Tire antiskid self-adaptive control method based on road surface friction

Abstract

The invention provides a tire antiskid self-adaptive control method based on road surface friction, and relates to the technical field of self-adaptive control, and the method comprises the following steps: collecting real-time driving data of a target vehicle based on a preset sensor, carrying out data processing to obtain first driving data, and obtaining real-time road surface information of the target vehicle based on the preset sensor; acquiring a real-time road surface friction condition from a road surface information base based on the real-time road surface information, and determining a road surface friction coefficient of the target vehicle in combination with the first driving data; acquiring a real-time anti-skid target of the target vehicle, and determining anti-skid control parameters of the target vehicle based on the real-time anti-skid target in combination with the pavement friction coefficient and the real-time driving data; and performing anti-skid control on the target vehicle based on the anti-skid control parameters, performing stability verification on an anti-skid control result, and performing adaptive optimization on the anti-skid control parameters based on the stability verification result. The tire anti-skid self-adaptive control of the target vehicle can be more accurate and effective, and the anti-skid control adjustment can be carried out in time.

Description

Technical Field

[0001] The invention relates to the technical field of adaptive control, and in particular to a tire anti-skid adaptive control method based on road friction. Background Art

[0002] At present, wheel slip is a common phenomenon during driving, which not only affects driving safety but may also cause the vehicle to lose control. Wheel slip is mainly caused by insufficient friction between the tire and the road surface, especially on wet or icy roads, where the friction will be significantly reduced, causing the wheels to slip easily.

[0003] However, existing anti-skid measures, such as installing anti-skid chains and using snow tires, can play a certain anti-skid role, but the effect is limited. Therefore, a more intelligent and efficient anti-skid control method is needed.

[0004] Therefore, the present invention provides a tire anti-skid adaptive control method based on road friction. Summary of the invention

[0005] The present invention provides a tire anti-skid adaptive control method based on road friction, which is used to optimize the road friction coefficient to obtain more accurate anti-skid control parameters to perform anti-skid control on a target vehicle, so that the tire anti-skid adaptive control of the target vehicle can be more accurate and effective, and the anti-skid control adjustment can be performed in time.

[0006] The present invention provides a tire anti-skid adaptive control method based on road friction, comprising: Step 1: collecting real-time driving data of the target vehicle based on a preset sensor, and performing data processing to obtain first driving data, and obtaining real-time road surface information of the target vehicle based on the preset sensor; Step 2: acquiring real-time road friction conditions from a road information database based on the real-time road information, and determining a road friction coefficient of the target vehicle in combination with the first driving data; Step 3: Obtain the real-time anti-skid target of the target vehicle, and determine the anti-skid control parameters of the target vehicle based on the real-time anti-skid target combined with the road friction coefficient and the real-time driving data; Step 4: Perform anti-skid control on the target vehicle based on the anti-skid control parameters, perform stability verification on the anti-skid control results, and adaptively optimize the anti-skid control parameters based on the stability verification results.

[0007] According to the present invention, real-time driving data of a target vehicle is collected based on a preset sensor, and data processing is performed to obtain first driving data, and real-time road surface information of the target vehicle is obtained based on the preset sensor, including: Step 11: collecting real-time driving data of the target vehicle based on a preset sensor, and filtering and denoising the real-time driving data to obtain first driving data; Step 12: Capturing the road surface image and point cloud data of the target vehicle in real time based on the preset sensor to obtain first road surface data; Step 13: Analyze the first road surface data to determine the real-time road surface information of the road on which the target vehicle is traveling.

[0008] According to the present invention, the real-time road surface friction condition is obtained from a road surface information database based on the real-time road surface information, and the road surface friction coefficient of the target vehicle is determined in combination with the first driving data, including: Step 21: Based on the type of road surface on which the target vehicle is traveling, the wetness of the road surface, and the corresponding friction coefficient, a corresponding road surface information database is established; Step 22: Match the real-time road surface information with the data in the road surface information database, so as to find the road surface type and slipperiness degree with the highest similarity to the real-time road surface information as reference road surface information; Step 23: Obtaining a road friction coefficient corresponding to the reference road information from the road information database as an initial road friction coefficient corresponding to the real-time road information; Step 24: The initial road friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road friction coefficient of the target vehicle.

[0009] According to the present invention, the initial road friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road friction coefficient of the target vehicle, including: Step 241: obtaining a real-time environmental index of the target vehicle, and extracting an environmental index that will affect the road friction coefficient of the target vehicle from the real-time environmental index to obtain a first environmental index; Step 242: Acquire driving data that affects the road surface friction coefficient in the first driving data to obtain second driving data; Step 243: determining a driving influence weight corresponding to the second driving data based on the influence degree of the coefficient of the road friction coefficient of the second driving data, and determining an environmental influence weight corresponding to the first environmental index based on the influence degree of the coefficient of the road friction coefficient of the first environmental index; Step 244: The driving influence weight and the second driving data are combined to determine a first optimization reference of the road friction coefficient of the target vehicle, and at the same time, the environmental influence weight and the first environmental index are combined to determine a second optimization reference of the road friction coefficient of the target vehicle; Step 245: performing reference conversion on the first optimization reference and the second optimization reference in combination with corresponding data types, thereby obtaining an optimization coefficient T of the road friction coefficient of the target vehicle; ; Where T is the optimization coefficient of the road friction coefficient of the target vehicle, is the driving influence weight of the first optimization reference, is the i-th sub-driving data in the second driving data, is the data normalization conversion coefficient of the i-th sub-driving data in the second driving data, is the data type influencing factor corresponding to the i-th sub-driving data in the second driving data, is the friction coefficient conversion factor, The environmental impact weight for the second optimization reference, is the jth sub-environmental index in the first environmental index of the target vehicle, is the index conversion factor of the jth sub-environmental index, n is the number of sub-driving data in the second driving data, and m is the number of sub-environmental indexes in the first environmental index; Step 246: The optimization coefficient of the target vehicle is combined with the initial road friction coefficient, thereby achieving adjustment and optimization of the initial road friction coefficient and obtaining the road friction coefficient of the target vehicle.

