Automobile safe driving early warning method and vehicle-mounted early warning system

Abstract

The invention discloses an automobile safe driving early warning method and a vehicle-mounted early warning system. The automobile safe driving early warning method comprises the steps of building a CNN (Convolutional Neural Network) deep learning framework model, and simulating a complex environment of the driving of a driver and state indicators of the driving of the driver; performing detectionon the face of the driver, and judging the face state of the driver in the driving process; detecting a fatigue driving behavior of the driver; detecting a safe driving behavior of the driver; acquiring time information or distance information of continuous driving of a vehicle in real time; monitoring location information and running trajectory of the automobile, and recognizing identity information of the driver at regular time. According to the invention, comparison is performed on acquired driver face images through the CNN deep learning framework model to judge the driving safety state of the driver, the algorithm is high in operating efficiency, the accuracy is high, the hardware configuration requirement is low, the practicability is high, the location information and driving parameters of the vehicle are acquired in real time through a positioning system unit, and driving behavior management for the driver, vehicle driving management and vehicle loss prevention management arerealized.

Description

technical field [0001] The invention relates to the technical field of automobile driving early warning, in particular to an automobile safe driving early warning method and a vehicle early warning system based on a CNN deep learning framework model. Background technique [0002] Car safety driving is a content that people are very concerned about in daily life. In the field of car safety driving early warning, the technical solutions commonly used in the existing technology are: first, it needs to be connected to the Internet. There is no unsafe driving behavior. This method relies heavily on the network and is greatly limited by the network transmission speed. Especially in some areas without wireless network signal coverage, this method cannot be implemented; the second is to use image comparison algorithms, but the current Technological algorithms are generally simple and cannot accurately judge the driver's facial state information. For example, the Harr detection metho...

AI Technical Summary

Problems solved by technology

[0002] Car safety driving is a content that people are very concerned about in daily life. In the field of car safety driving early warning, the technical solutions commonly used in the existing technology are: first, it needs to be connected to the Internet. There is no unsafe driving behavior. This method relies heavily on the network and is greatly limited by the network transmission speed. Especially in some areas without wireless network signal coverage, this method cannot be implemented; the second is to use image comparison algorithms, but the current Technological algorithms are generally simple and cannot accurately judge the driver's facial state information. For example, the Harr detection method that comes with Opencv is difficult to capture the face image in a complex real environment, and can only judge the driver's large-scale movement The state information of the position, at the same time, the cost of the image comparison algorithm in the prior art is high, and the misjudgment rate is high

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