63 lines
2.4 KiB
Markdown
63 lines
2.4 KiB
Markdown
# ScenarioNet
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**Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling** [](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb)
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[
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[**Webpage**](https://metadriverse.github.io/scenarionet/) |
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[**Code**](https://github.com/metadriverse/scenarionet) |
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[**Video**](https://youtu.be/3bOqswXP6OA) |
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[**Paper**](http://arxiv.org/abs/2306.12241) |
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[**Documentation**](https://scenarionet.readthedocs.io/en/latest/) |
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[**Colab Example**](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb)
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]
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ScenarioNet allows users to load scenarios from real-world dataset like Waymo, nuPlan, nuScenes, l5 and synthetic
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dataset such as procedural generated ones and safety-critical ones generated by adversarial attack.
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The built database provides tools for building training and test sets for ML applications.
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Powered by [MetaDrive Simulator](https://github.com/metadriverse/metadrive), the scenarios can be reconstructed for
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various applications like AD stack test, reinforcement learning, imitation learning, scenario generation and so on.
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## Installation
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The detailed installation guidance is available
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at [documentation](https://scenarionet.readthedocs.io/en/latest/install.html).
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A simplest way to do this is as follows.
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```
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# create environment
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conda create -n scenarionet python=3.9
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conda activate scenarionet
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# Install MetaDrive Simulator
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git clone git@github.com:metadriverse/metadrive.git
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cd metadrive
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pip install -e.
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# Install ScenarioNet
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git clone git@github.com:metadriverse/scenarionet.git
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cd scenarionet
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pip install -e .
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```
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## API reference
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All operations and API reference is available at
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our [documentation](https://scenarionet.readthedocs.io/en/latest/operations.html).
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If you already have ScenarioNet installed, you can check all operations by `python -m scenarionet.list`.
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## Citation
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If you used this project in your research, please cite
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```latex
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@article{li2023scenarionet,
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title={ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling},
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author={Li, Quanyi and Peng, Zhenghao and Feng, Lan and Duan, Chenda and Mo, Wenjie and Zhou, Bolei and others},
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journal={arXiv preprint arXiv:2306.12241},
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year={2023}
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}
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```
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