A method for establishing a beach skid model of a four-wheel vehicle

Abstract

The application provides a four-wheel vehicle beach sliding model establishing method, which comprises the following steps: collecting vehicle motion state data in a beach environment, inputting a kinematics model to obtain displacement prediction data; obtaining a sliding prediction formula based on the displacement prediction data and a dynamics model; constructing a sliding model according to the sliding prediction formula, decomposing a sliding coefficient of the sliding model to generate a coefficient factor, and generating a target coefficient factor by updating and optimizing the coefficient factor in real time according to vehicle motion data and variable change trends; outputting a sliding prediction value according to the sliding model configured with the target coefficient factor, and controlling the vehicle according to the sliding prediction value. The application improves the precision of vehicle control in the automatic driving in beach areas or other areas prone to sliding problems, and enhances the accuracy of beach vehicle sliding position prediction.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and specifically to a method for establishing a four-wheeled vehicle tidal flat skidding model. Background Technology

[0002] In vehicle dynamics, slippage occurs when the lateral force exceeds the friction between the tires and the ground during a turn. In autonomous driving systems, slippage not only affects the vehicle's trajectory and safety but also directly impacts the efficiency of path planning and obstacle avoidance. Therefore, accurate slippage prediction and real-time control are crucial for achieving precise autonomous driving functions, especially given the current research gaps in slippage prediction in tidal flat environments.

[0003] Due to the complex and varied topography of tidal flats, such as sediment deposition and tidal action, they are often accompanied by soft soil and swamps, which poses more challenges to unmanned driving in tidal flats. When autonomous vehicles drive in tidal flat environments, the reduced coefficient of friction makes it impossible to achieve the same stable driving process as on hard surfaces, and vehicle slippage is more likely to occur. This makes it impossible for autonomous driving equipment to operate according to the set route, resulting in poor accuracy and a significant reduction in prediction accuracy.

[0004] Therefore, there is an urgent need for a method to establish a four-wheeled vehicle slippage model in tidal flats to solve the problem of low slippage prediction accuracy for unmanned vehicles in the aforementioned tidal flat environment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a method for establishing a four-wheeled vehicle tidal flat skidding model.

[0006] The first aspect of this invention discloses a method for establishing a four-wheeled vehicle tidal flat skidding model, comprising:

[0007] Collect vehicle motion state data in the tidal flat environment and input it into the kinematic model to obtain displacement prediction data;

[0008] Based on the displacement prediction data and dynamic model, a slip prediction formula is obtained;

[0009] A slip model is constructed based on the slip prediction formula. The slip coefficient of the slip model is decomposed to generate coefficient factors. The coefficient factors are updated and optimized in real time based on vehicle motion data and variable change trends to generate target coefficient factors.

[0010] The slip prediction is output based on the slip model with configured target coefficient factors, and the vehicle is controlled based on the slip prediction.

[0011] As an optional implementation, in the first aspect of the present invention, the collection of vehicle motion data in the tidal flat environment includes:

[0012] The vehicle status data is collected in real time by sensors deployed on the vehicle. The vehicle status data includes vehicle speed information, steering angle information, heading angle information, mass information, position information and acceleration information under different road surface types.

[0013] The linearity standard determines whether the vehicle speed and steering angle meet the preset standard values. If they do, the vehicle state data collected in the current cycle is used as the reference data for inputting the kinematic model. If they do not meet the standard, a reference standard value is generated based on the historical data collected by the sensors as the reference data.

[0014] As an optional implementation, in the first aspect of the invention, the input kinematic model obtains displacement prediction data, including:

[0015] A static tensile test was conducted on the vehicle, and the static displacement formula of the vehicle was derived. The static displacement formula is as follows: ,in This indicates the displacement of the vehicle when it is stationary. This represents the coefficient of friction of a vehicle when it is stationary. This represents the force used to pull the vehicle during a static tensile test, and The value of the force lies between the vehicle's dynamic friction force and static friction force. This represents the unit time within the test cycle in a static tensile test;

[0016] Displacement prediction data are calculated based on the kinematic model equations, where the kinematic model equations are:

[0017] ;

[0018] ;

[0019] ;

[0020] in, This represents the predicted displacement data value in the x-axis direction. This represents the predicted displacement data value in the y-axis direction. This represents the predicted displacement data value of the heading angle. This represents the vehicle speed in the vehicle motion data. This represents the heading angle in the vehicle motion data. This represents the steering angle in the vehicle motion data. This represents the wheelbase between the front and rear axles in the vehicle motion data. This represents the radius of the circular motion of the vehicle with the rear wheel as the center.

