Towards Driver's State Recognition on Real Driving Conditions

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.