Tire anti-slip adaptive control method based on road friction

Abstract

The application provides a tire anti-skid adaptive control method based on road surface friction, and relates to the technical field of adaptive control, which comprises the following steps: collecting real-time driving data of a target vehicle based on a preset sensor, and performing data processing to obtain first driving data, and simultaneously acquiring real-time road surface information of the target vehicle based on the preset sensor; acquiring real-time road surface friction conditions from a road surface information library 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 an anti-skid control parameter of the target vehicle based on the real-time anti-skid target in combination with the road surface friction coefficient and the real-time driving data; performing anti-skid control on the target vehicle based on the anti-skid control parameter, verifying the stability of an anti-skid control result, and adaptively optimizing the anti-skid control parameter based on a stability verification result. The tire anti-skid adaptive control of the target vehicle can be more accurate and effective, and the anti-skid control can be adjusted in a timely manner.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of adaptive control, in particular to a tire anti-skid adaptive control method based on road friction. BACKGROUND

[0002] Currently, during driving, wheel skidding is a common phenomenon, which not only affects driving safety, but also may cause vehicle out of control. Wheel skidding is mainly caused by insufficient friction between tire and road surface, especially on wet or icy road surface, the friction will be significantly reduced, causing the wheel to be easy to skid.

[0003] However, existing anti-skid measures, such as installing anti-skid chains, using snow tires, etc., although 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 application provides a tire anti-skid adaptive control method based on road friction. SUMMARY

[0005] The present application provides a tire anti-skid adaptive control method based on road friction, which optimizes the coefficient of road friction coefficient to obtain more accurate anti-skid control parameters for target vehicle anti-skid control, which can make the tire anti-skid adaptive control of target vehicle more accurate and effective, and can timely adjust the anti-skid control.

[0006] The present application provides a tire anti-skid adaptive control method based on road friction, which includes:

[0007] Step 1: based on the preset sensor to collect the real-time driving data of the target vehicle, and process the data to obtain the first driving data, and based on the preset sensor to obtain the real-time road surface information of the target vehicle;

[0008] Step 2: based on the real-time road surface information, the real-time road friction condition is obtained from the road surface information library, and the road friction coefficient of the target vehicle is determined combined with the first driving data;

[0009] Step 3: obtaining the real-time anti-skid target of the target vehicle, and determining 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;

[0010] Step 4: based on the anti-skid control parameters to control the target vehicle, and verify the stability of the anti-skid control result, and based on the stability verification result to adaptively optimize the anti-skid control parameters.

[0011] According to the application, the real-time driving data of the target vehicle is collected based on the preset sensor, and the first driving data is obtained through data processing. Meanwhile, the real-time road surface information of the target vehicle is obtained based on the preset sensor, including:

[0012] Step 11: The real-time driving data of the target vehicle is collected based on the preset sensor, and the first driving data is obtained through filtering and denoising processing of the real-time driving data.

[0013] Step 12: The real-time road surface image and point cloud data of the target vehicle are captured based on the preset sensor, and the first road surface data is obtained.

[0014] Step 13: The first road surface data is analyzed to determine the real-time road surface information of the driving road surface of the target vehicle.

[0015] According to the application, the real-time road surface friction condition is obtained from the road surface information library 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:

[0016] Step 21: The corresponding road surface information library is established based on the driving road surface type, road surface wetness degree and corresponding friction coefficient of the target vehicle.

[0017] Step 22: The real-time road surface information is matched with the data in the road surface information library to find the road surface type and wetness degree with the highest similarity to the real-time road surface information as the reference road surface information.

[0018] Step 23: The road surface friction coefficient corresponding to the reference road surface information is obtained from the road surface information library as the initial road surface friction coefficient corresponding to the real-time road surface information.

[0019] Step 24: The initial road surface friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road surface friction coefficient of the target vehicle.

[0020] According to the application, the initial road surface friction coefficient is adjusted and optimized in combination with the first driving data to obtain the road surface friction coefficient of the target vehicle, including:

[0021] Step 241: The real-time environment index of the target vehicle is obtained, and the environment index in the real-time environment index that will affect the road surface friction coefficient of the target vehicle is extracted to obtain the first environment index.

[0022] Step 242: The driving data in the first driving data that will affect the road surface friction coefficient is obtained to obtain the second driving data.

