Abstract

Driver drowsiness detection is an important aspect of intelligent transportation systems that aim to reduce fatigue-related accidents. The existing schemes based on threshold-based method, or deep-learning based models often found to be associated with issues in terms of flexibility, computational efficiency, or capacity for real-time performance. This paper presents a development of two-stage hybrid framework for driver drowsy detection, where the first stage utilizes a fuzzy-logic based approach applied to physiological measures, facial feature, head position, blink duration, and eye movements to produce lightweight and adaptive analyses of sleepiness in drivers. The second stage consists of a hybrid quantum-classical neural network (HQCNN), in which convolutional neural networks (CNN) extract spatial features whereas quantum fully connected (QFC) components apply entanglement-based transformations to improve both feature characterization and classification accuracy. The experimental result validates effectiveness of the proposed hybrid method with 94% accuracy, and better than traditional CNNs with real-time capability. The proposed framework is developed to achieve a balance between computational efficiency and classification/decision quality thereby making it suitable for driver monitoring in real-time application.