Eyelid-based driver state classification under simulated and real driving conditions

On account of the increase in vehicle accidents due to driver drowsiness over the last years, the development of reliable drowsiness assistant systems by a reference drowsiness measure is highlighted. Since eyelid features have shown acceptable correlation with driver vigilance in driving simulators, this study focuses on 18 blink features of 43 subjects collected by electrooculography under both simulated and real driving conditions during 67 hours of driving. We have assessed the driver state by artificial neural network, support vector machine and k-nearest neighbors classifiers for both binary and multi-class cases. There, binary classifiers are trained both subject-independent and subject-dependent to address the generalization aspects of the results for unseen data. The drawback of driving simulators in comparison to real driving is also discussed and to this end we have performed a data reduction approach as a remedy. For the binary driver state prediction (awake vs. drowsy) by eyelid features, we have attained an average detection rate of 82% by each classifier separately. For 3-class classification (awake vs. medium vs. drowsy), however, the result was only 66%, possibly due to inaccurate self-rated vigilance states.