Method and system for predicting friction coefficient between vehicle and ground based on complex environment

Abstract

The invention provides a method and system for predicting a friction coefficient between a vehicle and the ground based on a complex environment, relates to the technical field of virtual driving systems, and comprehensively judges the friction coefficient through three factors of the ground, vehicle operation and the environment. Comprehensive analysis is carried out from three aspects of environmental factors, ground factors and vehicle operation factors; relevant data are collected to generate an environment compensation friction evaluation coefficient reflecting the influence of environment factors on the friction coefficient, a vehicle drift compensation friction evaluation coefficient reflecting the influence of vehicle operation factors on the friction coefficient, and a ground friction force compensation evaluation coefficient reflecting the influence of ground factors on the friction coefficient; and the three factors are integrated for analysis to generate a comprehensive friction coefficient evaluation index which is used for judging the confidence coefficient of vehicle stability and outputting a comprehensive slip level to assist workers in predicting the anti-slip degree of the vehicle and the ground in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of virtual driving system technology, specifically to a method and system for predicting the friction coefficient between a vehicle and the ground under complex environments. Background Technology

[0002] A virtual driving system is a system that uses computer technology, sensors, simulation software, and virtual reality technology to simulate a real driving environment and driving experience. It is commonly used in the following areas: driver training, where a virtual driving system can provide a safe and controllable environment for training and assessing novice drivers. This system can simulate various driving conditions and emergency situations, helping drivers gain experience before driving on real roads; vehicle testing and development, where automakers can use virtual driving systems to test the performance of new models or technologies in a virtual environment, reducing the time and cost of actual road testing; research and development, where researchers can use virtual driving systems to study driving behavior, traffic flow, accident causes, etc., helping to develop more effective traffic policies and safety measures; and entertainment and simulation experiences, where virtual driving systems are also widely used in the gaming and entertainment fields, providing users with a realistic driving experience. Virtual driving systems involve the issue of vehicle maneuverability in different environments; a good control system can improve the driving feel.

[0003] Virtual driving systems need to simulate and set the friction coefficient between the vehicle and the ground under different road conditions. Generally, staff will set different friction coefficients for different roads, environments, and vehicle speeds based on experience. Since it is difficult to accurately judge the vehicle's anti-skid performance under all three conditions, the parameters set by the staff are somewhat unreasonable.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting the friction coefficient between a vehicle and the ground under complex environments, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The method for predicting the friction coefficient between vehicles and the ground under complex environments includes the following steps:

[0008] S1. Collect calibration environmental parameters, including ground static friction, calibration temperature and calibration humidity; collect real-time environmental parameters, which are the actual environmental parameters during driving, including actual temperature and actual humidity.

[0009] S2. Perform correlation analysis on environmental parameters and calibrated environmental parameters to generate environmental compensation friction evaluation coefficient HBM, wherein the environmental compensation friction force is used to reflect the compensation coefficient of the external environment for friction force.

[0010] S3. Collect vehicle operating parameters, including vehicle speed and wheel speed;

[0011] S4. Perform correlation analysis on vehicle operating parameters to generate vehicle drift coefficient. The vehicle drift coefficient is used to reflect the sliding evaluation value between the vehicle tires and the ground. Perform correlation analysis on vehicle drift coefficient ε and ground static friction to generate vehicle drift compensation friction evaluation coefficient CPX. The vehicle drift compensation friction evaluation coefficient is used to reflect the compensation coefficient of friction when the vehicle slides.

[0012] S5. Collect ground parameters, including ground wear depth and ground humidity, and perform correlation analysis on the ground parameters to generate the ground friction compensation evaluation coefficient DMX.

[0013] S6. Perform correlation analysis on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX to generate the comprehensive friction coefficient evaluation index ZMP. The comprehensive friction coefficient evaluation index ZMP is used to reflect the comprehensive friction coefficient evaluation value between the vehicle and the ground.

[0014] S7. Compare the comprehensive friction coefficient evaluation index ZMP with the evaluation threshold to output the comprehensive slip level. The comprehensive slip level is used to reflect the degree of slip between the vehicle and the ground.

