Objective:In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians’ crossing intentions.Background:Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians’ interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI.Methods:Twenty-eight participants experienced an urban road environment from a pedestrian’s perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and “feel-safe” percentages were calculated.Results:For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs.Conclusion:An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous.Application:This research supports the development of automated vehicles that communicate with other road users.

Partially and fully automated vehicles (AVs) are being developed and tested in different countries. These vehicles are being designed to reduce and ultimately eliminate the role of human drivers in the future. However, other road users, such as pedestrians and cyclists will still be present and would need to interact with these automated vehicles. Therefore, external communication interfaces could be added to the vehicle to communicate with pedestrians and other non-automated road users. The first aim of this study is to investigate how the physical appearance of the AV and a mounted external human-machine interface (eHMI) affect pedestrians' crossing intention. The second aim is to assess the perceived realism of Virtual reality based on 360°videos for pedestrian crossing behavior for research purposes. The speed, time gap, and an eHMIs were included in the study as independent factors. Fifty-five individuals participated in our experiment. Their crossing intentions were recorded, as well as their trust in automation and perceived behavioral control. A mixed binomial logistic regression model was applied on the data for analysis. The results show that the presence of a zebra crossing and larger gap size between the pedestrian and the vehicle increase the pedestrian's intention to cross. In contrast to our expectations, participants intended to cross less often when the speed of the vehicle was lower. Despite that the vehicle type affected the perceived risk of the participants, no significant difference was found in crossing intention. Participants who recognized the vehicle as an AV had, overall, lower intentions to cross. A strong positive relationship was found between crossing intentions and perceived behavioral control. A difference in trust was found between participants who recognized the vehicle as automated, but this did not lead to a difference in crossing intentions. We assessed the research methodology using the presence questionnaire, the simulation sickness survey, and by comparing the results with previous literature. The method scored highly on the presence questionnaire and only 2 out of 55 participants stopped prematurely. Thus, the research methodology is useful for crossing behavior experiments.

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Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions between cyclists and AVs exists. This study aims to determine the main factors influencing cyclists’ crossing intentions when interacting with an automated vehicle as compared to a conventional vehicle (CV) using a 360° video-based virtual reality (VR) method. The considered factors in this study included vehicle type, gap size between cyclist and vehicle, vehicle speed, and right of way. Each factor had two levels. In addition, cyclist’s self-reported behavior and trust in automated vehicles were also measured. Forty-seven participants experienced 16 different crossing scenarios in a repeated measures study using VR. These scenarios are the result of combinations of the studied factors at different levels. In total, the experiment lasted 60 min. The results show that the gap size and the right of way were the primary factors affecting the crossing intentions of the individuals. The vehicle type and vehicle speed did not have a significant effect on the crossing intentions. Finally, the 360° video-based VR method scored relatively high as a research method and comparable with the results of a previous study investigating pedestrians’ crossing intentions confirming its suitability as a research methodology to study cyclists’ crossing intentions.

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