Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety

Abstract

When we travel from one place to another, the first priority during our journey is that, we all wish to reach safely at our destination. Ensuring driver wakefulness is crucial for road safety, as drowsiness is a leading cause of fatal accidents, resulting in physical injuries, financial losses, and loss of life. This paper proposes an anti-sleep driver detection algorithm designed specifically for four-wheelers and larger vehicles to mitigate accidents caused by driver drowsiness. The proposed algorithm leverages deep learning (DL) models, including InceptionV3, VGG16, and MobileNetV2, for real-time detection and classification of driver drowsiness. The models were trained and evaluated using comprehensive performance metrics, such as accuracy, precision, recall, F1 score, and confusion matrix. The proposed method outperforms the traditional approaches such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Haar Cascade Classifiers, and other DL architectures like Xception and VGG16, in terms of accuracy and efficiency. Among the tested models, InceptionV3 demonstrated superior performance, achieving an accuracy of 99.18%, a validation loss of 0.85%, and execution time of 0.2 s on Raspberry Pi platform. The results suggest that the proposed algorithm provides a robust and effective solution for real-time driver drowsiness detection thereby contributing towards enhanced safety.