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<div id="top">End-to-end Autonomous Driving
<!-- ![](https://img.shields.io/badge/Record-137-673ab7.svg) ![](https://img.shields.io/badge/License-MIT-lightgrey.svg) -->This repo is all you need for end-to-end autonomous driving research. We present awesome talks, comprehensive paper collections, benchmarks, and challenges.
Table of Contents
- At a Glance
- Learning Materials for Beginners
- Workshops and Talks
- Paper Collection
- Benchmarks and Datasets
- Competitions / Challenges
- Contributing
- License
- Citation
- Contact
At a Glance
The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction. In this survey, we provide a comprehensive analysis of more than 270 papers on the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. More details can be found in our survey paper.
<br/> <br/>End-to-end Autonomous Driving: Challenges and Frontiers
Li Chen<sup>1,2</sup>, Penghao Wu<sup>1</sup>, Kashyap Chitta<sup>3,4</sup>, Bernhard Jaeger<sup>3,4</sup>, Andreas Geiger<sup>3,4</sup>, and Hongyang Li<sup>1,2</sup>
<sup>1</sup> OpenDriveLab, Shanghai AI Lab, <sup>2</sup> University of Hong Kong, <sup>3</sup> University of Tübingen, <sup>4</sup> Tübingen AI Center
If you find some useful related materials, shoot us an email or simply open a PR!
Learning Materials for Beginners
Online Courses
- Lecture: Self-Driving Cars, Andreas Geiger, University of Tübingen, Germany
- Self-Driving Cars Specialization, University of Toronto, Coursera
- The Complete Self-Driving Car Course - Applied Deep Learning, Udemy
- Self-Driving Car Engineer Nanodegree Program, Udacity
Workshops and Talks
Workshops (recent years)
- [CVPR 2024] Foundation Models for Autonomous Systems
- [CVPR 2024] Tutorial: End-to-End Autonomy: A New Era of Self-Driving
- [CVPR 2024] Tutorial: Towards Building AGI in Autonomy and Robotics
- [CVPR 2023] Workshop on End-to-end Autonomous Driving
- [CVPR 2023] End-to-End Autonomous Driving: Perception, Prediction, Planning and Simulation
- [ICRA 2023] Scalable Autonomous Driving
Workshops (previous years)
<details>- [NeurIPS 2022] Machine Learning for Autonomous Driving
- [IROS 2022] Behavior-driven Autonomous Driving in Unstructured Environments
- [ICRA 2022] Fresh Perspectives on the Future of Autonomous Driving Workshop
- [NeurIPS 2021] Machine Learning for Autonomous Driving
- [NeurIPS 2020] Machine Learning for Autonomous Driving
- [CVPR 2020] Workshop on Scalability in Autonomous Driving
Talks
<details> <summary>Relevant talks from other workshops</summary>- Common Misconceptions in Autonomous Driving - Andreas Geiger, Workshop on Autonomous Driving, CVPR 2023
- Learning Robust Policies for Self-Driving - Andreas Geiger, AVVision: Autonomous Vehicle Vision Workshop, ECCV 2022
- Autonomous Driving: The Way Forward - Vladlen Koltun, Workshop on AI for Autonomous Driving, ICML 2020
- Feedback in Imitation Learning: Confusion on Causality and Covariate Shift - Sanjiban Choudhury and Arun Venkatraman, Workshop on AI for Autonomous Driving, ICML 2020
Paper Collection
We list key challenges from a wide span of candidate concerns, as well as trending methodologies. Please refer to this page for the full list, and the survey paper for detailed discussions.
- Survey
- Language / VLM for Driving
- World Model & Model-based RL
- Multi-sensor Fusion
- Multi-task Learning
- Interpretability
- Visual Abstraction / Representation Learning
- Policy Distillation
- Causal Confusion
- Robustness
- Affordance Learning
- BEV
- Transformer
- V2V Cooperative
- Distributed RL
- Data-driven Simulation
Benchmarks and Datasets
Closed-loop
- CARLA
- nuPlan
- Leaderboard (inactive after the CVPR 2023 challege)
- NAVSIM
Competitions / Challenges
- End-to-End Driving at Scale, Foundation Models for Autonomous Systems, CVPR 2024
- CARLA Autonomous Driving Challenge, Foundation Models for Autonomous Systems, CVPR 2024
- nuPlan planning, Workshop on End-to-end Autonomous Driving, CVPR 2023
- CARLA Autonomous Driving Challenge 2022, Machine Learning for Autonomous Driving, NeurIPS 2022
- CARLA Autonomous Driving Challenge 2021, Machine Learning for Autonomous Driving, NeurIPS 2021
- CARLA Autonomous Driving Challenge 2020, Machine Learning for Autonomous Driving, NeurIPS 2020
- Learn-to-Race Autonomous Racing Virtual Challenge, 2022
- INDY Autonomous Challenge
Contributing
Thank you for all your contributions. Please make sure to read the contributing guide before you make a pull request.
<p align="right">(<a href="#top">back to top</a>)</p>License
End-to-end Autonomous Driving is released under the MIT license.
<p align="right">(<a href="#top">back to top</a>)</p>Citation
If you find this project useful in your research, please consider citing:
@article{chen2023e2esurvey,
title={End-to-end Autonomous Driving: Challenges and Frontiers},
author={Chen, Li and Wu, Penghao and Chitta, Kashyap and Jaeger, Bernhard and Geiger, Andreas and Li, Hongyang},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024}
}
<p align="right">(<a href="#top">back to top</a>)</p>
Contact
Primary contact: hy@opendrivelab.com
. You can also contact: lichen@opendrivelab.com
.
Join OpenDriveLab Slack to chat with the commuty! Slack channel: #e2ead
.