CARLA is an open-source simulator for autonomous driving research.
The simulator addresses the challenge of safely and cost-effectively testing autonomous vehicle algorithms by providing a controlled, repeatable environment where researchers can develop and validate perception, planning, and control systems. It runs on top of Unreal Engine and offers a range of urban and highway scenarios with configurable weather, lighting, and traffic conditions. The tool generates synthetic sensor data including camera feeds, lidar point clouds, and radar measurements, allowing researchers to train and test machine learning models without requiring physical vehicles or real-world driving.
CARLA suits research teams and engineers developing autonomous driving systems who need a flexible, open-source platform for algorithm development and validation. It is particularly valuable for those working on computer vision, deep reinforcement learning, and imitation learning approaches, as well as for testing integration with the Robot Operating System. The simulator's cross-platform availability and support for multiple sensor modalities make it applicable to diverse research workflows, from perception pipeline development to end-to-end learning experiments.
The project maintains active development with regular updates to its simulation capabilities and sensor models. The codebase shows consistent refinement of core features and responsiveness to research community needs. Documentation and examples are regularly maintained to support new users. The project demonstrates sustained engagement with its research user base through ongoing feature development and bug fixes.
