CARLA

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.

Video

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.

Recommended reading

CARLA's functionality is covered extensively in the documentation. Here are some highlights covering some of CARLA's most useful and requested features.

Core features

• Quickstart: Getting started with CARLA is easy, this guide will show you how to install and run the simulator.
• Actors: CARLA's actors are entities that interact within the simulation like vehicles, pedestrians and traffic signals. Get to know them here.
• Sensors: CARLA boasts an impressive array of models of real world sensors like cameras, LIDAR and RADAR. The simulator also gives access to privileged information such as ground truth semantic segmentation and depth information.
• Traffic Manager: CARLA's Traffic Manager controls NPCs to challenge your autonomous driving agent.
• ROS bridge: CARLA's ROS bridge enables seamless connection with the Robot Operating System.

Tutorials

• First steps in CARLA: New to CARLA? Need some help with your first steps? This tutorial will get you up and running.
• Bounding boxes: Need ground truth bounding boxes for vehicles or map features? This tutorial shows you how to access them through CARLA's API.
• Creating custom maps for CARLA: Create your own custom maps for CARLA, using CARLA's built in asset library or importing your own assets.
• Creating custom vehicles for CARLA: Create your own custom vehicles for CARLA.
• Recording your simulation: Your CARLA simulations can be recorded and replayed exactly, enabling you to repeat and compare results for different sensors or configurations.

Paper

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}
}