Adaptive Driver Vigilance Monitoring System for Enhanced Road Safety

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

DOI: 10.65521/ijacect.v13i2.20

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Summary

This paper addresses the critical issue of driver fatigue and inattention, which are leading causes of road accidents globally, accounting for approximately 20% of incidents. The authors propose an Adaptive Driver Vigilance Monitoring System designed to enhance road safety by continuously monitoring driver alertness and dynamically responding to signs of fatigue or distraction. Unlike traditional systems that rely on static thresholds or limited data inputs, this solution utilizes advanced artificial intelligence, real-time data analysis, and sensor fusion to provide a personalized and dynamic approach to vigilance monitoring. The system aims to bridge the gap between research and practical implementation by offering a modular design that integrates seamlessly with in-vehicle systems and external networks. The proposed system operates through a multi-stage process involving data collection, feature extraction, adaptive analysis, risk assessment, and intervention. Data is collected via vision-based sensors capturing eye movements, blink rates, facial expressions, and head posture, as well as environmental sensors tracking lighting and traffic conditions. Vehicle data, including steering angle and lane deviation, and optional physiological sensors measuring heart rate and skin conductance, are also utilized. Machine learning algorithms analyze these features to recognize patterns indicating decreased vigilance. The system personalizes vigilance thresholds by learning from individual driving habits and adapting to contextual conditions, such as highway cruising versus urban traffic. Based on this analysis, the system classifies the driver’s state into normal, moderate risk, or high risk categories. The system employs a tiered alert mechanism to ensure driver safety. Mild alerts include visual or auditory cues, while moderate alerts utilize haptic feedback, such as seat or steering wheel vibrations. Critical alerts involve emergency warnings and recommendations to stop the vehicle. Additionally, the system integrates with cloud-based platforms and vehicle-to-everything (V2X) communication networks to log driving data, share safety alerts with nearby vehicles, and support fleet management. This connectivity allows for broader traffic safety initiatives and post-trip analysis. The paper highlights that multimodal alerts are more effective in capturing driver attention than single-mode alerts, though user-centered design is necessary to prevent annoyance. The significance of this system lies in its potential to reduce accidents caused by fatigue and distraction through proactive, personalized interventions. It offers applications in semi-autonomous vehicles, insurance risk assessment, driving training, and traffic management. By providing real-time feedback and continuous learning, the system enhances safety for diverse driver populations, including elderly and inexperienced drivers. The authors conclude that this adaptive approach sets a new standard in vehicle safety, contributing to smarter transportation systems and minimizing road accidents worldwide.

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Topics

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• drowsiness detection algorithms
• distraction detection algorithms
• dms validation
• vigilance
• gaze based attention detection

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: theory or model