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Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as tra#c participants. Human tra#c partici- pants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them. Although AVs are designed to act safely, e$ec- tive interaction in mixed tra#c scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations. Prosocial behavior, such as yielding one’s right of way, will be needed to resolve unclear tra#c situations or foster tra#c %ow. However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles but for all road users? Here, we describe a new scale to measure perceived social behavior in urban tra#c scenarios. Through an online survey on N = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Tra#c Scale and assessed it psychometrically. 

Automated vehicles are expected to communicate with vulnerable road users. In two longitudinal studies, we investigated the impact of external Human-Machine Interfaces (eHMI) on pedestrian safety and behavior when interacting with automated vehicles. Utilizing LED strips for communication, these studies probed various factors, including mixed traffic scenarios, presence of eHMIs, and being from Germany or the USA. Our experimental approaches included a Virtual Reality study with 24 participants in Germany and an online study with 28 participants from the USA and Germany. Results revealed that repeated interactions with automated vehicles featuring eHMI significantly enhance pedestrian Trust, Understanding, and perceived safety, while simultaneously diminishing mental workload. Notably, the positive effects of eHMI were consistent across the two countries. US participants exhibited a tendency for higher risk-taking in crossing situations and reported lower mental workloads, underscoring the importance of considering cultural nuances in designing eHMI systems for mixed-traffic environments. 

External Human-Machine Interfaces (eHMIs) have been evaluated to facilitate interactions between Automated Vehicles (AVs) and pedestrians. Most eHMIs are, however, visual/ light-based solutions, and multi-modal eHMIs have received little attention to date. We ran an experimental video study (𝑁 = 29) to systematically under- stand the effect on pedestrian’s willingness to cross the road and user preferences of a light-based eHMI (light bar on the bumper) and two sound-based eHMIs (bell sound and droning sound), and combinations thereof. We found no objective change in pedestri- ans’ willingness to cross the road based on the nature of eHMI, although people expressed different subjective preferences for the different ways an eHMI may communicate, and sometimes even strong dislike for multi-modal eHMIs. This shows that the modality of the evaluated eHMI concepts had relatively little impact on their effectiveness. Consequently, this lays an important groundwork for accessibility considerations of future eHMIs, and points towards the insight that provisions can be made for taking user preferences into account without compromising effectiveness. 

Automated vehicles are expected to become a part of the road traffic in the near future. This upcoming change raises concerns on how human road users, e.g., cyclists or pedestrians, would interact with them to ensure safe communication on the road. Previous work focused primarily on the scenario in which a young adult with- out impairments crosses a street in front of an automated vehicle. Several road user groups, such as children, seniors, or people with special needs, in roles of pedestrians and cyclists, are not consid- ered in this scenario. On top of this, cultural differences are rarely considered. To ensure that future traffic is safe and accessible for all citizens, we aim to address inclusive communication between automated vehicles and vulnerable road users. In this workshop, we will discuss and exchange methods, tools, and scenarios applicable for inclusive communication, identify the most relevant research gaps, and connect people for future collaborations.