[0021] As an optional implementation, in the first aspect of the invention, obtaining the slip prediction formula based on the displacement prediction data and the dynamic model includes:

[0022] The longitudinal force state of the vehicle under the displacement prediction data is determined based on the longitudinal dynamic model, and the longitudinal force state is expressed as follows:

[0023] ;

[0024] ;

[0025] in, Indicates the total driving force of the vehicle. This indicates the rolling resistance between the vehicle's front wheels and the ground. This represents the rolling resistance between the front and rear wheels of the vehicle and the ground. This indicates the wind resistance experienced by the vehicle. Indicates air density, Indicates vehicle speed. This indicates the vehicle's drag coefficient. Indicates the influence factor for vehicle reference;

[0026] Using displacement prediction data as a variable and quantitative factors under longitudinal stress as constants, the longitudinal slip formula is solved based on the longitudinal stress state. The longitudinal slip formula is expressed as:

[0027] ;

[0028] in, Indicates the longitudinal slip coefficient. This represents the longitudinal slip coefficient function obtained by fitting data based on vehicle speed and the vehicle's pressure on the ground as input. Indicates vehicle speed. This indicates the pressure exerted by the vehicle on the ground. This represents the fitting function for the longitudinal slip coefficient. This indicates the duration of force application on a vehicle under stress conditions.

[0029] As an optional implementation, in the first aspect of the invention, obtaining the slip prediction formula based on the displacement prediction data and the dynamic model includes:

[0030] The lateral force state of the vehicle under the displacement prediction data is determined based on the lateral dynamics model, and the lateral force state is expressed as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] in, Indicates vehicle mass. This represents the predicted lateral slippage data value of the vehicle perpendicular to the direction of vehicle movement. This represents the predicted longitudinal slippage data value of the vehicle parallel to the direction of vehicle movement. This represents the predicted displacement data value of the heading angle. This indicates the lateral force on the front wheel. This indicates the lateral force on the rear wheel. Indicates the rotational inertia of the vehicle. Indicates the distance between the front axle and the center of gravity. Indicates the distance between the rear axle and the center of gravity. This indicates the lateral stiffness of the front wheel. This indicates the lateral stiffness of the rear wheel. Indicates the vehicle's steering angle. Indicates the slip angle of the front wheel. Indicates the rear wheel slip angle. Indicates the longitudinal speed of the vehicle. Indicates the lateral velocity of the vehicle;

[0038] Using displacement prediction data as variable factors and quantitative factors under lateral stress as invariant factors, the heading slip formula and lateral slip formula are solved based on the lateral stress state. The heading slip formula is expressed as:

[0039] ;

[0040] in, Indicates the heading slip coefficient. Representing the heading slip equation, Indicates the vehicle's steering angle. Indicates vehicle speed. Indicates the mass of the vehicle. This represents the fitting function for the heading slip coefficient;

[0041] The lateral slip formula is expressed as follows:

[0042] ;

[0043] in, Indicates the lateral slip coefficient. Represents the lateral slip function. This indicates the lateral force on the vehicle. This represents the fitting function for the vehicle's lateral slip coefficient.

[0044] As an optional implementation, in the first aspect of the invention, constructing the slip model based on the slip prediction formula includes:

[0045] The vehicle trajectory formula is obtained by solving the longitudinal slip coefficient, directional slip coefficient, and lateral slip coefficient. A slip model is constructed based on the trajectory formula, which is expressed as:

[0046] ;

[0047] in, Indicates the trajectory of motion. Represents the displacement trajectory. Represents the trajectory of the heading angle. Indicates the time it takes for the vehicle to move. Indicates vehicle speed. Indicates the vehicle's steering angle. This represents the fitting function for the vehicle's longitudinal slip coefficient. This represents the fitting function for the vehicle's heading slip coefficient. This represents the fitting function for the vehicle's lateral slip coefficient.