[0023] Step 243: Determine a driving influence weight corresponding to the second driving data based on the influence of the second driving data on the road friction coefficient. Simultaneously, determine an environmental influence weight corresponding to the first environmental index based on the influence of the first environmental index on the road friction coefficient.

[0024] Step 244: Determine a first optimization reference for the road friction coefficient of the target vehicle by combining the driving influence weight and the second driving data. Simultaneously, determine a second optimization reference for the road friction coefficient of the target vehicle by combining the environmental influence weight and the first environmental index.

[0025] Step 245: performing reference conversion on the first optimization reference and the second optimization reference in combination with corresponding data types, thereby obtaining an optimized coefficient T of the road friction coefficient of the target vehicle;

[0026] ; 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 j-th sub-environmental index, n is the number of sub-driving data in the second driving data, and m is the number of sub-environmental indices in the first environmental index;

[0027] Step 246: The optimization coefficient of the target vehicle is combined with the initial road friction coefficient, thereby adjusting and optimizing the initial road friction coefficient to obtain the road friction coefficient of the target vehicle.

[0028] According to the present invention, the method of obtaining a real-time anti-skid target of a target vehicle and determining an anti-skid control parameter of the target vehicle based on the real-time anti-skid target in combination with a road friction coefficient and real-time driving data includes:

[0029] Step 31: determining a real-time anti-skid target for the target vehicle based on the real-time driving state of the target vehicle and the safety standard corresponding to the target vehicle;

[0030] 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.

[0031] 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 based on the stability verification result includes:

[0032] Step 41: performing anti-skid control on the target vehicle according to the anti-skid control parameter to obtain a first control result;

[0033] Step 42: Acquire real-time driving data of the target vehicle at each moment during the anti-skid control period to obtain a second driving data set;

[0034] Step 43: Classifying each type of second driving data in the second driving data set, sorting the second driving data based on the corresponding time of each type of second driving data, and obtaining a second driving curve for each type of second driving data based on the sorting result, thereby obtaining a second driving curve set;

[0035] Step 44: Based on the curve fluctuation of each second driving curve in the second driving curve set, comprehensively determining the vehicle stability of the target vehicle during the anti-skid control period;

[0036] 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;

[0037] 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.

[0038] 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:

[0039] Step 441: extracting third driving data of the target vehicle indicating vehicle instability in the vehicle stability condition, and simultaneously obtaining a data type of each third driving data;

[0040] 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;

[0041] Step 443: Inputting the parameter optimization solution 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;

[0042] 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.

[0043] The simulation test of the optimized anti-skid control method provided by the present invention specifically includes:

[0044] Step 01: Determine the real-time power distribution plan of the target vehicle based on the optimized anti-skid control parameters;

[0045] 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 to determine the feasibility of the real-time power distribution plan;

[0046] 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 for the target vehicle;

[0047] If the real-time power distribution plan is not feasible, the real-time power distribution plan is optimized.

[0048] 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 to perform anti-skid control on 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

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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 any creative work.

[0050] Figure 1 This 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

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1:

[0053] The embodiment of the present invention provides a tire anti-skid adaptive control method based on road friction, such as Figure 1 Shown, including:

[0054] Step 1: Collect real-time driving data of the target vehicle based on preset sensors and process the data to obtain first driving data, and obtain real-time road surface information of the target vehicle based on preset sensors;

[0055] Step 2: Obtain real-time road surface friction conditions from the road surface information library based on real-time road surface information, and determine the road surface friction coefficient of the target vehicle in combination with the first driving data;

[0056] 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 in combination with the road surface friction coefficient and the real-time driving data;

[0057] Step 4: Perform anti-skid control on the target vehicle based on the anti-skid control parameters, verify the stability of the anti-skid control result, and perform adaptive optimization on the anti-skid control parameters based on the stability verification result.

[0058] 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 real-time driving data (such as speed, acceleration, steering angle, etc.) and road surface information (such as road surface material, humidity, temperature, etc.) of the vehicle. The sensor may include radar, camera, accelerometer, gyroscope, pressure sensor, etc.

[0059] In this embodiment, real-time driving data refers to data collected by the preset sensor in real time during the actual driving of the vehicle. It can include vehicle speed, acceleration, steering angle, wheel speed, brake pressure, etc., which are used to analyze and evaluate the driving state and performance of the vehicle.

