Online identification method, device, equipment and storage medium for steering gear zero-point parameters

Abstract

A method, apparatus, device, and storage medium for online identification of steering gear zero-position parameters are disclosed, relating to the field of intelligent driving technology for automobiles. The method includes: acquiring lateral error observation data when a target vehicle is detected to be traveling in a straight line; updating an initial loss function constructed based on the least squares method according to the lateral error observation data to generate a target loss function, the target loss function including a steering gear deviation function; and solving the target loss function using the gradient descent method to obtain the target steering gear deviation value, thereby achieving the identification of the steering gear zero-position parameters. This application can effectively improve the identification accuracy of steering gear deviation parameters while reducing computational complexity and the requirements for computational resources and training data.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology for automobiles, specifically to a method, device, equipment, and storage medium for online identification of steering gear zero-position parameters. Background Technology

[0002] Identifying the steering gear zero-position parameters (i.e., steering gear deviation parameters) is crucial for improving system positioning accuracy, stability, control performance, and adaptability to different working environments. Therefore, achieving accurate identification of steering gear deviation parameters is essential in the field of intelligent driving control.

[0003] In related technologies, steering gear deviation parameters are often identified through manual offline methods or Long Short-Term Memory (LSTM) networks. However, manual offline identification suffers from significant errors. While LSTM networks can improve the accuracy of steering gear deviation parameter identification, they require more parameters and computational resources due to the introduction of gating and long-term memory mechanisms, resulting in high computational complexity. Furthermore, LSTM networks typically require extensive iterations and evaluation of individual fitness, which places high demands on computational resources, especially when solving complex problems, potentially requiring more time and computing power. In addition, LSTM networks require more parameters to train, thus needing more training data to avoid overfitting. Insufficient training data may lead to insufficient generalization ability in LSTM networks. Summary of the Invention

[0004] This application provides a method, device, equipment, and storage medium for online identification of steering gear zero-position parameters, which can effectively improve the identification accuracy of steering gear deviation parameters while reducing computational complexity and the requirements for computing resources and training data.

[0005] In a first aspect, embodiments of this application provide an online identification method for steering gear zero-position parameters, the online identification method for steering gear zero-position parameters comprising:

[0006] When the target vehicle is detected to be traveling in a straight line, acquire lateral error observation data;

[0007] The initial loss function constructed based on the least squares method is updated according to the lateral error observation data to generate a target loss function, which includes a steering gear deviation function;

[0008] The target loss function is solved by gradient descent to obtain the target steering gear deviation value, thereby enabling the identification of the steering gear zero-position parameters.

[0009] In conjunction with the first aspect, in one implementation, the target loss function is:

[0010] L(θ)=∑(Ef(θ)) 2

[0011] In the formula, L(θ) represents the target loss function, E represents the lateral error observation data, θ represents the steering gear deviation parameter, and f(θ) represents the steering gear deviation function, which is used to characterize the predicted lateral error value.

[0012] In conjunction with the first aspect, in one implementation, the step of solving the target loss function using the gradient descent method to obtain the target steering gear deviation value includes:

[0013] The initial steering gear deviation value is obtained by solving the target loss function using the gradient descent method.

[0014] The initial steering gear deviation value is input into a preset lateral error tracking model for simulation to output the target steering angle;

[0015] When the target steering angle is within the preset steering angle threshold range, the initial steering gear deviation value is used as the target steering gear deviation value.

[0016] In conjunction with the first aspect, in one embodiment, prior to the step of acquiring lateral error observation data when the target vehicle is detected to be traveling in a straight line, the method further includes:

[0017] Obtain curvature feedforward information of the target vehicle's location;

[0018] The target curvature value of the target vehicle's location is determined based on the curvature feedforward information.

[0019] When the target curvature value is less than or equal to a preset curvature threshold, the target vehicle is determined to be traveling in a straight line.

