Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control
OBJECTIVE In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. BACKGROUND Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. METHOD The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction--glance duration, glance history, and glance location--on how well the algorithms predicted crash risk. RESULTS Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history.Augmenting glance duration with other elements of glance behavior--1.5th power of duration and duration weighted by glance location--produced similar prediction performance as glance duration alone. CONCLUSIONS The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. APPLICATION The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.