Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings

arXiv:2404.09574v1 Announce Type: new
Abstract: Understanding and predicting pedestrian crossing behavior is essential for enhancing automated driving and improving driving safety. Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts. This task is particularly challenging at unsignalized crossings due to the ambiguous right of way, requiring pedestrians to constantly interact with vehicles and other pedestrians. This study addresses these challenges by utilizing simulator data to investigate scenarios involving multiple …

abstract arxiv automated behavior cs.ai cs.lg driving gap improving pedestrian safety systems type understanding