Enhancing pedestrian-automated vehicle interaction using external human-machine interfaces (eHMI): an experimental study considering vehicle-eHMI, Road-eHMI, and secondary task complexity

Automated vehicles (AVs), by lacking conventional interaction cues derived from human driver feedback, introduce greater uncertainty in pedestrian interactions, especially when secondary task engagement heightens safety risks. External human–machine interfaces (eHMI) are widely recognized as an effective means of simulating human driver communication, but current research largely remains vehicle-oriented. With the advancement of intelligent road infrastructure, it has gradually gained the capability to convey information to road users. In the context of vehicle-infrastructure cooperation, this study considers vehicle-eHMI and road-eHMI as an integrated system to explore their potential in pedestrian-AV interaction. A video-based experiment was conducted to investigate the effects of vehicle-eHMI, road-eHMI, and secondary task complexity on pedestrian crossing behavior and subjective responses. The results indicate that both vehicle-eHMI and road-eHMI improve interaction safety and enhance trust, but their influence on behavioral decisions depends on the risk level conveyed by implicit cues such as vehicle motion. Furthermore, the combined presentation of vehicle and road eHMIs yields the best performance. Increased secondary task complexity significantly elevates cognitive workload, reduces situational awareness and trust, and leads to more hesitant crossing decisions. Notably, road-eHMI demonstrated a pronounced compensatory effect under high cognitive load, as its location within the central visual field enables more efficient detection and interpretation when attention is constrained. This study highlights the necessity and feasibility of eHMIs in pedestrian-AV interaction and suggests that future designs should consider pedestrians’ cognitive states and integrate intelligent infrastructure and cooperative communication to support multi-agent interaction, providing new design insights and theoretical support for safe and efficient interaction.