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arXiv · 2407.04843

JaywalkerVR: A VR System for Collecting Safety-Critical Pedestrian-Vehicle Interactions

Kenta Mukoya·Erica Weng·Rohan Choudhury·Kris Kitani
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Abstract

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.

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Kenta Mukoya, Erica Weng, Rohan Choudhury, Kris Kitani. 2024-07-05. JaywalkerVR: A VR System for Collecting Safety-Critical Pedestrian-Vehicle Interactions. https://arxiv.org/abs/2407.04843

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