[0048] As an optional implementation, in the first aspect of the present invention, the step of decomposing the slip coefficient of the slip model to generate a coefficient factor, and updating and optimizing the coefficient factor in real time according to vehicle motion data and variable change trends to generate a target coefficient factor, includes:

[0049] Based on the matching relationship between independent and dependent variables in the current period of the slip model, the relationship between coefficient factors is determined, and the target coefficient factors are updated and optimized according to the relationship between the coefficient factors. The matching relationship is expressed as follows:

[0050] ;

[0051] Based on the matching relationship, the target coefficient factor is expressed as:

[0052] ;

[0053] in, Indicates the target coefficient factor. This represents the fitting function for the coefficients of change of the vehicle's surface and tires in a mudflat environment. This represents a fitting function that represents the coefficient of variation between the vehicle's speed and the amount of slip during a skid state. This represents a fitting function that represents the coefficient of variation between the vehicle's mass and the amount of slip under slip conditions. This is a fitting function representing the coefficient of variation between the steering angle and the amount of slip under vehicle slip conditions. This represents a fitting function that shows the coefficient of variation between the vehicle's center of gravity and the front wheelbase as the vehicle slips. A fitting function representing the coefficient of variation between the vehicle's center of gravity and the rear wheel axle distance and slip amount under slip conditions;

[0054] Based on the influence of changes in the matching relationship in the target coefficient factors, the motion trajectory formula will change to:

[0055] ;

[0056] in, This indicates the displacement of the vehicle when it is stationary.

[0057] A second aspect of this invention discloses a system for establishing a four-wheeled vehicle tidal flat skidding model, the system comprising:

[0058] The data acquisition module is used to collect vehicle motion state data in the tidal flat environment and input it into the kinematic model to obtain displacement prediction data.

[0059] The formula solving module is used to obtain the slip prediction formula based on the displacement prediction data and the dynamic model;

[0060] The model building module is used to build a slip model based on the slip prediction formula, decompose the slip coefficient of the slip model to generate coefficient factors, and update and optimize the coefficient factors in real time based on vehicle motion data and variable change trends to generate target coefficient factors.

[0061] The control slip module is used to output slip prediction based on the slip model with configured target coefficient factors, and to control the vehicle based on the slip prediction.

[0062] The third aspect of this invention discloses a device for establishing a four-wheeled vehicle tidal flat skidding model, comprising:

[0063] At least one processor, and,

[0064] A memory communicatively connected to the at least one processor; wherein,

[0065] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the four-wheeled vehicle tidal flat skidding model establishment method as disclosed in any of the first aspects of the present invention.

[0066] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a method for establishing a four-wheeled vehicle tidal flat skidding model as disclosed in any of the first aspects of the present invention.

[0067] Compared with the prior art, the present invention has the following advantages:

[0068] This invention establishes a formula with lateral and longitudinal forces as independent variables and slip as the dependent variable. By conducting driving tests on wet and soft surfaces such as mudflats, the motion behavior of the vehicle is analyzed, and a slip model is derived to express the relationship between slip and control variables such as front and rear wheel speeds, steering angle, and heading angle. This improves the accuracy of vehicle control in autonomous driving in mudflats or other areas prone to slip problems, and enhances the accuracy of predicting vehicle slip position in mudflats. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a flowchart of the method for establishing a four-wheeled vehicle tidal flat skidding model according to the present invention;

[0071] Figure 2 This is a schematic diagram of the system for establishing a four-wheeled vehicle tidal flat skidding model according to the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0073] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0074] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0075] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0076] Example 1

[0077] See Figure 1 This invention discloses a method for establishing a four-wheeled vehicle tidal flat skidding model, comprising:

[0078] 101. Collect vehicle motion state data in the tidal flat environment and input it into the kinematic model to obtain displacement prediction data;

[0079] 102. Obtain the slip prediction formula based on the displacement prediction data and dynamic model;

[0080] 103. Construct a slip model based on the slip prediction formula, decompose the slip coefficient of the slip model to generate coefficient factors, and update and optimize the coefficient factors in real time based on vehicle motion data and variable change trends to generate target coefficient factors;

[0081] 104. Output the slip prediction value according to the slip model with configured target coefficient factors, and control the vehicle according to the slip prediction value.

