title: Driver drowsiness detection based on non-intrusive metrics considering individual specifics.
abstract: OBJECTIVE
  Drowsy driving is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some drowsiness-related crashes could be prevented. The presentation of timely warnings, however, depends on reliable detection. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. The present study sought to develop a drowsiness detection model that accommodates the varying individual effects of drowsiness on driving performance.
  
  
  METHODS
  Nineteen driving behavior variables and four eye feature variables were measured as participants drove a fixed road course in a high fidelity motion-based driving simulator after having worked an 8-h night shift. During the test, participants were asked to report their drowsiness level using the Karolinska Sleepiness Scale at the midpoint of each of the six rounds through the road course. A multilevel ordered logit (MOL) model, an ordered logit model, and an artificial neural network model were used to determine drowsiness.
  
  
  RESULTS
  The MOL had the highest drowsiness detection accuracy, which shows that consideration of individual differences improves the models' ability to detect drowsiness. According to the results, percentage of eyelid closure, average pupil diameter, standard deviation of lateral position and steering wheel reversals was the most important of the 23 variables.
  
  
  CONCLUSIONS
  The consideration of individual differences on a drowsiness detection model would increase the accuracy of the model's detection accuracy.
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China; Road and Traffic Key Laboratory, Ministry of Education, Shanghai 201804, China.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Chengdu 610031, China. Electronic address: xuchuan@home.swjtu.edu.cn.
authors.forename: Chuan
authors.initials: C
authors.lastname: Xu
mesh_terms: D000328:Adult; D001334:Automobile Driving / Q000523:psychology*; D003198:Computer Simulation; D006801:Humans; D007206:Individuality; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D012449:Safety; D012894:Sleep Stages
keywords: Driving behavior; Driving simulator; Drowsiness detection; Eye feature; Multilevel ordered logit model; Non-intrusive