Are eHMIs always helpful? Investigating how eHMIs interfere with pedestrian behavior on multi-lane streets: An eye-tracking virtual reality experiment

2026-01-06

arXiv.org

AI Summary

This study investigates how external human–machine interfaces (eHMIs) on autonomous vehicles influence pedestrian crossing decisions in unsignalized multilane environments, where the underlying mechanisms remain poorly understood. Using a virtual reality setup integrated with eye-tracking, the research systematically compares egocentric and allocentric eHMI designs in terms of their effects on pedestrians’ crossing behavior, visual attention patterns, and cognitive load. The findings reveal, for the first time, that eHMIs in complex multilane scenarios significantly increase pedestrians’ cognitive load and distraction risk: allocentric eHMIs tend to induce greater attentional dispersion, whereas egocentric eHMIs lead to higher misjudgment rates when signaling is asymmetric across non-interacting lanes. These results provide critical empirical evidence and actionable insights for the safe and effective design of eHMIs in real-world traffic contexts.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Appropriate communication is crucial for efficient and safe interactions between pedestrians and autonomous vehicles (AVs). External human-machine interfaces (eHMIs) on AVs, which can be categorized as allocentric or egocentric, are considered a promising solution. While the effectiveness of eHMIs has been extensively studied, in complex environments, such as unsignalized multi-lane streets, their potential to interfere with pedestrian crossing behavior remains underexplored. Hence, a virtual reality-based experiment was conducted to examine how different types of eHMIs displayed on AVs affect the crossing behavior of pedestrians in multi-lane streets environments, with a focus on the gaze patterns of pedestrians during crossing. The results revealed that the presence of eHMIs significantly influenced the cognitive load on pedestrians and increased the possibility of distraction, even misleading pedestrians in cases involving multiple AVs on multi-lane streets. Notably, allocentric eHMIs induced higher cognitive loads and greater distraction in pedestrians than egocentric eHMIs. This was primarily evidenced by longer gaze time and higher proportions of attention for the eHMI on the interacting vehicle, as well as a broader distribution of gaze toward vehicles in the non-interacting lane. However, misleading behavior was mainly triggered by eHMI signals from yielding vehicles in the non-interacting lane. Under such asymmetric signal configurations, egocentric eHMIs resulted in a higher misjudgment rate than allocentric eHMIs. These findings highlight the importance of enhancing eHMI designs to balance the clarity and consistency of the displayed information across different perspectives, especially in complex multi-lane traffic scenarios. This study provides valuable insights regarding the application and standardization of future eHMI systems for AVs.

Problem

• eHMI
• pedestrian behavior
• autonomous vehicles
• multi-lane streets
• cognitive load

Innovation

• eHMI
• eye-tracking
• virtual reality
• pedestrian behavior
• cognitive load