Uncovering pedestrian stress and crossing strategies via mixed-reality encounters with autonomous vehicles
本研究通过混合现实实验测量行人的皮肤电反应，发现不同紧急反应倾向的行人在面对自动驾驶汽车时，生理唤醒会中介交通条件对过街行为的影响，为设计更安全的车外人机界面和道路系统提供依据。
Pedestrian crossings when encountering autonomous vehicles (AVs) is a complex interplay between vehicle kinematics, external human-machine interface (eHMI) cues, and pedestrians' physiological states. However, studies have not yet integrated physiological arousal with observed crossing behaviour in AV settings, nor clarified how stress-related responses differ across pedestrian groups. In this study, we investigate how AV traffic scenarios (e.g., vehicle speed, temporal gaps, yielding behaviours, urgency prompts, and eHMI) shape physiological arousal which affects crossing decisions, aiming to explain both direct effects and arousal-mediated mechanisms to support safer eHMI and road design. A head-mounted mixed reality (MR) experiment combined with galvanic skin response (GSR) measurement was developed to project life-size virtual AVs into real streetscapes and to quantify sympathetic arousal. Participants were stratified into three emergency reactivity groups (low/medium/high) using the Emergency Reaction Questionnaire (ERQ) traits. A multi-group path model was adopted to identify how crossing behaviour is influenced directly and indirectly by traffic conditions through physiological arousal across different groups. Results indicate that across groups, roadway cues and eHMIs signals generally have direct effects and arousal-mediated effects on crossing behaviour, but such mediation is mainly observed in medium and high emergency reactivity groups. Under high-intensity cue conditions, salient stimuli increase sympathetic activation, which can prolong pre-step-off waiting and, under strong cues, may also attenuate or offset the net change in walking speed via arousal-mediated pathways, depending on emergency reactivity groups. The strength of mediation and behavioural elasticity varies considerably across different groups. These insights help formulate group targeted safety interventions, thus providing actionable guidance for the system design involving AV and pedestrian interactions.