[0015] Further, in S1, the calibration temperature is represented by T0, the calibration humidity by H0, the actual temperature by T, and the actual humidity by H. A correlation analysis is performed on the environmental parameters and the calibration environmental parameters to generate the environmental compensation friction evaluation coefficient, based on the following formula:

[0016] HBM=μ*(1+β T *ΔT+β H *ΔH)

[0017] Wherein, HBM is the environmental compensation friction evaluation coefficient, used to reflect the compensation coefficient of the external environment for friction, μ is the static friction force of the ground, and β is the friction factor of the ground. T β is the temperature weighting factor. H β is the humidity weighting factor. T +β H =1, and β H >β TThe formula used for the temperature change ΔT is: ΔT=T-T0, and the formula used for the humidity change ΔH is: ΔH=H-H0.

[0018] Furthermore, the formula used to generate the vehicle drift coefficient ε through correlation analysis of vehicle operating parameters is as follows:

[0019]

[0020] Among them, v 车 For vehicle speed, v 轮 This refers to wheel speed.

[0021] Furthermore, a correlation analysis was performed on the vehicle drift coefficient ε and the ground static friction μ to generate the vehicle drift compensation friction evaluation coefficient CPX, based on the following formula:

[0022]

[0023] Where, ε opt ε is the drift weighting factor. opt The value range is [0.1, 0.2]. The vehicle drift compensation friction evaluation coefficient CPX is used to reflect the compensation coefficient of friction force when the vehicle slips.

[0024] Furthermore, a correlation analysis was performed on the ground wear depth D to generate the ground depth compensation friction coefficient μ. D The formula used is:

[0025] μ D =μ*(1-k D *D)

[0026] Collect ground wear depth D - ground depth compensated friction coefficient μ D The data set, and the ground wear depth D - ground depth compensation friction coefficient μ D Substituting the data set into the above formula, the output k is obtained by fitting using the least squares method. D The value of k, where k D This is the wear depth coefficient, used to reflect the effect of wear depth on reducing the friction coefficient.

[0027] Furthermore, a correlation analysis was performed on the ground humidity R to generate the ground humidity-compensated friction coefficient μ, based on the following formula:

[0028] μ R =μ*(1-k R *R)

[0029] Ground humidity R - Ground humidity compensation friction coefficient μ R The data set, and the ground humidity R - ground humidity compensated friction coefficient μR Substituting the data set into the above formula, the output k is fitted using the least squares method. R The value of k, where k R This is the ground humidity coefficient, used to reflect the effect of ground humidity on reducing the coefficient of friction.

[0030] Furthermore, a correlation analysis was conducted on the ground humidity coefficient, wear depth coefficient, ground humidity, and wear depth to generate the ground friction compensation evaluation coefficient DMX, based on the following formula:

[0031] DMX=μ*(1-k D *Dk R *R)

[0032] The ground friction compensation evaluation coefficient DMX is used to reflect the compensation coefficient of ground parameters for friction.

[0033] Furthermore, a correlation analysis was conducted on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX to generate the comprehensive friction coefficient evaluation index ZMP. The formula used is as follows:

[0034]

[0035] The comprehensive friction coefficient evaluation index ZMP is used to reflect the comprehensive friction coefficient evaluation value between the vehicle and the ground.

[0036] This invention also provides a system for predicting the friction coefficient between a vehicle and the ground under complex environments, used to execute a method for predicting the friction coefficient between a vehicle and the ground under complex environments, including:

[0037] The environmental parameter acquisition module is used to collect calibration environmental parameters and real-time environmental parameters;

[0038] The environmental parameter analysis module is used to perform correlation analysis between calibrated environmental parameters and actual environmental parameters, and generate the environmental compensation friction evaluation coefficient HBM.

[0039] The vehicle operating parameter acquisition module is used to collect vehicle operating parameters;

[0040] The vehicle operation parameter analysis module is used to perform correlation analysis on vehicle operation parameters, generate vehicle drift coefficient, perform correlation analysis on vehicle drift coefficient ε and ground static friction, and generate vehicle drift compensation friction evaluation coefficient CPX.

