No Risk No Trust: Investigating Perceived Risk in Highly Automated Driving

Abstract

When evaluating drivers' trust in automated systems, perceived risk is an inevitable, yet underestimated component, especially during initial interaction. We designed two experimental studies focusing on how people assess risk in different driving environments and how introductory information about automation reliability influences trust and risk perception. First, we designed nine driving scenarios to determine which factors influence Perceived Situational Risk (PSR) and Perceived Relational Risk (PRR). Results showed that participants identified levels of risk based on traffic type and vehicles' abnormal behaviors. We then evaluated how introductory information and situational risk influence trust and PRR. Results showed that participants reported the highest level of trust, perceived automation reliability, and the lowest level of PRR when presented with information about a highly reliable system, and when driving in a low-risk situation. These results highlight the importance of incorporating perceived risk and introductory information to support the trust calibration in automated vehicles.