[0020] Secondly, embodiments of this application provide an online identification device for steering gear zero-position parameters, the online identification device for steering gear zero-position parameters comprising:

[0021] The data acquisition module is used to acquire lateral error observation data when the target vehicle is detected to be traveling in a straight line;

[0022] The function update module is used to update the initial loss function constructed based on the least squares method according to the lateral error observation data to generate a target loss function, which includes a steering gear deviation function;

[0023] The parameter identification module is used to solve the target loss function using the gradient descent method to obtain the target steering gear deviation value, thereby realizing the identification of the steering gear zero-position parameters.

[0024] In conjunction with the second aspect, in one implementation, the target loss function is:

[0025] L(θ)=Σ(Ef(θ)) 2

[0026] In the formula, L(θ) represents the target loss function, E represents the lateral error observation data, θ represents the steering gear deviation parameter, and f(θ) represents the steering gear deviation function, which is used to characterize the predicted lateral error value.

[0027] In conjunction with the second aspect, in one implementation, the parameter identification module is specifically used for:

[0028] The initial steering gear deviation value is obtained by solving the target loss function using the gradient descent method.

[0029] The initial steering gear deviation value is input into a preset lateral error tracking model for simulation to output the target steering angle;

[0030] When the target steering angle is within the preset steering angle threshold range, the initial steering gear deviation value is used as the target steering gear deviation value.

[0031] In conjunction with the second aspect, in one embodiment, the online identification device for steering gear zero-position parameters further includes a status identification module, which is used for:

[0032] Obtain curvature feedforward information of the target vehicle's location;

[0033] The target curvature value of the target vehicle's location is determined based on the curvature feedforward information.

[0034] When the target curvature value is less than or equal to a preset curvature threshold, the target vehicle is determined to be traveling in a straight line.

[0035] Thirdly, embodiments of this application provide an online steering gear zero-position parameter identification device, which includes a processor, a memory, and an online steering gear zero-position parameter identification program stored in the memory and executable by the processor. When the online steering gear zero-position parameter identification program is executed by the processor, it implements the steps of the aforementioned online steering gear zero-position parameter identification method.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium storing a steering gear zero-position parameter online identification program, wherein when the steering gear zero-position parameter online identification program is executed by a processor, it implements the steps of the aforementioned steering gear zero-position parameter online identification method.

[0037] The beneficial effects of the technical solutions provided in this application include:

[0038] The initial loss function, including the steering gear deviation function, constructed using the least squares method, is updated based on the lateral error observation data corresponding to the target vehicle's straight-line driving state to generate the target loss function. Then, the target loss function is solved using the gradient descent method to obtain the target steering gear deviation value, thereby identifying the steering gear zero-position parameter. This application achieves fast and reliable online identification of steering gear deviation parameters by using the least squares method, without requiring additional parameters or computational load. This not only effectively improves identification accuracy but also reduces computational complexity and the requirements for computational resources and training data. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating an embodiment of the online identification method for steering gear zero-point parameters of this application;

[0040] Figure 2 For this application Figure 1 A detailed flowchart of step S30;

[0041] Figure 3 This is a schematic diagram of the functional modules of an embodiment of the online identification device for steering gear zero-position parameters of this application;

[0042] Figure 4 This is a schematic diagram of the hardware structure of the online identification device for steering gear zero-position parameters involved in the embodiments of this application. Detailed Implementation

[0043] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] This application provides an online identification method for steering gear zero-position parameters, which can effectively improve the identification accuracy of steering gear deviation parameters, while reducing computational complexity and the requirements for computing resources and training data.

[0045] To achieve the above objectives, the overall approach of this application is as follows:

[0046] Step S10: When the target vehicle is detected to be traveling in a straight line, acquire lateral error observation data;

[0047] Step S20: Update the initial loss function constructed based on the least squares method according to the lateral error observation data to generate a target loss function, the target loss function including the steering gear deviation function;

[0048] Step S30: Solve the target loss function using the gradient descent method to obtain the target steering gear deviation value, so as to identify the steering gear zero-position parameter.

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0050] In a first aspect, embodiments of this application provide a method for online identification of steering gear zero-position parameters.

