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+########################
+ScenarioNet Documentation
+########################
+
+
+Welcome to the ScenarioNet documentation!
+ScenarioNet is an open-sourced platform for large-scale traffic scenario modeling and simulation with the following features:
+
+* ScenarioNet defines a unified scenario description format containing HD maps and detailed object annotations.
+* 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.
+* 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.
+
+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.
+
+This documentation brings you the information on installation, usages and more of ScenarioNet!
+You can also visit the `GitHub repo <https://github.com/metadriverse/scenarionet>`_ and `Webpage <https://metadriverse.github.io/scenarionet/>`_ for code and videos.
+Please feel free to contact us if you have any suggestions or ideas!
+
+
+Citation
+########
+
+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:
+
+.. code-block:: latex
+
+    @article{li2023scenarionet,
+      title={ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling},
+      author={Li, Quanyi and Peng, Zhenghao and Feng, Lan and Duan, Chenda and Mo, Wenjie and Zhou, Bolei and others},
+      journal={arXiv preprint arXiv:2306.12241},
+      year={2023}
+    }
+