Driver Drowsiness and Distraction Alert System Using AI-Based Behaviour Analytics | IJCT Volume 13 – Issue 2 | IJCT-V13I2P47
International Journal of Computer Techniques
ISSN 2394-2231
Author
Preethi G, Aishwarya A S, Saivarshini S
Abstract
Road accidents caused by driver drowsiness and distraction are a major concern worldwide. Continuous driving, lack of sleep, and mobile phone usage significantly reduce a driver’s alertness and reaction time. This project proposes an AI-based Driver Drowsiness and Distraction Alert System that uses computer vision and behaviour analytics to monitor the driver’s facial expressions, eye movements, head posture, and attention patterns in real time. The system detects signs of drowsiness such as frequent eye closure, yawning, and abnormal blink rates, as well as distraction indicators like head turning and mobile phone usage. Machine learning models analyze these behavioural patterns and trigger alerts to warn the driver, thereby reducing the risk of accidents. The proposed system is cost-effective, real-time, and can be deployed in vehicles to enhance road safety.
Keywords
Driver Drowsiness Detection, Driver Distraction Monitoring, Behaviour Analytics, Artificial Intelligence, Computer Vision, Real-time Alert System, Road Safety
Conclusion
The Driver Drowsiness and Distraction Alert System Using AI-Based Behaviour Analytics successfully integrates advanced monitoring and safety features to enhance driver protection, prevent accidents, and provide timely alerts during high-risk situations. By leveraging AI-based analysis of facial expressions, eye movements, and head orientation, the system effectively detects driver fatigue and distraction in real time, delivering immediate audio, visual, and haptic alerts to reduce unsafe driving behavior.
During testing, the system demonstrated high accuracy in detecting drowsiness (96.5%) and distraction events (94.8%), ensuring reliable monitoring under varied driving conditions. Alerts were triggered within seconds, allowing drivers to take corrective action promptly. Cloud integration enabled synchronization of driving behavior and alert logs, supporting analytics for both drivers and fleet managers to track risk patterns and improve overall road safety. The rechargeable battery setup ensured extended operation, making the system practical for long-duration driving.
Although the system showed significant improvements in driving safety, certain challenges were noted, including sensor sensitivity to environmental conditions and occasional delays in data transmission in areas with weak network coverage. Future improvements will focus on enhancing AI detection accuracy, refining the mobile dashboard for actionable analytics, and exploring alternative communication protocols to maintain connectivity in low-signal regions.
Overall, this system provides a practical, efficient, and scalable solution for mitigating risks associated with driver fatigue and distraction. By combining real-time AI-based monitoring with immediate alert mechanisms and cloud-based behavior analytics, it has the potential to significantly reduce vehicle-related accidents and improve driver awareness, making it an indispensable tool for modern road safety management.
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How to Cite This Paper
Preethi G, Aishwarya A S, Saivarshini S (2026). Driver Drowsiness and Distraction Alert System Using AI-Based Behaviour Analytics. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
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