Carla Dataset Tools: Synthetic Data Collection for Autonomous Driving

KevinLADLee authored this suite of data collection tools for the CARLA simulator, updated in January 2026, to facilitate the generation of synthetic autonomous driving data. The repository enables researchers to programmatically capture synchronized sensor outputs and vehicle states within simulated urban environments.

Use Cases

• Generating synthetic RGB camera frames for training 2D object detection algorithms
• Collecting LiDAR point clouds for testing 3D spatial mapping and localization
• Recording vehicle steering and throttle telemetry for end-to-end imitation learning

Strengths

• GPL-3.0 license allows for open-source modification and redistribution
• Automates the extraction of synchronized multi-modal sensor data from CARLA
• Updated in 2026, ensuring compatibility with recent simulation environments

Limitations

• Requires a functional CARLA simulator installation and compatible hardware
• Does not provide a pre-packaged static dataset; users must generate their own data
• Synthetic data may lack the visual noise and edge cases found in real-world driving footage

Provenance

Source

KevinLADLee via GitHub

Collection Method

synthetic

Freshness

Updated as recently as January 2026.

Geography

Virtual (CARLA simulation environments)