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Review
. 2021 Nov 27;21(23):7921.
doi: 10.3390/s21237921.

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Toshiya Arakawa  1
Affiliations
Review

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Toshiya Arakawa. Sensors (Basel). .

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.

Keywords: autonomous driving; biometric information; driver monitoring; drowsiness; graphic information; vehicle behavior.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
SAE’s autonomous driving level and subject of driving at each level.
Figure 2
Figure 2
The feature points of the eye.
Figure 3
Figure 3
Definition of d and di.
See this image and copyright information in PMC

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