Long-Term Evolution of Driver Visual Attention during Automated Driving in Real-Traffic: Investigating the Influence of Mental Model and Dynamic Learned Trust

A calibrated trust level is essential for the safe use of automated systems. In automated driving, overtrust can reduce the driver’s monitoring behavior and delay takeover times, which poses significant safety risks. This motivates the need for continuous, objective trust assessment for real-time system adaptations. Prior research identified eye-tracking as a promising approach. Therefore, this study examines the longitudinal relationship between dynamic learned trust and visual attention. Given that mental models influence both trust and visual attention, their role in this process is also explored over time. In a longitudinal study, twenty-three participants repeatedly operated an automated vehicle in real traffic while their visual attention was recorded via the vehicle’s built-in driver monitoring camera. Findings suggest an interrelation between dynamic learned trust and mental model formation, with mental models mediating the effect of dynamic learned trust on visual attention. This work contributes to advancing trust measurement during automated driving.