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<div id="top" align="center"> <p align="center"> <img src="assets/navsim_transparent.png" width="600"> <h2 align="center">Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking</h1> <h3 align="center"><a href="https://arxiv.org/abs/2406.15349">Paper</a> | Supplementary | <a href="https://www.youtube.com/watch?v=Qe76HRmPDe0">Talk</a> | <a href="https://opendrivelab.com/challenge2024/#end_to_end_driving_at_scale">2024 Challenge</a> | <a href="https://huggingface.co/spaces/AGC2024-P/e2e-driving-navsim">Leaderboard</a> </h3> </p> </div> <br/>

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking

Daniel Dauner<sup>1,2</sup>, Marcel Hallgarten<sup>1,5</sup>, Tianyu Li<sup>3</sup>, Xinshuo Weng<sup>4</sup>, Zhiyu Huang<sup>4,6</sup>, Zetong Yang<sup>3</sup>
Hongyang Li<sup>3</sup>, Igor Gilitschenski<sup>7,8</sup>, Boris Ivanovic<sup>4</sup>, Marco Pavone<sup>4,9</sup>, Andreas Geiger<sup>1,2</sup>, and Kashyap Chitta<sup>1,2</sup> <br>

<sup>1</sup>University of Tübingen, <sup>2</sup>Tübingen AI Center, <sup>3</sup>OpenDriveLab at Shanghai AI Lab, <sup>4</sup>NVIDIA Research
<sup>5</sup>Robert Bosch GmbH, <sup>6</sup>Nanyang Technological University, <sup>7</sup>University of Toronto, <sup>8</sup>Vector Institute, <sup>9</sup>Stanford University

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Highlights <a name="highlight"></a>

🔥 NAVSIM gathers simulation-based metrics (such as progress and time to collision) for end-to-end driving by unrolling simplified bird's eye view abstractions of scenes for a short simulation horizon. It operates under the condition that the policy has no influence on the environment, which enables efficient, open-loop metric computation while being better aligned with closed-loop evaluations than traditional displacement errors.

<p align="center"> <img src="assets/navsim_cameras.gif" width="800"> </p>

Table of Contents

  1. Highlights
  2. Getting started
  3. Changelog
  4. License and citation
  5. Other resources

Getting started <a name="gettingstarted"></a>

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Changelog <a name="changelog"></a>

<p align="right">(<a href="#top">back to top</a>)</p>

License and citation <a name="licenseandcitation"></a>

All assets and code in this repository are under the Apache 2.0 license unless specified otherwise. The datasets (including nuPlan and OpenScene) inherit their own distribution licenses. Please consider citing our paper and project if they help your research.

@article{Dauner2024ARXIV,
    title = {NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking},
    author = {Daniel Dauner and Marcel Hallgarten and Tianyu Li and Xinshuo Weng and Zhiyu Huang and Zetong Yang and Hongyang Li and Igor Gilitschenski and Boris Ivanovic and Marco Pavone and Andreas Geiger and Kashyap Chitta},
    journal = {arXiv},
    volume = {2406.15349},
    year = {2024}
} 
@misc{Contributors2024navsim,
    title={NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking},
    author={NAVSIM Contributors},
    howpublished={\url{https://github.com/autonomousvision/navsim}},
    year={2024}
} 
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Other resources <a name="otherresources"></a>

<a href="https://twitter.com/AutoVisionGroup" target="_blank"> <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/Awesome Vision Group?style=social&color=brightgreen&logo=twitter" /> </a> <a href="https://twitter.com/kashyap7x" target="_blank"> <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/Kashyap Chitta?style=social&color=brightgreen&logo=twitter" /> </a> <a href="https://twitter.com/DanielDauner" target="_blank"> <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/Daniel Dauner?style=social&color=brightgreen&logo=twitter" /> </a> <a href="https://twitter.com/MHallgarten0797" target="_blank"> <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/Marcel Hallgarten?style=social&color=brightgreen&logo=twitter" /> </a> <p align="right">(<a href="#top">back to top</a>)</p>