Driving style identification algorithm based on factor analysis and machine learning

Abstract

The invention belongs to the technical field of automobiles, and particularly relates to a driving style identification algorithm based on factor analysis and machine learning. According to the driving style identification algorithm, firstly, data strongly related to a driving style are selected as driving style characteristic parameters, dimensionality reduction is conducted on the characteristicparameters through factor analysis to obtain public factors, redundancy between driving data is reduced, and corresponding physical significance is given to the public factors; the public factors aretaken as an input, and labels of corresponding driving styles are marked for different drivers by adopting a Gaussian mixture model clustering algorithm; and a driving style identification model is trained by using a back propagation neural network optimized by a genetic algorithm. By fusing unsupervised learning and supervised learning, the identification cost can be effectively reduced. The genetic algorithm is used for optimizing the initial weight of the back propagation neural network, so that the identification precision of the model can be effectively improved, and the blank that the driving style cannot be identified statically is filled.

Description

technical field [0001] The invention belongs to the technical field of automobiles, and specifically relates to a driving style identification algorithm based on factor analysis and machine learning. Background technique [0002] With the continuous advancement of science and technology, the research on automobiles has gradually developed from the traditional driver's vehicle in the ring to the intelligent driving of unmanned vehicles. However, smart cars still have huge technological difficulties in the preparation of traffic regulations, vehicle safety technology redundancy, complex scene decision-making, etc., so drivers will still participate in the research and development of smart cars for a long time. [0003] Drivers often exhibit different driving styles due to different factors such as gender, driving age, and occupation. During the driving process of the vehicle, the driver will choose the appropriate driving operation according to his own driving style to obtain...

AI Technical Summary

Problems solved by technology

Using the genetic algorithm to optimize the initial weight of the backpropagation neural network can effectively improve the identification accuracy of the model, and fill the gap that the existing driving style cannot be accurately identified

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