Abstract
Innovation in technology has delivered aid to drivers primarily in the form of smart vehicle systems. Over the last decade, driver health issues and drowsiness have shown to be prime reasons for vehicular accidents. Thus, detecting driver fatigue in order to avoid accidents is a major focus of current research. Various techniques have been proposed for detecting driver drowsiness. The majority of techniques are dependent on heart rate variability, while others are based on extracting facial features such as yawning, eye flickering, and head movements. This chapter reviews two popular techniques for detecting driver drowsiness: driver heart rate measurement and driver behavior methods. It also reviews and analyzes various algorithms for detecting driver drowsiness such as support vector machines (SVMs), the Hidden Markov Model (HMM), and convolutional neural network (CNNs).
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