A vehicle control method and system in an emergency braking scenario

Abstract

The present application provides a vehicle control method and system in an emergency braking scenario. This method dynamically adjusts the target slip rate range by establishing a data set of the optimal slip rate under different road surface conditions and combining a digital model to simulate the contact characteristics between the tire and the road surface; collects vehicle motion state data, analyzes the mutation characteristics and the changing trend of the tire force, and generates a warning signal; inputs the target slip rate range and the warning signal into a predictive control model, calculates the braking force of each wheel and generates a control instruction; uses a digital tire model to simulate the expected slip state, and determines the pressure control value with reference to the friction force change law; corrects the model parameters and adjusts the pressure control value by comparing the actual measurement with the simulation result and combining the pressure change rate of the brake hydraulic system, so as to realize the automatic adjustment of the braking force. The technical solution provided by the present application significantly improves the stability and safety of the vehicle in an emergency braking situation.

Description

Technical Field

[0001] This application relates to the technical field of vehicle braking control, and particularly to a vehicle control method and system in an emergency braking scenario. Background Art

[0002] In an emergency braking scenario of a vehicle, due to the dynamic change of the road surface adhesion coefficient and the complex contact characteristics between the tire and the ground, it is necessary to accurately control the slip ratio of each wheel in real time to maintain the best braking performance and vehicle stability. Especially on low-adhesion road surfaces such as ice, snow, and wet roads or in mixed adhesion road conditions, it is difficult for traditional braking systems to balance braking distance and directional stability. Therefore, there is an urgent need for an intelligent control method that can identify road surface characteristics in real time, dynamically adjust the target slip ratio, and accurately distribute the braking force of each wheel.

[0003] Currently, a relatively advanced solution is an ABS system based on model predictive control (MPC). This solution predicts the vehicle state changes in the future time domain by establishing a vehicle dynamics model and combining parameters such as wheel speed and vehicle speed collected in real time, and rolling optimizes and calculates the optimal braking force distribution scheme. The system uses a preset multi-group road surface parameter model to match the current road condition characteristics in real time by the least squares method, thereby dynamically adjusting the control strategy.

[0004] However, this existing solution has significant deficiencies. First, the preset road surface parameter model is difficult to cover all possible actual road conditions, especially performing poorly on complex and changeable mixed adhesion road surfaces. Second, the real-time performance of model predictive control is limited by computing resources, and control delays may occur under extreme working conditions. Finally, the modeling accuracy of the existing solution for the non-linear characteristics of the tire is insufficient, resulting in a decline in the control effect when approaching the adhesion limit, affecting braking stability and safety. Therefore, a more adaptable and accurate emergency braking control method is needed. Summary of the Invention

[0005] The embodiments of this application provide a vehicle control method and system in an emergency braking scenario to solve the problems of insufficient slip ratio tracking accuracy and low yaw stability in the prior art.

[0006] In a first aspect, the embodiments of this application provide a vehicle control method in an emergency braking scenario, including:

[0007] Establish a data set including the optimal wheel slip ratio under different road surface conditions, and dynamically adjust the target slip ratio range by simulating the contact characteristics between the tire and the road surface through a digital model;

[0008] Collect vehicle motion state data, analyze the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, and at the same time detect the change trend of the tire force state, comprehensively judge the vehicle instability risk and generate a warning signal;

[0009] Input the target slip rate range and warning signal into the predictive control model, calculate the braking force required for each wheel according to the difference between the actual wheel speed and the target value, and generate a braking force control command in combination with the vehicle steering state;

[0010] According to the braking force control command, use the digital tire model to simulate and calculate the expected wheel slip state, and determine the pressure control value of each brake with reference to the friction change law in the data set;

[0011] By comparing the actually measured wheel slip state with the simulation calculation result, and combining the pressure change rate of the brake hydraulic system, correct the physical characteristic parameters of the digital tire model, adjust the pressure control value, and realize the automatic adjustment control of the braking force of the vehicle in an emergency braking situation.

[0012] Optionally, analyzing the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, and at the same time detecting the change trend of the tire force state, comprehensively judging the vehicle instability risk and generating a warning signal, including:

[0013] Obtain the rotation rate of the vehicle around the vertical axis and the pitch change rate around the transverse axis through the angular velocity measuring device installed on the vehicle body, perform time series analysis on the rotation rate, extract the moments when its change amplitude exceeds the preset threshold and the corresponding change amount, and perform frequency spectrum decomposition on the pitch change rate to separate the high-frequency component caused by road surface impact and the low-frequency component caused by vehicle body motion;

[0014] Perform weighted fusion on the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate to obtain an overall vehicle motion trend index;

[0015] Obtain the real-time forces of each tire in the traveling direction and the lateral direction through the tire force detection module, calculate the difference degree of the components of the forces of each tire in the traveling direction at the same moment, and the symmetry degree of the lateral components, and calculate the tire force coupling index according to the difference degree of the traveling direction forces and the symmetry degree of the lateral forces;

[0016] Combine the overall vehicle motion trend index and the tire force coupling index according to a preset rule, and output a grade signal reflecting the vehicle instability degree as a warning signal.

[0017] Optionally, combining the overall vehicle motion trend index and the tire force coupling index according to a preset rule, and outputting a grade signal reflecting the vehicle instability degree as a warning signal, including:

[0018] Based on the overall vehicle motion trend index, a two-dimensional coordinate system is established that includes the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate, and the boundary ranges of the stable region, the transition region, and the dangerous region are divided according to historical data;

[0019] In the two-dimensional coordinate system, the current vehicle position point is calibrated in real time, and the Euclidean distance between the current vehicle position point and the boundary of the nearest dangerous region is calculated as the motion trend risk degree;

[0020] The tire force coupling index is decomposed into a component of the difference in the acting force in the traveling direction and a component of the symmetry of the lateral acting force, a tire force state plane with two groups of components as coordinate axes is constructed, and in the tire force state plane, the distance between the current state point and the coordinate origin is calculated as the tire force coupling strength value, where the coordinate origin represents the ideal coupling state;

[0021] The motion trend risk degree and the tire force coupling strength value are input into a preset classification decision table, and the corresponding level signal is output according to the classification decision table. The change direction of the level signal within three consecutive control cycles is detected for consistency. If they are all in an increasing trend, the output level is increased, and the final level signal is determined as the warning signal;

[0022] Among them, the classification decision table is used to output the highest level signal when the motion trend risk degree is greater than the first threshold and the tire force anomaly coefficient is greater than the second threshold; when the motion trend risk degree or the tire force anomaly coefficient exceeds the corresponding threshold, an intermediate level signal is output; when both the motion trend risk degree and the tire force anomaly coefficient do not exceed the threshold, the lowest level signal is output.

