A survey on driver drowsiness detection using physiological, vehicular, and behavioral approaches - Mustafa Kamel Gatea; Sadik Kamel Gharghan; Raed Khalid Ibrahim; Adnan Hussein Ali (Bulletin of Electrical Engineering and Informatics, 2022)
Drowsiness is a significant reason for street mishaps and has huge ramifications for driver safety. A few lethal mishaps can be prohibited if the sleepy drivers are cautioned in time. There are a number of tiredness identification strategies that screen the drivers’ languor state while driving and caution unfocused drivers. Highlights may be gathered from outward appearances (e.g., yawning and eyes and head movement) to determine the degree of laziness. This paper presents a holistic investigation of current strategies for driver laziness discovery and gives an exploration of widely-used characterization procedures. We begin by organizing the current procedures into three categories: behavior, vehicular, and physiological boundaries-based procedures. Then, we survey top directed learning methods utilized for laziness discovery. Next, we examine the advantages and disadvantages of the various techniques. A similar examination indicated that none of these strategies is entirely precise. However, physiological boundaries-based procedures produce more exact outcomes than other types of procedures. Their non-intrusive nature may be decreased through utilizing remote sensors on various elements including the driver’s body, driver’s seat, seat covers, and steering wheel.