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<img src="assets/navsim_transparent.png" width="500">
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**NAVSIM:** *Data-Driven **N**on-Reactive **A**utonomous **V**ehicle **Sim**ulation*
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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.
> NAVSIM attempts to address some of the challenges faced by the community:
>
> 1. **Providing a principled evaluation** (by incorporating ideas + data from nuPlan)
> - Key Idea: **PDM Score**, a multi-dimensional metric implemented in open-loop with strong correlation to closed-loop metrics
> - Critical scenario sampling, focusing on situations with intention changes where the ego history cannot be extrapolated into a plan
> - Official leaderboard on HuggingFace that remains open and prevents ambiguity in metric definitions between projects
>
> 2. **Maintaining ease of use** (by emulating nuScenes)
> - Simple data format and reasonably-sized download (<nuPlan’s 20+ TB)
> - Large-scale publicly available test split for internal benchmarking
> - Continually-maintained devkit
🏁 **NAVSIM** will serve as a main track in the **`CVPR 2024 Autonomous Grand Challenge`**. The leaderboard for the challenge is open! For further details, please [check the challenge website](https://opendrivelab.com/challenge2024/)!
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<img src="assets/navsim_cameras.gif" width="800">
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## Table of Contents
1. [Highlights](#highlight)
2. [Getting started](#gettingstarted)
3. [Changelog](#changelog)
4. [License and citation](#licenseandcitation)
5. [Other resources](#otherresources)
## Getting started <a name="gettingstarted"></a>
- [Download and installation](docs/install.md)
- [Understanding and creating agents](docs/agents.md)
- [Understanding the data format and classes](docs/cache.md)
- [Dataset splits vs. filtered training / test splits](docs/splits.md)
- [Understanding the PDM Score](docs/metrics.md)
- [Submitting to the Leaderboard](docs/submission.md)
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## Changelog <a name="changelog"></a>
- **`[2024/04/21]`** NAVSIM v1.0 release (official devkit version for [AGC 2024](https://opendrivelab.com/challenge2024/))
- **IMPORTANT NOTE**: The name of the data split `competition_test` was changed to `private_test_e2e`. Please adapt your directory name accordingly. For details see [installation](docs/install.md).
- Parallelization of metric caching / evaluation
- Adds [Transfuser](https://arxiv.org/abs/2205.15997) baseline (see [agents](docs/agents.md#Baselines))
- Adds standardized training and test filtered splits (see [splits](docs/splits.md))
- Visualization tools (see [tutorial_visualization.ipynb](tutorial/tutorial_visualization.ipynb))
- Refactoring
- **`[2024/04/03]`** NAVSIM v0.4 release
- Support for test phase frames of competition
- Download script for trainval
- Egostatus MLP Agent and training pipeline
- Refactoring, Fixes, Documentation
- **`[2024/03/25]`** NAVSIM v0.3 release (official devkit version for warm-up phase)
- Changes env variable NUPLAN_EXP_ROOT to NAVSIM_EXP_ROOT
- Adds code for Leaderboard submission
- Major refactoring of dataloading and configs
- **`[2024/03/11]`** NAVSIM v0.2 release
- Easier installation and download
- mini and test data split integration
- Privileged `Human` agent
- **`[2024/02/20]`** NAVSIM v0.1 release (initial demo)
- OpenScene-mini sensor blobs and annotation logs
- Naive `ConstantVelocity` agent
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## License and citation <a name="licenseandcitation"></a>
All assets and code in this repository are under the [Apache 2.0 license](./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.
```BibTeX
@misc{Contributors2024navsim,
title={NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation},
author={NAVSIM Contributors},
howpublished={\url{https://github.com/autonomousvision/navsim}},
year={2024}
}
```
```BibTeX
@inproceedings{Dauner2023CORL,
title = {Parting with Misconceptions about Learning-based Vehicle Motion Planning},
author = {Daniel Dauner and Marcel Hallgarten and Andreas Geiger and Kashyap Chitta},
booktitle = {Conference on Robot Learning (CoRL)},
year = {2023}
}
```
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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>
- [SLEDGE](https://github.com/autonomousvision/sledge) | [tuPlan garage](https://github.com/autonomousvision/tuplan_garage) | [CARLA garage](https://github.com/autonomousvision/carla_garage) | [Survey on E2EAD](https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving)
- [PlanT](https://github.com/autonomousvision/plant) | [KING](https://github.com/autonomousvision/king) | [TransFuser](https://github.com/autonomousvision/transfuser) | [NEAT](https://github.com/autonomousvision/neat)
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