[0082] As an optional implementation, in the first aspect of the present invention, the collection of vehicle motion data in the tidal flat environment includes:

[0083] The vehicle status data is collected in real time by sensors deployed on the vehicle. The vehicle status data includes vehicle speed information, steering angle information, heading angle information, mass information, position information and acceleration information under different road surface types.

[0084] The linearity standard determines whether the vehicle speed and steering angle meet the preset standard values. If they do, the vehicle state data collected in the current cycle is used as the reference data for inputting the kinematic model. If they do not meet the standard, a reference standard value is generated based on the historical data collected by the sensors as the reference data.

[0085] As an optional implementation, in the first aspect of the invention, the input kinematic model obtains displacement prediction data, including:

[0086] A static tensile test was conducted on the vehicle, and the static displacement formula of the vehicle was derived. The static displacement formula is as follows: ,in This indicates the displacement of the vehicle when it is stationary. This represents the coefficient of friction of a vehicle when it is stationary. This represents the force used to pull the vehicle during a static tensile test, and The value of the force lies between the vehicle's dynamic friction force and static friction force. This represents the unit time within the test cycle in a static tensile test;

[0087] Displacement prediction data are calculated based on the kinematic model equations, where the kinematic model equations are:

[0088] ;

[0089] ;

[0090] ;

[0091] in, This represents the predicted displacement data value in the x-axis direction. This represents the predicted displacement data value in the y-axis direction. This represents the predicted displacement data value of the heading angle. This represents the vehicle speed in the vehicle motion data. This represents the heading angle in the vehicle motion data. This represents the steering angle in the vehicle motion data. This represents the wheelbase between the front and rear axles in the vehicle motion data. This represents the radius of the circular motion of the vehicle with the rear wheel as the center.

[0092] As an optional implementation, in the first aspect of the invention, obtaining the slip prediction formula based on the displacement prediction data and the dynamic model includes:

[0093] The longitudinal force state of the vehicle under the displacement prediction data is determined based on the longitudinal dynamic model, and the longitudinal force state is expressed as follows:

[0094] ;

[0095] ;

[0096] in, Indicates the total driving force of the vehicle. This indicates the rolling resistance between the vehicle's front wheels and the ground. This represents the rolling resistance between the front and rear wheels of the vehicle and the ground. This indicates the wind resistance experienced by the vehicle. Indicates air density, Indicates vehicle speed. This indicates the vehicle's drag coefficient. Indicating the influence factor of vehicle reference

[0097] Using displacement prediction data as a variable and quantitative factors under longitudinal stress as constants, the longitudinal slip formula is solved based on the longitudinal stress state. The longitudinal slip formula is expressed as:

[0098] ;

[0099] in, Indicates the longitudinal slip coefficient. This represents the longitudinal slip coefficient function obtained by fitting data based on vehicle speed and the vehicle's pressure on the ground as input. Indicates vehicle speed. This indicates the pressure exerted by the vehicle on the ground. This represents the fitting function for the longitudinal slip coefficient. This indicates the duration of force application on a vehicle under stress conditions.