[0023] Optionally, inputting the target slip rate range and the warning signal into a predictive control model, calculating the braking force required for each wheel according to the difference between the actual wheel speed and the target value, and generating a braking force control command in combination with the vehicle steering state includes:

[0024] Within the prediction time window, an allowable control interval including the upper and lower limits of the target slip rate is established, and the boundary range of the allowable control interval is dynamically shrunk according to the level signal corresponding to the warning signal, where the shrinkage amplitude is positively correlated with the level corresponding to the level signal;

[0025] After the number of rotation signals of each wheel is collected in real time and converted into the current actual slip rate, based on the vehicle dynamics equation in the predictive control model, the predicted slip rate within the next three control cycles is calculated, and the braking force adjustment amount is determined according to the predicted slip rate;

[0026] The vehicle steering intention is obtained through a steering wheel angle sensor, and the braking force correction weight of each wheel within the prediction time window is calculated using the steering dynamics relationship in the predictive control model;

[0027] Multiply the braking force adjustment amount by the braking force correction weight, and obtain the braking force change value of each wheel through the rolling optimization module of the predictive control model;

[0028] Superimpose the braking force change value on the current braking force to generate a braking force command set including independent control quantities of four wheels.

[0029] Optionally, the determining the braking force adjustment amount according to the predicted slip ratio includes:

[0030] When the predicted slip ratio is lower than the lower limit of the target interval, calculate the braking force reduction amount of each wheel according to the first function relationship; or, when the predicted slip ratio is higher than the upper limit of the target interval, calculate the braking force increase amount of each wheel according to the second function relationship; or, when the predicted slip ratio is within the target interval, optimize the braking force of each vehicle to maintain the current change trend to determine the corresponding braking force adjustment amount of each vehicle.

[0031] Optionally, the using the digital tire model to simulate and calculate the expected wheel slip state according to the braking force control instruction, and determining the pressure control value of each brake with reference to the friction change law in the data set includes:

[0032] Based on the independent control quantity of each wheel in the braking force control instruction, calculate the deformation characteristics of the contact area between the tire and the road surface through the digital tire model, and simulate and calculate the expected wheel slip state of each wheel after applying the independent control quantity according to the deformation characteristics of the contact area and the current wheel speed;

[0033] Extract the friction change characteristics matching the current road surface condition from the data set, perform matching analysis on the calculated expected wheel slip state and the friction change characteristics, and determine the pressure control value of each brake based on the matching result.

[0034] Optionally, the correcting the physical property parameters of the digital tire model and adjusting the pressure control value by comparing the actually measured wheel slip state with the simulation calculation result and combining the pressure change rate of the brake hydraulic system includes:

[0035] Calculate the deviation amount between the actually measured wheel slip state and the simulation calculation result within a preset time window, and simultaneously monitor the real-time pressure change rate of the brake hydraulic system. Based on the corresponding relationship between the deviation amount and the pressure change rate, dynamically correct the physical property parameters representing the tire stiffness and friction characteristics in the digital tire model;

[0036] According to the physical characteristic parameters of the corrected digital tire model, recalculate the expected wheel slip state, perform a secondary match between the recalculated slip state and the friction change law in the data set, and generate an adjustment amount for the pressure control value based on the secondary match result and update the final control instruction.

[0037] Optionally, extracting the friction change characteristics matching the current road surface condition from the data set, performing a matching analysis between the calculated expected wheel slip state and the friction change characteristics, and determining the pressure control values of each brake based on the matching result, including:

[0038] According to the current road surface condition, extract the friction change characteristics corresponding to this road surface condition from the data set, and the friction change characteristics include the friction force values and their change laws at different slip rates;

[0039] Based on the expected wheel slip state, determine the slip rate value corresponding to the expected wheel slip state, and according to the slip rate value, find the corresponding friction force value from the friction change characteristics;

[0040] Compare the tire force state under the expected wheel slip state with the friction force value, calculate the difference degree between the two, and according to the difference degree, adjust the magnitude of the independent control quantity so that the difference degree between the tire force state under the expected wheel slip state and the friction force value is minimized;

[0041] Based on the adjusted independent control quantity, calculate the braking force distribution required for each wheel and determine the pressure control values of each brake.

[0042] Optionally, after generating the torque control instruction, it further includes:

[0043] According to the interaction intensity between the virtual tread and the road surface particles reconstructed by digital twin technology, adjust the time domain window length of the model predictive control algorithm;

[0044] Update the torque control instruction according to the adjusted model predictive control algorithm.

[0045] In a second aspect, an embodiment of the present application provides a vehicle control system in an emergency braking scenario, including:

[0046] An adjustment section, establishing a data set containing the optimal wheel slip rates under different road surface conditions, and dynamically adjusting the target slip rate range by simulating the contact characteristics between the tire and the road surface through a digital model;

[0047] The acquisition module collects vehicle motion state data, analyzes the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, simultaneously detects the change trend of the tire force state, comprehensively judges the vehicle instability risk and generates a warning signal;

[0048] The calculation module inputs the target slip rate range and the warning signal into the predictive control model, calculates the braking force required for each wheel according to the difference between the actual wheel speed and the target value, and generates a braking force control command in combination with the vehicle steering state;

[0049] The construction module, according to the braking force control command, uses the digital tire model to simulate and calculate the expected wheel slip state, and determines the pressure control value of each brake with reference to the friction change law in the data set;

[0050] The correction module, by comparing the actually measured wheel slip state with the simulation calculation result, combines the pressure change rate of the brake hydraulic system, corrects the physical characteristic parameters of the digital tire model, adjusts the pressure control value, and realizes the automatic adjustment and control of the braking force of the vehicle in an emergency braking situation.

[0051] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a vehicle control method in an emergency braking scenario as described in the first aspect above.

[0052] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a vehicle control method in an emergency braking scenario as described in the first aspect.

[0053] In the embodiments of the present application, a data set including the optimal wheel slip ratio under different road surface conditions is established, and the contact characteristics between the tire and the road surface are simulated through a digital model to dynamically adjust the target slip ratio range; vehicle motion state data is collected, the mutation characteristics and frequency distribution characteristics of the vehicle motion state data are analyzed, and at the same time, the change trend of the tire force state is detected, the vehicle instability risk is comprehensively judged and a warning signal is generated; the target slip ratio range and the warning signal are input into a predictive control model, and the braking force required for each wheel is calculated according to the difference between the actual wheel speed and the target value, and a braking force control instruction is generated in combination with the vehicle steering state; according to the braking force control instruction, the expected wheel slip state is simulated and calculated by using a digital tire model, and the pressure control value of each brake is determined with reference to the friction change law in the data set; by comparing the actually measured wheel slip state with the simulation calculation result, and combining the pressure change rate of the brake hydraulic system, the physical characteristic parameters of the digital tire model are corrected, and the pressure control value is adjusted to realize the automatic adjustment and control of the braking force of the vehicle under emergency braking conditions.

