Deviant Behavior of Pedestrians: A Risk Gamble or Just Against Automated Vehicles? How About Social Control? - Şahin, Hatice; Hemesath, Sebastian; Boll, Susanne (Frontiers in Robotics and AI, 2022)
Recent evidence suggests the assumed conflict avoidant programming of autonomous vehicles will incentivize pedestrians to bully them. This frequent argument, however, disregards the embedded nature of social interaction. Rule violations are socially sanctioned by different forms of social control, which could moderate the rational incentive to abuse risk avoidant vehicles. Drawing on a gamified virtual-reality experiment (n=36) of urban traffic scenarios, we tested how vehicle type, different forms of social control, as well as the monetary benefit of rule violations, affect pedestrians' decision to jaywalk. In a second step, we also tested whether differences for those effects exist when we control for the risk of crash in conventional vehicles. We find that individuals do indeed jaywalk more frequently when faced with an automated vehicle (AV), and this effect is largely dependent on the associated risk, and not their automated nature. We further show that social control, especially in the form of formal traffic rules and norm-enforcement, can reduce jaywalking behavior for any type of vehicle. Our study sheds light on the interaction dynamics between humans and AVs, how this is influenced by different forms of social control, and contributes to the small gamification literature in human-computer interaction.