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These decisions are executed by a motion planner that ensures smooth, kinematically feasib","title":"Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles","url":"https://arxiv.org/abs/2605.16858","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.16858v1 Announce Type: cross \nAbstract: 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 observations into natural-language reasoning prompts, enabling the LLM to infer pedestrian intent, anticipate risk, and generate cautious tactical driving decisions. 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