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.