Towards Safer Roads: A Deep Learning and Fuzzy Logic-Based Driver Fatigue Detection System

This paper presents a real-time, vision-based framework for detecting driver fatigue using a single low-cost, road-facing camera, eschewing direct visual monitoring of the driver. Unlike conventional systems that rely on in-cabin facial or physiological analysis, the proposed architecture prioritizes privacy by inferring fatigue through vehicle dynamics and road interaction alone. Built upon the YOLOP deep learning model, the system performs lane segmentation and object detection to extract two critical indicators: lane deviation and inter-vehicle distance, both computed from monocular vision. These signals are interpreted via a fuzzy logic module that incorporates trapezoidal, triangular, and Gaussian membership functions, enabling context-sensitive and explainable fatigue assessment. Comparative evaluation of these functions illustrates trade-offs in responsiveness and generalization. Initial validation against expert human assessments shows promising alignment in perceived fatigue levels, suggesting the system can meaningfully approximate fatigue-related judgments. By aligning with emerging ethical frameworks for non-intrusive AI in mobility, the system marks a step toward socially responsible and practically deployable fatigue monitoring in intelligent transportation.