[0054] The technical solution of the present application has the following beneficial effects:

[0055] In the present application, by establishing a data set including the optimal wheel slip ratio under different road surface conditions and simulating the contact characteristics between the tire and the road surface through a digital model, the dynamic adjustment of the target slip ratio range is realized to ensure the best braking performance under different road conditions; by collecting vehicle motion state data and analyzing its mutation characteristics and frequency distribution characteristics, and at the same time detecting the change trend of the tire force state, the accurate judgment and warning of the vehicle instability risk are realized; the target slip ratio range and the warning signal are input into a predictive control model, and combined with the difference between the actual wheel speed and the target value and the vehicle steering state, an accurate braking force control instruction is generated; the expected wheel slip state is simulated and calculated by using a digital tire model, and the accurate pressure control value of each brake is determined with reference to the friction change law in the optimal slip ratio data set; finally, through the comparison between the real-time measurement and the simulation result, and combining the pressure change rate of the brake hydraulic system, the model parameters are dynamically corrected and the control value is adjusted to realize the automatic optimization and adjustment of the braking force under emergency braking conditions. This method realizes the adaptive braking control under different road conditions, and significantly improves the braking efficiency and stability of the vehicle.

[0056] Furthermore, the vehicle rotation and pitch rates are obtained through an angular velocity measuring device installed on the vehicle body, their change characteristics are analyzed and the high-frequency and low-frequency components are separated, and the coupling index is calculated by combining the difference degree and symmetry degree of the tire traveling direction and the lateral force. After fusing the vehicle motion trend index and the tire force coupling index, an instability level signal is output. This method realizes the comprehensive monitoring of the vehicle dynamic state and the accurate assessment of the instability risk, provides a reliable warning signal for the braking control system, and effectively improves the safety and stability of the vehicle under emergency braking conditions.

[0057] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 The flowchart of a vehicle control method in an emergency braking scenario provided by the present application is shown;

[0060] Figure 2 The structural schematic diagram of a vehicle control system in an emergency braking scenario provided by the present application is shown;

[0061] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0063] In some processes described in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0065] Figure 1The following is a flowchart of a vehicle control method in an emergency braking scenario provided by an embodiment of the present application. As Figure 1 shown, the method includes:

[0066] 101. Establish a data set containing the optimal wheel slip ratios under different road surface conditions, and dynamically adjust the target slip ratio range by simulating the contact characteristics between the tire and the road surface through a digital model;

[0067] Optimal wheel slip ratio data set: It refers to the range of slip ratios that can achieve the best grip and braking effect when the tire contacts the road surface under different road surface conditions (such as wet, dry, ice and snow, etc.).

[0068] Digital tire model: It is a simulation tool based on physical characteristics used to simulate the contact behavior between the tire and the road surface, including characteristics such as friction, deformation, and slip.

[0069] Target slip ratio range: It refers to the slip ratio interval dynamically adjusted by the system under different road surface conditions to achieve the best braking effect and vehicle stability.

[0070] In an embodiment of the present application, first, through experiments and actual road tests, wheel slip ratio data under different road surface conditions are collected, and an optimal slip ratio data set is established. Then, the digital tire model is used to simulate the contact characteristics between the tire and the road surface, and the relationship between friction, slip ratio, and road surface conditions is analyzed. Then, according to the simulation results, the target slip ratio range is dynamically adjusted to ensure that the best braking effect can be achieved under various road surface conditions. Finally, the adjusted target slip ratio range is stored in the system to provide a reference for subsequent braking control.

[0071] In an actual case, a vehicle manufacturer established an optimal slip ratio data set containing various road surface conditions through experiments and road tests. For example, on a dry road surface, the optimal slip ratio range is 10% - 15%; on a wet road surface, it is 5% - 10%; on an ice and snow road surface, it is 2% - 5%. Through simulation with the digital tire model, it is found that the optimal slip ratio range on the ice and snow road surface is relatively narrow, while on the dry road surface, the range is relatively wide. Based on this discovery, the system dynamically adjusted the target slip ratio range, significantly improving the braking performance of the vehicle on the ice and snow road surface.

[0072] 102. Collect vehicle motion state data, analyze the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, and at the same time detect the change trend of the tire force state, comprehensively judge the vehicle instability risk and generate a warning signal;

[0073] Vehicle motion state data: It includes information such as vehicle speed, acceleration, steering angle, etc.

[0074] Mutation characteristics: It refers to the characteristics that these data change significantly in a short period of time.

[0075] Frequency distribution characteristics: Reflect the regularity of data changes.

[0076] Tire force state: Refers to the longitudinal force, lateral force, vertical force, etc. that the tire receives during driving, and the change trend can be used to evaluate the vehicle's stability.

[0077] Warning signal: Is a prompt message generated according to the vehicle instability risk, used to alert the driver or trigger the braking system to intervene.

[0078] In the embodiments of the present application, first, the vehicle motion state data and tire force state data are collected in real time through in-vehicle sensors. Then, signal processing technology is used to analyze the mutation characteristics and frequency distribution characteristics of the motion state data to identify abnormal changes. At the same time, the change trend of the tire force state is detected to evaluate the vehicle's stability. Then, based on the above analysis results, the vehicle instability risk is comprehensively judged, and a warning signal is generated. Finally, the warning signal is transmitted to the control system to provide a basis for subsequent braking intervention.

[0079] Based on the data of the previous step, the system detected a mutation in the vehicle motion state data during a certain driving, for example, the vehicle speed dropped suddenly from 60 km / h to 40 km / h, and at the same time, the tire force state showed a significant increase in the lateral force, rising from 500 N to 800 N. After comprehensive judgment, the system generated a warning signal, prompting the driver to decelerate and adjust the driving direction, successfully avoiding the occurrence of a vehicle side-slip accident.

[0080] 103. Input the target slip rate range and the warning signal into the predictive control model, calculate the braking force required for each wheel according to the difference between the actual wheel speed and the target value, and generate a braking force control instruction in combination with the vehicle steering state;

[0081] The predictive control model is a control system based on a predictive algorithm, used to generate an optimal control instruction according to the input parameters.

[0082] The difference between the actual wheel speed and the target value: Reflects the deviation between the current slip rate and the target slip rate, and the braking force magnitude refers to the braking force value required to adjust the slip rate.

[0083] Vehicle steering state: Includes information such as steering angle and steering speed, used to optimize the braking force distribution.

[0084] Braking force control instruction: Is an instruction generated by the system, used to adjust the braking force of each wheel.