[0100] As an optional implementation, in the first aspect of the invention, obtaining the slip prediction formula based on the displacement prediction data and the dynamic model includes:

[0101] The lateral force state of the vehicle under the displacement prediction data is determined based on the lateral dynamics model, and the lateral force state is expressed as follows:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] in, Indicates vehicle mass. This represents the predicted lateral slippage data value of the vehicle perpendicular to the direction of vehicle movement. This represents the predicted longitudinal slippage data value of the vehicle parallel to the direction of vehicle movement. This represents the predicted displacement data value of the heading angle. This indicates the lateral force on the front wheel. This indicates the lateral force on the rear wheel. Indicates the rotational inertia of the vehicle. Indicates the distance between the front axle and the center of gravity. Indicates the distance between the rear axle and the center of gravity. This indicates the lateral stiffness of the front wheel. This indicates the lateral stiffness of the rear wheel. Indicates the vehicle's steering angle. Indicates the slip angle of the front wheel. Indicates the rear wheel slip angle. Indicates the longitudinal speed of the vehicle. Indicates the lateral velocity of the vehicle;

[0109] Using displacement prediction data as variable factors and quantitative factors under lateral stress as invariant factors, the heading slip formula and lateral slip formula are solved based on the lateral stress state. The heading slip formula is expressed as:

[0110] ;

[0111] in, Indicates the heading slip coefficient. Representing the heading slip equation, Indicates the vehicle's steering angle. Indicates vehicle speed. Indicates the mass of the vehicle. This represents the fitting function for the heading slip coefficient;

[0112] The lateral slip formula is expressed as follows:

[0113] ;

[0114] in, Indicates the lateral slip coefficient. Represents the lateral slip function. This indicates the lateral force on the vehicle. This represents the fitting function for the vehicle's lateral slip coefficient.

[0115] As an optional implementation, in the first aspect of the invention, constructing the slip model based on the slip prediction formula includes:

[0116] The vehicle trajectory formula is obtained by solving the longitudinal slip coefficient, directional slip coefficient, and lateral slip coefficient. A slip model is constructed based on the trajectory formula, which is expressed as:

[0117] ;

[0118] in, Indicates the trajectory of motion. Represents the displacement trajectory. Represents the trajectory of the heading angle. Indicates the time it takes for the vehicle to move. Indicates vehicle speed. Indicates the vehicle's steering angle. This represents the fitting function for the vehicle's longitudinal slip coefficient. This represents the fitting function for the vehicle's heading slip coefficient. This represents the fitting function for the vehicle's lateral slip coefficient.

[0119] As an optional implementation, in the first aspect of the present invention, the step of decomposing the slip coefficient of the slip model to generate a coefficient factor, and updating and optimizing the coefficient factor in real time according to vehicle motion data and variable change trends to generate a target coefficient factor, includes:

[0120] Based on the matching relationship between independent and dependent variables in the current period of the slip model, the relationship between coefficient factors is determined, and the target coefficient factors are updated and optimized according to the relationship between the coefficient factors. The matching relationship is expressed as follows:

[0121] ;

[0122] Based on the matching relationship, the target coefficient factor is expressed as:

[0123] ;

[0124] in, Indicates the target coefficient factor. This represents the fitting function for the coefficients of change of the vehicle's surface and tires in a mudflat environment. This represents a fitting function that represents the coefficient of variation between the vehicle's speed and the amount of slip during a skid state. This represents a fitting function that represents the coefficient of variation between the vehicle's mass and the amount of slip under slip conditions. This is a fitting function representing the coefficient of variation between the steering angle and the amount of slip under vehicle slip conditions. This represents a fitting function that shows the coefficient of variation between the vehicle's center of gravity and the front wheelbase as the vehicle slips. A fitting function representing the coefficient of variation between the vehicle's center of gravity and the rear wheel axle distance and slip amount under slip conditions;

[0125] Based on the influence of changes in the matching relationship in the target coefficient factors, the motion trajectory formula will change to:

[0126] ;

[0127] in, This indicates the displacement of the vehicle when it is stationary.