[0010] According to the present invention, the real-time anti-skid target of the target vehicle is obtained, and the anti-skid control parameters of the target vehicle are determined based on the real-time anti-skid target combined with the road friction coefficient and the real-time driving data, including: Step 31: determining a real-time anti-skid target of the target vehicle according to the real-time driving state of the target vehicle and the safety standard corresponding to the target vehicle; Step 32: Based on the real-time anti-skid target, the road friction coefficient and the first driving data of the target vehicle, the anti-skid control parameters of the target vehicle are input into a preset anti-skid control system.

[0011] According to the present invention, anti-skid control is performed on a target vehicle based on anti-skid control parameters, stability verification is performed on the anti-skid control result, and adaptive optimization of the anti-skid control parameters is performed based on the stability verification result, including: Step 41: performing anti-skid control on the target vehicle according to the anti-skid control parameter to obtain a first control result; Step 42: Acquire the real-time driving data of the target vehicle at each moment in the anti-skid control period to obtain a second driving data set; Step 43: classifying each type of second driving data in the second driving data set, and sorting the second driving data based on the corresponding time of each type of second driving data, and obtaining the second driving curve of each type of second driving data based on the sorting result, so as to obtain the second driving curve set; Step 44: Based on the curve fluctuation of each second driving curve in the second driving curve set, comprehensively determine the vehicle stability of the target vehicle during the anti-skid control period; If the vehicle stability of the target vehicle during the anti-skid control period is higher than the preset minimum vehicle stability, there is no need to adaptively optimize the anti-skid control parameters of the target vehicle; If the vehicle stability of the target vehicle is not higher than a preset minimum vehicle stability during the anti-skid control period, the anti-skid control parameters of the target vehicle are adaptively optimized based on the vehicle stability of the target vehicle.

[0012] According to the present invention, the anti-skid control parameters of the target vehicle are adaptively optimized based on the vehicle stability of the target vehicle, including: Step 441: extracting third driving data of vehicle instability in the vehicle stability condition of the target vehicle, and acquiring a data type of each third driving data; Step 442: extracting a parameter optimization solution corresponding to the data type from a parameter optimization database based on the data type of each third driving data; Step 443: inputting the parameter optimization scheme into a preset adaptive optimization algorithm of the target vehicle, thereby determining a parameter adjustment strategy for the anti-skid control parameters of the target vehicle; Step 444: Based on the parameter adjustment strategy, the anti-skid control parameters of the target vehicle are adjusted accordingly, thereby achieving adaptive optimization of the anti-skid control parameters.

[0013] The simulation test of the optimized anti-skid control method provided by the present invention specifically includes: Step 01: Determine the real-time power distribution scheme of the target vehicle based on the optimized anti-skid control parameters; Step 02: Input the real-time power distribution plan and the real-time driving data of the target vehicle into the virtual machine for simulation, so as to determine the feasibility of the real-time power distribution plan; If the real-time power distribution scheme is feasible, the real-time power distribution scheme is used as the real-time anti-skid control method of the target vehicle; If the real-time power distribution plan is not feasible, the real-time power distribution plan is optimized.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a tire anti-skid adaptive control method based on road friction, which optimizes the road friction coefficient to obtain more accurate anti-skid control parameters for anti-skid control of the target vehicle, making the tire anti-skid adaptive control of the target vehicle more accurate and effective, and enabling timely anti-skid control adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a tire anti-skid adaptive control method based on road friction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a tire anti-skid adaptive control method based on road friction, such as Figure 1 As shown, including: Step 1: collecting real-time driving data of the target vehicle based on a preset sensor, and performing data processing to obtain first driving data, and obtaining real-time road surface information of the target vehicle based on the preset sensor; Step 2: acquiring real-time road friction conditions from a road information database based on the real-time road information, and determining a road friction coefficient of the target vehicle in combination with the first driving data; Step 3: Obtain the real-time anti-skid target of the target vehicle, and determine the anti-skid control parameters of the target vehicle based on the real-time anti-skid target combined with the road friction coefficient and the real-time driving data; Step 4: Perform anti-skid control on the target vehicle based on the anti-skid control parameters, perform stability verification on the anti-skid control results, and adaptively optimize the anti-skid control parameters based on the stability verification results.

[0019] In this embodiment, the preset sensor refers to a sensor pre-installed on the vehicle or in the road system, which is used to collect vehicle driving data (such as speed, acceleration, steering angle, etc.) and road information (such as road material, humidity, temperature, etc.) in real time. The sensor may include radar, camera, accelerometer, gyroscope, pressure sensor, etc.

