Vehicle driving behavior detection method and device based on class imbalance algorithm

Abstract

The invention belongs to the technical field of vehicle driving detection, and discloses a vehicle driving behavior detection method based on a class imbalance algorithm, which includes acquiring vehicle driving data information; preprocessing and feature extraction of the vehicle driving data information to obtain sample data block information; The information input adopts the vehicle driving behavior recognition model trained by the class imbalance algorithm, and outputs the vehicle driving behavior information; sends the vehicle driving behavior information to the terminal; also discloses a device; uses the ratio of the majority class to the minority class and a fixed threshold for comparison, When the ratio is greater than the threshold, the current data block is considered to be class unbalanced, and then the current data block is oversampled to increase the number of minority class samples, so as to achieve the class balance of the entire data block; After the information is extracted, it is input to the vehicle driving behavior recognition model trained by the class imbalance algorithm, and the accurate output includes a kind of abnormal driving behavior or normal driving behavior information of the vehicle.

Description

technical field [0001] The invention belongs to the technical field of vehicle driving detection, and particularly relates to a vehicle driving behavior detection method and device based on a class imbalance algorithm. Background technique [0002] It's been 135 years since the first car came out. In the history of more than 100 years, automobiles have played a pivotal role in the development of human society. With the continuous iterative innovation of automobile technology and production process, the speed, power, transportation capacity, and output of automobiles have all developed by leaps and bounds. On the contrary, the traffic safety problems related to automobiles have become more and more prominent. According to the statistics of traffic accidents of the Ministry of Public Security in 2019, a total of 238,351 road traffic accidents occurred nationwide in 2019, resulting in 67,759 deaths, 275,125 injuries, and direct property losses of 910 million yuan. These figur...

AI Technical Summary

Problems solved by technology

[0003] However, the safe driving monitoring for drivers will find that since people are highly free individuals, the high degree of freedom and unpredictability of personal behavior will have a negative impact on the algorithm and reduce the accuracy of algorithm recognition; at this time, it is often necessary to arrange A large number of sensors or image processing methods are used to increase the richness of information, so as to make up for the problems caused by the high degree of freedom of individual behavior; but the increase in the amount of information means that the algorithm needs longer time to process the input, correspondingly However, the real-time performance of the driving behavior monitoring system using this solution is very likely to fail to meet the requirements; on the other hand, in the process of vehicle driving behavior detection, the duration of each driving behavior category is different. For For those driving behavior categories with a short duration, the number of data samples contained in them is relatively small, and machine learning algorithms often have a good recognition effect on driving behavior categories with a relatively large number of samples (ie, most categories), while for The recognition effect of these driving behavior categories with a small number of samples is poor

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