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

Sensors

Iván G. Daza¹  Luis M. Bergasa¹  Sebastián Bronte¹  J. Javier Yebes¹  Javier Almazán¹ 

[1] Department of Electronics, University of Alcalá, Alcalá de Henares, Madrid 28871, Spain;

关键词: ADAS;    driver drowsiness;    driver physical measures;    driving performance measures;    PERCLOS;    data fusion;    neural networks;    binary classification;    third generation simulator;   

DOI  :  10.3390/s140101106

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.