Design of the Adaptive Belief Rule Based Inference System for Driver Drowsiness Detection

This research focuses on detecting driver fatigue in real-time. Driver drowsiness is a serious issue that often leads to traffic accidents, making it a particularly hazardous concern. Therefore, preventing and reducing driver fatigue and distraction is an important area of research. The study aims to engineer a real-time, non-intrusive drowsiness detection system image processing and fuzzy logic techniques. To determine the level of fatigue, the system analyzes facial features, including the state of the eyes and mouth. However, the calculation of the states of the eyes and mouth involves various uncertainties, such as vagueness and imprecision. Therefore, a Belief Rule-Based Approach (BRBA) is implemented due to its proficiency in managing uncertainties. In a predefined rule base, each input to an antecedent attribute is mapped to a belief distribution. Subsequently, within this rule base, the Equilibrium Optimizer (EO) method is employed to execute inference processes. A novel Belief Rule-Based (BRB) system is developed by meticulously analyzing the associated risks of fatigue and drowsiness, with the objective of accurately determining the risk levels of various driver states.