Quantum Machine Learning for Real-Time Driver Drowsiness Detection and Accident Prevention
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.