@article{ref1,
title="How dangerous is looking away from the road? Algorithms predict crash risk from glance patterns in naturalistic driving",
journal="Human factors",
year="2012",
author="Liang, Yulan and Lee, John D. and Yekhshatyan, Lora",
volume="54",
number="6",
pages="1104-1116",
abstract="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.   Keywords: Driver distraction;<p /> <p>Language: en</p>",
language="en",
issn="0018-7208",
doi="",
url="http://dx.doi.org/"
}