Adaptive prediction time domain steering control method and device for autonomous vehicle

Abstract

The embodiment of the application discloses an adaptive prediction time domain steering control method and device for an autonomous vehicle. The method comprises the following steps: acquiring a vehicle running reference track, and forming a vehicle running reference track under a global coordinate system according to the acquired track; performing fitting based on map information acquired by a vehicle global positioning system (GPS) and a differential system to form a vehicle running expected track curve; building a prediction model with a course deviation between a vehicle running path and the reference track based on a vehicle kinematic model and the vehicle running expected track curve; designing an adaptive variable sampling period; and periodically adjusting the track of the vehiclein the running process based on the sampling period to realize track following. According to the method in the embodiment of the application, the reference track is followed in an adaptive period prediction control manner, and the vehicle has a very good track following control effect after the adoption of an adaptive period considering the road curvature change, so that the control accuracy is improved, and relatively high stability is achieved in the control process.

Description

technical field [0001] The present application relates to the field of vehicle control in automatic driving, and in particular to a method, device, device and computer-readable storage medium for adaptive predictive time-domain steering control of an automatic driving vehicle. Background technique [0002] With the continuous development of autonomous driving technology, more and more people pay attention to autonomous vehicles, and the steering system of a vehicle is an important part of autonomous driving technology. Path tracking control is the main research content of steering motion control of unmanned vehicles, that is, according to the reference path given by the path planning system and the real-time position and speed of the vehicle obtained by the positioning and orientation system, the steering wheel actuator is calculated according to a certain control algorithm. The amount of control allows the vehicle to quickly track the planned path. The current mainstream t...

AI Technical Summary

Problems solved by technology

The PID control algorithm is widely used in the industrial field, and it is also widely used in the trajectory tracking control of autonomous vehicles because of its simplicity and ease of operation. However, in the control process, constant trial and error parameters are required, especially when the vehicle speed changes or the road curvature changes. Obviously, the PID control algorithm needs to readjust the parameters, so this method has poor adaptability and it is difficult to achieve smooth trajectory tracking with changing curvature

The model predictive control method is widely used in trajectory tracking because of its good control stability and tracking ability. However, in the existing technology, the influence of road curvature and the degree of road curvature change is less considered, and the fixed-period prediction time domain is mostly used. state, when the vehicle is driving on a road surface with large changes in road curvature and curvature, the accuracy and stability of trajectory tracking are low, and it is difficult to achieve the desired trajectory tracking effect

Some scholars have combined the preview control method and the model prediction method to optimize the controller for this problem, and use the trajectory re-planning method to reduce the impact of the trajectory on the vehicle tracking control. design, it is difficult to guarantee the application of the vehicle in practical problems

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