Crowdsourced Assessment of 227 Text-Based eHMIs for a Crossing Scenario

Abstract

Future automated vehicles may be equipped with external human-machine interfaces (eHMIs) capable of signaling whether pedestrians can cross the road. Industry and academia have proposed a variety of eHMIs featuring a text message. An eHMI message can refer to the action to be performed by the pedestrian (egocentric message) or the automated vehicle (allocentric message). Currently, there is no consensus on the correct phrasing of the text message. We created 227 eHMIs based on text-based eHMIs observed in the literature. A crowdsourcing experiment (N = 1438) was performed with images depicting an automated vehicle equipped with an eHMI on the front bumper. The participants indicated whether they would (not) cross the road, and response times were recorded. Egocentric messages were found to be more compelling for participants to (not) cross than allocentric messages. Furthermore, Spanish-speaking participants found Spanish eHMIs more compelling than English eHMIs. Finally, it was established that some eHMI texts should be avoided, as signified by low compellingness, long responses, and high inter-subject variability.

Keywords: automated driving, human factors, crowdsourcing, ehmi

DOI: 10.54941/ahfe1002444