Adaptive Driver Vigilance Surveillance System

Bodhale, U.N.; Bhagwat, Sanjana Manoj; Damare, Nandini Abasaheb; Ingale, Ganesh Janardhan · 2025 · Crossref

DOI: 10.65521/ijaeee.v14i1.526

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Summary

This paper addresses the critical issue of driver fatigue and distraction, which are significant contributors to road accidents, with approximately 25–30% of crashes attributed to distracted driving and 20% of fatal crashes involving drowsiness. The authors propose an Adaptive Driver Vigilance Surveillance System designed to enhance road safety by continuously monitoring a driver’s alertness in real-time. The motivation stems from the need for a low-cost, accessible solution, particularly for developing regions where expensive Advanced Driver-Assistance Systems (ADAS) are not feasible. The system aims to detect signs of drowsiness, inattention, or alcohol impairment and provide timely interventions to prevent accidents. The proposed system is built around a Raspberry Pi single-board computer, which serves as the central processing unit. It employs a multi-sensor approach to capture both physiological and behavioral data. Key components include a webcam for vision-based analysis, an MQ3 sensor for detecting ethanol levels in the driver’s breath, and a micro-switch sensor to monitor steering wheel interaction. The webcam utilizes computer vision techniques to detect facial landmarks, specifically analyzing blinking patterns and yawning to assess drowsiness. The MQ3 sensor provides analog signals indicating alcohol presence, while the micro-switch detects if the driver’s hands are off the wheel. These inputs are processed via Python scripts running on the Raspberry Pi. The system outputs alerts through a buzzer and LED display, with different colors indicating varying levels of danger. Additionally, the setup includes network management tools like VNC Server for remote desktop control and Advanced IP Scanner for network diagnostics, facilitating system monitoring and maintenance. The system’s operation involves continuous real-time data acquisition and processing. The Raspberry Pi integrates inputs from the camera, MQ3 sensor, and micro-switch to evaluate the driver’s state. If the vision-based analysis detects sleepiness, or if the sensors register alcohol or lack of steering interaction, the system triggers audio-visual alerts. The design emphasizes non-intrusive monitoring and immediate feedback. The authors highlight that this integrated framework combines alcohol sensing, physical interaction detection, and visual fatigue analysis into a single, compact unit. The significance of this work lies in its cost-effectiveness and potential to reduce accident rates caused by human error. By providing a viable alternative to high-end ADAS, the system offers a practical solution for broader adoption. The authors conclude that the system represents a robust approach to proactive accident prevention. They suggest future enhancements, including the integration of deep learning for more accurate eye detection, mobile app synchronization via Bluetooth or Wi-Fi for real-time reporting, and direct integration with vehicle braking systems for emergency intervention. This research contributes to the field of intelligent transportation systems by demonstrating how low-cost embedded technologies can be effectively leveraged for critical safety applications.

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Topics

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• drowsiness detection algorithms
• dms validation
• distraction detection algorithms
• drowsiness
• vigilance
• alcohol detection systems

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software, validation psychometrics