A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inference

This paper presents a novel approach utilizing a vision-and-knowledge enhanced large language model to achieve generalizable inference of pedestrian crossing behavior. This development is crucial for advancing autonomous driving systems and intelligent urban planning, enabling more robust and safer interactions between autonomous vehicles and pedestrians.