Statistical Relationship among Driver's Drowsiness, Eye State and Head Posture (Journal of Informatics and Mathematical Sciences, 2016)
Many serious accidents in road traffic are resulted from driver's drowsiness, leading to special efforts in improving traffic safety by searching for optimal models to accurately detect and alert driver's drowsiness. Thus, numerous scholars worldwide have paid special interest in proposing a great number of detection methods, among which visual feature-based approaches, such as eye state, head movement, yawning, facial expressions, etc., have been most preferred as they are non-intrusive and effectively detect drowsiness. However, the current literature fails to show the statistical relationships among the driver's drowsiness, eye state and head posture. Thus, the statistical linear regression and binary logistic regression models found in this paper fill the existing gap; especially, the eye state should be determined by simultaneously monitoring the eye states of both eyes and it has greater impact on the detection ability than that of head posture. More importantly, the interactive combination of eye state and head posture provides better detection ability. Our proposed logistic regression model can correctly detect 99.1% of the total investigated observations in a practical experiment study.
Keywords: Driver's drowsiness; drowsiness detection; eye state; head posture; statistical relationships; Linear regression; Logistic regression