**NAVSIM:** *Data-Driven **N**on-Reactive **A**utonomous **V**ehicle **Sim**ulation*

## Highlights 🔥 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 ( - 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/)!

## Table of Contents 1. [Highlights](#highlight) 2. [Getting started](#gettingstarted) 3. [Changelog](#changelog) 4. [License and citation](#licenseandcitation) 5. [Other resources](#otherresources) ## Getting started - [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)

## Changelog - **`[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 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

- [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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