.
• Cityscapes [Website]
  • Urban scene understanding benchmark with fine semantic annotations.
• Mapillary Vistas [Website]
  • Large-scale, geographically diverse street-scene parsing dataset.
• BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning [Paper] [Website]
  • Large-scale multi-task driving dataset.
• Waymo Open Dataset [Website] [About]
  • Large-scale perception, motion, scenario generation, and end-to-end driving benchmark ecosystem.
• nuScenes [Website]
  • Multi-sensor dataset for detection, tracking, segmentation, prediction, and map-related tasks.
• nuPlan [Website] [Paper]
  • Closed-loop planning benchmark with simulation and scenario-based evaluation.
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting [Website] [Paper]
  • Covers sensor, lidar, motion forecasting, map-change detection, and related tasks.
• Waymo Open Motion Dataset / Waymax ecosystem [Waymax] [Docs]
  • Useful for behavior prediction, sim agents, scenario generation, and closed-loop evaluation.
• ApolloScape [Website]
  • Includes scene parsing, car instance, lane segmentation, self-localization, and trajectory tasks.
• SYNTHIA [Website]
  • Synthetic dataset for semantic segmentation and related perception tasks.
• Oxford RobotCar Dataset [Website]
  • Long-term autonomy dataset across weather, season, and lighting changes.
• Oxford Radar RobotCar Dataset [Website]
  • Adds radar and odometry for robust localization and adverse-condition research.
• NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking (NeurIPS 2024) [Paper] [GitHub]
  • Planning-focused benchmark with PDMS / EPDMS metrics; NAVSIM v2 adds pseudo-simulation for two-stage evaluation (CoRL 2025).
• Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving (NeurIPS 2024) [Paper] [GitHub]
  • CARLA-based closed-loop benchmark for end-to-end driving across 44 interactive scenario types.
• KAIST Urban Dataset / MulRan-style Korean localization datasets
  • Keep Korean-road and Korean-traffic-specific resources in this section where possible.

Courses

• Self-Driving Cars (University of Tübingen, Prof. Andreas Geiger) [Website] [YouTube]
  • Full lecture series covering imitation learning, direct perception, vehicle control, and planning — one of the best free end-to-end driving courses.
• CS231n: Deep Learning for Computer Vision (Stanford) [Website]
• Self-Driving Cars Specialization (University of Toronto / Coursera) [Website]
• Introduction to Self-Driving Cars [Website]
• MIT Deep Learning and Self-Driving Cars lecture series [Website]
• Practical Deep Learning for Coders [Website]
• Probabilistic Robotics and SLAM related graduate lectures
  • Search with: SLAM / visual localization / motion planning / multi-agent forecasting lecture series.

Books

• Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville [Book]
• Probabilistic Robotics — Sebastian Thrun, Wolfram Burgard, Dieter Fox [Book]
• Planning Algorithms — Steven M. LaValle [Book]
• Principles of Robot Motion: Theory, Algorithms, and Implementations — Howie Choset, et al. [Book]
• Computer Vision: Algorithms and Applications — Richard Szeliski [Book]

Videos

• Self-Driving Cars lecture videos — Andreas Geiger (Tübingen) [Playlist]
• CVPR Workshop on Autonomous Driving (WAD) [Website]
  • Annual workshop with keynotes from Waymo, NVIDIA, Wayve, and academia; talks are posted online each year.
• Computer Vision Foundation (CVF) Open Access / YouTube [Channel]
• ROSCon [Channel]
• Autonomous Driving talks from Waymo Research / NVIDIA / Motional / CARLA Summit
• Classical deep learning lectures
  • Andrew Ng, Geoffrey Hinton, Yann LeCun, Yoshua Bengio

Software

ROS and Autonomous Driving Stacks

• ROS 2 Documentation [Website]
• ROS Home [Website]
• Autoware [Website] [Docs] [GitHub]
  • Open-source autonomous driving software stack built on ROS.
• OpenADS [Website] [GitHub]
  • Collaborative open-source ecosystem for Automated Driving Systems, spanning a ROS 2 reference stack (OpenADStack), multi-simulator support via CARLA/SUMO (OpenADSim), development tooling (OpenADSuite), and scenario-based verification/validation (OpenADSafety).

Frameworks and Toolboxes

• PyTorch [Website]
• TensorFlow [Website]
• JAX [Website]
• MMDetection3D [Docs] [GitHub]
• OpenPCDet [GitHub]
• Detectron2 [GitHub]
• Nerfstudio [Website]
  • Increasingly useful for 3D scene reconstruction and radiance-field-based research.
• Superhighway [Guide]
  • Build a Python agent that researches automotive markets, OEM strategies, EV adoption metrics, and charging infrastructure using live web search. Pay-per-call with USDC via x402.

Simulation and Evaluation

• CARLA Simulator [Website] [Docs] [GitHub]
• Waymax [Website] [Docs] [GitHub]
• nuPlan Devkit [GitHub]
• CommonRoad [Website]
• NVIDIA Isaac Sim [Website]

Conference and Workshop Channels

• CVPR [Website]
• ICCV [Website]
• ECCV [Website]
• NeurIPS [Website]
• ICRA [Website]
• IROS [Website]
• IEEE Intelligent Vehicles Symposium (IV) [Website]
• CoRL (Conference on Robot Learning) [Website]
• Workshop keywords to watch
  • autonomous driving, embodied AI, world models, behavior prediction, foundation models, simulation, safety validation

Maintenance Notes

• Last content refresh: July 2026 (added VLA / world-model papers, NAVSIM & Bench2Drive benchmarks, and course links).
• Prefer full paper titles over abbreviations when adding new entries.
• Prefer official project pages, official code repositories, and arXiv / OpenAccess links.
• Mark deprecated toolchains clearly (for example, Torch7, Theano, Caffe2) instead of deleting history.
• For 2026+, the most active update zones are:
  • foundation models / VLM / LLM driving
  • world models and scenario generation
  • occupancy and BEV scene representation
  • end-to-end driving
  • motion forecasting and sim agents
  • planning benchmarks and closed-loop evaluation

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Suggested Next Cleanup for This Repository

• Add tags such as Classic, Recommended, 2024+, Code, Benchmark, and Survey.
• Split the README into papers.md, datasets.md, software.md, and courses.md if it becomes too long.
• Add a small section for Korean-road / Korean-traffic-light / Korean-map resources.
• Add benchmark tables for 3D detection, forecasting, planning, and end-to-end driving.