Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles
Autonomous Vehicles (AVs) must make reliable decisions in dense urban environments where pedestrian behavior is variable, sometimes abnormal, and often unseen during training. Reinforcement learning (RL)-based AV control systems perform well in structured traffic but struggle to generalize to unpredictable pedestrian interactions and out-of-distribution scenarios. Their reliance on handcrafted rewards and opaque decisions further limits their suitability for safety-critical, pedestrian-rich environments. To address these limitations, we introduce a Large Language Model (LLM)-based decision-making framework for pedestrian-aware behavioral planning. The system converts structured scene obse...
| Authors | Aidana Baimbetova, Haruki Yonekura, Hamada Rizk et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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