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CARLA Leaderboard 2.0
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Hidden Biases of End-to-End Driving Datasets
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning
CarLLaVA: Vision language models for camera-only closed-loop driving
DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving
Hidden Biases of End-to-End Driving Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.