Interactions Between Workers and Automated Guided Vehicles: Impact of eHMI Design

Abstract

As manufacturing facilities integrate Autonomated Guided Vehicles (AGVs) to improve workflow efficiency, enhancing human-AGV interaction remains critical for workplace safety. While prior research has focused on vehicle and pedestrian motion prediction, effective interaction requires two-way communication, where the AGVs clearly convey intentions to the workers to enhance safety. This study investigates the impact of external Human-Machine Interface (eHMI) integrated with a predictive model on AGV-worker interaction. We designed LED light strip patterns to convey intentions and selected optimal designs through an online survey. We deployed three types of AGVs in a virtual reality (VR) environment: Control, Prediction, and eHMI + Prediction. Participants completed tasks while interacting with AGVs, followed by subjective assessments of trust, perceived safety, perceived performance, and understandability. A one-way repeated measures ANOVA revealed a significant improvement in perceived safety from eHMI + Prediction condition compared to the Control condition, suggesting that explicit communication via eHMI enhances perceived safety in AGV interactions.