title: Can variations in visual behavior measures be good predictors of driver sleepiness? A real driving test study.
abstract: The primary purpose of this study was to examine the association between variations in visual behavior measures and subjective sleepiness levels across age groups over time to determine a quantitative method of measuring drivers' sleepiness levels.
  
  
  
  A total of 128 volunteer drivers in 4 age groups were asked to finish 2-, 3-, and 4-h continuous driving tasks on expressways, during which the driver's fixation, saccade, and blink measures were recorded by an eye-tracking system and the subjective sleepiness level was measured through the Stanford Sleepiness Scale. Two-way repeated measures analysis of variance was then used to examine the change in visual behavior measures across age groups over time and compare the interactive effects of these 2 factors on the dependent visual measures.
  
  
  
  Drivers' visual behavior measures and subjective sleepiness levels vary significantly over time but not across age groups. A statistically significant interaction between age group and driving duration was found in drivers' pupil diameter, deviation of search angle, saccade amplitude, blink frequency, blink duration, and closure duration. Additionally, change in a driver's subjective sleepiness level is positively or negatively associated with variation in visual behavior measures, and such relationships can be expressed in regression models for different period of driving duration.
  
  
  
  Driving duration affects drivers' sleepiness significantly, so the amount of continuous driving time should be strictly controlled. Moreover, driving sleepiness can be quantified through the change rate of drivers' visual behavior measures to alert drivers of sleepiness risk and to encourage rest periods. These results provide insight into potential strategies for reducing and preventing traffic accidents and injuries.
authors.affiliation: a School of Highway, Chang'an University , Xi'an , Shaanxi , China.
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Wang
authors.affiliation: a School of Highway, Chang'an University , Xi'an , Shaanxi , China.
authors.forename: Mengyang
authors.initials: M
authors.lastname: Xin
authors.affiliation: b Department of Transportation & Logistics Engineering , Shandong Jiaotong University , Jinan , Shandong , China.
authors.forename: Han
authors.initials: H
authors.lastname: Bai
authors.affiliation: c CCCC First Highway Consultants Co., Ltd. , Xi'an , Shaanxi , China.
authors.forename: Yangdong
authors.initials: Y
authors.lastname: Zhao
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control; D000293:Adolescent; D000328:Adult; D000368:Aged; D001334:Automobile Driving / Q000523:psychology*; D001767:Blinking / Q000502:physiology; D005221:Fatigue / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011930:Reaction Time / Q000502:physiology*; D013647:Task Performance and Analysis; D014785:Vision, Ocular / Q000502:physiology*; D055815:Young Adult
keywords: 2-way repeated measures ANOVA; Pearson correlation; Stanford Sleepiness Scale; Visual behavior measures; regression model; subjective sleepiness level