Tibor Petzoldt

In recent years, there has been a debate on whether automated vehicles (AVs) should be equipped with novel external human–machine interfaces (eHMIs). Many studies have demonstrated how eHMIs influence pedestrians’ attitudes (e.g., trust in AVs) and behavior when they activate (e.g., encourage crossing by lighting up). However, very little attention has been paid to their effects when they do not activate (e.g., discourage crossing by not lighting up). We conducted a video-based laboratory study with a mixed design to explore the potential of two different eHMI messages to facilitate pedestrian-AV interactions by means of activating or not activating. Our participants watched videos of an approaching AV equipped with either a state eHMI (“I am braking”) or intent eHMI (“I intend to yield to you”) from the perspective of a pedestrian about to cross the road. They indicated when they would initiate crossing and repeatedly rated their trust in the AV. Our results show that the activation of both the state and intent eHMI was effective in communicating the AV’s intent to yield and both eHMIs drew attention to a failure to yield when they did not activate. However, the two eHMIs differed in their potential to mislead pedestrians, as decelerations accompanied by the activation of the state eHMI were repeatedly misinterpreted as an intention to yield. Despite this, user experience ratings did not differ between the eHMIs. Following a failure to yield, trust declined sharply. In subsequent trials, crossing behavior recovered quickly, while trust took longer to recover.

The proportion of highly automated vehicles in traffic (i.e., the prevalence of AVs) is likely to increase over time. The aim of this study was to investigate whether the prevalence of AVs may influence how pedestrians interact with AVs and with conventional, human-driven vehicles (CVs). A video-based laboratory study was conducted using a two-group mixed design. Participants took the perspective of pedestrians about to cross the road in a situation where AVs (with eHMIs) and CVs were approaching their position. The prevalence of AVs was manipulated between groups (low/high). The participants indicated the moment they decided to cross in front of the vehicles. Our results show that AV prevalence did indeed significantly influence when participants decided to cross. Overall, participants decided to cross earlier in front of the more prevalent vehicle type. Therefore, taking into account the given prevalence of AVs could significantly benefit AVs in predicting pedestrian behavior.