Advancing VR Simulators for Autonomous Vehicle-Pedestrian Interactions: A Focus on Multi-Entity Scenarios

TL;DR

This paper analyzes VR simulations of complex autonomous vehicle-pedestrian interactions, identifying factors affecting presence and behavior, and proposing improvements for more realistic and effective training and research environments.

Contribution

It provides a retrospective analysis of VR-based studies on multi-entity AV-pedestrian scenarios, highlighting key factors influencing presence and interaction behaviors.

Findings

Factors affecting sense of presence identified

Controlled scenarios influence crossing behavior

Recommendations for more natural VR simulations

Abstract

Recent research has increasingly focused on how autonomous vehicles (AVs) communicate with pedestrians in complex traffic situations involving multiple vehicles and pedestrians. VR is emerging as an effective tool to simulate these multi-entity scenarios, offering a safe and controlled study environment. Despite its growing use, there is a lack of thorough investigation into the effectiveness of these VR simulations, leaving a notable gap in documented insights and lessons. This research undertook a retrospective analysis of two distinct VR-based studies: one focusing on multiple AV scenarios (N=32) and the other on multiple pedestrian scenarios (N=25). Central to our examination are the participants' sense of presence and their crossing behaviour. The findings highlighted key factors that either enhance or diminish the sense of presence in each simulation, providing considerations for future improvements. Furthermore, they underscore the influence of controlled scenarios on crossing behaviour and interactions with AVs, advocating for the exploration of more natural and interactive simulations that better reflect real-world AV and pedestrian dynamics. Through this study, we set a groundwork for advancing VR simulators to study complex interactions between AVs and pedestrians.