[0060] In this embodiment, real-time road surface information refers to road condition information obtained in real time by the preset sensor, such as road surface material (asphalt, concrete, etc.), humidity, temperature, road damage, etc. These information is crucial for evaluating the friction performance and safety of the road.

[0061] In this embodiment, the road surface information library is a database that stores a large amount of road surface information, including friction coefficients under various road conditions, road damage conditions, etc. By comparing with real-time road surface information, corresponding road surface friction conditions and other information can be obtained from the library.

[0062] In this embodiment, the road surface friction coefficient refers to the ratio of the friction force between the wheel and the road surface to the vertical load of the wheel, which is used to measure the road's grip ability under different conditions. The larger the friction coefficient, the greater the friction force between the wheel and the road, and the better the handling and stability of the vehicle.

[0063] In this embodiment, the real-time anti-skid target refers to a preset anti-skid performance target based on the driving state of the vehicle and the road conditions. For example, on a wet and slippery road, higher anti-skid performance may be required to ensure the stability and safety of the vehicle.

[0064] In this embodiment, the anti-skid control parameter refers to a parameter used to adjust the working state of the vehicle's 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.

[0065] In this embodiment, anti-skid control refers to adjusting the vehicle's brake system, power system, steering system, etc. to improve the vehicle's anti-skid performance on wet and slippery roads, ensuring the stability and safety of the vehicle.

[0066] In this embodiment, stability verification refers to verifying the results of anti-skid control to ensure the driving stability and safety of the vehicle under anti-skid control. This usually involves monitoring and analyzing the vehicle's driving trajectory, speed, acceleration, etc.

[0067] In this embodiment, adaptive optimization refers to adjusting and optimizing anti-skid control parameters based on stability verification results to improve the performance and effectiveness of anti-skid control. This optimization can be real-time or offline based on historical data.

[0068] The beneficial effects of the above technical solutions are: by optimizing the road friction coefficient, more accurate anti-skid control parameters are obtained for the target vehicle, which can make the tire anti-skid adaptive control of the target vehicle more accurate and effective, and can timely adjust the anti-skid control.

[0069] Embodiment 2:

[0070] Based on the basis of embodiment 1, the real-time road information of the target vehicle is obtained based on the preset sensor, including:

[0071] Step 11: Based on the preset sensor, the real-time driving data of the target vehicle is collected, and the real-time driving data is filtered and denoised to obtain the first driving data;

[0072] Step 12: Based on the preset sensor, the road surface image and point cloud data of the target vehicle are captured in real time to obtain the first road surface data;

[0073] Step 13: The first road surface data is analyzed to determine the real-time road information of the target vehicle's driving road surface.

[0074] In this embodiment, real-time driving data refers to various data collected in real time by preset sensors while the target vehicle is driving, including but not limited to vehicle speed, acceleration, steering angle, brake status, engine status, etc., reflecting the real-time driving status of the vehicle.

[0075] In this embodiment, filtering is a signal processing technique used to remove unwanted frequency components from a signal. In the processing of vehicle driving data, filtering can help remove noise and interference, thereby improving the accuracy and reliability of the data.

[0076] In this embodiment, denoising refers to removing noise from data to improve the signal-to-noise ratio of the data. During the acquisition of vehicle driving data, the data may contain noise due to various interference factors (such as sensor errors, environmental noise, etc.).

[0077] In this embodiment, the first driving data is driving data obtained after filtering and denoising.

[0078] In this embodiment, the preset sensors are pre-installed on the vehicle or in the road system to capture real-time road information of the target vehicle. These sensors may include cameras, laser radar (LiDAR), etc., which can capture images and point cloud data of the road surface.

[0079] In this embodiment, the road surface image is the visual information of the road surface captured by an image sensor such as a camera. This information is usually presented in the form of a two-dimensional image, which includes characteristics such as the texture, color, and shape of the road surface.

[0080] In this embodiment, point cloud data is three-dimensional spatial information captured by sensors such as lidar. This data is presented as points, each containing its position in three-dimensional space. Point cloud data can be used to construct three-dimensional models and analyze road surface characteristics such as shape and height.

[0081] 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.

[0082] 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 such as road material, wetness, damage, etc. can be determined.

