Exploration of Possibility of Driver’s Drowsiness Prediction with High Accuracy using Both Physiological and Behavioral Measures

Abstract

The aim of this study was to explore the effectiveness of physiological and behavioral evaluation measures for predicting drivers’ subjective drowsiness. EEG, heart rate variability (RRV3), and blink frequency were physiological measures. Behavioral measures included neck vending angle (horizontal and vertical), back pressure, foot pressure, COP on sitting surface, frequency of body movement, tracking error in driving simulator task, and standard deviation of quantity of pedal operation. Drowsy states were predicted by using multinomial logistic regression model where physiological and behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. The prediction accuracy was obtained for a variety of combinations of the evaluation measures above. The maximum and minimum prediction accuracies were 0.962 and 0.876, respectively. Almost all combinations led to the prediction accuracy of more than 0.9. Moreover, it has been made clear that the proper interval used for attaining higher prediction accuracy is a 20-s interval between 20s and 40s before prediction.

Keywords: Drowsy Driving, Traffic Accident, Physiological Measures, Behavioral Measures, Prediction Accuracy, Multinomial Logistic Regression

DOI: 10.54941/ahfe100155