Abstract
Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this dangerous condition. The major problem of the developed algorithms is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and the time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data are gathered from the deriver's brain Electroencephalogram (EEG) signals or from video streaming recordings of the driver behavior. This paper develops two distinct approaches, the first based on the use of machine learning classifiers and the second depends on the use of deep learning models to produce a high-performance fatigue detection system. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolutional Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time ( TT ) when compared to all other recent and efficient fatigue detection algorithms.