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：在自动驾驶车辆（AVs）与人类驾驶车辆（HDVs）共享道路的混合交通环境中，中段过街处的行人安全是一个关键问题。在与自动驾驶车辆相遇时，行人通常通过车辆运动推断其意图，这使得他们容易受到冲突裕度微小变化的影响。本研究探讨了虚拟现实（VR）过街实验是否可分离出不同的交互风险特征，以及纯自动驾驶车辆场景与纯人类驾驶车辆场景相比是否改变了这些特征的分布。利用来自加拿大安大略省多伦多市和英国纽卡斯尔市的大规模沉浸式VR实验数据，我们计算了替代安全指标（SSMs）并应用潜在特征分析（LPA）来识别从风险接受到高度谨慎的不同行人过街姿态。关键发现表明，在纯自动驾驶车辆场景中，纽卡斯尔表现出更高比例的高紧迫性风险特征，这表明自动驾驶车辆导致了更高风险的相遇。相比之下，多伦多的纯自动驾驶车辆场景与纯人类驾驶车辆场景之间未显示出显著差异，这表明情境因素影响了自动驾驶车辆对行人安全的作用。