arXiv Computer Vision By Bala Murali Manoghar Sai Sudhakar, Sourab Bapu Sridhar, Sandipan Das, Rahul Ahuja, Meda Lazar, Ashish Garg, Pratik Likhar, Senthil Yogamani

RedLight-VLA: Models for traffic-rule grounding and behavioral emphasis in driving policies

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arXiv AI
Jun 17

DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models

arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.

By Xinglong Sun, Kevin Xie, Jenny Schmalfuss, Despoina Paschalidou, Xiuming Zhang, Sanja Fidler, Kashyap Chitta, Jose M. Alvarez
arXiv AI
Aug 7

Visual Grounding in Zero-Shot Vision-Language Control

arXiv:2608. 06154v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as zero-shot controllers, but successful trajectories do not necessarily show that decisions are grounded in visual input: simulator dynamics and conservative action priors can produce favourable scores without meaningful perception.

By J. de Curt\`o, Dayani Plasencia, Diego S\'anchez, I. de Zarz\`a
arXiv AI
Jul 3

DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving

arXiv:2603. 18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the rich contextual understanding required for safe driving and make unsafe exploration unavoidable in real-world settings.

By Zilin Huang, Zihao Sheng, Zhengyang Wan, Yansong Qu, Junwei You, Sicong Jiang, Sikai Chen