Abstract
: Driver drowsiness is a significant factor contributing to road accidents, resulting in
severe injuries and fatalities. This study presents an optimized approach for detecting driver
drowsiness using machine learning techniques. The proposed system utilizes real-time data to
analyze driver behavior and physiological signals to identify signs of fatigue. Various machine
learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks
(CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system
incorporates an optimization technique—such as Genetic Algorithms (GA) or Particle Swarm
Optimization (PSO)—to enhance the accuracy and response time of the detection process. The
integration of optimization methods ensures that the model adapts to various driving
conditions and individual differences, providing a more reliable and robust detection
mechanism. Data from multiple sources, including camera feeds and wearable sensors, are
used to train and validate the models, ensuring a comprehensive understanding of drowsiness
indicators.