2 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving
Wenhao Yao, Xinglong Sun, Zhenxin Li +4
End-to-end planning has emerged as a dominant paradigm for autonomous driving, where recent models often adopt a scoring-selection framework to choose trajectories from a large set…
Generalized Trajectory Scoring for End-to-end Multimodal Planning
Zhenxin Li, Wenhao Yao, Zi Wang +7
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory s…
DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
Wenhao Yao, Zhenxin Li, Shiyi Lan +4
Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, as in regression-based approaches, usually does not explicitly asses…
Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
Chonghao Sima, Kashyap Chitta, Zhiding Yu +5
How can we rely on an end-to-end autonomous vehicle's complex decision-making system during deployment? One common solution is to have a ``fallback layer'' that checks the planned…
Enhancing Autonomous Driving Safety with Collision Scenario Integration
Zi Wang, Shiyi Lan, Xinglong Sun +4
Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits th…
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
Zhenxin Li, Shihao Wang, Shiyi Lan +3
End-to-end autonomous driving research currently faces a critical challenge in bridging the gap between open-loop training and closed-loop deployment. Current approaches are traine…