Driving intention identification method of self-adaptive double particle swarm optimization support vector machine

Abstract

The invention provides a driving intention identification method of a self-adaptive double particle swarm optimization support vector machine, and relates to the technical field of gear shifting control strategies of an automobile gearbox. Driving intention classification numbering is carried out according to collected data, in order to solve the influence of parameter selection of the support vector machine on the model learning capability, the self-adaptive double particle swarm optimization is utilized for carrying out parameter optimization of the support vector machine, then the optimizedsupport vector machine is trained and verified through a collected data set, and finally, the driving intention is identified through the data acquired in real time. According to the method, the support vector machine in machine learning is adopted, the support vector machine is optimized, and therefore the driving intention can be quickly identified, the identification accuracy is high, the method can be applied to the gear shifting control strategies of the automobile gearbox, the gear shifting rationality during automobile driving is further improved, and the gear shifting quality is improved.

Description

technical field [0001] The invention relates to the field of shift control of automobile gearboxes, in particular to a driving intention recognition method of an adaptive dual particle swarm optimization support vector machine. Background technique [0002] With the continuous development of automobile intelligence, more and more new technologies are applied to automobile products, and the gearbox, as one of the essential components in the automobile transmission system, is also developing in the direction of intelligence. The automatic transmission can be divided into sports mode, economic mode, etc. according to different driving modes to meet the driving habits of different drivers. The driver directly reflects his driving intention by operating devices such as the opening of the accelerator pedal, while traditional driving mode switching needs to be done manually. By collecting relevant data and using machine learning methods, the transmission can automatically recogniz...

AI Technical Summary

Problems solved by technology

Drivers directly reflect their driving intentions by operating devices such as the accelerator pedal opening, while traditional driving mode switching needs to be done manually

Images