[0085] In the embodiments of the present application, first, the target slip ratio range and the warning signal are input into the predictive control model as the core input parameters of the model. Then, according to the difference between the actual wheel speed and the target value, the braking force required for each wheel is calculated. Next, in combination with the vehicle steering state, the braking force distribution is optimized to generate a braking force control command. Finally, the control command is transmitted to the braking actuator to achieve precise control of the braking force of each wheel.

[0086] In practical applications, in a certain emergency braking, the system calculates and distributes the braking force of each wheel according to the target slip ratio range (5%-10% for a wet road surface) and the warning signal. For example, the braking force of the left front wheel is 1200N, the right front wheel is 1100N, the left rear wheel is 800N, and the right rear wheel is 750N. In combination with the vehicle steering state (steering angle is 15°), the braking force distribution is optimized to ensure the vehicle remains stable during braking and avoid the risk of steering out of control.

[0087] 104. According to the braking force control command, use the digital tire model to simulate and calculate the expected wheel slip state, and determine the pressure control value of each brake with reference to the friction change law in the data set;

[0088] Expected wheel slip state: It refers to the changing trend of the wheel slip ratio simulated according to the braking force control command.

[0089] Friction change law: It reflects the friction relationship between the tire and the road surface at different slip ratios.

[0090] Pressure control value: It refers to the hydraulic pressure value required to adjust the braking force and is used to precisely control the braking effect.

[0091] In the embodiments of the present application, first, according to the braking force control command, use the digital tire model to simulate and calculate the expected wheel slip state and predict the changing trend of the slip ratio. Then, determine the pressure control value of each brake with reference to the friction change law in the optimal slip ratio data set to ensure that the slip ratio is within the target range. Finally, transmit the pressure control value to the brake hydraulic system to achieve precise adjustment of the braking force.

[0092] Through simulation calculation, in a certain braking, the system predicted that the slip ratio of a certain wheel would exceed the target range (5%-10% for a wet road surface). For example, the slip ratio of the left front wheel was predicted to be 12%. Immediately, the pressure control value of this brake was adjusted from 8MPa to 6.5MPa, successfully controlling the slip ratio within the optimal range and improving the braking effect and vehicle stability.

[0093] 105. By comparing the actually measured wheel slip state with the simulation calculation results, and combining with the pressure change rate of the brake hydraulic system, the physical characteristic parameters of the digital tire model are corrected, and the pressure control value is adjusted to achieve automatic adjustment and control of the braking force of the vehicle in an emergency braking situation.

[0094] Actually measured wheel slip state: It refers to the wheel slip rate data collected in real time by sensors.

[0095] Simulation calculation results: It refers to the expected slip state generated by the digital tire model.

[0096] Pressure change rate of the brake hydraulic system: It reflects the response speed of the braking force adjustment.

[0097] Physical characteristic parameters: Include the friction coefficient, stiffness, etc. of the tire, which are used to optimize the model accuracy.

[0098] Automatic adjustment and control of the braking force: It means that the system dynamically adjusts the braking force according to real-time data to ensure the stability of the vehicle in an emergency braking situation.

[0099] In the embodiment of the present application, first, the actual wheel slip state data is collected in real time by sensors and compared with the simulation calculation results to identify the deviation. Then, combining with the pressure change rate of the brake hydraulic system, the physical characteristic parameters of the digital tire model are corrected to optimize the model accuracy. Then, according to the corrected model, the pressure control value is adjusted to achieve automatic adjustment of the braking force. Finally, the adjusted control value is transmitted to the brake actuator to ensure the stability of the vehicle in an emergency braking situation.

[0100] In actual application, during a certain emergency braking, by comparing the actual slip state with the simulation calculation results, it is found that there is a deviation in the model prediction. For example, the actual slip rate of the left front wheel is 11%, while the simulation result is 9%. Immediately, the physical characteristic parameters of the digital tire model are corrected (the friction coefficient is adjusted from 0.8 to 0.75), and the pressure control value is adjusted from 6.5 MPa to 6 MPa, successfully realizing the automatic adjustment of the braking force and ensuring the stability and safety of the vehicle.

[0101] In summary, steps 101 to 105 achieve automatic adjustment and control of the braking force of the vehicle in an emergency braking situation. By establishing the optimal slip rate data set, analyzing the vehicle motion state data, generating warning signals, optimizing the braking force distribution, simulating the slip state, and dynamically adjusting the model parameters, the present application provides an efficient and reliable vehicle braking control method. In actual application, this method significantly improves the braking performance and stability of the vehicle under various road conditions, reduces the accident risk, and provides strong support for the development of intelligent driving technology.

[0102] In order to achieve high-precision braking force control and stability optimization of the vehicle in emergency braking situations, in some embodiments, analyzing the mutation characteristics and frequency distribution characteristics of the vehicle motion state data in step 102, and simultaneously detecting the change trend of the tire force state, comprehensively judging the vehicle instability risk and generating an early warning signal, including:

[0103] 201. Obtain the rotation rate of the vehicle around the vertical axis and the pitch change rate around the transverse axis through an angular velocity measuring device installed on the vehicle body. Conduct time series analysis on the rotation rate, extract the moments when its change amplitude exceeds a preset threshold and the corresponding change amounts, and perform spectral decomposition on the pitch change rate to separate the high-frequency components caused by road surface impacts and the low-frequency components caused by vehicle body motion;

[0104] Angular velocity measuring device: Used to measure the rotation rates of the vehicle around the vertical axis and the transverse axis in real time, including devices such as gyroscopes or inertial measurement units (IMUs).

[0105] Time series analysis: Conduct statistical analysis on the characteristics of the rotation rate changing with time, and extract key change points.

[0106] Spectral decomposition: Decompose the pitch change rate into different frequency components through Fourier transform or wavelet transform to distinguish the effects of road surface impacts and vehicle body motion.

[0107] In the embodiments of the present application, first, the rotation rate of the vehicle around the vertical axis and the pitch change rate around the transverse axis are collected in real time through an angular velocity measuring device. Then, time series analysis is conducted on the rotation rate, and the moments when the change amplitude exceeds a preset threshold and the corresponding change amounts are extracted. For example, the rotation rate suddenly increases from 10° / s to 30° / s within 0.5 seconds. Then, spectral decomposition is performed on the pitch change rate to separate the high-frequency components (such as above 10 Hz) and the low-frequency components (such as below 1 Hz), where the high-frequency components are mainly caused by road surface impacts and the low-frequency components are caused by vehicle body motion. Finally, the extracted features are stored in the system to provide data support for subsequent analysis.

[0108] 202. Perform weighted fusion on the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate to obtain an overall vehicle motion trend index;

[0109] Weighted fusion: Comprehensively calculate the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate according to a preset weight.