[0041] Ground parameter acquisition module, used to collect ground parameters;

[0042] The ground parameter preprocessing module is used to perform correlation analysis on ground parameters to generate the ground friction compensation evaluation coefficient DMX;

[0043] The comprehensive analysis module is used to perform correlation analysis on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX, and generate the comprehensive friction coefficient evaluation index ZMP.

[0044] The comparison module is used to compare the comprehensive friction coefficient evaluation index ZMP with the evaluation threshold θ and output the comprehensive slip level, which reflects the degree of slip between the vehicle and the ground.

[0045] The evaluation threshold θ is set to 0.58. When ZMP ≥ θ, the output comprehensive slip level is Level 1, at which point the vehicle stability confidence level is 95%. At this time, the overall slip level is level two, and the vehicle stability confidence level is 70%; when At this time, the overall slip level is output as level three, and the vehicle stability confidence level is 10%.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] This invention analyzes the friction factors between a vehicle and the ground, comprehensively considering environmental factors, ground factors, and vehicle operating factors. It collects relevant data to generate an environmental compensation friction evaluation coefficient reflecting the impact of environmental factors on the friction coefficient, a vehicle drift compensation friction evaluation coefficient reflecting the impact of vehicle operating factors on the friction coefficient, and a ground friction compensation evaluation coefficient reflecting the impact of ground factors on the friction coefficient. The invention then integrates these three factors to generate a comprehensive friction coefficient evaluation index, used to assess the confidence level of vehicle stability and output a comprehensive slip level. This assists operators in predicting the anti-slip degree of vehicles and the ground in complex environments and setting it within a virtual driving system. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

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

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example:

[0052] Please see Figure 1 The present invention provides a technical solution:

[0053] The method for predicting the friction coefficient between vehicles and the ground under complex environments includes the following steps:

[0054] S1. Collect calibration environmental parameters, including ground static friction, calibration temperature and calibration humidity; collect real-time environmental parameters, which are the actual environmental parameters during driving, including actual temperature and actual humidity.

[0055] Increased temperature softens the rubber in car tires, increasing their stickiness and flexibility, thus increasing the coefficient of friction. Conversely, decreased temperature hardens the rubber, reducing its stickiness and flexibility, and lowering the coefficient of friction. High humidity or standing water forms a film of water between the ground and the tires, significantly reducing the coefficient of friction. This film reduces direct contact between the tires and the ground, lowering friction. Water and other liquids can act as lubricants, further reducing friction.

[0056] S2. Perform correlation analysis on environmental parameters and calibrated environmental parameters to generate environmental compensation friction evaluation coefficient HBM, wherein the environmental compensation friction force is used to reflect the compensation coefficient of the external environment for friction force.

[0057] Temperature and humidity are measured using temperature and humidity sensors, respectively. In step S1, the calibrated temperature is represented by T0, the calibrated humidity by H0, the actual temperature by T, and the actual humidity by H. Correlation analysis is performed on the environmental parameters and the calibrated environmental parameters to generate the environmental compensation friction evaluation coefficient. The formula used is as follows:

[0058] HBM=μ*(1+β T *ΔT+βH *ΔH)

[0059] Wherein, HBM is the environmental compensation friction evaluation coefficient, used to reflect the compensation coefficient of the external environment for friction, μ is the static friction force of the ground, and β is the friction factor of the ground. T β is the temperature weighting factor. H β is the humidity weighting factor. T +β H =1, and β H >β T The formula used for the temperature change ΔT is: ΔT=T-T0, and the formula used for the humidity change ΔH is: ΔH=H-H0.

[0060] S3. Collect vehicle operating parameters, including vehicle speed and wheel speed; wheel speed is measured by a magnetoelectric sensor.

[0061] S4. Perform correlation analysis on vehicle operating parameters to generate a vehicle drift coefficient, which reflects the slippage evaluation value between the vehicle tires and the ground. Perform correlation analysis on the vehicle drift coefficient ε and the static friction force of the ground to generate a vehicle drift compensation friction evaluation coefficient CPX, which reflects the compensation coefficient for friction force when the vehicle slips. There is a complex nonlinear relationship between the friction coefficient and the vehicle drift coefficient. When the vehicle drift coefficient is low, increasing the vehicle drift coefficient will increase the friction coefficient, but after the vehicle drift coefficient exceeds a certain value, the friction coefficient may decrease.

