External Human–Machine Interfaces Can Be Misleading: An Examination of Trust Development and Misuse in a CAVE-Based Pedestrian Simulation Environment

Anees Ahamed Kaleefathullah, Natasha Merat, Yee Mun Lee, Yke Bauke Eisma, Ruth Madigan, Jorge Garcia, Joost de Winter

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
13 Downloads (Pure)

Abstract

Objective: To investigate pedestrians’ misuse of an automated vehicle (AV) equipped with an external human–machine interface (eHMI). Misuse occurs when a pedestrian enters the road because of uncritically following the eHMI’s message. Background: Human factors research indicates that automation misuse is a concern. However, there is no consensus regarding misuse of eHMIs. Methods: Sixty participants each experienced 50 crossing trials in a Cave Automatic Virtual Environment (CAVE) simulator. The three independent variables were as follows: (1) behavior of the approaching AV (within-subject: yielding at 33 or 43 m distance, no yielding), (2) eHMI presence (within-subject: eHMI on upon yielding, off), and (3) eHMI onset timing (between-subjects: eHMI turned on 1 s before or 1 s after the vehicle started to decelerate). Two failure trials were included where the eHMI turned on, yet the AV did not yield. Dependent measures were the moment of entering the road and perceived risk, comprehension, and trust. Results: Trust was higher with eHMI than without, and the −1 Group crossed earlier than the +1 Group. In the failure trials, perceived risk increased to high levels, whereas trust and comprehension decreased. Thirty-five percent of the participants in the −1 and +1 Groups walked onto the road when the eHMI failed for the first time, but there were no significant differences between the two groups. Conclusion: eHMIs that provide anticipatory information stimulate early crossing. eHMIs may cause people to over-rely on the eHMI and under-rely on the vehicle-intrinsic cues. Application: eHMI have adverse consequences, and education of eHMI capability is required.

Original languageEnglish
Number of pages16
JournalHuman Factors
DOIs
Publication statusPublished - 2020

Keywords

  • automated driving
  • external human–machine interfaces
  • misuse
  • pedestrians
  • risk perception
  • trust

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