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# ScenarioNet
[![Documentation Status](https://readthedocs.org/projects/scenarionet/badge/?version=latest)](https://scenarionet.readthedocs.io/en/latest/?badge=latest)
[![build](https://github.com/metadriverse/scenarionet/workflows/test/badge.svg)](http://github.com/metadriverse/scenarionet/actions)
[![GitHub license](https://img.shields.io/github/license/metadriverse/scenarionet)](https://github.com/metadriverse/scenarionet/blob/main/LICENSE.txt)
**Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling**
[
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[**Webpage**](https://metadriverse.github.io/scenarionet/) |
[**Code**](https://github.com/metadriverse/scenarionet) |
[**Video**](https://youtu.be/3bOqswXP6OA) |
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[**Paper**](http://arxiv.org/abs/2306.12241) |
[**Documentation**](https://scenarionet.readthedocs.io/en/latest/)
]
***Colab example for running simulation with ScenarioNet:***
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/simulation.ipynb)
***Colab example for reading established ScenarioNet dataset:***
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metadriverse/scenarionet/blob/main/tutorial/read_established_scenarionet_dataset.ipynb)
ScenarioNet allows users to load scenarios from real-world datasets like Waymo, nuPlan,
nuScenes, l5 and synthetic dataset such as procedural generated ones and safety-critical
ones generated by adversarial attack. The built database provides tools for building
training and test sets for ML applications.
Powered by [MetaDrive Simulator](https://github.com/metadriverse/metadrive),
the scenarios can be reconstructed for various applications like AD stack test,
reinforcement learning, imitation learning, scenario generation and so on.
![system](docs/asset/system.png)
## Installation
The detailed installation guidance is available
at [documentation](https://scenarionet.readthedocs.io/en/latest/install.html).
A simplest way to do this is as follows.
```
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# create environment
conda create -n scenarionet python=3.9
conda activate scenarionet
# Install MetaDrive Simulator
cd ~/ # Go to the folder you want to host these two repos.
git clone https://github.com/metadriverse/metadrive.git
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cd metadrive
pip install -e.
# Install ScenarioNet
cd ~/ # Go to the folder you want to host these two repos.
git clone git@github.com:metadriverse/scenarionet.git
cd scenarionet
pip install -e .
```
## API reference
Add come updates for Neurips paper (#4) * scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
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All operations and API reference is available at
our [documentation](https://scenarionet.readthedocs.io/en/latest/operations.html).
If you already have ScenarioNet installed, you can check all operations by `python -m scenarionet.list`.
Add come updates for Neurips paper (#4) * scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
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## Citation
Add come updates for Neurips paper (#4) * scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
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If you used this project in your research, please cite:
Add come updates for Neurips paper (#4) * scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
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```latex
@article{li2023scenarionet,
2023-10-23 07:10:04 -07:00
title={ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling},
author={Li, Quanyi and Peng, Zhenghao and Feng, Lan and Liu, Zhizheng and Duan, Chenda and Mo, Wenjie and Zhou, Bolei},
journal={Advances in Neural Information Processing Systems},
year={2023}
}
```