[0051] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the online identification method for steering gear zero-position parameters of this application. Figure 1 As shown, the online identification method for steering gear zero-point parameters includes:

[0052] Step S10: When the target vehicle is detected to be traveling in a straight line, acquire lateral error observation data.

[0053] Exemplary and understandable, lateral error refers to the vertical distance between the vehicle's center of gravity and the lane centerline (or target trajectory), and steering deviation can be identified through lateral error. In this embodiment, steering deviation parameters are identified only when the target vehicle is traveling in a straight line. Therefore, when the target vehicle is detected to be traveling in a straight line, steering deviation parameters will not be identified. However, if the target vehicle is detected to be traveling in a straight line, lateral error observation data can be obtained through the vehicle controller, and the lateral error observation data can be cleaned to improve data quality. It should be noted that data cleaning includes, but is not limited to, missing value handling, outlier handling, and deduplication. The specific implementation methods and principles of data cleaning are common knowledge in the field, and therefore, for the sake of brevity, they are not limited here.

[0054] Furthermore, in one embodiment, before the step of acquiring lateral error observation data when the target vehicle is detected to be traveling in a straight line, the method further includes:

[0055] Obtain curvature feedforward information of the target vehicle's location;

[0056] The target curvature value of the target vehicle's location is determined based on the curvature feedforward information.

[0057] When the target curvature value is less than or equal to a preset curvature threshold, the target vehicle is determined to be traveling in a straight line.

[0058] Exemplary and understandable, curvature feedforward refers to using the curvature information of a reference path as input to feedforward control in a control system to adjust the controller's output in advance, thereby better responding to upcoming path changes. In this embodiment, the curvature feedforward information of the target vehicle's location can be obtained through the vehicle controller. Based on the relationship between curvature feedforward C and curvature c: C = K × c, the target curvature value of the target vehicle's position is determined, where K is a proportional parameter, and its specific value can be determined according to actual needs and is not limited here. Then, the relationship between the target curvature value and the curvature threshold is used to determine whether the target vehicle is located on a straight driving segment. If the target curvature value is less than the curvature threshold, it means that the target vehicle is located on a straight driving segment, and the target vehicle is determined to be in a straight driving state. If the target curvature value is greater than the curvature threshold, it means that the target vehicle is located on a non-straight driving segment, and the target vehicle is determined to be in a non-straight driving state. It should be noted that the specific value of the curvature threshold can be determined according to actual needs and is not limited here.

[0059] Step S20: Update the initial loss function constructed based on the least squares method according to the lateral error observation data to generate a target loss function, wherein the target loss function includes a steering gear deviation function; wherein the target loss function is:

[0060] L(θ)=Σ(Ef(θ)) 2

[0061] In the formula, L(θ) represents the target loss function, E represents the lateral error observation data, θ represents the steering gear deviation parameter, and f(θ) represents the steering gear deviation function, which is used to characterize the predicted lateral error value.

[0062] Exemplary, in this embodiment, the online identification of steering gear deviation parameters will be achieved using the least squares method, i.e., the sum of squares of the residuals will be preferred as the loss function. Specifically, the relationship between the steering gear deviation parameter θ and the lateral error can be represented as y = f(θ), where y represents the predicted lateral error value, and f is a function of the steering gear deviation parameter θ, i.e., f(θ) is the steering gear deviation function. The predicted lateral error value can then be obtained through the f(θ) function. It should be noted that the goal of this embodiment is to find the optimal parameter estimate θ that minimizes the sum of squares of the residuals between the observed lateral error data and the predicted lateral error value. It should be understood that the residual ∈ in this embodiment can be defined as the difference between the observed lateral error data E and the predicted lateral error value y, i.e., ∈ = Ef(θ), and the loss function is:

[0063] L(θ)=∑∈ 2 =∑(Ef(θ)) 2

[0064] Therefore, after obtaining the lateral error observation data E, the lateral error observation data E is substituted into the L(θ) loss function to update the loss function, thereby obtaining the target loss function L(θ)=∑(Ef(θ)). 2 .

