CN-122009169-A - Zero drift self-correction method, device, equipment and storage medium

CN122009169ACN 122009169 ACN122009169 ACN 122009169ACN-122009169-A

Abstract

The application discloses a zero drift self-correction method, a device, equipment and a storage medium, which relate to the technical field of new energy automobiles and comprise the steps of obtaining lane clamp angle information, vehicle yaw angle speed information, wheel speed difference information, road curvature information and vehicle speed information; the method comprises the steps of inputting predefined geometric straight line models based on lane clamp angle information, vehicle yaw angle speed information, wheel speed difference information, road curvature information and vehicle speed information, resolving zero drift values corresponding to steering wheel angles under a plurality of vehicle zero position learning windows, determining steering wheel angle zero drift values, determining corresponding target zero drift compensation amounts based on the steering wheel angle zero drift values, controlling vehicle correction steering based on the target zero drift compensation amounts, and completing zero drift self-correction.

Inventors

QIN HAOXIANG
JIANG WENHAI
SHI CONG
WU LIUYI
Yan Rungang
SU HAIYUAN
MENG YUZHEN
YANG SHIGANG
SHEN GUODONG
ZHANG JINZHOU

Assignees

东风柳州汽车有限公司

Dates

Publication Date: 20260512
Application Date: 20260128

Claims (10)

1. A zero drift self-correction method, the method comprising: Acquiring lane clamp angle information, vehicle yaw rate information, wheel speed difference information, road curvature information and vehicle speed information; Inputting predefined geometric linear models based on the lane clamp angle information, the vehicle yaw angle speed information, the wheel speed difference information, the road curvature information and the vehicle speed information to calculate zero drift values corresponding to steering wheel angles under a plurality of vehicle zero learning windows, and determining steering wheel angle zero drift values; determining a corresponding target zero drift compensation amount based on the steering wheel angle zero drift value; and controlling the vehicle to correct and steer based on the target zero drift compensation quantity, and completing zero drift self-correction.
2. The method of claim 1, wherein the determining a steering wheel angle zero drift value based on the lane clamp angle information, the vehicle yaw rate information, the wheel speed difference information, the road curvature information, and the vehicle speed information input to a predefined geometric straight line model to calculate zero drift values corresponding to steering wheel angles under a plurality of vehicle zero learning windows comprises: Inputting predefined geometric straight line models based on the lane clamp angle information, the vehicle yaw angle speed information, the wheel speed difference information, the road curvature information and the vehicle speed information to detect geometric straight lines corresponding to a vehicle driving route, and determining vehicle driving geometric straight line information; Determining a corresponding vehicle zero learning window based on the vehicle driving geometric straight line information; and resolving a zero drift value corresponding to the steering wheel angle under a predefined sampling period based on the vehicle zero learning window to obtain the steering wheel angle zero drift value.
3. The method of claim 2, wherein the step of determining vehicle travel geometric straight line information based on the lane clamp angle information, the vehicle yaw rate information, the wheel speed difference information, the road curvature information, and the vehicle speed information input to a predefined geometric straight line model to detect a geometric straight line corresponding to a vehicle travel route comprises: Acquiring historical zero drift information; inputting a predefined geometric straight line model based on the historical zero drift information for training, and determining a target geometric straight line model; And inputting the target geometric straight line model based on the lane clamp angle information, the vehicle yaw angle speed information, the wheel speed difference information, the road curvature information and the vehicle speed information to detect a geometric straight line corresponding to a vehicle driving route, and obtaining vehicle driving geometric straight line information.
4. The method of claim 2, wherein the step of calculating a steering wheel angle zero drift value based on the vehicle zero learning window corresponding to a steering wheel angle at a predefined sampling period comprises: Acquiring an original angle value of a steering wheel corner according to a predefined sampling period based on the vehicle zero position learning window, and determining a sequence of the original angle values of the steering wheel corner; And calculating the zero drift value corresponding to the steering wheel angle original angle value sequence by using an arithmetic average algorithm to obtain the steering wheel angle zero drift value.
5. The method of claim 1, wherein the step of determining a corresponding target zero drift compensation amount based on the steering wheel angle zero drift value comprises: Determining symbol information, drift value amplitude information and distribution variance information under a plurality of continuous vehicle zero learning windows based on the steering wheel angle zero drift value; Checking based on the symbol information, the drift value amplitude information and the distribution variance information to obtain a checking result; And determining the corresponding target zero drift compensation amount according to the verification result.
6. The method of claim 1, wherein the step of controlling vehicle correction steering based on the target zero drift compensation amount to accomplish zero drift self-correction comprises: Acquiring a real-time turning angle of a vehicle; Compensating and correcting the real-time turning angle amount of the vehicle based on the target zero drift compensation amount, and determining a corresponding target correcting turning angle of the vehicle; And controlling the vehicle to correct steering based on the vehicle target correction steering angle to finish zero drift self-correction.
7. The method of claim 6, wherein the step of controlling vehicle corrected steering based on the vehicle target corrected angle of rotation to accomplish zero drift self-correction comprises: Acquiring feedback information, wherein the feedback information comprises a current zero compensation value, data confidence coefficient and sensor information; according to the feedback information, the vehicle target correction rotation angle is adjusted, and a corresponding vehicle rotation angle instruction is generated; And controlling the vehicle to correct and steer based on the vehicle steering angle instruction, and completing zero drift self-correction.
8. A zero drift self-correction device, the device comprising: The acquisition module is used for acquiring lane clamp angle information, vehicle yaw angle speed information, wheel speed difference information, road curvature information and vehicle speed information; The processing module is used for inputting a predefined geometric straight line model based on the lane clamp angle information, the vehicle yaw angle speed information, the wheel speed difference information, the road curvature information and the vehicle speed information to calculate zero drift values corresponding to steering wheel angles under a plurality of vehicle zero learning windows and determining steering wheel angle zero drift values; The processing module is further used for determining a corresponding target zero drift compensation amount based on the steering wheel angle zero drift value; And the execution module is used for controlling the vehicle to correct and steer based on the target zero drift compensation quantity so as to finish zero drift self-correction.
9. A zero drift self-correction device, characterized in that it comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the zero drift self-correction method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the zero drift self-correction method according to any one of claims 1 to 7.

