多车道道路行人与自动驾驶卡车交互中遮挡引发的风险及干预措施：一项虚拟现实研究

中文摘要 AI 辅助

自动驾驶卡车（ATs）因体积庞大、制动能力受限、驾驶员沟通提示有限，且可能遮挡周围交通，或会带来独特的行人安全风险。本研究采用包含54名参与者的受控虚拟现实实验，调查无信号多车道过街场景下行人与ATs的交互风险，并评估针对遮挡的风险缓解策略。实验检验了近侧车辆类型、天气状况及远侧车辆让行策略对行人行为、感知风险和客观安全性的影响。基于具有代表性的高风险场景，设计并测试了三种针对性干预措施：环境感知型外部人机界面（eHMI）、投影式eHMI及听觉警告。结果显示，ATs提升了感知风险并促使行人采取更谨慎的过街行为，表明存在风险补偿效应；但该效应在雨天会减弱，此时与制动相关的安全裕度降低。AT引发的遮挡还会限制行人对隐藏车辆的识别，进而增加远侧交互风险。在三种干预措施中，投影式eHMI表现出最佳整体性能，可改善客观安全裕度、增强风险感知并支持行为调整。这些发现凸显了需制定针对ATs的界面与警告策略，以兼顾意图沟通与风险定位。

英文摘要

Autonomous trucks (ATs) may introduce distinct pedestrian-safety risks because of their large physical dimensions, constrained braking capability, limited driver-based communication cues, and potential to occlude surrounding traffic. This study employed a controlled virtual reality experiment with 54 participants to investigate pedestrian-AT interaction risk in an unsignalized multi-lane crossing scenario and to evaluate occlusion-targeted risk mitigation strategies. The experiment examined the effects of near-side vehicle type, weather condition, and far-side vehicle yielding strategy on pedestrian behavior, perceived risk, and objective safety. Based on a representative high-risk scenario, three targeted interventions were designed and tested: an environment-aware external human-machine interface (eHMI), a projected eHMI, and an auditory warning. The results showed that ATs increased perceived risk and encouraged more cautious crossing behavior, suggesting a risk-compensation effect. However, this compensation was weakened under rainy conditions, where braking-related safety margins were reduced. AT-induced occlusion further increased far-side interaction risk by limiting pedestrians' recognition of hidden vehicles. Among the three interventions, the projected eHMI showed the best overall performance, improving objective safety margins, enhancing risk awareness, and supporting behavioral adjustment. These findings highlight the need for AT-specific interface and warning strategies that address both intention communication and risk localization.

发表机构

• University College London（伦敦大学学院）
• Ningbo University（宁波大学）
• The University of Hong Kong（香港大学）
• Shanghai Maritime University（上海海事大学）
• Tongji University（同济大学）

机构由 AI 辅助整理，请以论文原文为准。