[0065] Step S30: Solve the target loss function using the gradient descent method to obtain the target steering gear deviation value, so as to identify the steering gear zero-position parameter.

[0066] In this exemplary embodiment, after obtaining the target loss function, it is necessary to minimize the loss function to obtain the steering gear deviation value, thereby achieving the identification of the steering gear zero-position parameter. Specifically, the derivative of the target loss function with respect to the steering gear deviation parameter θ is calculated, and the derivative of the target loss function is set to zero, i.e. Then, the above equations are solved using the gradient descent method to obtain the optimal steering gear deviation parameter θ. It should be noted that the method and principle of determining the optimal parameter using the gradient descent method are common knowledge in this field. Therefore, for the sake of simplicity, they will not be elaborated here. Furthermore, the gradient descent method includes, but is not limited to, stochastic gradient descent and batch gradient descent.

[0067] It should be understood that, after fitting the lateral error E and the steering gear deviation r in this embodiment, the relationship between the two can be obtained as follows:

[0068] E = k × r

[0069] Where k represents a proportionality coefficient, the specific value of which can be determined according to actual needs and is not limited here; it can be seen that the steering gear deviation parameter θ=k×r can be determined through the above relationship. Therefore, after calculating the steering gear deviation parameter θ, the target steering gear deviation value can be calculated according to θ=k×r, thereby realizing fast, accurate and reliable online identification of the steering gear deviation parameter, which can effectively improve the system's response performance, stability and robustness.

[0070] Therefore, this embodiment, when using the least squares method to identify steering gear deviation parameters, does not require gating or long-term memory mechanisms, meaning it requires fewer parameters and less computation, and does not need to perform numerous iterations or evaluate individual fitness, thus reducing the demand for computational resources. Furthermore, there are no additional parameters to train, meaning less training data is needed to avoid overfitting. Therefore, this embodiment not only effectively improves identification accuracy but also reduces computational complexity and the requirements for computational resources and training data.

[0071] Further, see Figure 2As shown, the step of solving the target loss function using the gradient descent method to obtain the target steering gear deviation value includes:

[0072] Step S301: Solve the target loss function using the gradient descent method to obtain the initial steering gear deviation value;

[0073] Step S302: Input the initial steering gear deviation value into a preset lateral error tracking model for simulation to output the target steering angle;

[0074] Step S303: When the target steering angle is within the preset steering angle threshold range, the initial steering gear deviation value is used as the target steering gear deviation value.

[0075] In this exemplary embodiment, after obtaining the initial steering gear deviation value by solving the target loss function using the gradient descent method, the steering gear deviation identification result (i.e., the initial steering gear deviation value) is substituted into the lateral error tracking model for simulation to output the simulation result (i.e., the target steering angle). This target steering angle can be either the front wheel steering angle or the steering wheel angle (i.e., the steering gear angle), which is not limited here. Then, the simulation result is judged to determine whether the influence of the zero position on the control algorithm has been weakened. That is, if the target steering angle is within the preset steering angle threshold range, it indicates that the influence of the zero position on the control algorithm has been weakened. In this case, the calculation of the steering gear deviation value is stopped, and the initial steering gear deviation value is used as the target steering gear deviation value and substituted into the existing lateral control algorithm for autonomous driving control. It should be noted that the specific structure and working principle of the lateral error tracking model are common knowledge in the field, so for the sake of simplicity, they will not be elaborated here. Furthermore, the specific value limit of the steering angle threshold range can be determined according to actual needs and is not limited here.

[0076] If the target steering angle is outside the steering angle threshold range, it means that the influence of the zero position on the control algorithm has not been weakened. At this time, it is necessary to re-solve the optimal steering gear deviation parameter θ until the optimal steering gear deviation value corresponding to the solved optimal steering gear deviation parameter θ is substituted into the lateral error tracking model and the output steering angle is within the steering angle threshold range. Then, the optimal steering gear deviation value is substituted into the existing lateral control algorithm as the target steering gear deviation value for automatic driving control.

