AI RESEARCH
GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic
arXiv CS.AI
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ArXi:2605.10386v1 Announce Type: new Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse safety threats, particularly in accident-prone scenarios. Recent safeguard mechanisms have shown promise by incorporating logical constraints, yet most rely on static formulations that lack temporally grounded safety reasoning over evolving traffic interactions, resulting in limited robustness in dynamic driving environments.