[0128] The working principle of this invention is as follows: Using longitudinal slip as the dependent variable, and the vehicle's front and rear wheel speeds, front wheel angles, vehicle mass, and directly related vehicle factors as independent variables, a longitudinal slip formula is derived. Using directional slip as the dependent variable, a directional slip formula is calculated using the same parameters. Using lateral slip as the dependent variable, a lateral slip formula is derived using the same parameters. Finally, the data obtained from directional and lateral slip are combined to derive a lateral dynamics model of the vehicle. The derivation process and results of mathematical modeling analysis based on collected data demonstrate that the slip behavior of a four-wheeled vehicle on soft terrain can be described through experimental modeling. The overall model adjusts the vehicle's direction of travel by controlling the front and rear wheel rotation speeds, steering angles, and heading angles. To better control slip, it is crucial to accurately simulate its motion behavior on loose terrain. By conducting driving tests on wet and soft road surfaces such as mudflats, the motion behavior of the vehicle was analyzed, and a mathematical model containing target coefficient factors was derived to express the relationship between slip and control variables such as front and rear wheel speeds, steering angle, and heading angle.

[0129] This invention establishes a formula with lateral and longitudinal forces as independent variables and slip as the dependent variable. By conducting driving tests on wet and soft surfaces such as mudflats, the motion behavior of the vehicle is analyzed, and a slip model is derived to express the relationship between slip and control variables such as front and rear wheel speeds, steering angle, and heading angle. This improves the accuracy of vehicle control in autonomous driving in mudflats or other areas prone to slip problems, and enhances the accuracy of predicting vehicle slip position in mudflats.

[0130] Example 2

[0131] like Figure 2 As shown, the second aspect of the present invention discloses a system for establishing a four-wheeled vehicle tidal flat skidding model, the system comprising:

[0132] The data acquisition module is used to collect vehicle motion state data in the tidal flat environment and input it into the kinematic model to obtain displacement prediction data.

[0133] The formula solving module is used to obtain the slip prediction formula based on the displacement prediction data and the dynamic model;

[0134] The model building module is used to build a slip model based on the slip prediction formula, decompose the slip coefficient of the slip model to generate coefficient factors, and update and optimize the coefficient factors in real time based on vehicle motion data and variable change trends to generate target coefficient factors.

[0135] The control slip module is used to output slip prediction based on the slip model with configured target coefficient factors, and to control the vehicle based on the slip prediction.

[0136] Example 3

[0137] The third aspect of this invention discloses a device for establishing a four-wheeled vehicle tidal flat skidding model, comprising:

[0138] At least one processor, and,

[0139] A memory communicatively connected to the at least one processor; wherein,

[0140] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the four-wheeled vehicle tidal flat skidding model establishment method as disclosed in any of the first aspects of the present invention.

[0141] The computer device can be a terminal, comprising a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for establishing a four-wheeled vehicle tidal flat skid model. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0142] Example 4

[0143] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a method for establishing a four-wheeled vehicle tidal flat skidding model as disclosed in any of the first aspects of the present invention.

[0144] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes described in the embodiments of the four-wheeled vehicle tidal flat skidding model establishment method. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0145] Alternatively, if the above-mentioned modules of the present invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, terminal, or network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, RAM, ROM, magnetic disks, or optical disks.

[0146] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0147] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0148] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0149] Finally, it should be noted that the method for establishing a four-wheeled vehicle tidal flat skidding model disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for establishing a four-wheeled vehicle tidal flat skidding model, characterized in that, The method includes: Collect vehicle motion state data in the tidal flat environment and input it into the kinematic model to obtain displacement prediction data; Based on the displacement prediction data and dynamic model, a slip prediction formula is obtained; A slip model is constructed based on the slip prediction formula. The slip coefficient of the slip model is decomposed to generate coefficient factors. The coefficient factors are updated and optimized in real time based on vehicle motion data and variable change trends to generate target coefficient factors. The slip prediction is output based on the slip model with configured target coefficient factors, and the vehicle is controlled based on the slip prediction. The construction of the slip model based on the slip prediction formula includes: The vehicle trajectory formula is obtained by solving the longitudinal slip coefficient, directional slip coefficient, and lateral slip coefficient. A slip model is constructed based on the trajectory formula, which is expressed as: ； in, Indicates the trajectory of motion. Represents the displacement trajectory. Represents the trajectory of the heading angle. Indicates the time it takes for the vehicle to move. Indicates vehicle speed. Indicates the vehicle's steering angle. This represents the fitting function for the vehicle's longitudinal slip coefficient. This represents the fitting function for the vehicle's heading slip coefficient. This represents the fitting function for the vehicle's lateral slip coefficient.