[0110] Overall vehicle motion trend index: Used to quantify the overall motion state of the vehicle during driving and reflect the stability of the vehicle.

[0111] In the embodiments of the present application, first, weights are respectively assigned to the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate (for example, the rotation rate weight is 0.6, and the pitch change rate weight is 0.4). Then, the overall vehicle motion trend index is calculated through a weighted fusion formula. For example, the index value = change amount of the rotation rate × 0.6 + amplitude of the low-frequency component × 0.4. Finally, the calculated index is stored in the system for subsequent instability risk assessment.

[0112] 203. The real-time forces of each tire in the traveling direction and the lateral direction are obtained through the tire force detection module, the difference degree of the forces of each tire in the traveling direction component at the same moment is calculated, as well as the symmetry degree of the lateral direction component, and the tire force coupling index is calculated according to the difference degree of the traveling direction force and the symmetry degree of the lateral direction force.

[0113] Tire force detection module: used to measure the forces of the tire in the traveling direction and the lateral direction in real time, including force sensors or tire pressure monitoring systems.

[0114] Difference degree of traveling direction force: reflecting the difference degree of the forces of each tire in the traveling direction.

[0115] Symmetry degree of lateral direction force: reflecting the symmetry degree of the forces of each tire in the lateral direction.

[0116] Tire force coupling index: used to quantify the distribution state of the tire forces and reflect the contact situation between the tire and the road surface.

[0117] In the embodiments of the present application, first, the force data of each tire in the traveling direction and the lateral direction are collected in real time through the tire force detection module. Then, the difference degree of the traveling direction forces of each tire at the same moment is calculated. For example, the difference degree = maximum traveling direction force - minimum traveling direction force. Then, the symmetry degree of the lateral direction force is calculated. For example, the symmetry degree = lateral direction force of the left front tire - lateral direction force of the right front tire. Finally, according to the difference degree and the symmetry degree, the tire force coupling index is calculated through a preset formula. For example, the coupling index = difference degree × 0.5 + symmetry degree × 0.5.

[0118] 204. The overall vehicle motion trend index and the tire force coupling index are combined according to a preset rule, and a grade signal reflecting the instability degree of the vehicle is output as a warning signal.

[0119] Preset rule combination: According to the numerical ranges of the overall vehicle motion trend index and the tire force coupling index, the instability level division rules are set.

[0120] Instability level signal: a classification signal used to reflect the instability degree of the vehicle, including low risk, medium risk, and high risk levels.

[0121] In the embodiments of the present application, first, according to the numerical ranges of the vehicle's overall motion trend index and the tire force coupling index, instability level division rules are set, such as low risk (index value < 0.3), medium risk (0.3 ≤ index value < 0.7), and high risk (index value ≥ 0.7). Then, the two indexes are combined according to a preset rule to generate an instability level signal. Finally, the level signal is transmitted to the warning system to prompt the driver or trigger the vehicle stability control system to intervene.

[0122] The following is a specific example:

[0123] During a highway driving process, the vehicle is equipped with an angular velocity measurement device and a tire force detection module. When the vehicle passes through a wet and slippery road section, the angular velocity measurement device detects that the rotation rate of the vehicle around the vertical axis suddenly increases from 10° / s to 30° / s within 0.5 seconds, and the amplitude of the low-frequency component of the pitch change rate is 0.8° / s. Through steps 201 and 202, the vehicle's overall motion trend index is calculated to be 0.68. At the same time, the tire force detection module detects that the difference degree of the acting forces of each tire in the traveling direction is 200 N, and the symmetry degree of the lateral acting forces is 150 N. Through step 203, the tire force coupling index is calculated to be 0.75. In step 204, the vehicle's overall motion trend index (0.68) and the tire force coupling index (0.75) are combined according to a preset rule to generate an instability level signal of high risk. The warning system immediately issues an alarm, prompting the driver to decelerate and adjust the driving direction, and at the same time triggering the vehicle stability control system to intervene, successfully avoiding the occurrence of a vehicle side-slip accident.

[0124] In summary, through steps 201 to 204, the accuracy and real-time performance of vehicle instability detection during driving are achieved. By the collaborative application of the angular velocity measurement device and the tire force detection module, combined with technologies such as time series analysis, spectrum decomposition, weighted fusion, and preset rule combination, the vehicle instability risk can be accurately identified and a warning signal can be generated. In practical applications, this method significantly improves the safety and stability of vehicle driving, providing reliable decision-making support for the driver and the vehicle stability control system.

[0125] To achieve high-precision instability detection and warning during vehicle driving, this study aims to accurately extract the coupling characteristics of vehicle motion trends and tire forces through the collaborative application of an angular velocity measurement device and a tire force detection module. The research and development idea is to combine the change amplitude of the rotation rate and the amplitude of the low-frequency component of the pitch change rate, and perform weighted fusion to obtain an overall vehicle motion trend index. By calculating the difference degree of the components of each tire force in the traveling direction and the symmetry degree of the lateral components, a tire force coupling index is generated. Based on the preset rule combination of the overall vehicle motion trend index and the tire force coupling index, a grade signal reflecting the vehicle instability degree is output, providing real-time warning support for vehicle driving safety. In some embodiments, in step 204, combining the overall vehicle motion trend index and the tire force coupling index according to a preset rule to output a grade signal reflecting the vehicle instability degree as a warning signal includes:

[0126] 301. According to the overall vehicle motion trend index, establish a two-dimensional coordinate system including the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate, and divide the boundary ranges of the stable region, the transition region, and the dangerous region according to historical data;

[0127] Two-dimensional coordinate system: A plane coordinate system with the change amount of the rotation rate as the horizontal axis and the amplitude of the low-frequency component of the pitch change rate as the vertical axis, used to describe the vehicle motion trend.

[0128] Stable region, transition region, and dangerous region: Different vehicle motion state regions divided based on historical data, representing safe, potential risk, and severe instability states respectively.

[0129] In the embodiments of the present application, first, according to the overall vehicle motion trend index, a two-dimensional coordinate system is established, where the horizontal axis is the change amount of the rotation rate and the vertical axis is the amplitude of the low-frequency component of the pitch change rate. Then, based on historical data, the boundary ranges of the stable region, the transition region, and the dangerous region are divided. For example, the range of the rotation rate change amount in the stable region is 0 - 10° / s, and the range of the amplitude of the low-frequency component is 0 - 1° / s; the range of the rotation rate change amount in the dangerous region is 20 - 30° / s, and the range of the amplitude of the low-frequency component is 2 - 3° / s. Finally, the division results are stored in the system to provide a reference for subsequent instability detection.

