2024/07/05 by Kenta Mukoya, Erica Weng, Mukoya, Kenta +5 · 1 citation
Engineering · Psychology · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Robotics (cs.RO) #Traffic and Road Safety
paper · pdf · doi:10.48550/arxiv.2407.04843
openalex publication_date 2024/07/05 · openalex created_date 2024/07/11 · openalex updated_date 2026/07/28
Developing autonomous vehicles that can safely interact with pedestrians requires large amounts of pedestrian and vehicle data in order to learn accurate pedestrian-vehicle interaction models. However, gathering data that include crucial but rare scenarios - such as pedestrians jaywalking into heavy traffic - can be costly and unsafe to collect. We propose a virtual reality human-in-the-loop simulator, JaywalkerVR, to obtain vehicle-pedestrian interaction data to address these challenges. Our system enables efficient, affordable, and safe collection of long-tail pedestrian-vehicle interaction data. Using our proposed simulator, we create a high-quality dataset with vehicle-pedestrian interaction data from safety critical scenarios called CARLA-VR. The CARLA-VR dataset addresses the lack of long-tail data samples in commonly used real world autonomous driving datasets. We demonstrate that models trained with CARLA-VR improve displacement error and collision rate by 10.7% and 4.9%, respectively, and are more robust in rare vehicle-pedestrian scenarios.