Driving behavior identification method based on intelligent mobile terminal

Abstract

The invention discloses a drive behavior identification method based on an intelligent mobile terminal. The method includes the steps of S1, using the intelligent mobile terminal to collect and screenvehicle original state data which comprises the acceleration and angular speed information of three axes; S2, preprocessing vehicle original motion state data; S3, using a main component analysis method to acquire driving behavior comprehensive feature vectors from the preprocessed data; S4, using a k-means clustering algorithm to perform cluster partition on the driving behavior comprehensive feature vectors to obtain optimal clustering number; S5, using an FCM algorithm to cluster the driving behavior comprehensive feature vectors according to the optimal clustering number to obtain the deblurred final clustering result; S6, collecting real-time vehicle state data, and identifying the vehicle driving behaviors according to the final clustering result. By the method, refined driving behavior data clustering is achieved, and vehicle driving behavior features are effectively clustered into turning, speed changing and lane changing.

Description

technical field [0001] The present invention relates to the technical field of vehicle assisted driving, in particular to a driving behavior recognition method and system based on an intelligent mobile terminal. Background technique [0002] With the rapid increase of the number of urban vehicles, driver's bad driving behavior has become an important factor of traffic problems. The research on vehicle driving behavior is beneficial to solve the problem of traffic jams and traffic accidents caused by bad driving behavior. At present, the research on driving behavior is mainly divided into two methods based on visual image and multi-sensor data fusion. [0003] For the analysis of driving behavior based on visual images, in practical applications, the uncertainty of light intensity, camera angle and the interference of the surrounding environment will affect the recognition effect. The fusion of multiple sensors to perceive the state of the vehicle is relatively less disturb...

AI Technical Summary

Problems solved by technology

The k-means clustering belongs to the hard clustering algorithm, and the sample points can only belong to or not belong to a certain category, so the classification effect has limitations; the FCM algorithm belongs to the soft clustering algorithm, and the membership function is used to indicate that the sample points belong to a certain category degree, the classification result is more refined, but the algorithm increases the amount of calculation, and the speed decreases compared with the k-means algorithm

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