AI Eyes on the Road: Cross-Cultural Perspectives on Traffic Surveillance

Abstract

AI-powered road surveillance systems are increasingly proposed to monitor infractions such as speeding, phone use, and jaywalking. While these systems promise to enhance safety by discouraging dangerous behaviors, they also raise concerns about privacy, fairness, and potential misuse of personal data. Yet empirical research on how people perceive AI-enhanced monitoring of public spaces remains limited. We conducted an online survey ($N=720$) using a 3$\times$3 factorial design to examine perceptions of three road surveillance modes -- conventional, AI-enhanced, and AI-enhanced with public shaming -- across China, Europe, and the United States. We measured perceived capability, risk, transparency, and acceptance. Results show that conventional surveillance was most preferred, while public shaming was least preferred across all regions. Chinese respondents, however, expressed significantly higher acceptance of AI-enhanced modes than Europeans or Americans. Our findings highlight the need to account for context, culture, and social norms when considering AI-enhanced monitoring, as these shape trust, comfort, and overall acceptance.