[0083] In this embodiment, real-time road surface information refers to detailed information about the road surface on which the target vehicle is traveling, obtained through data analysis. Real-time road surface information typically includes the road surface's physical properties (e.g., material, wetness), geometric properties (e.g., shape, height), and damage.

[0084] The beneficial effects of the above technical solution are: by analyzing the road surface information obtained by the sensor, the real-time road surface information is obtained, the road surface friction coefficient is determined, the coefficient optimization is performed, and the tire anti-skid adaptive control of the target vehicle is more accurate and effective.

[0085] Embodiment 3:

[0086] Based on the basis of embodiment 2, the road surface friction coefficient of the target vehicle is determined in combination with the first driving data, comprising:

[0087] Step 21: based on the driving road surface type, the road surface wetness degree and the corresponding friction coefficient of the target vehicle, a corresponding road surface information library is established;

[0088] Step 22: match the real-time road surface information with the data in the road surface information library, so as to find the road surface type and wetness degree with the highest similarity to the real-time road surface information as the reference road surface information;

[0089] Step 23: obtain the road surface friction coefficient corresponding to the reference road surface information from the road surface information library as the initial road surface friction coefficient corresponding to the real-time road surface information;

[0090] Step 24: adjust and optimize the initial road surface friction coefficient in combination with the first driving data to obtain the road surface friction coefficient of the target vehicle.

[0091] In this embodiment, the driving road surface 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 characteristics and friction performance.

[0092] In this embodiment, the road surface wetness degree is a state describing whether the road surface is wet or not, which is usually related to factors such as water content and oil pollution of the road surface. The higher the wetness degree, the lower the friction coefficient of the road surface, and the driving safety of the vehicle will also decrease accordingly.

[0093] In this embodiment, the friction coefficient is a physical quantity describing the friction between the road surface and the wheel. The larger the friction coefficient, the greater the friction between the wheel and the road surface, and the better the controllability and stability of the vehicle.

[0094] In this embodiment, the road surface information library is a database storing a large amount of road surface information, including different road surface types, wetness degrees and their corresponding friction coefficients, etc., which can be used for matching and comparing with the actually collected road surface information.

[0095] In this embodiment, the real-time road surface information is the road surface information collected in real time by the preset sensor, including the road surface type, the wetness degree, etc.

[0096] In this embodiment, matching refers to the process of searching for data in the road surface information database that is most similar to the real-time road surface information. The purpose of matching is to find reference information that is closest to the current actual road surface conditions.

[0097] In this embodiment, the reference road surface information is the road surface type and wetness level that are most similar to the real-time road surface information found in the road surface information database. This information will serve as a reference for subsequent analysis and processing.

[0098] 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.

[0099] In this embodiment, the adjustment optimization is the process of modifying the initial road friction coefficient based on 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.

[0100] 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.

[0101] The beneficial effect of the above technical solution is: by analyzing 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.

[0102] Example 4:

[0103] Based on Example 3, the road friction coefficient of the target vehicle is obtained, including:

[0104] Step 241: obtaining a real-time environmental index of the target vehicle, and extracting an environmental index that affects the road friction coefficient of the target vehicle from the real-time environmental index to obtain a first environmental index;

[0105] Step 242: Acquire driving data that affects the road surface friction coefficient in the first driving data to obtain second driving data;

[0106] Step 243: Determine a driving influence weight corresponding to the second driving data based on the influence of the second driving data on the road friction coefficient. Simultaneously, determine an environmental influence weight corresponding to the first environmental index based on the influence of the first environmental index on the road friction coefficient.

[0107] Step 244: Determine a first optimization reference for the road friction coefficient of the target vehicle by combining the driving influence weight and the second driving data. Simultaneously, determine a second optimization reference for the road friction coefficient of the target vehicle by combining the environmental influence weight and the first environmental index.

[0108] Step 245: combine the first optimization reference and the second optimization reference to convert the corresponding data type, so as to obtain the optimization coefficient T of the road surface friction coefficient of the target vehicle;

[0109] ; wherein T is the optimization coefficient of the road surface 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 influence factor corresponding to the i-th sub-driving data in the second driving data, is the friction coefficient conversion factor, is the environmental influence weight of the second optimization reference, is the j-th sub-environmental index in the first environmental index of the target vehicle, is the index conversion factor of the j-th 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;

[0110] Step 246: combine the optimization coefficient of the target vehicle with the initial road surface friction coefficient to realize the adjustment and optimization of the initial road surface friction coefficient, and obtain the road surface friction coefficient of the target vehicle.

