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.

翻译：路段中间过街的行人安全是自动驾驶车辆（AV）与人类驾驶车辆（HDV）共享道路的混合交通环境中的关键问题。行人在与自动驾驶车辆交互时往往通过车辆运动推断其意图，这使得他们易受冲突裕度微小变化的影响。本研究探究虚拟现实（VR）过街场景是否可分离为不同的交互风险剖面，以及相较于纯HDV场景，纯AV场景是否会改变剖面分布的普遍性。利用来自加拿大托伦多和英国纽卡斯尔的大规模沉浸式VR实验，我们计算了替代安全度量（SSMs）并应用潜在剖面分析（LPA）来识别从风险接受型到高度谨慎型的不同行人过街姿态。关键发现表明，在纯AV场景中，纽卡斯尔显示出更高比例的紧急风险剖面，说明自动驾驶车辆导致了更高风险的交互。相反，托伦多显示纯AV场景与纯HDV场景间无显著差异，表明情境因素会影响自动驾驶车辆对行人安全的影响。