DI-drive - Decision Intelligence Platform for Autonomous Driving simulation.
DI-drive is application platform under OpenDILab
DI-drive is an open-source application platform under OpenDILab. DI-drive applies different simulator/datasets/cases in Decision Intelligence Training & Testing for Autonomous Driving Policy. It aims to
- run Imitation Learning, Reinforcement Learning, GAIL etc. in a single platform and simple unified entry
- apply Decision Intelligence in any parts of driving simulation
- suit most of the driving simulators input & output
- run designed driving cases and scenarios
and most importantly, to put these all together!
DI-drive uses DI-engine, a Reinforcement Learning platform to build most of the running modules and demos. DI-drive currently supports Carla, an open-source Autonomous Drining simualtor to operate driving simualtion.
DI-drive needs to run Carla server for simulation. Besides, The client needs to have the following modules installed:
- PyTorch
- DI-engine
- Carla Python API
Please refer to the documentation for details about installation and user guide of DI-drive. We provide IL & RL tutorials and full guidance for quickly running existing policies for beginners.
Please refer to FAQ for frequently asked questions.
- BeV Speed RL
- Implicit Affordance
DI-drive Casezoo is a scenario set for training and testing of Autonomous Driving policy in simulator. Casezoo combines data collected by real vehicles and Shanghai Lingang road license test Scenarios. Casezoo supports both evaluating and training, whick makes the simulation closer to real driving.
Please see casezoo instruction for details about Casezoo.
We appreciate all contributions to improve DI-drive, both algorithms and system designs.
DI-engine released under the Apache 2.0 license.
@misc{didrive,
title={{DI-drive: OpenDILab} Decision Intelligence platform for Autonomous Driving simulation},
author={DI-drive Contributors},
publisher = {GitHub},
howpublished = {\url{https://github.com/opendilab/DI-drive}},
year={2021},
}