[0111] In this embodiment, the real-time environmental index refers to a series of indicators describing the environmental state of the target vehicle obtained in real time through preset sensors or other monitoring means. These indicators may include temperature, humidity, air pressure, wind speed, visibility, etc., which collectively reflect the environmental conditions during vehicle driving.

[0112] In this embodiment, the first environmental index is extracted from the real-time environmental index and has a significant impact on the road surface friction coefficient of the target vehicle. These indexes may be temperature, humidity, etc. parameters that directly affect the road surface friction performance.

[0113] In this embodiment, the first driving data is the vehicle driving data obtained after preprocessing, which reflects the driving state of the vehicle, such as vehicle speed, acceleration, steering angle, etc.

[0114] In this embodiment, the second driving data is selected from the first driving data and has a significant impact on the road surface friction coefficient. These data may be vehicle speed, brake state, etc. parameters directly related to road surface friction.

[0115] In this embodiment, the driving influence weight is used to describe the weight of the influence of each parameter in the second driving data on the road friction coefficient. Different driving parameters may have different effects on the friction coefficient, so corresponding weights need to be assigned according to their influence.

[0116] 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.

[0117] 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 taking into account the impact of the driving state on the friction coefficient.

[0118] 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.

[0119] 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.

[0120] In this embodiment, the optimization coefficient is obtained by reference conversion, which comprehensively considers the impact 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.

[0121] 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.

[0122] In this embodiment, adjustment optimization is the process of combining the optimized coefficient of the target vehicle with the initial road friction coefficient to obtain a more accurate road friction coefficient. This process corrects and optimizes the initial friction coefficient to make it more consistent with actual driving conditions and environmental conditions.

[0123] 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.

[0124] 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.

[0125] Example 5:

[0126] 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 in combination with the road friction coefficient and real-time driving data, including:

[0127] Step 31: determining a real-time anti-skid target for the target vehicle based on the real-time driving state of the target vehicle and the safety standard corresponding to the target vehicle;

[0128] 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.

[0129] 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 speed, acceleration, steering angle, braking state, etc., reflecting the dynamic behavior of the vehicle.

[0130] In this embodiment, the safety standards corresponding to the target vehicle refer to safety performance requirements formulated based on the target vehicle type, purpose, etc. Safety standards generally include indicators such as vehicle braking performance, stability, and maneuverability, and are used to ensure that the vehicle remains safe under various driving conditions.

[0131] In this embodiment, the real-time anti-skid target is the anti-skid performance target that the vehicle should achieve under current driving conditions, determined based on the target vehicle's real-time driving status and safety standards. This target may be a limit on parameters such as braking distance, sideslip angle, and wheel slip rate, and is used to guide the operation of the anti-skid control system.

[0132] 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.

[0133] In this embodiment, the road friction coefficient is a physical quantity that describes the friction between the road surface and the wheels, and has a significant impact 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.

[0134] In this embodiment, the preset anti-skid control system is a pre-designed control system that determines the vehicle's anti-skid control parameters based on input information (such as real-time anti-skid targets, road friction coefficient, driving data, etc.). These parameters may include brake pressure, steering angle adjustment, wheel drive force distribution, etc., used to achieve vehicle anti-skid control.

[0135] In this embodiment, the anti-skid control parameter is the output of a preset anti-skid control system and is used to guide the vehicle's anti-skid operation. The specific value of the anti-skid control parameter depends on factors such as the real-time anti-skid target, the road surface friction coefficient, and driving data. It is used to ensure that the vehicle maintains stable anti-skid performance under various driving conditions.

[0136] 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 timely anti-skid control adjustments.

[0137] Example 6:

[0138] Based on Example 5, the anti-skid control parameters are adaptively optimized based on the stability verification results, including:

[0139] Step 41: performing anti-skid control on the target vehicle according to the anti-skid control parameter to obtain a first control result;

[0140] Step 42: Acquire real-time driving data of the target vehicle at each moment during the anti-skid control period to obtain a second driving data set;

[0141] Step 43: Classifying each type of second driving data in the second driving data set, sorting the second driving data based on the corresponding time of each type of second driving data, and obtaining a second driving curve for each type of second driving data based on the sorting result, thereby obtaining a second driving curve set;

[0142] Step 44: Based on the curve fluctuation of each second driving curve in the second driving curve set, comprehensively determining the vehicle stability of the target vehicle during the anti-skid control period;

[0143] 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;

[0144] 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.

