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CARLA Leaderboard 2.0

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.

Papers

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Showing 1–6 of 6 papers

TitleStatusHype
Hidden Biases of End-to-End Driving DatasetsCode4
CarLLaVA: Vision language models for camera-only closed-loop drivingCode3
Hidden Biases of End-to-End Driving ModelsCode2
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving—0
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