Longitudinal bicyclist, driver, and pedestrian perceptions of autonomous vehicle communication strategies

Abstract

We sought to better understand how autonomous vehicle (AV) communication strategies impact human road users’ perceptions. More specifically, we explored the impact of different external human-machine interface (eHMI) designs on task load, comfort, trust, and acceptance. To accomplish this, we created virtual reality (VR) scenarios where human participants interacted with AVs in biking, driving, and pedestrian simulators. Participants were brought back after initial testing to explore acclimation and learning effects. eHMI designs included a text-based grille eHMI, a text-based roof eHMI, a text-based driver-side door eHMI, anon-textual LED windshield strip eHMI, and a non-textual side mirror arrow eHMI. The presence of an eHMI was the strongest positive predictor of comfort, trust, and acceptance outcomes in the statistical models when controlling for all other variables. There was a clear divide between text-based eHMIs and non-text eHMIs with text-based eHMIs experiencing better perception scores. The LED Windshield experienced the worst perception scores. There were perception acclimation effects detected which were most notable for task load (which decreased over time) and comfort (which increased over time). Perception scores for the different eHMI designs tended to cluster over time. However, the acclimation effects had less of an impact than the presence of an eHMI. Perception outcomes had weaker relationships with participant characteristics than with AV characteristics. Results suggest that eHMI presence and design, AV behavior, and acclimation are most impactful in terms of perceptions.

Keywords

• Vehicle automation
• Communication
• Human-automation interaction
• Technology acceptance
• Virtual reality