[0020] In this embodiment, real-time driving data refers to data collected in real time by preset sensors during the actual driving process of the vehicle, which may include vehicle speed, acceleration, steering angle, wheel speed, brake pressure, etc., and is used to analyze and evaluate the driving status and performance of the vehicle.

[0021] In this embodiment, real-time road surface information refers to road condition information obtained in real time by preset sensors, such as road material (asphalt, concrete, etc.), humidity, temperature, road damage, etc. This information is crucial for evaluating the friction performance and safety of the road surface.

[0022] In this embodiment, the road surface information database is a database storing a large amount of road surface information, including friction coefficients under various road surface conditions, road surface damage conditions, etc. By comparing with the real-time road surface information, corresponding road surface friction conditions and other information can be obtained from the database.

[0023] In this embodiment, the road friction coefficient refers to the ratio between the friction between the wheel and the road surface and the vertical load of the wheel, which is used to measure the grip of the road surface under different conditions. The larger the friction coefficient, the greater the friction between the wheel and the road surface, and the better the handling and stability of the vehicle.

[0024] In this embodiment, the real-time anti-skid target refers to an anti-skid performance target preset according to the driving state of the vehicle and the road conditions. For example, on a slippery road, a higher anti-skid performance may be required to ensure the stability and safety of the vehicle.

[0025] In this embodiment, the anti-skid control parameters refer to parameters used to adjust the working state of the vehicle anti-skid system, such as brake pressure, wheel speed adjustment, engine torque adjustment, etc. By adjusting these parameters, the anti-skid performance of the vehicle can be controlled.

[0026] In this embodiment, anti-skid control refers to improving the anti-skid performance of the vehicle on slippery roads by adjusting the vehicle's brake system, power system, steering system, etc., to ensure the stability and safety of the vehicle.

[0027] In this embodiment, stability verification refers to verifying the anti-skid control result to ensure the driving stability and safety of the vehicle under anti-skid control, which usually involves monitoring and analyzing the vehicle's driving trajectory, speed, acceleration and other parameters.

[0028] In this embodiment, the adaptive optimization refers to adjusting and optimizing the anti-skid control parameters according to the stability verification result to improve the performance and effect of the anti-skid control. This optimization can be real-time or offline optimization based on historical data.

[0029] The beneficial effect of the above technical solution is: by optimizing the road friction coefficient, more accurate anti-skid control parameters are obtained to perform anti-skid control on the target vehicle, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective, and can make anti-skid control adjustments in time.

[0030] Embodiment 2: Based on Example 1, the real-time road surface information of the target vehicle is obtained based on a preset sensor, including: Step 11: collecting real-time driving data of the target vehicle based on a preset sensor, and filtering and denoising the real-time driving data to obtain first driving data; Step 12: Capturing the road surface image and point cloud data of the target vehicle in real time based on the preset sensor to obtain first road surface data; Step 13: Analyze the first road surface data to determine the real-time road surface information of the road on which the target vehicle is traveling.

[0031] In this embodiment, real-time driving data refers to various data collected in real time by preset sensors during the driving process of the target vehicle, including but not limited to vehicle speed, acceleration, steering angle, brake status, engine status, etc., reflecting the real-time driving status of the vehicle.

[0032] In this embodiment, filtering is a signal processing technique used to remove unnecessary frequency components from a signal. In the processing of vehicle driving data, filtering can help remove noise and interference and improve the accuracy and reliability of the data.

[0033] In this embodiment, denoising refers to removing the noise part in the data to improve the signal-to-noise ratio of the data. During the collection of vehicle driving data, due to various interference factors (such as sensor error, environmental noise, etc.), the data may contain noise.

[0034] In this embodiment, the first driving data is driving data obtained after filtering and denoising.

[0035] In this embodiment, the preset sensors are pre-installed on the vehicle or in the road system to capture the road information of the target vehicle in real time. These sensors may include cameras, LiDAR, etc., which can capture images and point cloud data of the road surface.

[0036] In this embodiment, the road surface image is the visual information of the road surface captured by an image sensor such as a camera, etc. Such information is usually presented in the form of a two-dimensional image, including features such as the texture, color, and shape of the road surface.

[0037] In this embodiment, point cloud data is three-dimensional spatial information captured by sensors such as laser radar. These data are presented as points, and each point contains its position information in three-dimensional space. Point cloud data can be used to build a three-dimensional model and analyze the shape, height and other characteristics of the road surface.

[0038] In this embodiment, the first road surface data is a road surface image and point cloud data captured in real time by a preset sensor, which together constitute a comprehensive description of the road surface on which the target vehicle is traveling.

[0039] In this embodiment, data analysis refers to processing and analyzing the collected data to extract useful information. By analyzing the data, real-time road surface information of the road surface on which the target vehicle is traveling, such as road surface material, wetness, damage, etc., can be determined.

[0040] In this embodiment, the real-time road surface information refers to detailed information about the road surface on which the target vehicle is traveling, obtained through data analysis. The real-time road surface information generally includes the physical properties (such as material, wetness), geometric properties (such as shape, height) and damage of the road surface.

[0041] The beneficial effect of the above technical solution is: by performing data analysis on the road surface information obtained by the sensor, real-time road surface information is obtained, thereby determining the road surface friction coefficient and optimizing the coefficient, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective.

