article · IEEE Access
Driver fatigue is a major factor in road traffic accidents, creating a strong requirement for reliable systems that can recognise tiredness and alert drivers promptly. Existing detection algorithms often struggle with balancing high accuracy against the rapid processing speed required for timely warnings. To address this, two distinct approaches were developed using different inputs. The first relies on machine learning to process brain signals from electroencephalograms, where a Support Vector Machine classifier achieved up to 98 per cent detection accuracy across six tested classifiers. The second method uses deep learning to analyse video streams of driver behaviour, with a Convolutional Neural Network achieving up to 99 per cent accuracy among three evaluated models. Experimental evaluations show that both proposed approaches deliver superior detection accuracy and shorter testing times compared to recent alternative fatigue detection algorithms.
Road accidents caused by driver fatigue pose severe safety risks. By improving the speed and accuracy of drowsiness identification through either brain signals or camera feeds, these techniques support more dependable monitoring systems that can alert drivers quickly before fatigue leads to dangerous errors.
This research could support driver monitoring systems for automotive manufacturers, commercial fleet operators, and safety technology developers. It demonstrates dual pathways using either brain-signal sensors or video streams. The work appears to be applied and tested within experimental settings using collected datasets, though real-time deployment in operational vehicles is not reported.
AI-generated from the published abstract. Always read the original work before citing.
Because of the increased number 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 potentially dangerous condition. The developed algorithms’ main problem is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and 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 video streaming recordings of the driver’s behavior. This paper proposes two distinct approaches to producing a high-performance fatigue detection system, the first based on the use of machine learning classifiers and the second depending on the use of deep learning models. 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.
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DOI: 10.1109/access.2022.3185251
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