Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Driver drowsiness is a serious issue that poses significant threat to road safety, as it can lead accidents and injuries. In response this problem, thorough review of machine learning techniques for detecting driver was conducted. The examined range techniques, including more recent approaches use deep algorithms well different types data sources behaviours, physiological signals, vehicle behaviours. primary objective paper critically analyse provide comprehensive overview the current state-of-the-art in drowsiness, evaluate effectiveness each technique terms accuracy reliability, identify potential areas future research improvement. order achieve this, systematic relevant studies undertaken. determined learning-based improve reliability detection systems. However, certain limitations, such need large amounts data, feature extraction, model structure, must be addressed. By overcoming these systems have enhance safety prevent accidents. conclusion, provides detection, evaluates their effectiveness, identifies directions, highlights significance contribution safety. insights gained from study used guide development effective community.