MetaDrive is a driving simulator with the following key features:
- Compositional: It supports generating infinite scenes with various road maps and traffic settings for the research of generalizable RL.
- Lightweight: It is easy to install and run. It can run up to +1000 FPS on a standard PC.
- Realistic: Accurate physics simulation and multiple sensory input including Lidar, RGB images, top-down semantic map and first-person view images.
Install MetaDrive via:
git clone https://github.com/metadriverse/metadrive.git
cd metadrive
pip install -e .
Note that the program is tested on both Linux and Windows. Some control and display issues in MacOS wait to be solved
You can verify the installation of MetaDrive via running the testing script:
# Go to a folder where no sub-folder calls metadrive
python -m metadrive.examples.profile_metadrive
Note that please do not run the above command in a folder that has a sub-folder called ./metadrive
.
We provide examples to demonstrate features and basic usages of MetaDrive after the local installation.
There is an .ipynb
example which can be directly opened in Colab.
Also, you can try examples in the documentation directly in Colab! See more details in Documentations.
Run the following command to launch a simple driving scenario with auto-drive mode on. Press W, A, S, D to drive the vehicle manually.
python -m metadrive.examples.drive_in_single_agent_env
Run the following command to launch a safe driving scenario, which includes more complex obstacles and cost to be yielded.
python -m metadrive.examples.drive_in_safe_metadrive_env
You can also launch an instance of Multi-Agent scenario as follows
python -m metadrive.examples.drive_in_multi_agent_env --env roundabout
--env
accepts following parmeters: roundabout
(default), intersection
, tollgate
, bottleneck
, parkinglot
, pgmap
.
Adding --top_down
can launch top-down pygame renderer.
Running the following script enables driving in a scenario constructed from nuScenes dataset or Waymo dataset.
python -m metadrive.examples.drive_in_real_env
The default real-world dataset is nuScenes.
Use --waymo
to visualize Waymo scenarios.
Traffic vehicles can not response to surrounding vchicles if directly replaying them.
Add argument --reactive_traffic
to use an IDM policy control them and make them reactive.
Press key r
for loading a new scenario, and b
or q
for switching perspective.
To build the RL environment in python script, you can simply code in the Farama Gymnasium format as:
from metadrive.envs.metadrive_env import MetaDriveEnv
env = MetaDriveEnv(config={"use_render": True})
obs, info = env.reset()
for i in range(1000):
obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
if terminated or truncated:
env.reset()
env.close()
Please find more details in: https://metadrive-simulator.readthedocs.io
The documentation is built with .ipynb
so every example can run locally
or with colab. For Colab running, on the Colab interface, click βGitHub,β enter the URL of MetaDrive:
https://github.com/metadriverse/metadrive, and hit the search icon.
After running examples, you are expected to get the same output and visualization results as the documentation!
For example, hitting the following icon opens the source .ipynb
file of the documentation section: Environments.
If you use MetaDrive in your own work, please cite:
@article{li2022metadrive,
title={Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning},
author={Li, Quanyi and Peng, Zhenghao and Feng, Lan and Zhang, Qihang and Xue, Zhenghai and Zhou, Bolei},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022}
}
The simulator can not be built without the help from Panda3D community and the following open-sourced projects:
- panda3d-simplepbr: https://github.com/Moguri/panda3d-simplepbr
- panda3d-gltf: https://github.com/Moguri/panda3d-gltf
- RenderPipeline (RP): https://github.com/tobspr/RenderPipeline
- Water effect for RP: https://github.com/kergalym/RenderPipeline
- procedural_panda3d_model_primitives: https://github.com/Epihaius/procedural_panda3d_model_primitives
- DiamondSquare for terrain generation: https://github.com/buckinha/DiamondSquare
- KITSUNETSUKI-Asset-Tools: https://github.com/kitsune-ONE-team/KITSUNETSUKI-Asset-Tools