[0077] In summary, this embodiment provides an efficient parameter identification algorithm, which uses the least squares method to perform real-time calculation of steering gear zero-position parameters to achieve fast and reliable parameter estimation, thereby obtaining high-accuracy identification results with strong anti-interference capabilities. Furthermore, reasonable algorithm design and optimization reduce computational complexity and shorten the time required for parameter identification. Simultaneously, the data used in this embodiment can be easily obtained from the vehicle, ensuring its feasibility and practicality in real-time control systems. Therefore, the online steering gear zero-position parameter identification method provided in this embodiment can identify steering gear deviation parameters in the vehicle's lateral control algorithm in real time and can be applied to parameter adjustment in intelligent driving scenarios. When applying the online steering gear zero-position parameter identification method provided in this embodiment, the characteristics and requirements of the actual system can be considered to ensure that the proposed parameter identification method can operate effectively in various actual environments and application scenarios and meet the requirements of the actual control system.

[0078] Secondly, embodiments of this application also provide an online identification device for steering gear zero-position parameters.

[0079] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the online identification device for steering gear zero-position parameters of this application. Figure 3 As shown, the online identification device for steering gear zero-point parameters includes:

[0080] The data acquisition module is used to acquire lateral error observation data when the target vehicle is detected to be traveling in a straight line;

[0081] The function update module is used to update the initial loss function constructed based on the least squares method according to the lateral error observation data to generate a target loss function, which includes a steering gear deviation function;

[0082] The parameter identification module is used to solve the target loss function using the gradient descent method to obtain the target steering gear deviation value, thereby realizing the identification of the steering gear zero-position parameters.

[0083] Furthermore, in one embodiment, the target loss function is:

[0084] L(θ)=∑(Ef(θ)) 2

[0085] In the formula, L(θ) represents the target loss function, E represents the lateral error observation data, θ represents the steering gear deviation parameter, and f(θ) represents the steering gear deviation function, which is used to characterize the predicted lateral error value.

[0086] Furthermore, in one embodiment, the parameter identification module is specifically used for:

[0087] The initial steering gear deviation value is obtained by solving the target loss function using the gradient descent method.

[0088] The initial steering gear deviation value is input into a preset lateral error tracking model for simulation to output the target steering angle;

[0089] When the target steering angle is within the preset steering angle threshold range, the initial steering gear deviation value is used as the target steering gear deviation value.

[0090] Furthermore, in one embodiment, the online identification device for steering gear zero-position parameters further includes a status identification module, which is used for:

[0091] Obtain curvature feedforward information of the target vehicle's location;

[0092] The target curvature value of the target vehicle's location is determined based on the curvature feedforward information.

[0093] When the target curvature value is less than or equal to a preset curvature threshold, the target vehicle is determined to be traveling in a straight line.

[0094] The functions of each module in the above-mentioned online identification device for steering gear zero-position parameters correspond to the steps in the above-mentioned online identification method for steering gear zero-position parameters, and their functions and implementation processes will not be described in detail here.

[0095] Thirdly, embodiments of this application provide an online identification device for steering gear zero-position parameters. The online identification device for steering gear zero-position parameters can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0096] Reference Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of the online steering gear zero-position parameter identification device involved in the embodiments of this application. In this embodiment, the online steering gear zero-position parameter identification device may include a processor, a memory, a communication interface, and a communication bus.

[0097] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0098] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the online steering gear zero-position parameter identification device, as well as interfaces used for interconnecting the device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0099] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0100] The processor can be a general-purpose processor, which can call the online identification program for steering gear zero-position parameters stored in the memory and execute the online identification method for steering gear zero-position parameters provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the online identification program for steering gear zero-position parameters is called can be referred to in various embodiments of the online identification method for steering gear zero-position parameters of this application, and will not be repeated here.

[0101] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0102] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0103] The present application has a readable storage medium storing a steering gear zero-position parameter online identification program, wherein when the steering gear zero-position parameter online identification program is executed by a processor, the steps of the steering gear zero-position parameter online identification method as described above are implemented.