[0130] 302. In the two-dimensional coordinate system, calibrate the current vehicle position point in real time, and calculate the Euclidean distance between the current vehicle position point and the boundary of the nearest dangerous region as the motion trend risk degree;

[0131] Current vehicle position point: In the two-dimensional coordinate system, the vehicle state point calibrated according to the real-time rotation rate change amount and the amplitude of the low-frequency component.

[0132] Euclidean distance: It is used to calculate the geometric distance between the current vehicle position point and the boundary of the dangerous area, reflecting the degree of the vehicle approaching the unstable state.

[0133] Danger degree of motion trend: It is used to quantify the degree to which the vehicle's motion trend approaches the dangerous area. The smaller the value, the higher the risk.

[0134] In the embodiment of the present application, first, in a two-dimensional coordinate system, the current vehicle position point is calibrated according to the real-time change amount of the rotation rate and the amplitude of the low-frequency component. Then, the Euclidean distance between the current vehicle position point and the boundary of the nearest dangerous area is calculated. For example, if the current vehicle position point is (15° / s, 1.5° / s) and the dangerous area boundary point is (20° / s, 2° / s), then the Euclidean distance is 5.1. Finally, the calculated danger degree of the motion trend is stored in the system for subsequent judgment of the instability level.

[0135] 303. Decompose the tire force coupling index into the difference degree component of the acting force in the traveling direction and the symmetry degree component of the acting force in the lateral direction, construct a tire force state plane with the two groups of components as the coordinate axes, and in the tire force state plane, calculate the distance between the current state point and the coordinate origin as the tire force coupling strength value, where the coordinate origin represents the ideal coupling state;

[0136] Tire force state plane: A plane coordinate system with the difference degree component of the acting force in the traveling direction as the horizontal axis and the symmetry degree component of the acting force in the lateral direction as the vertical axis, used to describe the distribution state of the tire force.

[0137] Tire force coupling strength value: It is used to quantify the deviation degree of the tire force distribution state from the ideal coupling state. The larger the value, the worse the coupling state.

[0138] In the embodiment of the present application, first, the tire force coupling index is decomposed into the difference degree component of the acting force in the traveling direction and the symmetry degree component of the acting force in the lateral direction. Then, a tire force state plane is constructed and the current state point is calibrated therein. Then, the Euclidean distance between the current state point and the coordinate origin is calculated as the tire force coupling strength value. For example, if the current state point is (200N, 150N), then the tire force coupling strength value is √(200² + 150²) = 250N. Finally, the calculated tire force coupling strength value is stored in the system for subsequent judgment of the instability level.

[0139] 304. Input the danger degree of the motion trend and the tire force coupling strength value into a preset classification decision table, output the corresponding level signal according to the classification decision table, perform consistency detection on the change direction of the level signals in three consecutive control cycles. If they are all in an increasing trend, then increase the output level and determine the final level signal as the warning signal;

[0140] Classification decision table: A preset rule table used to output corresponding instability level signals based on the risk degree of the motion trend and the tire force coupling strength value. Among them, the classification decision table is used to output the highest-level signal when the risk degree of the motion trend is greater than the first threshold and the tire force anomaly coefficient is greater than the second threshold; when the risk degree of the motion trend or the tire force anomaly coefficient exceeds the corresponding threshold, output the intermediate-level signal; when neither the risk degree of the motion trend nor the tire force anomaly coefficient exceeds the threshold, output the lowest-level signal.

[0141] First threshold and second threshold: Preset values used to divide the instability levels. For example, the first threshold is 5.0 and the second threshold is 200 N.

[0142] Highest-level signal, intermediate-level signal, and lowest-level signal: Instability level signals representing high risk, medium risk, and low risk respectively.

[0143] Consistency detection: Used to determine whether the change trends of the level signals in three consecutive control cycles are consistent. If they are all in an increasing trend, the output level is upgraded.

[0144] Warning signal: A classification signal used to reflect the degree of vehicle instability, including low risk, medium risk, and high risk levels.

[0145] In the embodiments of the present application, first, the risk degree of the motion trend and the tire force coupling strength value are input into the classification decision table to output the corresponding level signal. For example, if the risk degree of the motion trend is 5.1 and the tire force coupling strength value is 250 N, then the medium-risk level signal is output. Then, consistency detection is performed on the level signals in three consecutive control cycles. If they are all in an increasing trend, the output level is upgraded. For example, if the level signals in three consecutive control cycles are low risk, medium risk, and medium risk respectively, then the output level is upgraded to high risk. Finally, the final level signal is transmitted to the warning system to prompt the driver or trigger the vehicle stability control system to intervene.

[0146] Specifically, it is possible to first determine whether the risk degree of the motion trend and the tire force coupling strength value exceed the first threshold and the second threshold. For example, if the risk degree of the motion trend is 5.1 (greater than the first threshold of 5.0) and the tire force coupling strength value is 250 N (greater than the second threshold of 200 N), then the highest-level signal is output. Then, the level signal is transmitted to the warning system to prompt the driver or trigger the vehicle stability control system to intervene.

[0147] The following is a specific example:

[0148] During a highway driving process, the vehicle is equipped with an angular velocity measuring device and a tire force detection module. When the vehicle passes through a wet and slippery road surface, the angular velocity measuring device detects that the rotation rate of the vehicle around the vertical axis suddenly increases from 10° / s to 30° / s within 0.5 seconds, and the amplitude of the low-frequency component of the pitch change rate is 1.5° / s. Through steps 301 and 302, the calculated motion trend risk level is 5.1. At the same time, the tire force detection module detects that the difference in the acting forces of each tire in the traveling direction is 200 N, and the symmetry of the lateral acting forces is 150 N. Through step 303, the calculated tire force coupling strength value is 250 N. In steps 304 and 305, the motion trend risk level (5.1) and the tire force coupling strength value (250 N) are input into the classification decision table, and the highest-level signal is output. The warning system immediately issues an alarm, prompting the driver to decelerate and adjust the driving direction, and at the same time triggering the vehicle stability control system to intervene, successfully avoiding the occurrence of a vehicle skidding accident.

[0149] In summary, through steps 301 to 305, the hierarchical early warning and dynamic optimization of vehicle instability detection during driving are realized. By establishing a two-dimensional coordinate system, dividing the regional boundaries, calculating the motion trend risk level and the tire force coupling strength value, and combining the classification decision table and the consistency detection technology, the vehicle instability risk can be accurately identified and warning signals can be generated. In practical applications, this method significantly improves the safety and stability of vehicle driving, providing reliable decision-making support for drivers and vehicle stability control systems.