Description

Zero drift self-correction method, device, equipment and storage medium Technical Field The application relates to the technical field of new energy automobiles, in particular to a zero drift self-correction method, a zero drift self-correction device, zero drift self-correction equipment and a storage medium. Background The steering-by-wire system realizes the transverse control of the vehicle through the rotation angle sensor and the execution motor, the zero position accuracy directly influences the straight running performance, the automatic driving track following precision and the driving safety of the vehicle, and in the long-term running process of the vehicle, the electric zero position of the steering wheel can be gradually deviated due to factors such as temperature drift, tooth gaps, mechanical abrasion, power failure disturbance, sensor aging and the like, so that the system outputs a target rotation angle based on the wrong zero position, the vehicle is deviated from the geometric center line, and the running safety and the control precision are influenced. At present, the existing method is to control the steering motor angle to be zero and rely on the course angle value acquired by an inertial navigation unit to carry out zero offset automatic compensation, however, the existing method relies on motor active zero locking and course angle stability, the straight running working condition is easy to misjudge under complex scenes such as crosswind, ramps and special-shaped roads, only single sensor information is used, the existing multi-source data of the vehicle cannot be utilized for cross verification, the automatic self-learning capability in running is not achieved, and zero on-line maintenance and continuous correction of the full life cycle of the vehicle cannot be realized. Therefore, how to perform zero drift self-correction more efficiently and accurately is a problem to be solved. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide a zero drift self-correction method, device, equipment and storage medium, which aim at solving the technical problem of how to more efficiently and accurately perform zero drift self-correction. In order to achieve the above object, the present application provides a zero drift self-correction method, which includes: Acquiring lane clamp angle information, vehicle yaw rate information, wheel speed difference information, road curvature information and vehicle speed information; Inputting predefined geometric linear models based on the lane clamp angle information, the vehicle yaw angle speed information, the wheel speed difference information, the road curvature information and the vehicle speed information to calculate zero drift values corresponding to steering wheel angles under a plurality of vehicle zero learning windows, and determining steering wheel angle zero drift values; determining a corresponding target zero drift compensation amount based on the steering wheel angle zero drift value; and controlling the vehicle to correct and steer based on the target zero drift compensation quantity, and completing zero drift self-correction. In an embodiment, the step of determining the steering wheel angle zero drift value by calculating zero drift values corresponding to steering wheel angles under a plurality of vehicle zero learning windows based on the lane clamp angle information, the vehicle yaw rate information, the wheel speed difference information, the road curvature information, and the vehicle speed information input predefined geometric straight line model includes: Inputting predefined geometric straight line models based on the lane clamp angle information, the vehicle yaw angle speed information, the wheel speed difference information, the road curvature information and the vehicle speed information to detect geometric straight lines corresponding to a vehicle driving route, and determining vehicle driving geometric straight line information; Determining a corresponding vehicle zero learning window based on the vehicle driving geometric straight line information; and resolving a zero drift value corresponding to the steering wheel angle under a predefined sampling period based on the vehicle zero learning window to obtain the steering wheel angle zero drift value. In an embodiment, the step of determining the vehicle driving geometric straight line information includes detecting a geometric straight line corresponding to a vehicle driving route based on the lane clamping angle information, the vehicle yaw rate information, the wheel speed difference information, the road curvature information and the vehicle speed information input into a predefined geometric straight line model: Acquiring historical zero drift informat