Abstract
Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver’s graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.
Highlights
The National Highway Traffic Safety Administration (NHTSA) estimated that drowsy driving accounted for 91,000 traffic accidents, which caused approximately 50,000 injuries and 800 deaths, as reported by the police in 2017
The High-frequency coupling (HFC) drowsiness scale is classified into nine points, and the observer-rated sleepiness is classified into two types, D-ORS used for vehicle behavior and B-ORS used for driver behavior
No particular method is currently being used in wakefulness detection and estimation technology
Summary
The National Highway Traffic Safety Administration (NHTSA) estimated that drowsy driving accounted for 91,000 traffic accidents, which caused approximately 50,000 injuries and 800 deaths, as reported by the police in 2017. Often occurs between midnight and 6 a.m. or in the late afternoon During both times, the circadian rhythm, the body’s internal clock that controls sleep, is reduced; In many cases, a single driver (without a passenger) has run off the road at high speed with no sign of braking; Often occurs on local roads and highways. Fatigue while driving decreases attention and reduces the amount of information received by the driver It decreases the driver’s response and level of understanding of the situation, resulting in incorrect decision-making. The driver’s arousal level may decrease, and sleepiness might follow, which might cause loss of vehicle control. To prevent these situations, driver monitoring systems that can detect the driver’s state of drowsiness are necessary.
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