Real-World Validation of Dynamic Text-based eHMIs for Pedestrian Interaction with Autonomous Shuttles

Abstract

External human-machine interfaces (eHMIs) are designed to explicitly communicate autonomous vehicles' (AVs) intentions, thereby enhancing safety in complex traffic interactions. This study evaluated the effectiveness of a dynamic text-based eHMI on an autonomous shuttle operating in a naturalistic setting at unsignalized crosswalks in South Korea. Through field observations, we identified scenarios in which traditional yielding or stopping messages were insufficient, especially under conditions of continuous pedestrian flow causing vehicle delays. Using this scenario, explicitly communicating the vehicle's imminent departure, was tested against a static control condition. Post-interaction surveys of 60 pedestrians revealed that the dynamic eHMI significantly improved message visibility, comprehension, and perceived system support. Additionally, pedestrians exposed to the dynamic eHMI prioritized explicit textual cues over implicit vehicle cues when deciding to cross, leading to increased trust in AV technology. These results highlight the practical value of context-sensitive, explicit eHMIs for enhancing real-world AV-pedestrian interactions.