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Sensors 2014, 14(9), 17832-17847; doi:10.3390/s140917832

Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss

1
Khaje Nasir Toosi University of Technology, Faculty of Mechanical Engineering, Tehran 19991-43344, Iran
2
Graz University of Technology, Automotive Engineering Faculty, Inffeldgasse 11, Graz 8010, Austria
*
Author to whom correspondence should be addressed.
Received: 28 July 2014 / Revised: 27 August 2014 / Accepted: 1 September 2014 / Published: 25 September 2014
(This article belongs to the Section Physical Sensors)
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Abstract

This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs. View Full-Text
Keywords: sensors data fusion; driver drowsiness detection; driver assistance system; eye tracking; vehicle lateral position; steering angle; vehicle safety; driving simulator sensors data fusion; driver drowsiness detection; driver assistance system; eye tracking; vehicle lateral position; steering angle; vehicle safety; driving simulator
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Samiee, S.; Azadi, S.; Kazemi, R.; Nahvi, A.; Eichberger, A. Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss. Sensors 2014, 14, 17832-17847.

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