Table of Contents
International Journal of Vehicular Technology
Volume 2011 (2011), Article ID 617210, 14 pages
Research Article

Towards Driver's State Recognition on Real Driving Conditions

1Department of Computer Science, University of Ioannina, 451 10 Ioannina, Greece
2Department of Economics, University of Ioannina, 451 10 Ioannina, Greece
3Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece

Received 15 November 2010; Revised 25 March 2011; Accepted 28 April 2011

Academic Editor: Panayotis Mathiopouloss

Copyright © 2011 George Rigas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.