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

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.

Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrians' crossing behaviour

“…Authors found an effect of eHMI when the approaching AV was in the 'Gentle Brake' and 'Early Brake' conditions, but no effect of eHMI was shown in the 'Aggressive' braking and 'Constant Speed' conditions. Conversely, in a CAVE-based pedestrian simulator study, Kaleefathullah et al (2020) demonstrated that after a series of exposures to a functioning eHMI conveying a yielding message, approximately 35% of pedestrians walked onto the road when the eHMI was presented but the approaching vehicle was not decelerating. This suggests that pedestrians may become over-reliant on information conveyed through eHMI, to the point where they start to ignore the vehicle movement behaviour.…”

Investigating the Interplay between eHMI and dHMI for Automated Buses: How Do Contradictory Signals Influence a Pedestrian's Willingness to Cross?

“…Focusing on the design of eHMIs for different vehicle types, a study by Lau et al [12] investigated the application of eHMI communication strategies on an automated bus and revealed eHMIs as contribution to a well-working interaction in terms of pedestrians' perceived safety and perceived information quality [12]. However, current research also pointed out negative effects of eHMIs, e.g., overtrust, which can affect the overall safety in urban traffic [13][14][15] and, therefore, need to be investigated for larger vehicle types, e.g., buses, particularly as negative effects here in a pedestrian interaction might lead to serious accidents or even fatalities.…”

Toward a Holistic Communication Approach to an Automated Vehicle's Communication With Pedestrians: Combining Vehicle Kinematics With External Human-Machine Interfaces for Differently Sized Automated Vehicles

“…If the vehicle kinematics contradicted the message of the eHMI, pedestrians primarily based their willingness to cross on the vehicle kinematics rather than the eHMI communication (Dey et al, 2020a). In contrast, a study by Kaleefathullah et al (2020) revealed that when the eHMI was on, but the AV did not indicate a braking process, pedestrians still crossed the street. This result demonstrated possible negative effects, i.e., over-trust, which have been also found by other studies (Kitazaki and Daimon, 2018;Holländer et al, 2019;Lee et al, 2021).…”