Precise Driver’s Drowsiness Detection Using a Combination of Proven Methods with a Neuro-Dynamic Structure

Drowsiness can impair reaction time, increasing the risk of severe accidents. Many current studies concentrate on a single symptom of drowsiness, which can lead to false alerts. This paper presents a new method for detecting drowsiness in real time. The proposed approach utilizes four deep learning architectures based on convolutional neural networks: AlexNet for extracting environmental features, ResNet50V2 for recognizing hand gestures, VGG-FaceNet for facial feature extraction, and FlowImageNet for analyzing behavioral features. To maximize the benefits of the aforementioned methods, we suggest using a single-layer neural network. Since drowsiness is a dynamic phenomenon, capturing its evolving features requires a dynamic neural network with adaptive delays, specifically an Adaptive Time Delay Neural Network (ATDNN) with adjustable weights. Our implementation of this neuro-dynamic approach on the NTHUDDD and our custom datasets demonstrates that it achieves greater accuracy (99.1% and 98.6%, respectively) compared to existing methods in the literature.