[0145] In this embodiment, the anti-skid control parameters are calculated based on a pre-set anti-skid control system and are designed to adjust the vehicle's dynamic characteristics to prevent wheel slip. These parameters may include brake system operating pressure, wheel drive force distribution, steering system adjustments, and so on.

[0146] In this embodiment, the first control result refers to the actual state or performance of the vehicle after executing the anti-skid control, such as the reduction of wheel slip, the stability of vehicle trajectory, etc.

[0147] In this embodiment, the anti-skid control period refers to the time period during which the anti-skid control is executed, usually a continuous time window, for evaluating the effectiveness of the anti-skid control.

[0148] In this embodiment, the real-time driving data refers to the driving data of the vehicle at each moment within the anti-skid control period, such as vehicle speed, acceleration, steering angle, wheel speed, etc.

[0149] In this embodiment, the second driving data set is a collection of all real-time driving data collected within the anti-skid control period, for subsequent analysis and evaluation.

[0150] In this embodiment, classification is grouping data in the second driving data set according to type (such as vehicle speed, acceleration, etc.).

[0151] In this embodiment, sorting is sorting the same type of driving data based on the time point corresponding to each second driving data, to reflect the trend of data changes over time.

[0152] In this embodiment, the second driving curve refers to drawing the sorted same type of driving data into a curve to visually display the changes of this type of driving data within the anti-skid control period.

[0153] In this embodiment, the second driving curve set is a set containing all types of second driving curves, for comprehensive evaluation of the driving state of the vehicle.

[0154] In this embodiment, the curve fluctuation refers to the shape, amplitude, frequency, etc. of the second driving curve, for reflecting the stability and changes of the driving data.

[0155] In this embodiment, the vehicle stability situation is based on the second driving curve set to comprehensively evaluate the driving stability of the vehicle within the anti-skid control period. Usually involves quantitative analysis of curve fluctuation.

[0156] In this embodiment, the preset minimum vehicle stability is a set threshold for determining whether the stability of the vehicle within the anti-skid control period is sufficient. Usually determined according to vehicle type, driving conditions, etc.

[0157] In this embodiment, adaptive optimization is to automatically adjust the anti-skid control parameters to improve the stability of the vehicle according to the stability situation of the vehicle within the anti-skid control period.

[0158] 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.

[0159] Example 7:

[0160] 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:

[0161] Step 441: extracting third driving data of the target vehicle indicating vehicle instability in the vehicle stability condition, and simultaneously obtaining a data type of each third driving data;

[0162] 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;

[0163] Step 443: Inputting the parameter optimization solution 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;

[0164] 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.

[0165] In this embodiment, the vehicle stability condition refers to the driving stability state of the vehicle during the execution of anti-skid control, and is usually evaluated through a series of driving data.

[0166] In this embodiment, the third vehicle unstable driving data is driving data identified as causing vehicle instability during vehicle stability evaluation.

[0167] 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.

[0168] In this embodiment, the parameter optimization database is a database that stores anti-skid control parameter optimization solutions for different data types and vehicle stability issues. The anti-skid control parameter optimization solutions are usually derived based on historical data, expert experience, or machine learning algorithms.

[0169] 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.

[0170] In this embodiment, the preset adaptive optimization algorithm is a pre-designed algorithm for determining an adjustment strategy for anti-slip control parameters according to a parameter optimization scheme, and may involve knowledge in fields such as machine learning, optimization theory, or control theory.

[0171] In this embodiment, the parameter adjustment strategy is the output of the adaptive optimization algorithm, specifically describing 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 drive force distribution, changing the steering angle, etc.

[0172] 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.

[0173] In this embodiment, adaptive optimization continuously improves vehicle stability by continuously evaluating vehicle stability, extracting instability data, obtaining optimization solutions from a database, applying adaptive optimization algorithms, and adjusting anti-skid control parameters. This process is dynamic and automatically adjusts control strategies based on real-time driving conditions.

[0174] 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.

