Abstract
In this work a methodology for detecting drivers' stress and fatigue and predicting driving performance is presented. The proposed methodology exploits a set of features obtained from three different sources: (i) physiological signals from the driver (ECG, EDA, and respiration), (ii) video recordings from the driver's face, and (iii) environmental information. The extracted features are examined in terms of their contribution to the classification of the states under investigation. The most significant indicators are selected and used for classification using various classifiers. The approach has been validated on an annotated dataset collected during real-world driving. The results obtained from the combination of physiological signals, video features, and driving environment parameters indicate high classification accuracy (88% using three fatigue scales and 86% using two stress scales). A series of experiments on a simulation environment confirms the association of fatigue states with driving performance.
Highlights
Real-life car driving requires accurate and fast decisions by the driver, given only incomplete information in real time
Stress could be defined as the awareness of not being able to cope with the demands of one’s environment, when this realization is of concern to the person and associated with a negative emotional response, while fatigue as the temporary inability, or decrease in ability, or strong disinclination, to respond to a situation, because of previous overactivity, either mental, emotional, or physical [1]
We examine the performance of four different classifiers on the driver state recognition accuracy
Summary
Real-life car driving requires accurate and fast decisions by the driver, given only incomplete information in real time. The majority of relative works is based on in-lab experiments, mainly focusing on face monitoring and blink detection to calculate eye activation [2], while the vehicular experiments serve for indirect fatigue recognition through its impact on driving issues (speed maintenance, steering control). These methods, are suitable for the recognition of rather late stages of the fatigue (drowsiness) when the effects on driver’s face are quite noticeable and performance change has already become critical. This has been confirmed by numerous studies, which followed similar approaches for driver fatigue estimation, making use of biosignals obtained from the driver [6,7,8,9]
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