Towards Social Autonomous Vehicles: Understanding Pedestrian-Driver Interactions

Cooperative interaction in traffic is vital for resolving a wide range of ambiguities arising from road users' actions. Autonomous vehicles are no exception and require the ability to understand the intention of road users and communicate with them in order to ensure their safety and maintain traffic flow. In this paper, we address the problem of traffic interaction by analyzing a large sample of pedestrians communicating with drivers. We highlight the ways pedestrians communicate and use a logistic regression model to identify what factors influence communication patterns of pedestrians and how. We also discuss practical challenges regarding the recognizing and understanding of pedestrians' intention and how our theoretical findings can help to solve them. Our analysis suggests that pedestrians predominantly rely on implicit communication cues such as stepping onto the road to transmit their intention of crossing. In addition, we found that the presence of traffic signal, street width, and pedestrian group size can influence the frequency and type of pedestrian communication, while factors such as pedestrians' age and gender did not show any significant impact.