[0042] Embodiment 3: Based on the second embodiment, the road friction coefficient of the target vehicle is determined in combination with the first driving data, including: Step 21: Based on the type of road surface on which the target vehicle is traveling, the wetness of the road surface, and the corresponding friction coefficient, a corresponding road surface information database is established; Step 22: Match the real-time road surface information with the data in the road surface information database, so as to find the road surface type and slipperiness degree with the highest similarity to the real-time road surface information as reference road surface information; Step 23: Obtaining a road friction coefficient corresponding to the reference road information from the road information database as an initial road friction coefficient corresponding to the real-time road information; Step 24: The initial road friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road friction coefficient of the target vehicle.

[0043] In this embodiment, the driving road type refers to the type of road surface on which the vehicle is driving, such as asphalt road, cement road, gravel road, etc. Different types of road surfaces have different physical properties and friction performances.

[0044] In this embodiment, the road surface slipperiness describes whether the road surface is slippery, and is usually related to factors such as the moisture content and oil pollution of the road surface. The higher the slipperiness, the lower the friction coefficient of the road surface, and the driving safety of the vehicle will be reduced accordingly.

[0045] In this embodiment, the friction coefficient is a physical quantity that describes the friction between the road surface and the wheels. The larger the friction coefficient, the greater the friction between the wheels and the road surface, and the better the handling and stability of the vehicle.

[0046] In this embodiment, the road surface information database is a database that stores a large amount of road surface information, including information such as different road surface types, wetness and slipperiness, and their corresponding friction coefficients, which can be used for matching and comparison with the actually collected road surface information.

[0047] In this embodiment, the real-time road surface information is road surface information collected in real time by preset sensors, including road surface type, wetness, etc.

[0048] In this embodiment, matching refers to the process of searching for data with the highest similarity to the real-time road information in the road information database. The purpose of matching is to find reference information that is closest to the current actual road condition.

[0049] In this embodiment, the reference road surface information is the road surface type and wetness level with the highest similarity to the real-time road surface information found in the road surface information database. This information will be used as a reference for subsequent analysis and processing.

[0050] In this embodiment, the initial road surface friction coefficient is a road surface friction coefficient obtained from a road surface information database based on reference road surface information.

[0051] In this embodiment, the adjustment optimization is a process of correcting the initial road friction coefficient according to the first driving data. The purpose of the adjustment is to make the friction coefficient more accurately reflect the actual road conditions, thereby improving the driving safety and controllability of the vehicle.

[0052] In this embodiment, the road friction coefficient of the target vehicle refers to the road friction coefficient obtained after adjustment and optimization, which more accurately reflects the friction performance of the road surface on which the target vehicle is currently traveling.

[0053] The beneficial effect of the above technical solution is: by analyzing the real-time road information, the corresponding initial road friction coefficient is selected, and the coefficient is optimized to determine the anti-skid control parameters, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective.

[0054] Embodiment 4: Based on Example 3, the road friction coefficient of the target vehicle is obtained, including: Step 241: obtaining a real-time environmental index of the target vehicle, and extracting an environmental index that will affect the road friction coefficient of the target vehicle from the real-time environmental index to obtain a first environmental index; Step 242: Acquire driving data that affects the road surface friction coefficient in the first driving data to obtain second driving data; Step 243: determining a driving influence weight corresponding to the second driving data based on the influence degree of the coefficient of the road friction coefficient of the second driving data, and determining an environmental influence weight corresponding to the first environmental index based on the influence degree of the coefficient of the road friction coefficient of the first environmental index; Step 244: The driving influence weight and the second driving data are combined to determine a first optimization reference of the road friction coefficient of the target vehicle, and at the same time, the environmental influence weight and the first environmental index are combined to determine a second optimization reference of the road friction coefficient of the target vehicle; Step 245: performing reference conversion on the first optimization reference and the second optimization reference in combination with corresponding data types, thereby obtaining an optimization coefficient T of the road friction coefficient of the target vehicle; ; Where T is the optimization coefficient of the road friction coefficient of the target vehicle, is the driving influence weight of the first optimization reference, is the i-th sub-driving data in the second driving data, is the data normalization conversion coefficient of the i-th sub-driving data in the second driving data, is the data type influencing factor corresponding to the i-th sub-driving data in the second driving data, is the friction coefficient conversion factor, The environmental impact weight for the second optimization reference, is the jth sub-environmental index in the first environmental index of the target vehicle, is the index conversion factor of the jth sub-environmental index, n is the number of sub-driving data in the second driving data, and m is the number of sub-environmental indexes in the first environmental index; Step 246: The optimization coefficient of the target vehicle is combined with the initial road friction coefficient, thereby achieving adjustment and optimization of the initial road friction coefficient and obtaining the road friction coefficient of the target vehicle.

[0055] In this embodiment, the real-time environmental index refers to a series of indicators that are obtained in real time through preset sensors or other monitoring means, describing the environmental status of the target vehicle. These indicators may include temperature, humidity, air pressure, wind speed, visibility, etc., which together reflect the environmental conditions when the vehicle is driving.

[0056] In this embodiment, the first environmental index is extracted from the real-time environmental index and has a significant impact on the road friction coefficient of the target vehicle. These indices may be parameters such as temperature and humidity that directly affect the road friction performance.

[0057] In this embodiment, the first driving data is vehicle driving data obtained after preprocessing, and these data reflect the driving state of the vehicle, such as vehicle speed, acceleration, steering angle, etc.

