Detection of Driver Drowsiness to Avoid Accidents and Prevent Alertness

Khekale, Lalit · 2025 · Crossref

DOI: 10.22214/ijraset.2025.67039

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Summary

This paper addresses the critical safety issue of driver drowsiness, a leading cause of road accidents resulting in severe injuries and fatalities globally. The authors motivate their work by highlighting that drowsy driving significantly impairs response times, awareness, and decision-making, posing risks comparable to driving under the influence of alcohol. The primary objective is to develop a non-intrusive, real-time Driver Drowsiness Detection System that integrates into Intelligent Transportation Systems (ITS) and Advanced Driver Assistance Systems (ADAS) to prevent accidents by alerting fatigued drivers. The proposed methodology utilizes computer vision and machine learning technologies to monitor a driver’s facial expressions and eye movements. The system employs a camera module to capture video frames, which are processed using OpenCV for video capture and Dlib for facial landmark detection. Key indicators of drowsiness analyzed include blink frequency, duration of eye closure, head pose, and head nodding frequency. Upon detecting these signs of fatigue, the system triggers an alert mechanism, such as an alarm or visual warning, to encourage the driver to rest or regain focus. The design aims for energy efficiency and adaptability across various vehicle types, utilizing pre-trained models for face and eye recognition. The paper outlines several limitations and challenges inherent to the system. Performance accuracy may be compromised by varying lighting conditions, shadows, glare, and nighttime driving. Individual differences in facial features, skin tone, and eye shape can affect tracking algorithm efficiency. Additionally, unexpected head movements or user actions may lead to false positives or negatives. The authors note constraints regarding real-time processing on lower-powered hardware, potential latency, and data privacy concerns associated with capturing physiological data. Hardware costs and driver adaptability, including perceptions of invasiveness, are also cited as barriers. Furthermore, integration with existing ADAS in different vehicle models and dependence on internet connectivity for cloud-based AI processing present additional technical hurdles. In conclusion, the authors assert that the developed system serves as a powerful tool for enhancing road safety by providing timely notifications of driver tiredness. By leveraging machine learning algorithms and geometric analysis for real-time observation, the system identifies early indicators of drowsiness to avert potential accidents. The study emphasizes the importance of non-intrusive, real-time detection methods in reducing the economic and social costs associated with drowsy driving, thereby contributing to the advancement of safer transportation infrastructure.

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Topics

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• drowsiness detection algorithms
• distraction detection algorithms
• drowsiness
• dms validation
• microsleep

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software