2. The method for establishing a four-wheeled vehicle tidal flat skidding model according to claim 1, characterized in that, The collection of vehicle movement data in the tidal flat environment includes: The vehicle status data is collected in real time by sensors deployed on the vehicle. The vehicle status data includes vehicle speed information, steering angle information, heading angle information, mass information, position information and acceleration information under different road surface types. The linearity standard determines whether the vehicle speed and steering angle meet the preset standard values. If they do, the vehicle state data collected in the current cycle is used as the reference data for inputting the kinematic model. If they do not meet the standard, a reference standard value is generated based on the historical data collected by the sensors as the reference data.

3. The method for establishing a four-wheeled vehicle tidal flat skidding model according to claim 2, characterized in that, The input kinematic model obtains displacement prediction data, including: A static tensile test was conducted on the vehicle, and the static displacement formula of the vehicle was derived. The static displacement formula is as follows: ,in This indicates the displacement of the vehicle when it is stationary. This represents the coefficient of friction of a vehicle when it is stationary. This represents the force used to pull the vehicle during a static tensile test, and The value of the force lies between the vehicle's dynamic friction force and static friction force. This represents the unit time within the test cycle in a static tensile test; Displacement prediction data are calculated based on the kinematic model equations, where the kinematic model equations are: ； ； ； in, This represents the predicted displacement data value in the x-axis direction. This represents the predicted displacement data value in the y-axis direction. This represents the predicted displacement data value of the heading angle. This represents the vehicle speed in the vehicle motion data. This represents the heading angle in the vehicle motion data. This represents the steering angle in the vehicle motion data. This represents the wheelbase between the front and rear axles in the vehicle motion data. This represents the radius of the circular motion of the vehicle with the rear wheel as the center.

4. The method for establishing a four-wheeled vehicle tidal flat skidding model according to claim 3, characterized in that, The process of obtaining the slip prediction formula based on the displacement prediction data and the dynamic model includes: The longitudinal force state of the vehicle under the displacement prediction data is determined based on the longitudinal dynamic model, and the longitudinal force state is expressed as follows: ； ； in, Indicates the total driving force of the vehicle. This indicates the rolling resistance between the vehicle's front wheels and the ground. This represents the rolling resistance between the front and rear wheels of the vehicle and the ground. This indicates the wind resistance experienced by the vehicle. Indicates air density, Indicates vehicle speed. This indicates the vehicle's drag coefficient. Indicates the influence factor for vehicle reference; Using displacement prediction data as a variable and quantitative factors under longitudinal stress as constants, the longitudinal slip formula is solved based on the longitudinal stress state. The longitudinal slip formula is expressed as: ； in, Indicates the longitudinal slip coefficient. This represents the longitudinal slip coefficient function obtained by fitting data based on vehicle speed and the vehicle's pressure on the ground as input. Indicates vehicle speed. This indicates the pressure exerted by the vehicle on the ground. This represents the fitting function for the longitudinal slip coefficient. This indicates the duration of force application on a vehicle under stress conditions.

