RT info:eu-repo/semantics/article
T1 Fusion of optimized indicators from Advanced Driver Assistance Systems (ADAS) for driver drowsiness detection
A1 García Daza, Iván
A1 Bergasa Pascual, Luis Miguel
A1 Bronte Palacios, Sebastián
A1 Yebes Torres, José Javier
A1 Almazán Yagüe, Javier
A1 Arroyo Contera, Roberto
K1 ADAS
K1 Driver drowsiness
K1 Driver physical measures
K1 Driving performance measures
K1 PERCLOS
K1 Data fusion
K1 Neural networks
K1 Binary classification
K1 Third generation simulator
K1 Electrónica
K1 Electronics
AB 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.
PB MDPI
SN 1424-8220
YR 2014
FD 2014-01-09
LK http://hdl.handle.net/10017/43135
UL http://hdl.handle.net/10017/43135
LA eng
NO Ministerio de Economía y Competitividad
DS MINDS@UW
RD 23-abr-2024