Fatigue driving detection method and system

Abstract

The invention relates to the field of automobile driving safety, in particular to a fatigue driving detection method and system, and the method comprises the following steps: S1, collecting driving information, and building a data set; the data set comprises an image data set and an operation data set; the image data set is used for storing a face image of a driver; the operation data set is used for storing driving information of the vehicle; s2, inputting the image data set into a first detection model, and calculating a first fatigue value; s3, inputting the operation data set into a second detection model, and calculating a second fatigue value; s4, judging whether the driver is in a fatigue state or not according to the first fatigue value and the second fatigue value; and S5, taking corresponding measures according to a judgment result. Compared with the prior art, the method has higher applicability.

Description

technical field [0001] The invention relates to the field of vehicle driving safety, and more particularly, to a fatigue driving detection method and system. Background technique [0002] Fatigue driving is very easy to cause traffic accidents, which means that after driving continuously for a long time, the physiological function and psychological function of the driver are out of balance, and the driving skill declines objectively. Drivers have poor or insufficient sleep quality and are prone to fatigue after driving for a long time. Driving fatigue can affect the driver's attention, feeling, perception, thinking, judgment, will, decision and movement. If you continue to drive the vehicle after fatigue, you will feel drowsy, drowsy, weak in limbs, inattention, decline in judgment ability, even in a trance or instant memory loss, late or early action, operation pause or improper correction time and other unsafe factors, Road traffic accidents are very likely to occur. Wi...

AI Technical Summary

Problems solved by technology

Although the installation is simple and cost-effective based on the facial features of the driver, it is difficult to judge the opening and closing of the human eyes when the driver is moving, and in the case of weak light at night, it is difficult for the existing face detection to accurately detect the facial features. recognition, resulting in lower accuracy at night detection

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