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cs.RO2025
TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data
Deqing Liu, Yinfeng Gao, Deheng Qian +9
Existing end-to-end autonomous driving methods typically rely on imitation learning (IL) but face a key challenge: the misalignment between open-loop training and closed-loop deplo…
cs.CV2025
World knowledge-enhanced Reasoning Using Instruction-guided Interactor in Autonomous Driving
Mingliang Zhai, Cheng Li, Zengyuan Guo +7
The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have revitalized autonomous driving, particularly in reasoning tasks within perceivable regions. Howeve…