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Automated vehicles (AVs) are expected to encounter various am- biguous space-sharing conflicts in urban traffic. Bottleneck sce- narios, where one of the parts needs to resolve the conflict by yielding priority to the other, could be utilized as a representative ambiguous scenario to understand human behavior in experimental settings. We conducted a controlled field experiment with a Wizard of Oz automated car in a bottleneck scenario. 24 participants at- tended the study by driving their own cars. They made yielding, or priority-taking decisions based on implicit and explicit locomotion cues on AV realized with an external display. Results indicate that acceleration and deceleration cues affected participants’ driving choices and their perception regarding the social behavior of AV, which further serve as ecological validation of related simulation studies. 

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. 

As a third party to both automated and non-automated vehicles, pedestrians are among the most vulnerable participants in traffic. Currently, there is no way for them to communicate their intentions to an automated vehicle (AV). In this work, we explore the interactions between pedestrians and AVs at unmarked crossings. We propose a virtual reality testbed, in which we conducted a pilot study to compare three conditions: crossing a street before a car that (1) does not give information, (2) displays its locomotion, or (3) displays its locomotion and reacts to pedestrians' gestures. Our results show that gestures introduce a new point of failure, which can increase pedestrians' insecurity. However, communicating the vehicle's locomotion supports pedestrians, helping them to make safer decisions.