[0062] The formula used to generate the vehicle drift coefficient ε by performing correlation analysis on the vehicle operating parameters is as follows:

[0063]

[0064] Among them, v 车 For vehicle speed, v 轮 This refers to wheel speed.

[0065] A correlation analysis was performed on the vehicle drift coefficient ε and the ground static friction μ to generate the vehicle drift compensation friction evaluation coefficient CPX, based on the following formula:

[0066]

[0067] Where, ε opt ε is the drift weighting factor. opt The value range is [0.1, 0.2]. The vehicle drift compensation friction evaluation coefficient CPX is used to reflect the compensation coefficient of friction force when the vehicle slips.

[0068] S5. Collect ground parameters, including ground wear depth and ground humidity, and perform correlation analysis on the ground parameters to generate the ground friction compensation evaluation coefficient DMX.

[0069] Correlation analysis was performed on the ground wear depth D to generate the ground depth compensation friction coefficient μ. D The formula used is:

[0070] μ D =μ*(1-k D *D)

[0071] Collect ground wear depth D - ground depth compensated friction coefficient μ D The data set, and the ground wear depth D - ground depth compensation friction coefficient μ D Substituting the data set into the above formula, the output k is obtained by fitting using the least squares method. D The value of k, where k D This is the wear depth coefficient, used to reflect the effect of wear depth on reducing the friction coefficient.

[0072] Correlation analysis was performed on the ground humidity R to generate the ground humidity-compensated friction coefficient μ. R The formula used is:

[0073] μ R =μ*(1-k R *R)

[0074] Ground humidity R - Ground humidity compensation friction coefficient μ R The data set, and the ground humidity R - ground humidity compensated friction coefficient μ R Substituting the data set into the above formula, the output k is fitted using the least squares method. R The value of k, where k R This is the ground humidity coefficient, used to reflect the effect of ground humidity on reducing the coefficient of friction.

[0075] A correlation analysis was performed on the ground humidity coefficient, wear depth coefficient, ground humidity, and wear depth to generate the ground friction compensation evaluation coefficient DMX. The formula used is as follows:

[0076] DMX=μ*(1-k D *Dk R *R)

[0077] The ground friction compensation evaluation coefficient DMX is used to reflect the compensation coefficient of ground parameters for friction.

[0078] S6. Perform correlation analysis on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX to generate the comprehensive friction coefficient evaluation index ZMP. The comprehensive friction coefficient evaluation index ZMP is used to reflect the comprehensive friction coefficient evaluation value between the vehicle and the ground.

[0079] S7. Compare the comprehensive friction coefficient evaluation index ZMP with the evaluation threshold to output the comprehensive slip level. The comprehensive slip level is used to reflect the degree of slip between the vehicle and the ground.

[0080] Correlation analysis was performed on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX to generate the comprehensive friction coefficient evaluation index ZMP. The formula used is as follows:

[0081]

[0082] The comprehensive friction coefficient evaluation index ZMP is used to reflect the comprehensive friction coefficient evaluation value between the vehicle and the ground.

[0083] This invention also provides a system for predicting the friction coefficient between a vehicle and the ground under complex environments, used to execute a method for predicting the friction coefficient between a vehicle and the ground under complex environments, including:

[0084] The environmental parameter acquisition module is used to collect calibration environmental parameters and real-time environmental parameters;

[0085] The environmental parameter analysis module is used to perform correlation analysis between calibrated environmental parameters and actual environmental parameters, and generate the environmental compensation friction evaluation coefficient HBM.

[0086] The vehicle operating parameter acquisition module is used to collect vehicle operating parameters;

[0087] The vehicle operation parameter analysis module is used to perform correlation analysis on vehicle operation parameters, generate vehicle drift coefficient, perform correlation analysis on vehicle drift coefficient ε and ground static friction, and generate vehicle drift compensation friction evaluation coefficient CPX.