[0104] The method implemented when the online identification program for steering gear zero-position parameters is executed can be referred to in various embodiments of the online identification method for steering gear zero-position parameters of this application, and will not be repeated here.

[0105] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0106] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0107] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0108] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0109] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0111] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for online identification of zero-position parameters of a steering gear, characterized in that, The online identification method for zero-point parameters of the steering gear includes: When the target vehicle is detected to be traveling in a straight line, acquire lateral error observation data; The initial loss function constructed based on the least squares method is updated according to the lateral error observation data to generate a target loss function, which includes a steering gear deviation function; The target loss function is solved by gradient descent to obtain the target steering gear deviation value, thereby enabling the identification of the steering gear zero-position parameters. The step of solving the target loss function using the gradient descent method to obtain the target steering gear deviation value includes: The initial steering gear deviation value is obtained by solving the target loss function using the gradient descent method. The initial steering gear deviation value is input into a preset lateral error tracking model for simulation to output the target steering angle; When the target steering angle is within the preset steering angle threshold range, the initial steering gear deviation value is used as the target steering gear deviation value.

2. The online identification method for steering gear zero-position parameters as described in claim 1, characterized in that, The target loss function is: In the formula, Describes the target loss function. This represents the lateral error observation data. This indicates the steering gear deviation parameter. The steering gear deviation function is used to characterize the predicted lateral error.

3. The online identification method for steering gear zero-position parameters as described in claim 1, characterized in that, Before the step of acquiring lateral error observation data when the target vehicle is detected to be traveling in a straight line, the method further includes: Obtain curvature feedforward information of the target vehicle's location; The target curvature value of the target vehicle's location is determined based on the curvature feedforward information. When the target curvature value is less than or equal to a preset curvature threshold, the target vehicle is determined to be traveling in a straight line.

4. A device for online identification of zero-position parameters of a steering gear, characterized in that, The online identification device for zero-point parameters of the steering gear includes: The data acquisition module is used to acquire lateral error observation data when the target vehicle is detected to be traveling in a straight line; The function update module is used to update the initial loss function constructed based on the least squares method according to the lateral error observation data to generate a target loss function, which includes a steering gear deviation function; The parameter identification module is used to solve the target loss function by gradient descent to obtain the target steering gear deviation value, so as to identify the steering gear zero-position parameter. The parameter identification module is specifically used for: The initial steering gear deviation value is obtained by solving the target loss function using the gradient descent method. The initial steering gear deviation value is input into a preset lateral error tracking model for simulation to output the target steering angle; When the target steering angle is within the preset steering angle threshold range, the initial steering gear deviation value is used as the target steering gear deviation value.

5. The online identification device for steering gear zero-position parameters as described in claim 4, characterized in that, The target loss function is: In the formula, Describes the target loss function. This represents the lateral error observation data. This indicates the steering gear deviation parameter. The steering gear deviation function is used to characterize the predicted lateral error.

6. The online identification device for steering gear zero-position parameters as described in claim 4, characterized in that, The online identification device for steering gear zero-point parameters also includes a status identification module, which is used for: Obtain curvature feedforward information of the target vehicle's location; The target curvature value of the target vehicle's location is determined based on the curvature feedforward information. When the target curvature value is less than or equal to a preset curvature threshold, the target vehicle is determined to be traveling in a straight line.

7. A device for online identification of zero-position parameters of a steering gear, characterized in that, The online steering gear zero-position parameter identification device includes a processor, a memory, and an online steering gear zero-position parameter identification program stored in the memory and executable by the processor, wherein when the online steering gear zero-position parameter identification program is executed by the processor, it implements the steps of the online steering gear zero-position parameter identification method as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a steering gear zero-position parameter online identification program, wherein when the steering gear zero-position parameter online identification program is executed by a processor, it implements the steps of the steering gear zero-position parameter online identification method as described in any one of claims 1 to 3.

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