arXiv cs.CY· Rulla Al-Haideri, Bilal Farooq, Elisabetta Cherchi·· 12 小时前AI 评分18
基于 VR 实验的中段过街行人-车辆交互潜在风险画像评估
Assessment of Latent Pedestrian-Vehicle Interaction Risk Profiles at Midblock Crossing in VR
AI 导读
研究利用加拿大多伦多与英国纽卡斯尔的沉浸式 VR 过街实验，计算替代安全指标（SSM）并用潜在剖面分析（LPA）识别出从风险接受到高度谨慎的行人过街姿态。纽卡斯尔在仅自动驾驶车辆（AV）场景下高紧迫性风险画像占比更高，多伦多则无显著差异，表明情境因素影响 AV 对行人安全的作用。
正文
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.
| Subjects: | Physics and Society (physics.soc-ph); Computers and Society (cs.CY) |
| Cite as: | arXiv:2603.16688 [physics.soc-ph] |
| (or arXiv:2603.16688v4 [physics.soc-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2603.16688 arXiv-issued DOI via DataCite |
Submission history
From: Rulla Al-Haideri [view email]
[v1]
Tue, 17 Mar 2026 15:45:42 UTC (718 KB)
[v2]
Tue, 31 Mar 2026 21:47:28 UTC (718 KB)
[v3]
Mon, 21 Sep 2026 15:15:14 UTC (718 KB)
[v4]
Fri, 23 Oct 2026 23:24:25 UTC (718 KB)
来源：arXiv cs.CY · arxiv.org