Vehicle steering control method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a vehicle steering control method and device, electronic equipment and storage medium, and relates to the field of artificial intelligence such as automatic driving and intelligent transportation. The method can include: obtaining a compensated to-be-optimized steering signal generated by previous processing as a target steering signal, performing steering control on the vehicle according to the target steering signal, and obtaining vehicle state information after the steering control; determining a gap parameter according to the target steering signal and the vehicle state information; obtaining the to-be-optimized steering signal, performing inverse gap compensation on the to-be-optimized steering signal according to the gap parameter, and obtaining a compensated to-be-optimized steering signal generated by current processing. The scheme disclosed in the present disclosure can improve the stability and safety of vehicle driving.

Description

Vehicle steering control methods, devices, electronic equipment and storage media Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and in particular to vehicle steering control methods, devices, electronic devices and storage media in the fields of autonomous driving and intelligent transportation. Background Technology

[0002] For steering systems of autonomous vehicles, the gap problem (or gap nonlinearity problem) is a relatively common nonlinear problem. The gap comes from the clearance between transmission components such as gears and shafts. This nonlinearity problem can lead to inadequate steering control, especially in straight-line driving scenarios, where it will be particularly noticeable, such as causing the vehicle to sway from side to side. Summary of the Invention

[0003] This disclosure provides a vehicle steering control method, apparatus, electronic device, and storage medium.

[0004] A vehicle steering control method, comprising:

[0005] The compensation-adjusted steering signal generated in the previous processing is obtained and used as the target steering signal. The vehicle is then steered according to the target steering signal, and the vehicle status information after steering control is obtained.

[0006] The clearance parameters are determined based on the target steering signal and the vehicle status information;

[0007] Obtain the steering signal to be optimized, and perform inverse clearance compensation on the steering signal to be optimized according to the clearance parameter to obtain the compensated steering signal to be optimized generated in this process.

[0008] A vehicle steering control device includes: a control identification module and a signal compensation module;

[0009] The control identification module is used to acquire the compensated steering signal to be optimized generated in the previous processing, take it as the target steering signal, perform steering control on the vehicle according to the target steering signal, acquire the vehicle status information after steering control, determine the gap parameter according to the target steering signal and the vehicle status information, and provide the gap parameter to the signal compensation module.

[0010] The signal compensation module is used to acquire the steering signal to be optimized, perform inverse clearance compensation on the steering signal to be optimized according to the clearance parameter, obtain the compensated steering signal to be optimized generated in this process, and provide it to the control identification module.

[0011] An electronic device, comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described above.

[0015] A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.

[0016] A computer program product includes a computer program / instructions that, when executed by a processor, implement the method described above.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0018] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0019] Figure 1 is a flowchart of an embodiment of the vehicle steering control method described in this disclosure;

[0020] Figure 2 is a schematic diagram of the equivalent representation of the vehicle steering system described in this disclosure;

[0021] Figure 3 is a schematic diagram of the reverse gap compensation process described in this disclosure;

[0022] Figure 4 is a schematic diagram of the overall implementation process of the vehicle steering control method described in this disclosure;

[0023] Figure 5 is a schematic diagram of the composition structure of embodiment 500 of the vehicle steering control device described in this disclosure;

[0024] Figure 6 shows a schematic block diagram of an electronic device 600 that can be used to implement embodiments of the present disclosure. Detailed Implementation

[0025] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0026] Furthermore, it should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0027] Figure 1 is a flowchart of an embodiment of the vehicle steering control method described in this disclosure. As shown in Figure 1, the specific implementation methods include the following.

[0028] In step 101, the compensated steering signal to be optimized generated in the previous processing is obtained and used as the target steering signal. The vehicle is then steered according to the target steering signal, and the vehicle status information after steering control is obtained.

[0029] In step 102, the clearance parameters are determined based on the target steering signal and vehicle status information.

[0030] In step 103, the steering signal to be optimized is obtained, and the steering signal to be optimized is compensated in reverse according to the clearance parameter to obtain the compensated steering signal to be optimized generated in this process. Then, step 101 is repeated.

[0031] In the above-described method embodiments, an adaptive approach can be used to identify and compensate for the clearance nonlinearity problem of the vehicle steering system online. This can effectively improve the problem of inadequate vehicle control caused by clearance nonlinearity, such as effectively eliminating the problem of left and right swaying when the vehicle is driving straight, thereby improving the stability and safety of vehicle driving.

