Vehicle safe driving detection method, device and storage medium based on artificial neural network

Abstract

The embodiment of the invention relates to the technical field of automobile electronic control, and discloses a vehicle safe driving detection method based on an artificial neural network, a device and a computer-readable storage medium. In the invention, the method comprises the following steps of: collecting characteristic sample parameters of a historical driving process; Establishing artificial neural network model including front neural network and back neural network, and train artificial neural network model by using characteristic parameters of dangerous driving. The first kind of characteristic parameters and the second kind of characteristic parameters of the driver are obtained in real time during the current driving process of the vehicle. The first and second characteristic parameters are inputted into the artificial neural network model, and the classification probability value of dangerous driving characteristics is obtained. Then the driver is judged to be in dangerousdriving state and the reminder information is sent. The invention provides a vehicle safe driving detection method based on an artificial neural network, a device and a computer-readable storage medium, which can improve reliability and accuracy of dangerous driving evaluation.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of automotive electronic control, and in particular to a method, device, and computer-readable storage medium for detecting safe driving of a vehicle based on an artificial neural network. Background technique [0002] With the development of social economy and the improvement of people's living standards, the demand for transportation in the whole society is increasing day by day. The rapid growth of traffic volume has deteriorated road traffic conditions, leading to a series of serious social problems such as traffic safety accidents, environmental pollution, traffic congestion, and economic losses. Especially, major traffic accidents involving passenger vehicles have attracted widespread attention from the society. . Dangerous driving of drivers is one of the main causes of serious traffic accidents. Statistics show that: the traffic accidents caused by driving fatigue account for ...

AI Technical Summary

Problems solved by technology

[0003] The inventors found that at least the following problems exist in the prior art: the traditional anti-dangerous driving uses the same detection model, so for different drivers, the accuracy and universality are further reduced due to individual differences, resulting in the reliability of dangerous driving evaluation. The safety and accuracy are not high. For example, the traditional dangerous driving detection model analyzes the user's fatigue through the user's eye blink data, but some people will blink frequently when they are energetic, and the system will also detect it as dangerous driving.

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