1 citations · 1 across the 12 of their papers we have counts for
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Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
Zhenxin Li, Nadine Chang, Xinglong Sun +8
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implaus…
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…
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
Zhenxin Li, Nadine Chang, Wenhao Yao +9
Human demonstrations are widely considered the cornerstone of end-to-end (E2E) autonomous driving despite human demonstration's scarcity for long-tail and safety-critical scenarios…
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…
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…