Fatigue driving state monitoring method

Abstract

The invention discloses a fatigue driving state monitoring method. The fatigue driving state monitoring method comprises the steps of transmitting collected electrocardio data and triaxial acceleration data of hands to a control unit, performing statistics on the transmitted data by the control unit to obtain variability indexes and hand exercise coefficient indexes, constructing a fatigue forecasting model by a K-means method, inputting the heart rate and heart rate variability indexes and hand exercise coefficient into the fatigue forecasting model, and according to the output value of the fatigue forecasting model, performing grading on the fatigue forecasting result. The method is simple to operate and high in accuracy, the fatigue degree of a driver can be in real-time on-line recognition and detection, the driver can be reminded, and relevant improvement basis can be provided for the driver or handlers.

Description

technical field [0001] The invention belongs to the field of automobile safety driving auxiliary tools, and in particular relates to a fatigue driving state monitoring method. Background technique [0002] The driver is the direct operator of the vehicle, the user of the road, and the direct sensor of the traffic environment. He occupies a dominant position in the entire traffic system, and his driving behavior has a direct impact on road traffic safety. Accident data show that driver's unsafe driving behavior is the main cause of commercial vehicle accidents, and fatigue driving is the most important unsafe driving behavior. Therefore, it is of great significance to monitor and analyze the abnormal state of driver fatigue driving, and it is one of the important ways to reduce the traffic accident rate. [0003] There are three main types of existing driver fatigue driving state monitoring schemes. The first type of method is a detection method based on the driver's physio...

AI Technical Summary

Problems solved by technology

These two types of early warnings will have false positives and false positives. At the same time, the equipment is too complicated, and it is necessary to install a camera and be equipped with a camera information processing device. The cycle of processing facial information is long, and it is difficult to detect in real time.

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