Toward Driver State Models that Explain Interindividual Variability of Distraction for Adaptive Automation

Although there exist many models of distracted driving, identifying a distracted driver is still challenging as distraction might appear differently for different drivers but also within an individual driver in different situations. Here we present a driver state model that focusses on safety-relevant driver-distraction by conceptualizing driving control as influenced by both environmental factors and individual preferences. Also, the model differentiates compensatory control from exploratory control movements to better diagnose driving distraction. We then test several predictions that are derived from this model in a driving-simulator study. In this study participants drove the same road with or without a secondary task while their eye movements and driving performance was recorded. Our results are consistent with previous findings that overall steering control actions increase in the distraction condition but also that exploratory steering movements are apparently more sensitive indicators for distraction than compensatory control actions.