Real-Time Anti-Sleep Alert Algorithm to Prevent Road Accidents to Ensure Road Safety

Abhishek Kumar Pathak ¹ Ankit Kumar Singh ² Pankaj Kumar ³ Vimal Bhatia ^1* Ondrej Krejcar ⁴

• ¹ Electrical Engineering, Indian Institute of Technology Indore, Indore, India
• ² Department of Computer Science and Engineering Motihari College of Engineering, 845401, India, Motihari, India
• ³ Department of Computer Science and Engineering Vaishali Engineering College, India, Vaishali, India
• ⁴ Škoda Auto University, Mladá Boleslav, Central Bohemia, Czechia

Whenever 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. Among the tested models, InceptionV3 demonstrated superior performance, achieving an accuracy of 99.18%, a validation loss of 0.85%, and an execution time of 0.2 seconds on a Raspberry Pi platform.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. The results suggest that the proposed algorithm provides a robust and effective solution for real-time driver drowsiness detection, contributing to enhanced safety.