5. The method for establishing a four-wheeled vehicle tidal flat skidding model according to claim 4, characterized in that, The process of obtaining the slip prediction formula based on the displacement prediction data and the dynamic model includes: The lateral force state of the vehicle under the displacement prediction data is determined based on the lateral dynamics model, and the lateral force state is expressed as follows: ； ； ； ； ； ； in, Indicates vehicle mass. This represents the predicted lateral slippage data value of the vehicle perpendicular to the direction of vehicle movement. This represents the predicted longitudinal slippage data value of the vehicle parallel to the direction of vehicle movement. This represents the predicted displacement data value of the heading angle. This indicates the lateral force on the front wheel. This indicates the lateral force on the rear wheel. Indicates the rotational inertia of the vehicle. Indicates the distance between the front axle and the center of gravity. Indicates the distance between the rear axle and the center of gravity. This indicates the lateral stiffness of the front wheel. This indicates the lateral stiffness of the rear wheel. Indicates the vehicle's steering angle. Indicates the slip angle of the front wheel. Indicates the rear wheel slip angle. Indicates the longitudinal speed of the vehicle. Indicates the lateral velocity of the vehicle; Using displacement prediction data as variable factors and quantitative factors under lateral stress as invariant factors, the heading slip formula and lateral slip formula are solved based on the lateral stress state. The heading slip formula is expressed as: ； in, Indicates the heading slip coefficient. Representing the heading slip equation, Indicates the vehicle's steering angle. Indicates vehicle speed. Indicates the mass of the vehicle. This represents the fitting function for the heading slip coefficient; The lateral slip formula is expressed as follows: ； in, Indicates the lateral slip coefficient. Represents the lateral slip function. This indicates the lateral force on the vehicle. This represents the fitting function for the vehicle's lateral slip coefficient.

6. The method for establishing a four-wheeled vehicle tidal flat skidding model according to claim 1, characterized in that, The process of decomposing the slip coefficient of the slip model to generate coefficient factors, and updating and optimizing the coefficient factors in real time based on vehicle motion data and variable change trends to generate target coefficient factors, includes: Based on the matching relationship between independent and dependent variables in the current period of the slip model, the relationship between coefficient factors is determined, and the target coefficient factors are updated and optimized according to the relationship between the coefficient factors. The matching relationship is expressed as follows: ； Based on the matching relationship, the target coefficient factor is expressed as: ； in, Indicates the target coefficient factor. This represents the fitting function for the coefficients of change of the vehicle's surface and tires in a mudflat environment. This represents a fitting function that represents the coefficient of variation between the vehicle's speed and the amount of slip during a skid state. This represents a fitting function that represents the coefficient of variation between the vehicle's mass and the amount of slip under slip conditions. This is a fitting function representing the coefficient of variation between the steering angle and the amount of slip under vehicle slip conditions. This represents a fitting function that shows the coefficient of variation between the vehicle's center of gravity and the front wheelbase as the vehicle slips. A fitting function representing the coefficient of variation between the vehicle's center of gravity and the rear wheel axle distance and slip amount under slip conditions; Based on the influence of changes in the matching relationship in the target coefficient factors, the motion trajectory formula will change to: ； in, This indicates the displacement of the vehicle when it is stationary.

7. A system for establishing a four-wheeled vehicle tidal flat skidding model, characterized in that, The system includes: The data acquisition module is used to collect vehicle motion state data in the tidal flat environment and input it into the kinematic model to obtain displacement prediction data. The formula solving module is used to obtain the slip prediction formula based on the displacement prediction data and the dynamic model; The model building module is used to build a slip model based on the slip prediction formula, decompose the slip coefficient of the slip model to generate coefficient factors, and update and optimize the coefficient factors in real time based on vehicle motion data and variable change trends to generate target coefficient factors. A control slip module is used to output a slip prediction based on a slip model configured with target coefficient factors, and to control the vehicle based on the slip prediction. The construction of the slip model based on the slip prediction formula includes: The vehicle trajectory formula is obtained by solving the longitudinal slip coefficient, directional slip coefficient, and lateral slip coefficient. A slip model is constructed based on the trajectory formula, which is expressed as: ； in, Indicates the trajectory of motion. Represents the displacement trajectory. Represents the trajectory of the heading angle. Indicates the time it takes for the vehicle to move. Indicates vehicle speed. Indicates the vehicle's steering angle. This represents the fitting function for the vehicle's longitudinal slip coefficient. This represents the fitting function for the vehicle's heading slip coefficient. This represents the fitting function for the vehicle's lateral slip coefficient.

8. A device for establishing a four-wheeled vehicle tidal flat skidding model, characterized in that, include: At least one processor, and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the four-wheeled vehicle tidal flat skidding model establishment method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the four-wheeled vehicle tidal flat skidding model establishment method as described in any one of claims 1 to 6.

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