Driver Drowsiness Detection Based on Convolutional Neural Network Architecture Optimization Using Genetic Algorithm

In today's analysis of traffic accident reports it becomes evident that most driving accidents result from driver drowsiness, fatigue, and lack of alertness. At these moments, drivers cannot react quickly to such changes in their state. Characteristics of drowsy driving include closed eyes, sem...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.45709-45726
Hauptverfasser:	Jebraeily, Yashar, Sharafi, Yousef, Teshnehlab, Mohammad
Format:	Artikel
Sprache:	eng
Schlagworte:	Accidents Alertness Artificial neural networks Brain modeling convolutional neural network (CNN) Convolutional neural networks Datasets Driver drowsiness detection Driver fatigue Fatigue Feature extraction genetic algorithm Genetic algorithms neural architecture search Neural networks Sleepiness Traffic accidents Vehicle driving Vehicle safety Vehicles Yaw
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