[0058] In this embodiment, the second driving data is selected from the first driving data and is driving data that has a significant impact on the road friction coefficient. These data may be parameters directly related to road friction, such as vehicle speed and braking status.

[0059] In this embodiment, the driving influence weight is used to describe the weight of the influence degree of each parameter in the second driving data on the road friction coefficient. Different driving parameters may have different influences on the friction coefficient, so it is necessary to assign corresponding weights according to their influence degrees.

[0060] In this embodiment, the environmental impact weight is a weight used to measure the degree of influence of the parameter on the road friction coefficient. Similar to the driving impact weight, different environmental parameters may have different effects on the friction coefficient.

[0061] In this embodiment, the first optimization reference is a preliminary optimization reference for the road friction coefficient obtained by comprehensive calculation based on the second driving data and its corresponding driving influence weight. This reference value is the result after considering the influence of the driving state on the friction coefficient.

[0062] In this embodiment, the second optimization reference is another preliminary optimization reference for the road friction coefficient obtained by comprehensive calculation based on the first environmental index and its corresponding environmental impact weight. This reference value is the result after considering the impact of environmental conditions on the friction coefficient.

[0063] In this embodiment, reference conversion is a process of converting or integrating the first optimization reference and the second optimization reference in combination with their corresponding data types, for example, operations such as data standardization and weighted summation may be involved.

[0064] In this embodiment, the optimization coefficient is obtained by reference conversion, which comprehensively considers the influence of driving state and environmental conditions on the road friction coefficient. This coefficient more accurately reflects the road friction performance of the target vehicle under the current driving state and environmental conditions.

[0065] In this embodiment, the initial road surface friction coefficient is a preliminary estimated value of the road surface friction coefficient obtained from a road surface information database based on information such as road surface type and wetness.

[0066] In this embodiment, the adjustment optimization is a process of combining the optimization coefficient of the target vehicle with the initial road friction coefficient to obtain a more accurate road friction coefficient. This process realizes the correction and optimization of the initial friction coefficient to make it more consistent with the actual driving state and environmental conditions.

[0067] In this embodiment, the road friction coefficient of the target vehicle refers to the road friction coefficient obtained after adjustment and optimization, which more accurately reflects the friction performance of the road surface on which the target vehicle is currently traveling.

[0068] The beneficial effect of the above technical solution is: by optimizing the initial road friction coefficient in combination with environmental factors and the second driving data, the anti-skid control parameters are determined, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective.

[0069] Embodiment 5: Based on Example 3, the real-time anti-skid target of the target vehicle is obtained, and the anti-skid control parameters of the target vehicle are determined based on the real-time anti-skid target combined with the road friction coefficient and the real-time driving data, including: Step 31: determining a real-time anti-skid target of the target vehicle according to the real-time driving state of the target vehicle and the safety standard corresponding to the target vehicle; Step 32: Based on the real-time anti-skid target, the road friction coefficient and the first driving data of the target vehicle, the anti-skid control parameters of the target vehicle are input into a preset anti-skid control system.

[0070] In this embodiment, the real-time driving state of the target vehicle refers to the driving condition of the target vehicle at the current moment, including but not limited to the vehicle speed, acceleration, steering angle, braking state, etc., which reflects the dynamic behavior of the vehicle.

[0071] In this embodiment, the safety standard corresponding to the target vehicle refers to the safety performance requirements formulated for the target vehicle type, purpose, etc. The safety standard generally includes indicators such as the braking performance, stability, and maneuverability of the vehicle, which are used to ensure that the vehicle can maintain safety under various driving conditions.

[0072] In this embodiment, the real-time anti-skid target is the anti-skid performance target that the vehicle should achieve under the current driving conditions, determined based on the real-time driving state and safety standards of the target vehicle. It may be the limit values ​​of parameters such as the braking distance, sideslip angle, and wheel slip rate of the vehicle, which are used to guide the operation of the anti-skid control system.

[0073] In this embodiment, the real-time anti-skid target is used to guide the parameter setting of the anti-skid control system, and reflects the anti-skid performance level that the vehicle needs to achieve under the current driving conditions.

[0074] In this embodiment, the road friction coefficient is a physical quantity that describes the friction between the road surface and the wheels, and has an important influence on the anti-skid performance of the vehicle. The larger the road friction coefficient, the greater the friction between the wheels and the road surface, and the better the anti-skid performance of the vehicle.

[0075] In this embodiment, the preset anti-skid control system is a pre-designed control system for determining the anti-skid control parameters of the vehicle according to input information (such as real-time anti-skid target, road friction coefficient, driving data, etc.). These parameters may include brake pressure, steering angle adjustment, wheel driving force distribution, etc., for achieving anti-skid control of the vehicle.

[0076] In this embodiment, the anti-skid control parameter is the result of the output of the preset anti-skid control system, which is used to guide the anti-skid operation of the vehicle. The specific value of the anti-skid control parameter depends on factors such as the real-time anti-skid target, the road friction coefficient and the driving data, and is used to ensure that the vehicle can maintain stable anti-skid performance under various driving conditions.

[0077] The beneficial effect of the above technical solution is: by optimizing the road friction coefficient, more accurate anti-skid control parameters are obtained to perform anti-skid control on the target vehicle, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective, and can make anti-skid control adjustments in time.

