CARLA

Categories: Coding & Developer Tools | Pricing: Free | Official Website ↗

Provides an open-source urban driving simulator for developing, training, and validating autonomous driving systems.

CARLA provides an open-source urban driving simulator with open digital assets, allowing full control over simulation environments and actors for developing, training, and validating autonomous driving systems. It offers a flexible API to control traffic generation, pedestrian behaviors, weather conditions, and diverse sensor suites including LIDARs, cameras, and GPS. The platform supports fast simulation for planning and control by disabling rendering, and enables users to generate custom maps following the ASAM OpenDRIVE standard. CARLA also integrates with ROS and provides autonomous driving baselines, making it a comprehensive tool for researchers and developers in the autonomous driving domain.

Key Features

Scalability via a server multi-client architecture
Flexible API for controlling simulation aspects
Configurable autonomous driving sensor suites (LIDARs, cameras, depth sensors, GPS)
Fast simulation mode for planning and control without rendering
Maps generation using ASAM OpenDRIVE standard
Traffic scenarios simulation with ScenarioRunner engine
ROS integration via ROS-bridge
Autonomous Driving baselines as runnable agents (AutoWare, Conditional Imitation Learning)

Pros

Open-source code and protocols
Provides open digital assets (urban layouts, buildings, vehicles)
Supports flexible specification of sensor suites and environmental conditions
Full control of all static and dynamic actors
Allows creation of custom maps and vehicles
Enables recording and replaying simulations for comparison
Active community and development with frequent updates

Cons

Requires technical knowledge for setup and use
No direct mention of customer support channels beyond a forum
Focuses specifically on urban driving simulation, not general robotics

Use Cases

Developing autonomous driving algorithms
Training AI models for perception and control
Validating autonomous vehicle software in virtual environments
Researching urban driving scenarios
Creating custom maps for autonomous vehicle testing
Simulating traffic behaviors
Integrating with Robot Operating System projects
Benchmarking autonomous driving agents

Best For

Developers of autonomous driving systems
Researchers in robotics and AI
Engineers validating autonomous vehicle software
Students learning about autonomous driving

Integrations: ROS, AutoWare, Conditional Imitation Learning, ASAM OpenDRIVE, RoadRunner, ScenarioRunner

Platforms: web, desktop

Watch demo on YouTube ↗

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