[0032] Preferably, the vehicle is an autonomous vehicle. During the vehicle's operation, the scheme described in the above method embodiment can be repeatedly executed, thereby continuously generating target steering signals, and for each generated target steering signal, the processes shown in steps 101-103 can be executed respectively.

[0033] Specifically, for each acquired target steering signal, the vehicle can be steered according to the target steering signal, and the vehicle status information after steering control can be obtained.

[0034] The specific information included in the vehicle status information can be determined according to actual needs. For example, it may include various information related to the steering operations performed by the vehicle.

[0035] Then, the clearance parameter can be determined based on the target steering signal and vehicle status information. Preferably, the vehicle status information corresponding to the previous target steering signal adjacent to the target steering signal can be obtained as the vehicle historical status information, and the clearance parameter can be determined based on the target steering signal, vehicle status information, and vehicle historical status information.

[0036] In other words, in addition to obtaining the vehicle status information corresponding to the current target steering signal, it can also obtain the vehicle status information corresponding to the previous (adjacent) target steering signal as the vehicle historical status information. Accordingly, the required clearance parameters can be determined by combining the target steering signal, vehicle status information and vehicle historical status information, thereby further improving the accuracy of the determined clearance parameters.

[0037] Preferably, the required clearance parameters can be determined by recursive least squares (RLS) based on the target steering signal, vehicle status information, vehicle historical status information, and a pre-determined first mathematical model.

[0038] Preferably, the first mathematical model can be a mathematical model determined by combining the second and third mathematical models. The second mathematical model can be a mathematical model corresponding to the first-order inertial element, and the third mathematical model can be a mathematical model corresponding to the gap nonlinearity. In this case, the vehicle steering system is equivalent to a series combination model of the first-order inertial element and the gap nonlinearity.

[0039] In addition, preferably, the gap parameter may include a first gap parameter and a second gap parameter.

[0040] Figure 2 is a schematic diagram of the equivalent representation of the vehicle steering system described in this disclosure. As shown in Figure 2, the vehicle steering system can be equivalently represented as a series combination model of a first-order inertial element and a clearance nonlinearity. Based on this model, steering control and clearance parameter identification are completed, where u represents the target steering signal, w represents intermediate result information, and y represents vehicle state information.

[0041] Among them, by combining Euler's backward difference method and other methods, the discrete form of the mathematical model (second mathematical model) corresponding to the first-order inertial element can be determined as follows:

[0042] w(k)=a*w(k-1)+b*u(k); (1)

[0043] in,

[0044] T represents the sampling time interval of the Euler backward difference method, k represents the current time, k-1 represents the previous time, w(k) represents the latest intermediate result information, w(k-1) represents the previous intermediate result information, and a0 and a1 are model coefficients.

[0045] Furthermore, the mathematical model corresponding to the gap nonlinearity (the third mathematical model) is as follows:

[0046]

[0047] Where y(k) represents vehicle status information, y(k-1) represents vehicle historical status information, and d l d represents the first gap parameter. r This represents the second gap parameter; additionally, d l and d r These are the left and right clearance parameters (left and right clearance values).

[0048] Combining formulas (2) and (3), we get the following form:

[0049]

[0050] in, Let y(k-1) be the derivative of y(k-1).

[0051] Let ψ = [y(k-1), u(k), 1], Θ l =[a,b,(1-a)d l ] T Θ r =[a,b,(a-1)d r ] T Equation (4) can be transformed into the following standard least squares form, i.e., the first mathematical model:

[0052]

[0053] Accordingly, the required clearance parameter d can be determined using the existing RLS method based on the target steering signal u(k), vehicle state information y(k), vehicle historical state information y(k-1), and formula (5). l and d r .

[0054] In the above processing method, by making an equivalent expression of the vehicle steering system and combining the second and third mathematical models, a first mathematical model corresponding to the vehicle steering system can be effectively constructed. Then, based on the various information obtained and the first mathematical model, the required clearance parameters can be determined efficiently and accurately, thus laying a good foundation for subsequent processing.

