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

TL;DR

A retrospective analysis of two distinct VR-based studies on how autonomous vehicles communicate with pedestrians in complex traffic situations involving multiple vehicles and pedestrians set a groundwork for advancing VR simulators to study complex interactions between AVs and pedestrians.

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.

Figures (6)

• Figure 1: Physical space used in the multi-vehicle study (left) and in the multi-pedestrian study (right).
• Figure 2: Settings of the multi-vehicle simulation: (A) Mixed traffic with AVs and manually-driven vehicles; (B) Human activities in the background; (C) A mid-block crossing where participants were tasked with reaching the bus stop.
• Figure 3: Settings of the multi-pedestrian simulation: (A) Traffic consists of one AV approaching from the left-hand side; (B) Two pedestrians crossing in the scenario; (C) A mid-block crossing where participants were tasked with reaching the shop in front of them.
• Figure 4: Visualisation of the responses to the iGroup Presence Questionnaire across various dimensions of presence: Spatial Presence, Involvement, Realism, and General Presence. Statements that were reverse-scored are indicated with an asterisk '*'. A higher score signifies a greater sense of presence.
• Figure 5: Visualisation of the responses to the Multimodal Presence Scale across various dimensions of presence: Physical Presence, Social Presence, and Self-Presence. A higher score signifies a greater sense of presence.