151e38ce8d
* Improved braking, collision negotiation. * Improved braking algorithm for smoother approach to lead vehicle. * Implemented smoother path boundary modification to aid smoother braking. * Re-worked collision negotiation algorithm. * Improved collision candidate filtering. * Added safe-guard in case of vehicle removal in collision stage. * Used local variable for heavily referenced object in localization stage. * Implemented vector relative velocities for motion planner's collision consideration. * Moved collision candidate sorting logic from collision stage to localization stage. * Sorting collision candidates using their ids instead of shared pointers to avoid memory corruption. * Improved conditions for collision consideration for greater efficiency. * Removed fps limit in async mode. * Hybrid physics mode * Introduced hybrid physics mode parameter * Implemented physics independent velocity computation * Modified localization stage to be physics agnostic * Fixing velocity compute interval in sync and async mode. Made motion planner stage work with internally computed velocities. * Made collision stage agnostic to actor physics * Sampling waypoint buffer for teleportation window * WIP: Teleportation changes * WIP2: Teleportation changes * Fixes waypoint window and vehicle spawning * hotfix to performance benchmark * comment out debugs * changelog * fixes collision bug * fixes package error and out_of_range bug * changes after review * Left & Right Transit of a lane: Waypoint mapping Co-authored-by: Praveen Kumar <pravinblaze@hotmail.com> Co-authored-by: Soumyadeep <soumyadeep.dhar@kpit.com> |
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| Import | ||
| LibCarla | ||
| PythonAPI | ||
| Unreal/CarlaUE4 | ||
| Util | ||
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| CHANGELOG.md | ||
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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.
CARLA Ecosystem
Repositories associated to the CARLA simulation platform:
- 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.
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.
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.