[0055] It should be noted that the above-described method for determining the clearance parameters is merely illustrative and is not intended to limit the technical solutions disclosed herein. For example, the target steering signal and vehicle status information (or the target steering signal, vehicle status information, and historical vehicle status information) can also be input into a pre-trained prediction model to determine the clearance parameters.

[0056] Given the determined clearance parameters, inverse clearance compensation can be achieved by using them. This allows us to obtain the steering signal to be optimized, perform inverse clearance compensation on the steering signal to be optimized based on the clearance parameters, and use the compensated steering signal to be optimized as the new target steering signal.

[0057] Figure 3 is a schematic diagram of the backlash compensation process described in this disclosure. As shown in Figure 3, v represents the steering signal to be optimized, which can be a steering signal generated using an existing steering control algorithm, and u represents the new target steering signal obtained after backlash compensation.

[0058] Preferably, the method of performing inverse clearance compensation on the steering signal to be optimized according to the clearance parameter may include: obtaining the derivative of the steering signal to be optimized; in response to determining that the derivative is less than 0, obtaining the difference between the steering signal to be optimized and the first clearance parameter, and using the difference as the compensated steering signal to be optimized generated in this process, i.e., as the target steering signal; in response to determining that the derivative is greater than 0, obtaining the sum of the steering signal to be optimized and the second clearance parameter, and using the sum as the target steering signal.

[0059] According to the different values ​​of the derivative, the reverse clearance compensation of the steering signal to be optimized can be performed in a corresponding manner, thereby making the compensation more targeted and further improving the accuracy of the compensation results.

[0060] Alternatively, preferably, in response to determining that the derivative is equal to 0, the steering signal to be optimized can be used as the target steering signal.

[0061] That is, when the derivative is equal to 0, there is no need to perform reverse backlash compensation, and the steering signal to be optimized can be directly used as the target steering signal, thereby improving processing efficiency, etc.

[0062] Accordingly, we can have:

[0063]

[0064] in, This represents the derivative of the steering signal to be optimized.

[0065] Based on the foregoing description, Figure 4 is a schematic diagram of the overall implementation process of the vehicle steering control method described in this disclosure. As shown in Figure 4, the vehicle can be steered according to the target steering signal at the current moment, and the vehicle state information after steering control can be obtained. Correspondingly, gap identification can be performed based on the target steering signal and the vehicle state information, i.e., the gap parameter can be determined. In addition, the steering control algorithm can generate a steering signal to be optimized, i.e., the steering signal at the next moment, based on the vehicle state information. Then, the gap parameter can be used to perform inverse gap compensation on the steering signal to be optimized, thereby obtaining the target steering signal at the next moment. Then, the vehicle can be steered using the target steering signal at the next moment, and the process can be repeated continuously.

[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this disclosure. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0067] In summary, the solution described in the embodiments of this disclosure can effectively improve the problem of inadequate vehicle control caused by gap nonlinearity. For example, it can effectively eliminate the problem of left and right swaying when the vehicle is traveling straight due to gap nonlinearity, thereby improving the stability and safety of vehicle driving. Moreover, the method is simple and convenient to implement, reducing implementation and maintenance costs.

[0068] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0069] Figure 5 is a schematic diagram of the composition of an embodiment 500 of the vehicle steering control device described in this disclosure. As shown in Figure 5, it includes: a control identification module 501 and a signal compensation module 502.

[0070] The control identification module 501 is used to acquire the compensated steering signal to be optimized generated in the previous processing, use it as the target steering signal, perform steering control on the vehicle according to the target steering signal, acquire the vehicle status information after steering control, determine the gap parameter according to the target steering signal and the vehicle status information, and provide the gap parameter to the signal compensation module 502.

[0071] The signal compensation module 502 is used to acquire the steering signal to be optimized, perform reverse clearance compensation on the steering signal to be optimized according to the clearance parameter, obtain the compensated steering signal to be optimized generated in this process, and provide it to the control identification module 501.

[0072] In the above-described device embodiment, an adaptive approach can be used to identify and compensate for the clearance nonlinearity problem of the vehicle steering system online. This can effectively improve the problem of inadequate vehicle control caused by clearance nonlinearity, such as effectively eliminating the problem of left and right swaying when the vehicle is driving straight due to clearance nonlinearity, thereby improving the stability and safety of vehicle driving.