[0078] Embodiment 6: Based on Example 5, the anti-skid control parameters are adaptively optimized based on the stability verification results, including: Step 41: performing anti-skid control on the target vehicle according to the anti-skid control parameter to obtain a first control result; Step 42: Acquire the real-time driving data of the target vehicle at each moment in the anti-skid control period to obtain a second driving data set; Step 43: classifying each type of second driving data in the second driving data set, and sorting the second driving data based on the corresponding time of each type of second driving data, and obtaining the second driving curve of each type of second driving data based on the sorting result, so as to obtain the second driving curve set; Step 44: Based on the curve fluctuation of each second driving curve in the second driving curve set, comprehensively determine the vehicle stability of the target vehicle during the anti-skid control period; If the vehicle stability of the target vehicle during the anti-skid control period is higher than the preset minimum vehicle stability, there is no need to adaptively optimize the anti-skid control parameters of the target vehicle; If the vehicle stability of the target vehicle is not higher than a preset minimum vehicle stability during the anti-skid control period, the anti-skid control parameters of the target vehicle are adaptively optimized based on the vehicle stability of the target vehicle.

[0079] In this embodiment, the anti-skid control parameters are calculated based on a preset anti-skid control system, and are intended to adjust the dynamic characteristics of the vehicle to prevent wheel slippage. The parameters may include the working pressure of the braking system, the distribution of wheel driving force, the adjustment of the steering system, etc.

[0080] In this embodiment, the first control result refers to the actual state or performance of the vehicle after the anti-skid control is executed, such as the reduction of wheel slippage, the stabilization of the vehicle's driving trajectory, etc.

[0081] In this embodiment, the anti-skid control cycle refers to a time period for executing the anti-skid control, which is usually a continuous time window used to evaluate the effect of the anti-skid control.

[0082] In this embodiment, the real-time driving data refers to the driving data of the vehicle at each moment during the anti-skid control cycle, such as vehicle speed, acceleration, steering angle, wheel speed, etc.

[0083] In this embodiment, the second driving data set is a set of all real-time driving data collected during the anti-skid control period, which is used for subsequent analysis and evaluation.

[0084] In this embodiment, classification is to group the data in the second driving data set according to type (such as vehicle speed, acceleration, etc.).

[0085] In this embodiment, the sorting is based on the time point corresponding to each second driving data, and the driving data of the same type are sorted to reflect the changing trend of the data over time.

[0086] In this embodiment, the second driving curve refers to drawing the sorted driving data of the same type into a curve to intuitively show the changes of the driving data of this type in the anti-skid control cycle.

[0087] In this embodiment, the second driving curve set is a set including all types of second driving curves, and is used to comprehensively evaluate the driving state of the vehicle.

[0088] In this embodiment, the curve fluctuation refers to the shape, amplitude, frequency and other characteristics of the second driving curve, which is used to reflect the stability and changes of the driving data.

[0089] In this embodiment, the vehicle stability is based on the second driving curve set, and the driving stability of the vehicle in the anti-skid control period is comprehensively evaluated, which usually involves quantitative analysis of the curve fluctuation.

[0090] In this embodiment, the preset minimum vehicle stability is a set threshold value used to determine whether the stability of the vehicle during the anti-skid control period is sufficient, and is usually determined based on factors such as vehicle type and driving conditions.

[0091] In this embodiment, the adaptive optimization is to automatically adjust the anti-skid control parameters according to the stability of the vehicle during the anti-skid control period to improve the vehicle stability.

[0092] The beneficial effect of the above technical solution is: by judging the vehicle stability of the target vehicle, more accurate anti-skid control parameters are obtained to perform anti-skid control on the target vehicle, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective.

[0093] Embodiment 7: Based on Example 6, the anti-skid control parameters of the target vehicle are adaptively optimized based on the vehicle stability of the target vehicle, including: Step 441: extracting third driving data of vehicle instability in the vehicle stability condition of the target vehicle, and acquiring a data type of each third driving data; Step 442: extracting a parameter optimization solution corresponding to the data type from a parameter optimization database based on the data type of each third driving data; Step 443: inputting the parameter optimization scheme into a preset adaptive optimization algorithm of the target vehicle, thereby determining a parameter adjustment strategy for the anti-skid control parameters of the target vehicle; Step 444: Based on the parameter adjustment strategy, the anti-skid control parameters of the target vehicle are adjusted accordingly, thereby achieving adaptive optimization of the anti-skid control parameters.

[0094] In this embodiment, the vehicle stability condition refers to the driving stability state of the vehicle during the anti-skid control process, which is usually evaluated through a series of driving data.

[0095] In this embodiment, the third vehicle unstable driving data is driving data that is identified as causing vehicle instability in the vehicle stability evaluation.

[0096] In this embodiment, the data type refers to a specific category of the third driving data, such as vehicle speed data, acceleration data, steering angle data, etc. These data types are helpful for subsequently extracting corresponding optimization solutions from the parameter optimization database.

[0097] In this embodiment, the parameter optimization database is a database storing anti-skid control parameter optimization solutions for different data types and vehicle stability problems. The anti-skid control parameter optimization solutions are usually derived based on historical data, expert experience or machine learning algorithms.

[0098] In this embodiment, the parameter optimization solution corresponding to the data type is a relevant optimization solution retrieved from the parameter optimization database according to the data type of the third driving data.

