CARLA

Open-source simulator for autonomous driving research.

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Introduction

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, environmental conditions, full control of all static and dynamic actors, maps generation and much more.

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Highlighted features

  • Scalability via a server multi-client architecture: multiple clients in the same or in different nodes can control different actors.
  • Flexible API: CARLA exposes a powerful API that allows users to control all aspects related to the simulation, including traffic generation, pedestrian behaviors, weathers, sensors, and much more.
  • Autonomous Driving sensor suite: users can configure diverse sensor suites including LIDARs, multiple cameras, depth sensors and GPS among others.
  • Fast simulation for planning and control: this mode disables rendering to offer a fast execution of traffic simulation and road behaviors for which graphics are not required.
  • Maps generation: users can easily create their own maps following the OpenDrive standard via tools like RoadRunner.
  • Traffic scenarios simulation: our engine ScenarioRunner allows users to define and execute different traffic situations based on modular behaviors.
  • ROS integration: CARLA is provided with integration with ROS via our ROS-bridge
  • Autonomous Driving baselines: we provide Autonomous Driving baselines as runnable agents in CARLA, including an AutoWare agent and a Conditional Imitation Learning agent.

Paper

CARLA: An Open Urban Driving Simulator

BiBTex

@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}
}