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Learning type path tracking prediction control method for automatic driving vehicle

A technology of predictive control and path tracking, applied in the direction of control devices, etc., can solve problems such as external interference and unfavorable tracking effects, and achieve good robustness and precise tracking accuracy

Active Publication Date: 2021-08-27
HUNAN UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, with these current tracking methods, the vehicle is easily affected by factors such as random noise, road undulations, and external interference during the path tracking process, which is not conducive to the tracking effect.

Method used

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  • Learning type path tracking prediction control method for automatic driving vehicle
  • Learning type path tracking prediction control method for automatic driving vehicle
  • Learning type path tracking prediction control method for automatic driving vehicle

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Embodiment Construction

[0084] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0085] An embodiment of the present invention provides a learning-type path tracking predictive control method for an automatic driving vehicle. The automatic driving vehicle includes: a vehicle state acquisition device, which may be a GPS device, an inertial measurement unit (IMU), and other sensors for measuring the vehicle state. The state of the system includes, but is not limited to, the collection of vehicle speed, lateral position, yaw angle, center of mass side slip angle, and yaw angle change rate, as well as other quantities related to vehicle travel that need to be collected and measured. The system state of the vehicle is only a title, which is used to indicate the driving state of the vehicle, and may also have other names, such as the running state of the vehicle, etc., which is not limited herein. A system may refer to the entirety of d...

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Abstract

The embodiment of the invention discloses learning type path tracking prediction control method for an automatic driving vehicle. The method comprises the following steps: acquiring a current system state of a vehicle at a current sampling moment; obtaining future N expected states of the vehicle; predicting the future state of the vehicle according to the current state and the learning type prediction control model, and obtaining an optimal control sequence in combination with N future expected states, a preset objective function and system constraints; and controlling operation of the vehicle by using the first quantity in the obtained optimal control sequence until the next sampling moment arrives, calculating to obtain the next optimal control sequence, and repeating the steps, and calculating at each sampling moment until the vehicle travels for a whole path.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of automatic driving vehicles, in particular to a learning-type path tracking predictive control method for automatic driving vehicles. Background technique [0002] Autonomous driving has the advantages of reducing traffic congestion, improving traffic efficiency, and reducing driver workload. It has received extensive attention in recent years. Among them, path tracking is a key technology for realizing autonomous driving. [0003] The current path tracking methods mainly include path tracking based on geometric models, path tracking based on model-free feedback control, and path tracking based on model-based feedback control. However, in the current tracking methods, the vehicle is easily affected by factors such as random noise, road undulations, and external interference during the path tracking process, which is not conducive to the tracking effect. Contents of the invention ...

Claims

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Application Information

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IPC IPC(8): B60W60/00B60W50/00
CPCB60W60/001B60W50/00B60W2050/0031
Inventor 边有钢张田田胡云卿刘海涛尚敬胡满江徐彪秦兆博秦洪懋王晓伟秦晓辉谢国涛丁荣军
Owner HUNAN UNIV
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