[0150] In order to realize the hierarchical early warning and dynamic optimization of vehicle instability detection during driving, in some embodiments, in step 103, the target slip rate range and the warning signal are input into the predictive control model, and the braking force required for each wheel is calculated according to the difference between the actual wheel speed and the target value, and a braking force control instruction is generated in combination with the vehicle steering state, including:

[0151] 401. Within the prediction time window, an allowable control interval including the upper and lower limits of the target slip rate is established, and the boundary range of the allowable control interval is dynamically shrunk according to the level signal corresponding to the warning signal, where the shrinkage amplitude is positively correlated with the level corresponding to the level signal;

[0152] Prediction time window: The time range used for predicting and controlling the slip rate, usually several future control cycles.

[0153] Allowable control interval: The upper and lower limit ranges of the target slip rate, used to limit the adjustment amplitude of the braking force.

[0154] Dynamic shrinkage: According to the level signal of the warning signal, gradually narrow the boundary range of the allowable control interval to improve the control accuracy.

[0155] In the embodiments of the present application, first, within the prediction time window, an allowable control interval including the upper and lower limits of the target slip ratio is established. For example, the target slip ratio range is 10% - 20%. Then, according to the level signal corresponding to the warning signal, the boundary range of the allowable control interval is dynamically shrunk. For example, the shrinkage amplitude corresponding to the low - risk level signal is 5%, and the shrinkage amplitude corresponding to the high - risk level signal is 20%. Finally, the shrunk allowable control interval is stored in the system to provide a reference for subsequent braking force adjustment.

[0156] 402. After the rotation cycle number signals of each wheel are collected in real - time and converted into the current actual slip ratio, based on the vehicle dynamics equation in the predictive control model, calculate the predicted slip ratio within the next three control cycles, and determine the braking force adjustment amount according to the predicted slip ratio;

[0157] Rotation cycle number signal: The number of rotations of the wheel collected in real - time by the wheel speed sensor, which is used to calculate the actual slip ratio.

[0158] Vehicle dynamics equation: A mathematical model used to describe the vehicle motion state, including the relationship between the slip ratio and the braking force.

[0159] Braking force adjustment amount: The value of the change in braking force required to adjust the predicted slip ratio to the target slip ratio.

[0160] In the embodiments of the present application, first, the rotation cycle number signals of each wheel are collected in real - time through the wheel speed sensor and converted into the current actual slip ratio. Then, based on the vehicle dynamics equation in the predictive control model, calculate the predicted slip ratio within the next three control cycles. Then, determine the braking force adjustment amount according to the difference between the predicted slip ratio and the target slip ratio. For example, if the predicted slip ratio is 25% and the target slip ratio is 15%, the braking force adjustment amount is to increase by 1000N.

[0161] 403. Obtain the vehicle steering intention through the steering wheel angle sensor, and use the steering dynamics relationship in the predictive control model to calculate the braking force correction weights of each wheel within the prediction time window;

[0162] Steering wheel angle sensor: Used to detect the steering wheel angle in real - time, reflecting the driver's steering intention.

[0163] Steering dynamics relationship: A mathematical model used to describe the distribution of braking forces of each wheel during the steering process.

[0164] Braking force correction weight: Used to adjust the proportion of the braking force distribution of each wheel to optimize the steering stability.

[0165] In the embodiments of the present application, first, the vehicle steering intention is obtained through a steering wheel angle sensor. For example, the steering wheel angle is 30°. Then, using the steering dynamics relationship in the predictive control model, the braking force correction weights of each wheel within the prediction time window are calculated. For example, the correction weight of the left front wheel is 1.2, the right front wheel is 0.8, the left rear wheel is 1.0, and the right rear wheel is 1.0.

[0166] 404. Multiply the braking force adjustment amount by the braking force correction weight, and through the rolling optimization module of the predictive control model, obtain the braking force change values of each wheel;

[0167] Rolling optimization module: A calculation module for dynamically optimizing the braking force distribution within the prediction time window.

[0168] Braking force change value: The braking force adjustment amount of each wheel within the prediction time window, used for independent control.

[0169] In the embodiments of the present application, first, multiply the braking force adjustment amount by the braking force correction weight to obtain the braking force change values of each wheel. For example, the braking force change value of the left front wheel is 1000N × 1.2 = 1200N, and the right front wheel is 1000N × 0.8 = 800N. Then, through the rolling optimization module of the predictive control model, dynamically optimize the braking force change values of each wheel to ensure that they meet the requirements of the allowable control interval.

[0170] 405. On the basis of the current braking force, superimpose the braking force change value to generate a braking force command set including independent control quantities for the four wheels.

[0171] Braking force command set: A command set including independent braking force control quantities for the four wheels, used to achieve precise braking force distribution.

[0172] In the embodiments of the present application, first, superimpose the braking force change value on the basis of the current braking force to generate the braking force commands for each wheel. For example, the braking force of the left front wheel is 5000N + 1200N = 6200N, and the right front wheel is 5000N + 800N = 5800N. Then, summarize the braking force commands of each wheel into a braking force command set and transmit it to the braking actuator to achieve independent control of the four wheels.

[0173] The following is a specific example:

[0174] During an emergency braking process, the vehicle is equipped with wheel speed sensors and a steering wheel angle sensor. When the vehicle passes through a wet and slippery road surface, the system detects that the current actual slip ratio is 25%, and the target slip ratio range is 10% - 20%. Through step 401, according to the high-risk level signal, the boundary range of the allowable control interval is dynamically shrunk to 12% - 18%. Then, through step 402, based on the vehicle dynamics equation, the predicted slip ratio within the next three control cycles is calculated to be 28%, and the braking force adjustment amount is determined to be an increase of 1000N. At the same time, through step 403, according to the steering wheel angle of 30°, the braking force correction weights for each wheel are calculated as 1.2 for the left front wheel, 0.8 for the right front wheel, 1.0 for the left rear wheel, and 1.0 for the right rear wheel. Through step 404, the braking force adjustment amount is multiplied by the correction weight to obtain the braking force change values for each wheel as 1200N for the left front wheel, 800N for the right front wheel, 1000N for the left rear wheel, and 1000N for the right rear wheel. Finally, through step 405, on the basis of the current braking force, the braking force change values are superimposed to generate a braking force instruction set of 6200N for the left front wheel, 5800N for the right front wheel, 6000N for the left rear wheel, and 6000N for the right rear wheel. The braking actuator independently controls the four wheels according to the instruction set, successfully adjusting the slip ratio to 15% and ensuring the stability of the vehicle.

[0175] In summary, through steps 401 to 405, high-precision braking force control and stability optimization of the vehicle in an emergency braking situation are achieved. This method significantly improves the safety and stability of vehicle braking by dynamically shrinking the allowable control interval, calculating the predicted slip ratio, optimizing the braking force distribution, and generating independent control instructions, providing reliable technical support for the driver and the vehicle stability control system.

