Yulan Liang1, John D Lee, Lora Yekhshatyan. 1. Liberty Mutual Research Institute for Safety, 71 Frankland Rd., Hopkinton, MA 01748, USA. yulan.liang@libertymutual.com
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.
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.
Authors: Bruce G Simons-Morton; Feng Guo; Sheila G Klauer; Johnathon P Ehsani; Anuj K Pradhan Journal: J Adolesc Health Date: 2014-05 Impact factor: 5.012
Authors: Carrie Huisingh; Emily B Levitan; Marguerite R Irvin; Paul MacLennan; Virginia Wadley; Cynthia Owsley Journal: Invest Ophthalmol Vis Sci Date: 2017-06-01 Impact factor: 4.799