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README.md
CARLA Simulator
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions.
If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.
Recommended system
- Intel i7 gen 9th - 11th / Intel i9 gen 9th - 11th / AMD ryzen 7 / AMD ryzen 9
- +16 GB RAM memory
- NVIDIA RTX 2070 / NVIDIA RTX 2080 / NVIDIA RTX 3070, NVIDIA RTX 3080
- Ubuntu 18.04
CARLA Ecosystem
Repositories associated to the CARLA simulation platform:
- CARLA Autonomous Driving leaderboard: Automatic platform to validate Autonomous Driving stacks
- Scenario_Runner: Engine to execute traffic scenarios in CARLA 0.9.X
- ROS-bridge: Interface to connect CARLA 0.9.X to ROS
- Driving-benchmarks: Benchmark tools for Autonomous Driving tasks
- Conditional Imitation-Learning: Training and testing Conditional Imitation Learning models in CARLA
- AutoWare AV stack: Bridge to connect AutoWare AV stack to CARLA
- Reinforcement-Learning: Code for running Conditional Reinforcement Learning models in CARLA
- Map Editor: Standalone GUI application to enhance RoadRunner maps with traffic lights and traffic signs information
Like what you see? Star us on GitHub to support the project!
Paper
If you use CARLA, please cite our CoRL’17 paper.
CARLA: An Open Urban Driving Simulator
Alexey Dosovitskiy, German Ros,
Felipe Codevilla, Antonio Lopez, Vladlen Koltun; PMLR 78:1-16
[PDF]
[talk]
@inproceedings{Dosovitskiy17,
title = {{CARLA}: {An} Open Urban Driving Simulator},
author = {Alexey Dosovitskiy and German Ros and Felipe Codevilla and Antonio Lopez and Vladlen Koltun},
booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
pages = {1--16},
year = {2017}
}
Building CARLA
Use git clone or download the project from this page. Note that the master branch contains the latest fixes and features, for the latest stable code may be
best to switch to the stable branch.
Then follow the instruction at How to build on Linux or How to build on Windows.
The Linux build needs for an UE patch to solve some visualization issues regarding Vulkan. Those already working with a Linux build should install the patch and make the UE build again using the following commands.
# Download and install the UE patch
cd ~/UnrealEngine_4.24
wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/UE_Patch/430667-13636743-patch.txt ~/430667-13636743-patch.txt
patch --strip=4 < ~/430667-13636743-patch.txt
# Build UE
./Setup.sh && ./GenerateProjectFiles.sh && make
Unfortunately we don't have official instructions to build on Mac yet, please check the progress at issue #150.
Contributing
Please take a look at our Contribution guidelines.
F.A.Q.
If you run into problems, check our FAQ.
CARLA Talks
The team creates some additional content for users, besides the docs. This is a great way to cover different subjects such as detailed explanations for a specific module, latest improvements in a feature, future work and much more.
CARLA Talks 2020 (May):
- General
- Modules
- Features
License
CARLA specific code is distributed under MIT License.
CARLA specific assets are distributed under CC-BY License.
The ad-rss-lib library compiled and linked by the RSS Integration build variant introduces LGPL-2.1-only License.
Note that UE4 itself follows its own license terms.