A NEW INTELLIGENT DRIVER VIGILANCE SYSTEM DESIGN USING TILT SENSORS

Zuhair Ghazi Zahid, Ali; Shakir Muttair, Karrar; Ahmed Shareef, Oras; Ali Diwan, Abbas; Baqir Taher, Hazeem; Hameed Chyad Alfilh, Raed; Mahmood Farhan, Ahmed; Farhan Mosleh, Mahmood; Arith, Faiz · 2025 · Crossref

DOI: 10.30572/2018/kje/160103

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Summary

This paper addresses the critical safety issue of driver drowsiness, a leading cause of motor vehicle accidents resulting in significant fatalities and injuries globally. The authors aim to design an inexpensive, intelligent driver vigilance system capable of detecting sleep onset and alerting drivers when their alertness levels drop below safe thresholds. The motivation stems from the high prevalence of fatigue-related crashes, which are often underreported, and the need for accessible technologies to mitigate risks associated with tiredness, sleep deprivation, and distraction. The proposed system utilizes a Raspberry Pi Model B as the central processing unit, integrating specific hardware components to monitor driver status. The primary detection mechanism employs SW520D tilt sensors attached to the driver’s head via a plastic strap to track head inclination and vibration. These sensors detect changes in head angle—such as tilting down, up, or to the sides—which indicate drowsiness or loss of attention. Additionally, a USB web camera is mounted near the dashboard to visually monitor the driver’s face and record video data. A mini piezo buzzer serves as the alert mechanism, triggering an audible alarm when the sensors detect significant head tilting. The system’s logic processes sensor inputs to determine if the driver’s head position deviates from the upright, attentive posture, subsequently activating the alarm to restore awareness. Experimental results demonstrate that the system effectively recognizes head tilting in various directions with a response reliability of 95%. The authors report that the system performs well in practical scenarios, distinguishing between normal driving movements and drowsy postures with minimal false positives. Comparative analysis with previous studies indicates that this approach offers superior response accuracy compared to methods using wristbands (80%), thermal cameras (56–82%), or visible-light and thermal camera combinations (90%). The system’s low cost and high reliability make it a viable solution for preventing accidents caused by driver inattention. The significance of this work lies in its provision of a cost-effective, hardware-based solution for real-time driver monitoring. By combining tilt sensors with visual monitoring, the system offers a robust method for detecting fatigue without relying on complex, expensive computational architectures. The authors conclude that such systems can significantly reduce casualties and property damage by prompting drivers to regain attention or take breaks. Future work will focus on enhancing the system by incorporating eye-drowsiness detection algorithms to further improve accuracy and provide multi-modal warning signals.

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Topics

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

Information type

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