[0099] In this embodiment, the preset adaptive optimization algorithm is a pre-designed algorithm for determining an adjustment strategy for anti-skid control parameters according to a parameter optimization scheme, and may involve knowledge in the fields of machine learning, optimization theory, or control theory.

[0100] In this embodiment, the parameter adjustment strategy is the result of the adaptive optimization algorithm output, which specifically describes how to adjust the anti-skid control parameters to improve vehicle stability. For example, the strategy may include increasing or decreasing brake pressure, adjusting wheel driving force distribution, changing steering angle, etc.

[0101] In this embodiment, the anti-skid control parameter adjustment refers to the process of actually adjusting the anti-skid control parameters of the target vehicle according to the parameter adjustment strategy. This process usually involves direct operation of the vehicle control system.

[0102] In this embodiment, the adaptive optimization continuously improves the driving stability of the vehicle by continuously evaluating the vehicle stability, extracting the instability data, obtaining the optimization scheme from the database, applying the adaptive optimization algorithm and adjusting the anti-skid control parameters. This process is dynamic and can automatically adjust the control strategy according to the real-time driving conditions.

[0103] The beneficial effect of the above technical solution is: by judging the vehicle stability of the target vehicle, more accurate anti-skid control parameters are obtained to perform anti-skid control on the target vehicle, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective.

[0104] Embodiment 8: Based on Example 7, after the adaptive optimization is performed, the method includes: performing a simulation test on the optimized anti-skid control method, specifically including: Step 01: Determine the real-time power distribution scheme of the target vehicle based on the optimized anti-skid control parameters; Step 02: Input the real-time power distribution plan and the real-time driving data of the target vehicle into the virtual machine for simulation, so as to determine the feasibility of the real-time power distribution plan; If the real-time power distribution scheme is feasible, the real-time power distribution scheme is used as the real-time anti-skid control method of the target vehicle; If the real-time power distribution plan is not feasible, the real-time power distribution plan is optimized.

[0105] In this embodiment, the real-time power distribution scheme is a power distribution strategy formulated for the target vehicle based on the optimized anti-skid control parameters to ensure that the wheels obtain optimal traction and prevent skidding.

[0106] In this embodiment, in vehicle engineering and control systems, virtual machines are often used to simulate the dynamic behavior and control strategies of a vehicle so as to perform testing and verification without actually operating the vehicle.

[0107] In this embodiment, simulation is a process of simulating an actual system or process using a computer model. In vehicle engineering, simulation can be used to evaluate the impact of different power distribution schemes on vehicle driving stability and safety.

[0108] In this embodiment, the feasibility of the real-time power distribution scheme is a process for evaluating whether the real-time power distribution scheme can be effectively implemented under actual driving conditions and achieve the expected effect. It may involve comprehensive consideration of the technical feasibility, economic feasibility and safety of the scheme.

[0109] In this embodiment, the scheme optimization is the process of improving and adjusting the real-time power distribution scheme to improve its feasibility and effect. The scheme optimization may involve further adjustment of anti-slip control parameters, improvement of power distribution logic or adjustment of other related control strategies.

[0110] The beneficial effect of the above technical solution is that by simulating the optimized anti-skid control method, the control performance of the anti-skid control method can be judged timely and accurately, and timely adjustments can be made, so that the anti-skid control method of the target vehicle can be made more accurate and effective.

[0111] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tire anti-skid adaptive control method based on road friction, characterized in that: include: Step 1: collecting real-time driving data of the target vehicle based on a preset sensor, and performing data processing to obtain first driving data, and obtaining real-time road surface information of the target vehicle based on the preset sensor; Step 2: acquiring real-time road friction conditions from a road information database based on the real-time road information, and determining a road friction coefficient of the target vehicle in combination with the first driving data; Step 3: Obtain the real-time anti-skid target of the target vehicle, and determine the anti-skid control parameters of the target vehicle based on the real-time anti-skid target combined with the road friction coefficient and the real-time driving data; Step 4: Perform anti-skid control on the target vehicle based on the anti-skid control parameters, perform stability verification on the anti-skid control results, and adaptively optimize the anti-skid control parameters based on the stability verification results.

2. The tire anti-skid adaptive control method based on road friction according to claim 1, characterized in that: The real-time driving data of the target vehicle is collected based on the preset sensor, and the data is processed to obtain the first driving data, and the real-time road surface information of the target vehicle is obtained based on the preset sensor, including: Step 11: collecting real-time driving data of the target vehicle based on a preset sensor, and filtering and denoising the real-time driving data to obtain first driving data; Step 12: Capturing the road surface image and point cloud data of the target vehicle in real time based on the preset sensor to obtain first road surface data; Step 13: Analyze the first road surface data to determine the real-time road surface information of the road surface on which the target vehicle is traveling.

3. The tire anti-skid adaptive control method based on road friction according to claim 2, characterized in that: Acquiring real-time road friction conditions from a road information database based on real-time road information, and determining a road friction coefficient of the target vehicle in combination with the first driving data, including: Step 21: Based on the type of road surface on which the target vehicle is traveling, the degree of road surface slipperiness, and the corresponding friction coefficient, a corresponding road surface information database is established; Step 22: Match the real-time road surface information with the data in the road surface information database, so as to find the road surface type and slipperiness degree with the highest similarity to the real-time road surface information as reference road surface information; Step 23: Obtaining a road friction coefficient corresponding to the reference road information from a road information database as an initial road friction coefficient corresponding to the real-time road information; Step 24: The initial road friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road friction coefficient of the target vehicle.

