Current Volume 9
Abstract
Driver drowsiness is a critical factor contributing to a significant number of road accidents globally, posing serious threats to public safety. Early detection of driver fatigue is essential to prevent such incidents and enhance transportation safety systems. Conventional methods, including physiological signal monitoring and manual observation, are often intrusive, costly, and lack real-time efficiency. In this paper, a real-time driver drowsiness detection and alert system based on Machine Learning and Computer Vision is proposed. The system utilizes visual features extracted from live video streams to monitor driver behavior. Key indicators such as eye closure, blink rate, yawning frequency, and head movement are analyzed using facial landmark detection techniques and metrics like Eye Aspect Ratio (EAR). A threshold-based decision mechanism is employed to identify drowsiness conditions accurately. Artificial Intelligence and Machine Learning technologies are transforming the transportation industry by enabling intelligent monitoring and automated safety systems. Driver monitoring systems are becoming increasingly important because they help reduce accidents caused by human fatigue and distraction. Real-time Computer Vision techniques allow systems to analyze facial expressions, eye movements, blinking behavior, and head posture continuously. Upon detection of fatigue, the system generates immediate alerts through audio signals to warn the driver, thereby reducing the risk of accidents. The proposed approach is non-intrusive, cost-effective, and capable of real-time performance, making it suitable for integration into modern vehicles and advanced driver assistance systems. Experimental analysis demonstrates that the system achieves high accuracy and reliability under varying conditions. The results indicate that the proposed model can serve as an effective solution for enhancing driver safety and supporting intelligent transportation systems. Unlike traditional sensor-based systems, the proposed approach is non-intrusive, affordable, and suitable for real-world implementation. The research demonstrates how intelligent transportation systems can improve road safety and reduce accidents caused by fatigue.
Keywords
Driver Drowsiness Detection, Computer Vision, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Eye Aspect Ratio (EAR), Facial Landmark Detection, Real-Time Monitoring, Intelligent Transportation Systems, Driver Safety, Advanced Driver Assistance Systems (ADAS).
Citations
IRE Journals:
Sahana Belcy M, Prof. Rakshitha B S "Driver Drowsiness Detection and Alert System Using Machine Learning and Computer Vision" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2652-2661 https://doi.org/10.64388/IREV9I11-1717949
IEEE:
Sahana Belcy M, Prof. Rakshitha B S
"Driver Drowsiness Detection and Alert System Using Machine Learning and Computer Vision" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717949
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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