[0073] Preferably, the vehicle is an autonomous vehicle. During the vehicle's operation, the process corresponding to the scheme described in the above device embodiments can be repeatedly executed.

[0074] For each target steering signal acquired, the control identification module 501 can perform steering control on the vehicle based on the target steering signal, and can acquire the vehicle status information after steering control. Then, the clearance parameter can be determined based on the target steering signal and the vehicle status information.

[0075] The specific information included in the vehicle status information can be determined according to actual needs. For example, it may include various information related to the steering operations performed by the vehicle.

[0076] Preferably, the control identification module 501 can acquire the vehicle status information corresponding to the previous target steering signal adjacent to the target steering signal, as the vehicle historical status information, and can determine the gap parameter based on the target steering signal, the vehicle status information and the vehicle historical status information.

[0077] In other words, in addition to obtaining the vehicle status information corresponding to the current target steering signal, it can also obtain the vehicle status information corresponding to the previous (adjacent) target steering signal as the vehicle historical status information. Accordingly, the required gap parameter can be determined by combining the target steering signal, vehicle status information and vehicle historical status information.

[0078] Preferably, the control identification module 501 can determine the required clearance parameters using the RLS method based on the target steering signal, vehicle status information, vehicle historical status information, and a pre-determined first mathematical model.

[0079] Preferably, the first mathematical model can be a mathematical model determined by combining the second and third mathematical models. The second mathematical model can be a mathematical model corresponding to the first-order inertial element, and the third mathematical model can be a mathematical model corresponding to the gap nonlinearity. In this case, the vehicle steering system is equivalent to a series combination model of the first-order inertial element and the gap nonlinearity.

[0080] In addition, preferably, the determined gap parameters may include: a first gap parameter and a second gap parameter.

[0081] The control identification module 501 can send the determined gap parameters to the signal compensation module 502. Accordingly, for the obtained gap parameters, the signal compensation module 502 can use them to implement reverse gap compensation, that is, obtain the steering signal to be optimized, perform reverse gap compensation on the steering signal to be optimized according to the gap parameters, and use the compensated steering signal to be optimized as the new target steering signal.

[0082] Preferably, the signal compensation module 502 may perform inverse clearance compensation on the steering signal to be optimized according to the clearance parameter in the following manner: obtaining the derivative of the steering signal to be optimized; in response to determining that the derivative is less than 0, obtaining the difference between the steering signal to be optimized and the first clearance parameter, and using the difference as the compensated steering signal to be optimized generated in this process, i.e., as the target steering signal; in response to determining that the derivative is greater than 0, obtaining the sum of the steering signal to be optimized and the second clearance parameter, and using the sum as the target steering signal.

[0083] Alternatively, preferably, in response to determining that the derivative is equal to 0, the signal compensation module 502 can use the steering signal to be optimized as the target steering signal.

[0084] That is, when the derivative is equal to 0, there is no need to perform reverse backlash compensation, and the steering signal to be optimized can be directly used as the target steering signal.

[0085] Furthermore, the signal compensation module 502 can send the compensated steering signal to be optimized generated in this process as a new target steering signal to the control identification module 501. Accordingly, the control identification module 501 will repeat its own processing.

[0086] The specific workflow of the device embodiment shown in Figure 5 can be found in the relevant descriptions in the foregoing method embodiments, and will not be repeated here.

[0087] In summary, the solution described in the embodiments of this disclosure can effectively improve the problem of inadequate vehicle control caused by gap nonlinearity. For example, it can effectively eliminate the problem of left and right swaying when the vehicle is traveling straight due to gap nonlinearity, thereby improving the stability and safety of vehicle driving. Moreover, the method is simple and convenient to implement, reducing implementation and maintenance costs.

[0088] The solutions described in this disclosure can be applied to the field of artificial intelligence, particularly in areas such as autonomous driving and intelligent transportation. Artificial intelligence is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It involves both hardware and software technologies. Artificial intelligence hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. Artificial intelligence software technologies mainly include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0089] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0090] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0091] Figure 6 illustrates a schematic block diagram of an electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0092] As shown in Figure 6, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0093] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0094] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as those described in this disclosure. For example, in some embodiments, the methods described in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the methods described in this disclosure can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the methods described in this disclosure by any other suitable means (e.g., by means of firmware).

