Evaluating Visual eHMI Formats for Pedestrian Crossing Confirmation in Electric Autonomous Vehicles: A Comprehension-Time Study with Simulation and Preliminary Field Validation

Abstract

Effective communication between electric autonomous vehicles (EAVs) and pedestrians is critical for safety, yet the absence of a driver removes traditional cues such as eye contact or gestures. While external human–machine interfaces (eHMIs) have been proposed, few studies have systematically compared visual formats across demographic groups and validated findings in both simulation and real-world settings. This study addresses this gap by evaluating various eHMI designs using combinations of textual cues (“WALK” and “CROSS”), symbolic indicators (pedestrian and arrow icons), and display colors (white and green). Twenty simulated scenarios were developed in the CARLA simulator, where 100 participants observed an EAV equipped with eHMIs and responded by pressing a button upon understanding the vehicle’s intention. The results showed that green displays facilitated faster comprehension than white, “WALK” was understood more quickly than “CROSS,” and pedestrian symbols outperformed arrows in clarity. The fastest overall comprehension occurred with the green pedestrian symbol paired with the word “WALK.” A subsequent field experiment using a Level 3 autonomous vehicle with a smaller participant group and differing speed/distance conditions provided preliminary support for the consistency of these observed trends. The novelty of this work lies in combining simulation with preliminary field validation, using comprehension time as the primary metric, and comparing results across four age groups to derive evidence-based eHMI design recommendations. These findings offer practical guidance for enhancing pedestrian safety, comprehension, and trust in EAV–pedestrian interactions.