“Development of a Smart Driver Drowsiness Detection Model with Cloud Integration”

Abstract

Early detection of driver drowsiness plays a crucial role in minimizing road accidents and enhancing transportation safety. Fatigue usually develops progressively, making it difficult for drivers to recognize the decline in their alertness and reaction capability at the initial stage. Recent advancements in monitoring technologies, such as computer vision techniques and physiological signal analysis, have shown significant potential in identifying early symptoms of drowsiness. These methods analyse parameters including eye blink duration, yawning frequency, head posture variations, and heart rate fluctuations to detect fatigue before it leads to critical driving mistakes. This research presents “Driving Alertness to the Cloud: Intelligent Drowsiness Detection with Cloud-Assisted Machine Learning,” an advanced framework designed to identify real-time indicators of driver fatigue through facial and physiological monitoring. The proposed system captures features such as extended eye closure, irregular blinking behaviour, yawning patterns, and abnormal head movements. The acquired information is pre-processed and transferred to a cloud-supported machine learning environment, where intelligent algorithms analyse behavioural patterns and classify the driver’s alertness condition with high accuracy. The integration of cloud computing enables scalable storage and processing capabilities, continuous enhancement of machine learning models, and seamless implementation across multiple vehicular platforms. Experimental results demonstrate that the system can effectively provide timely warnings to drivers, thereby reducing the likelihood of fatigue-related accidents. Consequently, the proposed cloud-based framework delivers a reliable, adaptive, and intelligent solution for improving safety in modern transportation systems. In addition, this work evaluates the effectiveness of multiple machine learning algorithms for detecting driver drowsiness using facial and physiological characteristics. Auto Colour Correlogram (ACC) features were extracted from driver images, and six classifiers—Additive Regression (AR), Naive Bayes (NB), Linear Regression (LR), Attribute Selected Classifier (ASC), Naive Bayes Multinomial (NBM), and Logistic Regression—were analysed and compared. Among these models, the Additive Regression classifier achieved superior performance across different evaluation parameters, demonstrating its suitability for real-time drowsiness detection applications.