[0088] Ground parameter acquisition module, used to collect ground parameters;

[0089] The ground parameter preprocessing module is used to perform correlation analysis on ground parameters to generate the ground friction compensation evaluation coefficient DMX;

[0090] The comprehensive analysis module is used to perform correlation analysis on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX, and generate the comprehensive friction coefficient evaluation index ZMP.

[0091] The comparison module is used to compare the comprehensive friction coefficient evaluation index ZMP with the evaluation threshold θ and output the comprehensive slip level, which reflects the degree of slip between the vehicle and the ground.

[0092] The evaluation threshold θ is set to 0.58. When ZMP ≥ θ, the output comprehensive slip level is Level 1, at which point the vehicle stability confidence level is 95%. At this time, the overall slip level is level two, and the vehicle stability confidence level is 70%; when At this time, the output comprehensive slip level is level three, and the vehicle stability confidence level is 10%. The higher the value of the comprehensive friction coefficient evaluation index ZMP, the higher the vehicle stability confidence level. The vehicle stability confidence level is used to reflect the degree of anti-slip of the vehicle. The higher the confidence level, the higher the degree of anti-slip.

[0093] The overall slip level is divided into three levels, which can be reflected as a linear drift difficulty when mapped to the simulation system, thereby enhancing the realism of the simulation system and improving the feel of operation.

[0094] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0095] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0096] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0097] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for predicting the friction coefficient between a vehicle and the ground under complex environments, characterized in that, The specific steps include: S1. Collect calibration environmental parameters, including ground static friction, calibration temperature and calibration humidity; collect real-time environmental parameters, which are the actual environmental parameters during driving, including actual temperature and actual humidity. S2. Perform correlation analysis on environmental parameters and calibrated environmental parameters to generate environmental compensation friction evaluation coefficient HBM, wherein the environmental compensation friction force is used to reflect the compensation coefficient of the external environment for friction force. S3. Collect vehicle operating parameters, including vehicle speed and wheel speed; S4. Perform correlation analysis on vehicle operating parameters to generate vehicle drift coefficient. The vehicle drift coefficient is used to reflect the sliding evaluation value between the vehicle tires and the ground. Perform correlation analysis on vehicle drift coefficient ε and ground static friction to generate vehicle drift compensation friction evaluation coefficient CPX. The vehicle drift compensation friction evaluation coefficient is used to reflect the compensation coefficient of friction when the vehicle slides. S5. Collect ground parameters, including ground wear depth and ground humidity, and perform correlation analysis on the ground parameters to generate the ground friction compensation evaluation coefficient DMX. S6. Perform correlation analysis on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX to generate the comprehensive friction coefficient evaluation index ZMP. The comprehensive friction coefficient evaluation index ZMP is used to reflect the comprehensive friction coefficient evaluation value between the vehicle and the ground. S7. Compare the comprehensive friction coefficient evaluation index ZMP with the evaluation threshold to output the comprehensive slip level. The comprehensive slip level is used to reflect the degree of slip between the vehicle and the ground.

2. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to claim 1, characterized in that: In S1, the calibration temperature is represented by T0, the calibration humidity by H0, the actual temperature by T, and the actual humidity by H. A correlation analysis is performed on the environmental parameters and the calibration environmental parameters to generate the environmental compensation friction evaluation coefficient. The formula used is as follows: HBM=μ*(1+β T *ΔT+β H *ΔH) Wherein, HBM is the environmental compensation friction evaluation coefficient, used to reflect the compensation coefficient of the external environment for friction, μ is the static friction force of the ground, and β is the friction factor of the ground. T β is the temperature weighting factor. H β is the humidity weighting factor. T +β H =1, and β H >β T The formula used for the temperature change ΔT is: ΔT=T-T0, and the formula used for the humidity change ΔH is: ΔH=H-H0.

3. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to claim 2, characterized in that: The formula used to generate the vehicle drift coefficient ε by performing correlation analysis on the vehicle operating parameters is as follows: Among them, v 车 For vehicle speed, v 轮 This refers to wheel speed.

4. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to claim 3, characterized in that: A correlation analysis was performed on the vehicle drift coefficient ε and the ground static friction μ to generate the vehicle drift compensation friction evaluation coefficient CPX, based on the following formula: Where, ε opt ε is the drift weighting factor. opt The value range is [0.1, 0.2]. The vehicle drift compensation friction evaluation coefficient CPX is used to reflect the compensation coefficient of friction force when the vehicle slips.

5. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to claim 2, characterized in that: Correlation analysis was performed on the ground wear depth D to generate the ground depth compensation friction coefficient μ. D The formula used is: m D =μ*(1-k D *D) Collect ground wear depth D - ground depth compensated friction coefficient μ D The data set, and the ground wear depth D - ground depth compensation friction coefficient μ D Substituting the data set into the above formula, the output k is obtained by fitting using the least squares method. D The value of k, where k D This is the wear depth coefficient, used to reflect the effect of wear depth on reducing the friction coefficient.

6. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to claim 2, characterized in that: Correlation analysis was performed on the ground humidity R to generate the ground humidity-compensated friction coefficient μ. R The formula used is: m R =μ*(1-k R *R) Ground humidity R - Ground humidity compensation friction coefficient μ R The data set, and the ground humidity R - ground humidity compensated friction coefficient μ R Substituting the data set into the above formula, the output k is fitted using the least squares method. R The value of k, where k R This is the ground humidity coefficient, used to reflect the effect of ground humidity on reducing the coefficient of friction.

7. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to any one of claims 5 and 6, characterized in that: A correlation analysis was performed on the ground humidity coefficient, wear depth coefficient, ground humidity, and wear depth to generate the ground friction compensation evaluation coefficient DMX. The formula used is as follows: DMX=μ*(1-k D *D-k R *R) The ground friction compensation evaluation coefficient DMX is used to reflect the compensation coefficient of ground parameters for friction.

8. The method for predicting the friction coefficient between a vehicle and the ground under complex environments according to claim 1, characterized in that: Correlation analysis was performed on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX to generate the comprehensive friction coefficient evaluation index ZMP. The formula used is as follows: The comprehensive friction coefficient evaluation index ZMP is used to reflect the comprehensive friction coefficient evaluation value between the vehicle and the ground.

9. A system for predicting the friction coefficient between a vehicle and the ground under complex environments, used to execute the method for predicting the friction coefficient between a vehicle and the ground under complex environments as described in claim 1, characterized in that, include: The environmental parameter acquisition module is used to collect calibration environmental parameters and real-time environmental parameters; The environmental parameter analysis module is used to perform correlation analysis between calibrated environmental parameters and actual environmental parameters, and generate the environmental compensation friction evaluation coefficient HBM. The vehicle operating parameter acquisition module is used to collect vehicle operating parameters; The vehicle operation parameter analysis module is used to perform correlation analysis on vehicle operation parameters, generate vehicle drift coefficient, perform correlation analysis on vehicle drift coefficient ε and ground static friction, and generate vehicle drift compensation friction evaluation coefficient CPX. Ground parameter acquisition module, used to collect ground parameters; The ground parameter preprocessing module is used to perform correlation analysis on ground parameters to generate the ground friction compensation evaluation coefficient DMX; The comprehensive analysis module is used to perform correlation analysis on the environmental compensation friction evaluation coefficient HBM, the vehicle drift compensation friction evaluation coefficient CPX, and the ground friction compensation evaluation coefficient DMX, and generate the comprehensive friction coefficient evaluation index ZMP. The comparison module is used to compare the comprehensive friction coefficient evaluation index ZMP with the evaluation threshold θ and output the comprehensive slip level, which reflects the degree of slip between the vehicle and the ground. The evaluation threshold θ is set to 0.

58. When ZMP ≥ θ, the output comprehensive slip level is Level 1, at which point the vehicle stability confidence level is 95%. At this time, the overall slip level is level two, and the vehicle stability confidence level is 70%; when At this time, the overall slip level is output as level three, and the vehicle stability confidence level is 10%.

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