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Sensors 2014, 14(1), 1106-1131; doi:10.3390/s140101106
Article

Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

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Received: 30 October 2013; in revised form: 17 December 2013 / Accepted: 18 December 2013 / Published: 9 January 2014
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2013)
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Abstract: This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study.
Keywords: ADAS; driver drowsiness; driver physical measures; driving performance measures; PERCLOS; data fusion; neural networks; binary classification; third generation simulator ADAS; driver drowsiness; driver physical measures; driving performance measures; PERCLOS; data fusion; neural networks; binary classification; third generation simulator
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.

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MDPI and ACS Style

Daza, I.G.; Bergasa, L.M.; Bronte, S.; Yebes, J.J.; Almazán, J.; Arroyo, R. Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection. Sensors 2014, 14, 1106-1131.

AMA Style

Daza IG, Bergasa LM, Bronte S, Yebes JJ, Almazán J, Arroyo R. Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection. Sensors. 2014; 14(1):1106-1131.

Chicago/Turabian Style

Daza, Iván G.; Bergasa, Luis M.; Bronte, Sebastián; Yebes, J. J.; Almazán, Javier; Arroyo, Roberto. 2014. "Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection." Sensors 14, no. 1: 1106-1131.


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