4. The tire anti-skid adaptive control method based on road friction according to claim 3, characterized in that: The initial road friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road friction coefficient of the target vehicle, including: Step 241: obtaining a real-time environmental index of the target vehicle, and extracting an environmental index that will affect the road friction coefficient of the target vehicle from the real-time environmental index to obtain a first environmental index; Step 242: Acquire driving data that affects the road surface friction coefficient in the first driving data to obtain second driving data; Step 243: determining a driving influence weight corresponding to the second driving data based on the influence degree of the coefficient of the road friction coefficient of the second driving data, and determining an environmental influence weight corresponding to the first environmental index based on the influence degree of the coefficient of the road friction coefficient of the first environmental index; Step 244: The driving influence weight and the second driving data are combined to determine a first optimization reference of the road friction coefficient of the target vehicle, and at the same time, the environmental influence weight and the first environmental index are combined to determine a second optimization reference of the road friction coefficient of the target vehicle; Step 245: performing reference conversion on the first optimization reference and the second optimization reference in combination with corresponding data types, thereby obtaining an optimization coefficient T of the road friction coefficient of the target vehicle; ; Where T is the optimization coefficient of the road friction coefficient of the target vehicle, is the driving influence weight of the first optimization reference, is the i-th sub-driving data in the second driving data, is the data normalization conversion coefficient of the i-th sub-driving data in the second driving data, is the data type influencing factor corresponding to the i-th sub-driving data in the second driving data, is the friction coefficient conversion factor, The environmental impact weight for the second optimization reference, is the jth sub-environmental index in the first environmental index of the target vehicle, is the index conversion factor of the jth sub-environmental index, n is the number of sub-driving data in the second driving data, and m is the number of sub-environmental indexes in the first environmental index; Step 246: The optimization coefficient of the target vehicle is combined with the initial road friction coefficient, thereby achieving adjustment and optimization of the initial road friction coefficient and obtaining the road friction coefficient of the target vehicle.

5. The tire anti-skid adaptive control method based on road friction according to claim 3, characterized in that: Obtain the real-time anti-skid target of the target vehicle, and determine the anti-skid control parameters of the target vehicle based on the real-time anti-skid target combined with the road friction coefficient and real-time driving data, including: Step 31: determining a real-time anti-skid target of the target vehicle according to the real-time driving state of the target vehicle and the safety standard corresponding to the target vehicle; Step 32: Based on the real-time anti-skid target, the road friction coefficient and the first driving data of the target vehicle, the anti-skid control parameters of the target vehicle are input into a preset anti-skid control system.

6. The tire anti-skid adaptive control method based on road friction according to claim 5, characterized in that: Anti-skid control is performed on the target vehicle based on the anti-skid control parameters, and stability verification is performed on the anti-skid control results. Based on the stability verification results, the anti-skid control parameters are adaptively optimized, including: Step 41: performing anti-skid control on the target vehicle according to the anti-skid control parameter to obtain a first control result; Step 42: Acquire the real-time driving data of the target vehicle at each moment in the anti-skid control period to obtain a second driving data set; Step 43: classifying each type of second driving data in the second driving data set, and sorting the second driving data based on the corresponding time of each type of second driving data, and obtaining the second driving curve of each type of second driving data based on the sorting result, so as to obtain the second driving curve set; Step 44: Based on the curve fluctuation of each second driving curve in the second driving curve set, comprehensively determine the vehicle stability of the target vehicle during the anti-skid control period; If the vehicle stability of the target vehicle during the anti-skid control period is higher than the preset minimum vehicle stability, there is no need to adaptively optimize the anti-skid control parameters of the target vehicle; If the vehicle stability of the target vehicle is not higher than a preset minimum vehicle stability during the anti-skid control period, the anti-skid control parameters of the target vehicle are adaptively optimized based on the vehicle stability of the target vehicle.

7. The tire anti-skid adaptive control method based on road friction according to claim 6, characterized in that: Adaptively optimize the anti-skid control parameters of the target vehicle based on the vehicle stability of the target vehicle, including: Step 441: extracting third driving data of vehicle instability in the vehicle stability condition of the target vehicle, and acquiring a data type of each third driving data; Step 442: extracting a parameter optimization solution of a corresponding data type from a parameter optimization database based on the data type of each third driving data; Step 443: inputting the parameter optimization scheme into a preset adaptive optimization algorithm of the target vehicle, thereby determining a parameter adjustment strategy for the anti-skid control parameters of the target vehicle; Step 444: Based on the parameter adjustment strategy, the anti-skid control parameters of the target vehicle are adjusted accordingly, thereby achieving adaptive optimization of the anti-skid control parameters.

8. The tire anti-skid adaptive control method based on road friction according to claim 6, characterized in that: After the adaptive optimization, including: simulation test of the optimized anti-skid control method, including: Step 01: Determine the real-time power distribution scheme of the target vehicle based on the optimized anti-skid control parameters; Step 02: Input the real-time power distribution plan and the real-time driving data of the target vehicle into the virtual machine for simulation, so as to determine the feasibility of the real-time power distribution plan; If the real-time power distribution scheme is feasible, the real-time power distribution scheme is used as the real-time anti-skid control method of the target vehicle; If the real-time power distribution plan is not feasible, the real-time power distribution plan is optimized.

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