[0095] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0099] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0100] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0101] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0102] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A vehicle steering control method, comprising: The compensation-adjusted steering signal generated in the previous processing is obtained and used as the target steering signal. The vehicle is then steered according to the target steering signal, and the vehicle status information after steering control is obtained. Based on the target steering signal and the vehicle state information, a clearance parameter is determined, including a first clearance parameter and a second clearance parameter. The process involves obtaining a steering signal to be optimized and performing inverse clearance compensation on the steering signal based on the clearance parameter, including: obtaining the derivative of the steering signal to be optimized; in response to determining that the derivative is less than 0, obtaining the difference between the steering signal to be optimized and the first clearance parameter, using the difference as the compensated steering signal to be optimized generated in this process; and in response to determining that the derivative is greater than 0, obtaining the sum of the steering signal to be optimized and the second clearance parameter, using the sum as the compensated steering signal to be optimized generated in this process.

2. The method according to claim 1, wherein, The step of determining the gap parameter based on the target steering signal and the vehicle status information includes: obtaining the vehicle status information corresponding to the previous target steering signal adjacent to the target steering signal, as the vehicle historical status information; and determining the gap parameter based on the target steering signal, the vehicle status information, and the vehicle historical status information.

3. The method according to claim 2, wherein, The step of determining the gap parameter based on the target steering signal, the vehicle status information, and the vehicle historical status information includes: determining the gap parameter by recursive least squares method based on the target steering signal, the vehicle status information, the vehicle historical status information, and a pre-determined first mathematical model.

4. The method according to claim 3, wherein, The first mathematical model is a mathematical model determined by combining the second and third mathematical models; the second mathematical model is a mathematical model corresponding to the first-order inertial element, and the third mathematical model is a mathematical model corresponding to the gap nonlinearity. The steering system of the vehicle is equivalent to a series combination model of the first-order inertial element and the gap nonlinearity.

5. The method according to claim 1, further comprising: In response to determining that the derivative is equal to 0, the steering signal to be optimized is taken as the target steering signal.

6. A vehicle steering control device, comprising: Control identification module and signal compensation module; The control identification module is used to acquire the compensated steering signal to be optimized generated in the previous processing, use it as the target steering signal, perform steering control on the vehicle according to the target steering signal, acquire vehicle state information after steering control, determine clearance parameters according to the target steering signal and the vehicle state information, and provide the clearance parameters to the signal compensation module. The clearance parameters include a first clearance parameter and a second clearance parameter. The signal compensation module is used to acquire the steering signal to be optimized, and perform inverse clearance compensation on the steering signal to be optimized according to the clearance parameters, including: acquiring the derivative of the steering signal to be optimized; in response to determining that the derivative is less than 0, acquiring the difference between the steering signal to be optimized and the first clearance parameter, using the difference as the compensated steering signal to be optimized generated in the current processing; in response to determining that the derivative is greater than 0, acquiring the sum of the steering signal to be optimized and the second clearance parameter, using the sum as the compensated steering signal to be optimized generated in the current processing, and providing the compensated steering signal to be optimized generated in the current processing to the control identification module.

7. The apparatus according to claim 6, wherein, The control identification module acquires the vehicle status information corresponding to the previous target steering signal adjacent to the target steering signal, as the vehicle historical status information, and determines the gap parameter based on the target steering signal, the vehicle status information, and the vehicle historical status information.

8. The apparatus according to claim 7, wherein, The control identification module determines the gap parameter by means of recursive least squares based on the target steering signal, the vehicle status information, the vehicle historical status information and a pre-determined first mathematical model.

9. The apparatus according to claim 8, wherein, The first mathematical model is a mathematical model determined by combining the second and third mathematical models; the second mathematical model is a mathematical model corresponding to the first-order inertial element, and the third mathematical model is a mathematical model corresponding to the gap nonlinearity. The steering system of the vehicle is equivalent to a series combination model of the first-order inertial element and the gap nonlinearity.

10. The apparatus according to claim 6, wherein, The signal compensation module is further configured to, in response to determining that the derivative is equal to 0, use the steering signal to be optimized as the target steering signal.

11. An electronic device, comprising: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

13. A computer program product comprising a computer program / instructions that, when executed by a processor, implement the method of any one of claims 1-5.

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