Assessment of Latent Pedestrian-Vehicle Interaction Risk Profiles at Midblock Crossing in VR

TL;DR

This study uses large-scale VR experiments to identify distinct pedestrian interaction risk profiles at midblock crossings and examines how autonomous vehicle presence influences these profiles across different cities.

Contribution

It introduces a novel application of latent profile analysis to VR crossing data to understand pedestrian risk perceptions and how AVs affect safety profiles in different urban contexts.

Findings

New risk profiles identified ranging from risk-accepting to cautious.

Higher prevalence of high-urgency risk profiles in AV sessions in Newcastle.

No significant difference in risk profiles between AV and HDV sessions in Toronto.

Abstract

Pedestrian safety at midblock crossings is a critical concern in mixed traffic environments where autonomous vehicles (AVs) and human-driven vehicles (HDVs) share the road. Pedestrians often infer intent from vehicle motion in AV encounters, making them vulnerable to small shifts in conflict margins. This study investigates whether virtual reality (VR) crossing sessions separate into distinct interaction risk profiles and whether AV-only sessions shift profile prevalence compared to HDV-only sessions. Using large-scale immersive VR experiments from Toronto, Canada, and Newcastle, England, we compute surrogate safety measures (SSMs) and apply latent profile analysis (LPA) to identify distinct pedestrian crossing stances, ranging from risk-accepting to highly cautious. Key findings show that Newcastle exhibits a higher prevalence of high-urgency risk profiles in AV-only sessions, indicating that AVs contribute to higher-risk encounters. In contrast, Toronto shows no significant difference between AV-only and HDV-only sessions, suggesting that contextual factors influence the impact of AVs on pedestrian safety.