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 drivers' monitoring behavior and delay takeover times, which poses significant safety risks.This motivates the need for continuous, objective trust assessment to enable real-time system adaptations.Prior research has identified eye-tracking as a promising approach.Therefore, this study examines the longitudinal evolution of dynamic learned trust as well as its relationship with visual attention.Given that mental models influence both trust and visual attention, their role in this process is also examined 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.The study indicates that the mental model is a key factor within the field of trust evolution and visual attention.This work contributes to advancing trust measurement in automated driving.