A driver model-based intelligent vehicle direction control method and control system

Abstract

The invention discloses an automobile direction intelligent control method based on a driver model. The method includes the steps of firstly, predicting a position where an automobile can reach after a moment T of a preview period according to the current running state of the automobile, comparing the position with a target position after the moment T, obtaining deviation of the two positions, obtaining the ratio of the deviation and T to serve as a predicted automobile transverse velocity, and then comparing the predicted automobile transverse velocity with an actual automobile transverse velocity to obtain a predicted automobile transverse velocity difference; secondly, obtaining a steering wheel turning angle through calculation, and controlling an automobile steering wheel according to the obtained steering wheel turning angle. The invention further discloses an automobile direction intelligent control system based on the drive model. The system comprises a preview module, a prediction module, a comparing module, a calculation module and a control module. Compared with the prior art, parameters of the driver model built in the method are directly obtained through parameters of the whole automobile, the driver module has the advantages of being simple in parameter and clear in physical meaning, and control of the automobile is more accurate and real.

Description

technical field [0001] The invention relates to an automobile intelligent control method, in particular to an automobile direction intelligent control method and control system based on a driver model, and belongs to the technical field of automatic control. Background technique [0002] With the advancement of science and technology and the improvement of social level, people's demand for cars is increasing, and the number of private cars has increased year by year, which has caused traffic safety problems to become increasingly prominent. This situation is particularly evident in our country, where the number of deaths due to traffic safety accidents has ranked first in the world for ten consecutive years. Therefore, the issue of vehicle safety becomes critical. [0003] In order to solve traffic safety problems, it is not only necessary to pay attention to the formulation of various traffic safety regulations, but also to study intelligent transportation systems, smart c...

AI Technical Summary

Problems solved by technology

However, when it turns at high speed, the tracking accuracy is not high and the steering fluctuation phenomenon occurs, so its application at high speed has certain limitations; the LQR model adopts the optimization method, the theory is rigorous, and the tracking accuracy is high

However, its physical concept is not clear, and the use of multi-point preview at the same time leads to a large amount of optimization calculations, making it difficult to apply in practice

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