title: How dangerous is looking away from the road? Algorithms predict crash risk from glance patterns in naturalistic driving.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.
authors: Liberty Mutual Research Institute for Safety, 71 Frankland Rd., Hopkinton, MA 01748, USA. yulan.liang@libertymutual.com
authors: Yulan
authors: Y
authors: Liang
authors: John D
authors: JD
authors: Lee
authors: Lora
authors: L
authors: Yekhshatyan
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000465:Algorithms; D001288:Attention; D001334:Automobile Driving; D005133:Eye Movements; D006801:Humans; D016017:Odds Ratio; D018570:Risk Assessment