  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.
