Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 20 references
Abstract
Drowsiness-related traffic accidents are a major challenge in ensuring road safety in the whole world. One of the biggest challenges is to develop a reliable real-time driver monitoring system that works even under adverse conditions, i.e., low illumination and partial occlusion of the face. A number of existing methods have issues in scaling, cost, and real-world performance. This paper proposes a real-time driver drowsiness detection and accident prevention framework that integrates quantum machine learning in addition to the conventional learning methods. The underlying system looks at the facial video streams to detect various behavioral cues such as eye closure duration, blink frequency, yawning, and head movements. The proposed quantum-enhanced learning provides efficient feature representation and quick inference that is suitable for a real-time application. On detecting drowsiness, the system provides timely audio and visual alerts to inform the driver. Simulation results demonstrate state-of-the-art detection accuracy with very low latency, thereby establishing the efficacy and practicality of the proposed framework.
Driver fatigue and distraction play a crucial role in causing road traffic accidents, which necessitates an effective real-time driver monitoring system that is feasible in a resource-constrained embedded environment. In this paper, a system tailored for the user employing MediaPipe FaceMesh for extracting real-time fa...
Ajay Amirth K, Govardhan Karunanidhi· International Conference Com...· 0 citations
Road traffic accidents caused by driver fatigue continue to be a major public safety concern, highlighting the need for intelligent and real-time monitoring systems. This paper presents a Smart Driver Drowsiness Detection and Advanced Emergency Communication System that combines computer vision, deep learning, and auto...
Dhanalakshmi, Venkata Yamuna Chirumalla· International Journal of Eng...· 0 citations
One of the leading causes of mortality worldwide is traffic accidents brought on by sleepy drivers, as the driver gradually loses focus without direct awareness. And although there are traditional techniques for monitoring the driver, most of them suffer from limited accuracy or slow response. We created a method to id...
F. J. Kadhim· Al-Noor Journal of Engineeri...· 0 citations
In long-haul freight transportation, driver fatigue and drowsiness are among the primary causes of road traffic accidents. To address this safety hazard, this focuses on long-haul truck driving scenarios and designs and implements a vision-based real-time monitoring and early warning system for long-haul truck driver d...
Yan Yang, Jing Li, Nan Jin et al.· 2026 IEEE International Conf...· 0 citations
Drowsiness is a leading cause of human error in transportation and in shift-based occupational work, yet delivering reliable real-time detection on affordable, resource-constrained hardware remains difficult. This study aims to develop and evaluate a vision-based drowsiness detection system that behaves consistently ac...
Rafi'e· Indonesian Journal of Electr...· 0 citations
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