TL;DR
This research used a VR approach (Meta Quest 3 Headset) to examine the efficiency of different types of symbols displayed by the eHMI of an AV to communicate its intentions to a VRU, and evaluated the effectiveness of the selected symbols.
Effective visual communication is key to improving road safety whenever vulnerable road users (VRUs), e.g., pedestrians and cyclists, interact in daily traffic. The importance of such communication continues to grow as higher levels of automation are achieved and fully automated vehicles (AVs) become increasingly preva...
The advent of automated vehicles (AVs) prompted AutomotiveUI researchers to investigate how AVs can safely share the road with Human Road Users (HRUs). Researchers developed interfaces to facilitate AV-HRU interaction, e.g., on-vehicle eHMIs. However, these were mostly evaluated through online surveys with stationary p...
AutomotiveUI research has increasingly explored interfaces for automated vehicles (AVs) to explicitly communicate their yielding intentions and resolve space-sharing conflicts with surrounding road users. However, the focus was primarily on walking pedestrians, overlooking runners, who have distinct requirements. In ou...
Results suggest that the eHMI supported earlier crossing initiation and increased perceived safety and trust, and indicate that the signal is learnable in a controlled dyadic setting and motivate future VR studies of multi-pedestrian interaction, social influence, and traffic complexity.
With the advancement of automated vehicles (AVs), ensuring safe interaction between pedestrians and AVs become a critical challenge. External human–machine interfaces (eHMIs) have emerged as a solution for communicating vehicle intentions to pedestrians. In recent years, virtual reality (VR) has been increasingly used...
Understanding how pedestrians perceive roadway conditions and vehicle-state communication is important for examining pedestrian interactions with automated vehicles (AVs). This study used an immersive virtual reality experiment to examine three trial-level perception outcomes—perceived crash likelihood, perceived envir...
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