title: Development of a method to predict crash risk using trend analysis of driver behavior changes over time.
abstract: OBJECTIVE
  This study aimed at identifying and predicting in advance the point in time with a high risk of a virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure.
  
  
  METHODS
  Behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with a high risk of a virtual accident (in a real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of a virtual accident).
  
  
  RESULTS
  On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with a high risk of a virtual accident before the point in time of a virtual accident.
  
  
  CONCLUSIONS
  The proposed method is a promising technique for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving and for warning drivers of such a drowsy and risky state.
authors.affiliation: a Department of Intelligent Mechanical Systems , Graduate School of Natural Science and Technology, Okayama University , Okayama , Japan.
authors.forename: Atsuo
authors.initials: A
authors.lastname: Murata
authors.affiliation: a Department of Intelligent Mechanical Systems , Graduate School of Natural Science and Technology, Okayama University , Okayama , Japan.
authors.forename: Kohei
authors.initials: K
authors.lastname: Fukuda
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000523:psychology* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D018570:Risk Assessment / Q000379:methods; D012894:Sleep Stages; D055815:Young Adult
keywords: behavioral measure; drowsiness; identification of point in time with a high risk of crash; point in time of a virtual accident; trend analysis