1 citations · 2 across the 10 of their papers we have counts for
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Grounding What Shapes the Plan: Rethinking Groundedness for Physical Intelligence in Autonomous Driving
Minkyoung Cho, Zewei Zhou, Wenhao Ding +11
Driving models increasingly ground reasoning in causal relations, spatial structure, perceptual evidence, and predicted futures. These advances make reasoning more faithful to the…
CtrlWAM: Controllable World Action Models with Aligned Intent and Foresight
Chensheng Peng, Wenhao Ding, Ran Tian +9
World action models (WAMs) jointly predict actions (intent) and visual future (foresight). Standard training adds noise to recorded actions and video simultaneously, but such train…
Vision-Language-Action Autonomous Driving Agent with Language-based Memory
Kai Yan, Xiangyu Chen, Yulong Cao +9
Vision-Language-Action (VLA) foundation models have recently emerged as one of the prevailing solutions for autonomous driving, as they can utilize knowledge acquired during vision…
Accelerating Structured Chain-of-Thought in Autonomous Vehicles
Yi Gu, Yan Wang, Yuxiao Chen +8
Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving, but its autoregressive nature introduces signific…
TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs
Chenchen Lin, Sanbao Su, Rachel Luo +4
Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utili…