It matters who is crossing: Users' trust when the automated vehicle stops for children and blind pedestrians

Abstract

In urban environments, automated driving offers the opportunity to improve traffic flow, heighten user comfort and also increase traffic safety for vulnerable road users. To realize these benefits, users need to trust and use automated driving functions when they interact with vulnerable road users such as crossing pedestrians. So far, previous studies have mainly focused on adult pedestrians and their interaction with automated vehicles. However, other pedestrians are even more vulnerable in crossing scenarios such as children or visually impaired people. In addition, some crossing events may be more critical than others depending on the crossing infrastructure or the visibility of the pedestrians. To examine the influence of these situational characteristics on users' perceived criticality of the situation and trust in the automated driving system, we conducted an online experiment. In short videos, the participants (N = 69) experienced automated rides from the users' perspective, where the vehicle stopped to let pedestrians cross. In a within-subjects design, we manipulated whether the crossing pedestrian was an adult, a child or a blind person and how critical the crossing event was (critical vs. non-critical). In the non-critical event, the participants perceived the crossing of a blind pedestrian as more critical than that of a child or an adult. In the critical event, the child was perceived as more critical than the adult. Participants also trusted the automated driving system less under a crossing event with a blind pedestrian compared to with an adult. More research is needed to determine if there are also implications for the use of automated driving systems in such situations and what additional information users might need from the automated driving system. In summary, this study shows the importance of including vulnerable pedestrians such as children and the visually impaired in the research and development of automated driving in cities.