65 lines
2.3 KiB
ReStructuredText
65 lines
2.3 KiB
ReStructuredText
##########################
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ScenarioNet Documentation
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##########################
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Welcome to the ScenarioNet documentation!
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ScenarioNet is an open-sourced platform for large-scale traffic scenario modeling and simulation with the following features:
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* ScenarioNet defines a unified scenario description format containing HD maps and detailed object annotations.
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* ScenarioNet provides tools to build and manage databases built from various data sources including real-world datasets like Waymo, nuScenes, Lyft L5, and nuPlan datasets and synthetic datasets like the procedural generated ones and safety-critical ones.
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* Scenarios recorded in this format can be replayed in the digital twins with multiple views, ranging from Bird-Eye-View layout to realistic 3D rendering.
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It can thus support several applications including large-scale scenario generation, AD testing, imitation learning, and reinforcement learning in both single-agent and multi-agent settings. The results imply scaling up the training data brings new research opportunities in machine learning and autonomous driving.
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This documentation brings you the information on installation, usages and more of ScenarioNet!
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You can also visit the `GitHub repo <https://github.com/metadriverse/scenarionet>`_ and `Webpage <https://metadriverse.github.io/scenarionet/>`_ for code and videos.
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Please feel free to contact us if you have any suggestions or ideas!
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.. toctree::
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:maxdepth: 2
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:hidden:
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:caption: Quick Start
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install.rst
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example.rst
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operations.rst
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.. toctree::
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:hidden:
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:maxdepth: 2
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:caption: Supported Dataset
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datasets.rst
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nuplan.rst
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nuscenes.rst
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waymo.rst
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PG.rst
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lyft.rst
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new_data.rst
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.. toctree::
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:hidden:
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:maxdepth: 2
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:caption: System Design
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description.rst
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simulation.rst
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Citation
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########
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You can read `our white paper <https://arxiv.org/pdf/2306.12241.pdf>`_ describing the details of ScenarioNet! If you use ScenarioNet in your own work, please cite:
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.. code-block:: 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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