Elisabetta Cherchi

Rulla Al-Haideri, Bilal Farooq, Elisabetta Cherchi

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.

Bilal Farooq, Elisabetta Cherchi, Anae Sobhani

Stated preference experiments have been known to suffer from the lack of realism. This issue is particularly visible when the scenario doesn't have a well understood prior reference e.g. in case of the autonomous vehicles related scenarios. We present Virtual Immersive Reality Environment (VIRE) that is capable of developing highly realistic, immersive, and interactive choice scenario. We demonstrate the use of VIRE in the pedestrian preferences related to autonomous vehicles and associated infrastructure changes on urban streets of Montréal. The results are compared with predominantly used approaches i.e. text-only and visual aid. We show that VIRE results in better understanding of the scenario and consistent results.