The main goal of the CARLA Autonomous Driving Leaderboard is to evaluate the driving proficiency of autonomous agents in realistic traffic scenarios. The leaderboard serves as an open platform for the community to perform fair and reproducible evaluations of autonomous vehicle agents, simplifying the comparison between different approaches. Leaderboard is currently at version 2.0, version 1.0 is still supported.
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CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation). Source: Synthetic Data for Deep Learning