Identification of driver cognitive workload using support vector machines with driving performance, physiology and eye movement in a driving simulator

Abstract

This paper suggests experimental approaches for identifying driver’s cognitive workload using support vector machines (SVMs) with driving performance, physiological response and eye movement data. In order to construct a classification model for detecting high cognitive workload condition, driving simulation experiments were conducted. For the experiments, 30 participants (15 younger males in the 25–35 age range (M = 27.9, SD = 3.13) and 15 older males in the 60–69 (M = 63.2, SD = 1.74)) drove a simulated highway in a fixed-base driving simulator. While driving through 37 km of straight highway, participants conducted three levels of cognitive secondary tasks, i.e. an auditory delayed digit recall task, at specified segments for 10 minutes and their driving performance, physiological response and eye movement data were collected. In this study, the model performances with different combination of measures were assessed with the nested cross-validation method. As a result, it was demonstrated that the proposed SVM models were able to identify driver’s cognitive workload with high accuracy. The best performance was achieved with a combination of the standard deviation of lane position (SDLP), physiology and gaze information. The best model obtained 89.0% accuracy, sensitivity of 86.4% and specificity of 91.7%.