GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic
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. To address these limitations, we propose GuardAD, a model-agnostic safeguard that formulates AD safety as an evolving Markovian logical state. GuardAD introduces Neuro-Symbolic Logic Formalization, which represents safe...
| Authors | Tianyuan Zhang, Peng Yue, Zihao Peng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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