Road Safety by Detecting Drowsiness while Driving using Machine Learning

Abstract

Driver drowsiness is one of the major causes of road accidents, leading to countless injuries, fatalities, and property damage every year. In this paper, we introduce a real-time drowsiness detection system that uses computer vision to monitor the driver\'s alertness. The system works with a regular webcam and tracks facial landmarks to analyze eye aspect ratio and blinking behavior. If the driver shows signs of drowsiness, such as slow blinking or prolonged eye closure, the system immediately triggers an audible alarm to wake them up and avoid a possible accident. We\'ve built the system using Python along with OpenCV, dlib, and pygame libraries, which makes it affordable and easy to implement. Our experiments show that it performs well in normal lighting conditions, offering a practical and non-intrusive way to improve road safety.

Introduction

Driver drowsiness is a significant cause of road accidents globally, impairing reaction time and decision-making. Traditional prevention methods like awareness campaigns have limitations. Advances in embedded systems and computer vision enable real-time, non-intrusive driver drowsiness detection systems using facial landmark tracking and head pose estimation. These systems alert drivers early, promoting safer driving.

Key Components:

• Hardware: Cameras, embedded boards (Raspberry Pi, NVIDIA Jetson), processors.

• Software: Python, OpenCV, Dlib, and other libraries for facial analysis.

Advantages:

• Real-time, continuous monitoring without discomfort.

• Cost-effective using affordable hardware and open-source tools.

• Early warning alerts and scalable for various vehicles.

Limitations:

• Reduced accuracy in poor lighting or with sunglasses.

• Privacy concerns due to continuous monitoring.

• Rapid head movements may disrupt detection.

Applications:

• Personal vehicles to alert sleepy drivers.

• Public transport to enhance passenger safety.

• Fleet management to reduce liability.

• Integration in vehicle manufacturing for advanced driver assistance and autonomous driving support.

Future Directions:

• IoT and cloud connectivity for remote monitoring and alerts.

• Multimodal systems combining eye tracking, heart rate, steering behavior, and voice recognition for improved accuracy.

• Support for semi-autonomous vehicles to ensure driver alertness.

• Use of deep learning (CNNs) for better fatigue detection across diverse conditions.

Conclusion

alerts, offering a practical and non-intrusive solution to enhance road safety and reduce the risk of accidents caused by drowsiness. Experimental results demonstrate that the system performs reliably under typical conditions and can be integrated into modern vehicles as an intelligent driver assistance feature.

References

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Copyright

Copyright © 2025 Piyush Pardhi, Nishant Bhoskar, Pratik Motghare, Nayan Meshram, Nikhil Murchule. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.