End-to-end Autonomous Driving
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
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.
End-to-end Autonomous Driving: Challenges and Frontiers
Li Chen1,2, Penghao Wu1, Kashyap Chitta3,4, Bernhard Jaeger3,4, Andreas Geiger3,4, and Hongyang Li1,2
1 OpenDriveLab, Shanghai AI Lab, 2 University of Hong Kong, 3 University of Tübingen, 4 Tübingen AI Center
If you find some useful related materials, shoot us an email or simply open a PR!
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Learning Materials for Beginners
Online Courses
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Workshops and Talks
Workshops (recent years)
Workshops (previous years)
Talks
Relevant talks from other workshops
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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.
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Benchmarks and Datasets
Real-world deployment is the final benchmark for autonomous driving.
However, testing in the real world is expensive. For academic benchmarking, we recommend you read this write-up from Jaeger et al. 2024: Common mistakes in benchmarking.
Closed-loop
Open-loop
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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
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Contributing
Thank you for all your contributions. Please make sure to read the contributing guide before you make a pull request.
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License
End-to-end Autonomous Driving is released under the MIT license.
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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}
}
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Primary contact: [email protected]. You can also contact: [email protected].
Join OpenDriveLab Slack to chat with the commuty! Slack channel: #e2ead.
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