Pedestrians' Interaction with eHMI-equipped Autonomous Vehicles: A Bibliometric Analysis and Systematic Review

Autonomous vehicles (AVs) should prioritise pedestrian safety in a traffic accident. External human-machine interfaces (eHMIs), which enhance communication through visual and auditory signals, become essential as AVs become prevalent. This study aimed to investigate the current state of research on eHMIs, with a specific focus on pedestrian interactions with eHMI-equipped AVs. A bibliometric analysis of 234 papers published between January 2014 and December 2023 was conducted using the Web of Science database. The analysis revealed a remarkable increase in eHMI research since 2018, with the principal research topics on crossing behaviour and eHMI evaluations of pedestrians. Subsequently, 38 articles were selected for a systematic review. The systematic review, conducted through a detailed examination of each selected article, showed that pedestrian crossing behaviour is usually measured using crossing initiation time, response time, walking speed and eye tracking data. The eHMI evaluations of pedestrians were made through questionnaires that measure clarity, preference and acceptance. Research findings showed that pedestrians' crossing behaviour and eHMI evaluations are influenced by human factors (age and nationality), vehicle factors (eHMI type, eHMI colour and eHMI position) and environmental factors (signalisation and distractions). The results also revealed that current eHMI experiments often use virtual reality and video methodologies, which do not fully replicate the complexities of real-world environments. Additionally, the exploration regarding the impact of human factors, such as gender and familiarity with AVs, on pedestrian crossing behaviour is lacking. Furthermore, the investigation of multimodal eHMI systems is limited. This review highlighted the importance of standardising eHMI design, and the key gaps in the current eHMI research were revealed. These insights will guide future research towards effective eHMI solutions through informed theoretical studies and practical applications in autonomous driving.