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.
- Record URL:
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Authors:
- Rigas, George
- Goletsis, Yorgos
- Bougia, Panagiota
- Fotiadis, Dimitrios I
- Publication Date: 2011
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 14 p
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Serial:
- International Journal of Vehicular Technology
- Volume: 2011
- Publisher: Hindawi Publishing Corporation
- Serial URL: http://www.hindawi.com/journals/ijvt/
Subject/Index Terms
- TRT Terms: Bayes' theorem; Data collection; Driving; Eye movements; Fatigue (Physiological condition); High risk drivers; Stress (Physiology)
- Candidate Terms: Face recognition
- Subject Areas: Highways; Safety and Human Factors; I70: Traffic and Transport; I83: Accidents and the Human Factor;
Filing Info
- Accession Number: 01352775
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 28 2011 9:21AM