[0175] Example 8:

[0176] Based on Example 7, after the adaptive optimization is performed, the following steps are included: performing a simulation test on the optimized anti-slip control method, specifically including:

[0177] Step 01: Determine the real-time power distribution plan of the target vehicle based on the optimized anti-skid control parameters;

[0178] 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 to determine the feasibility of the real-time power distribution plan;

[0179] 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 for the target vehicle;

[0180] If the real-time power distribution plan is not feasible, the real-time power distribution plan is optimized.

[0181] 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.

[0182] In this embodiment, in the vehicle engineering and control system, virtual machines are often used to simulate the dynamic behavior and control strategies of vehicles, so as to test and verify without actually operating the vehicle.

[0183] 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.

[0184] In this embodiment, the feasibility of the real-time power distribution scheme is a process of evaluating whether the real-time power distribution scheme can be effectively implemented and achieve the expected effect under actual driving conditions. It may involve comprehensive consideration of the technical feasibility, economic feasibility and safety of the scheme.

[0185] In this embodiment, scheme optimization is a process of improving and adjusting the real-time power distribution scheme to improve its feasibility and effectiveness. Scheme optimization may involve further adjustment of anti-skid control parameters, improvement of power distribution logic or adjustment of other related control strategies.

[0186] The beneficial effects of the above technical solutions are: through simulation of the optimized anti-skid control method, the control performance of the anti-skid control method can be judged in time and accurately, and adjustment can be made in time, so that the anti-skid control method of the target vehicle is more accurate and effective.

[0187] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

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, while simultaneously obtaining real-time road surface information of the target vehicle based on the preset sensor; Step 2: obtaining 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 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; 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 target vehicle's driving road surface type, road surface wetness, and 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 to find the road surface type and wetness degree that are most similar to the real-time road surface information as the reference road surface information; Step 23: Obtain the road friction coefficient corresponding to the reference road information from the road information database as the initial road friction coefficient corresponding to the real-time road information; Step 24: adjusting and optimizing the initial road surface friction coefficient in combination with the first driving data to obtain the road surface friction coefficient of the target vehicle; The initial road friction coefficient is adjusted and optimized based on 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 affects 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: Determine a driving influence weight corresponding to the second driving data based on the influence of the second driving data on the road friction coefficient. Simultaneously, determine an environmental influence weight corresponding to the first environmental index based on the influence of the first environmental index on the road friction coefficient. Step 244: Determine a first optimization reference for the road friction coefficient of the target vehicle by combining the driving influence weight and the second driving data. Simultaneously, determine a second optimization reference for the road friction coefficient of the target vehicle by combining the environmental influence weight and the first environmental index. Step 245: performing reference conversion on the first optimization reference and the second optimization reference in combination with corresponding data types, thereby obtaining an optimized 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 indices in the first environmental index; Step 246: The optimization coefficient of the target vehicle is combined with the initial road friction coefficient, thereby adjusting and optimizing the initial road friction coefficient to obtain the road friction coefficient of the target vehicle.

2. The tire anti-skid adaptive control method based on road friction according to claim 1, characterized in that: The method includes collecting real-time driving data of a target vehicle based on a preset sensor, processing the data to obtain first driving data, and obtaining real-time road surface information of the target vehicle 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 a road surface image and point cloud data of the target vehicle in real time based on a 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.

3. The tire anti-skid adaptive control method based on road friction according to claim 1, 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 for the target vehicle based on 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.

4. The tire anti-skid adaptive control method based on road friction according to claim 3, characterized in that: 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, 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 real-time driving data of the target vehicle at each moment during 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, sorting the second driving data based on the corresponding time of each type of second driving data, and obtaining a second driving curve for each type of second driving data based on the sorting result, thereby obtaining a second driving curve set; Step 44: Based on the curve fluctuation of each second driving curve in the second driving curve set, comprehensively determining 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.

5. The tire anti-skid adaptive control method based on road friction according to claim 4, 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 the target vehicle indicating vehicle instability in the vehicle stability condition, and simultaneously obtaining 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 solution 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.

6. The tire anti-skid adaptive control method based on road friction according to claim 4, characterized in that: After the adaptive optimization is performed, simulation tests are conducted on the optimized anti-skid control method, specifically including: Step 01: Determine the real-time power distribution plan 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 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 for 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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