Analysis of User Acceptance and Perception for External HMIs Based on Driving Situations

With the advancement of automated driving technology, the role of vehicle drivers is increasingly shifting to that of passengers, necessitating a transformation in non- automated verbal communication methods among road users. In this context, visual communication through external Human-Machine Interfaces (eHMIs) on automated driving vehicles has emerged as a critical area of focus. This study systematically analyses user preferences for eHMI messages across various driving scenarios (normal driving, single-lane roads, adverse weather, branching roads, and mixed traffic conditions) and user groups (drivers, pedestrians, and automated ride-hailing passengers). Additionally, it examines the influence of demographic factors such as age, as well as user understanding and favourability toward automated driving technologies, on message preferences. Data were collected through an online survey involving 900 licensed drivers, who were asked to rank the top three most useful eHMI message types for each scenario. The collected data were analysed using cross-tabulation and regression analysis to identify variations in message preferences across different variables. The findings reveal that preferred eHMI messages vary significantly depending on the driving scenario. For instance, messages such as “Informaion for Rear Driver,” “Intention to Proceed,” and “Emergency Alerts” were highly favoured in vehicle-related scenarios. In pedestrian-related contexts, the "Pedestrian Detected" message was deemed most important, while in ride-hailing situations, user identification and risk alert messages were recognized as critical. Although age-related differences in preferences were observed, their effect sizes were relatively small. Similarly, understanding and favourability toward automated driving technologies exerted modest influences in certain scenarios. Based on these findings, the study highlights the need for situation-specific eHMI designs, standardized interaction cues that promote mutual awareness, intuitive designs accommodating older adults, and adaptive systems capable of responding to dynamic driving contexts. To further validate these findings, future research will involve experimental studies to assess the applicability and effectiveness of these eHMI messages in real-world driving environments.