[0176] In some embodiments, determining the braking force adjustment amount according to the predicted slip ratio in step 402 includes: when the predicted slip ratio is lower than the lower limit of the target interval, calculating the braking force reduction amount for each wheel according to the first functional relationship; or, when the predicted slip ratio is higher than the upper limit of the target interval, calculating the braking force increase amount for each wheel according to the second functional relationship; or, when the predicted slip ratio is within the target interval, optimizing the braking force of each vehicle to maintain the current change trend to determine the braking force adjustment amount corresponding to each vehicle.

[0177] Predicted slip ratio: The future slip ratio value calculated based on the vehicle dynamics equation, used to predict the motion state of the vehicle.

[0178] Target interval: The upper and lower limit ranges of the slip ratio, used to limit the adjustment range of the braking force.

[0179] First functional relationship: The mathematical model used to calculate the braking force reduction amount, usually a linear or non-linear function.

[0180] Second functional relationship: A mathematical model for calculating the increase in braking force, usually a linear or non-linear function.

[0181] Braking force adjustment amount: The change value of the braking force required to adjust the slip ratio to the target range.

[0182] In the embodiments of the present application, first, according to the relationship between the predicted slip ratio and the target range, it is determined whether the slip ratio is lower than the lower limit, higher than the upper limit, or within the range. When the predicted slip ratio is lower than the lower limit of the target range, the braking force reduction amount of each wheel is calculated according to the first functional relationship. For example, if the predicted slip ratio is 8% and the lower limit of the target range is 10%, the braking force reduction amount is a reduction of 500N. When the predicted slip ratio is higher than the upper limit of the target range, the braking force increase amount of each wheel is calculated according to the second functional relationship. For example, if the predicted slip ratio is 22% and the upper limit of the target range is 20%, the braking force increase amount is an increase of 800N. When the predicted slip ratio is within the target range, the braking force of each vehicle is optimized to maintain the current change trend, such as keeping the braking force unchanged or making fine adjustments. Finally, the calculated braking force adjustment amount is stored in the system to provide a reference for subsequent braking force control.

[0183] The following is a specific example:

[0184] During an emergency braking process, a vehicle is equipped with wheel speed sensors and a braking force control system. When the vehicle passes through a wet road surface, the system detects that the predicted slip ratio is 22% and the target range is 10% - 20%. Through step 501, it is determined that the predicted slip ratio is higher than the upper limit of the target range, and the braking force increase amount of each wheel is calculated according to the second functional relationship as 800N. The braking actuator increases the braking force of the four wheels according to the calculation result, successfully adjusting the slip ratio to 18% and ensuring the stability of the vehicle.

[0185] In summary, through the above steps, high-precision braking force adjustment and stability optimization of the vehicle in emergency braking situations are achieved. This method dynamically calculates the braking force adjustment amount by judging the relationship between the predicted slip ratio and the target range, significantly improving the safety and stability of vehicle braking, and providing reliable technical support for drivers and vehicle stability control systems.

[0186] In some embodiments, step 104 of simulating and calculating the expected wheel slip state using the digital tire model according to the braking force control instruction and determining the pressure control value of each brake according to the friction change law in the data set includes:

[0187] 601. Based on the independent control quantity of each wheel in the braking force control instruction, calculate the deformation characteristics of the contact area between the tire and the road surface through a digital tire model, and simulate and calculate the expected wheel slip state of each wheel after applying the independent control quantity according to the deformation characteristics of the contact area and the current wheel speed;

[0188] Digital tire model: A mathematical model used to simulate the contact behavior between the tire and the road surface, including characteristics such as deformation, slip, and friction.

[0189] Deformation characteristics of the contact area: The degree and distribution of deformation in the contact area between the tire and the road surface, reflecting the grip and stability of the tire.

[0190] Expected wheel slip state: The predicted future slip rate state based on the independent control quantity and the current wheel speed.

[0191] In the embodiment of the present application, first, according to the independent control quantity of each wheel in the braking force control instruction, calculate the deformation characteristics of the contact area between the tire and the road surface through a digital tire model. For example, the independent control quantity of the left front wheel is 6200N, and that of the right front wheel is 5800N. Then, according to the deformation characteristics of the contact area and the current wheel speed, simulate and calculate the expected wheel slip state of each wheel after applying the independent control quantity. For example, the expected slip rate of the left front wheel is 15%, and that of the right front wheel is 14%. Finally, store the calculation results in the system to provide a reference for subsequent braking force optimization.

[0192] 602. Extract the friction force change characteristics matching the current road surface conditions from the data set, perform a matching analysis on the calculated expected wheel slip state and the friction force change characteristics, and determine the pressure control value of each brake based on the matching result.

[0193] Optimal slip rate data set: A data set containing the optimal slip rate range under different road surface conditions, used to optimize the braking force control.

[0194] Friction force change characteristics: Characteristic parameters reflecting the change law of the friction force between the tire and the road surface at different slip rates.

[0195] Pressure control value: The hydraulic pressure value required to adjust the braking force, used to precisely control the braking effect.

[0196] In the embodiment of the present application, first, extract the friction force change characteristics matching the current road surface conditions from the optimal slip rate data set. For example, on a wet road surface, the optimal slip rate range is 10% - 20%. Then, perform a matching analysis on the calculated expected wheel slip state and the friction force change characteristics to determine the pressure control value of each brake. For example, the pressure control value of the left front wheel is 8MPa, and that of the right front wheel is 7.5MPa. Finally, transmit the pressure control value to the brake actuator to achieve precise adjustment of the braking force.

[0197] The following is a specific example:

[0198] During an emergency braking process, the vehicle is equipped with wheel speed sensors and a braking force control system. When the vehicle passes through a wet and slippery road surface, the system detects that the current actual slip ratio is 25%, and the target slip ratio range is 10% - 20%. Through step 601, based on the independent control quantities in the braking force control instruction (6200N for the left front wheel and 5800N for the right front wheel), the deformation characteristics of the contact area between the tire and the road surface are calculated through a digital tire model, and the expected slip states of each wheel are simulated and calculated (15% for the left front wheel and 14% for the right front wheel). Then, through step 602, the friction force change characteristics matching the wet and slippery road surface are extracted from the optimal slip ratio data set, and the expected slip state is matched and analyzed with the friction force change characteristics to determine the pressure control values of each brake (8MPa for the left front wheel and 7.5MPa for the right front wheel). The braking actuator precisely adjusts the braking forces of the four wheels according to the pressure control values, successfully adjusting the slip ratio to 15% and ensuring the stability of the vehicle.

[0199] In summary, through steps 601 to 602, high-precision braking force control and stability optimization of the vehicle in an emergency braking situation are achieved. This method simulates the contact characteristics between the tire and the road surface through a digital tire model, combines the optimal slip ratio data set and the friction force change characteristics, and dynamically adjusts the pressure control values of each brake, significantly improving the safety and stability of vehicle braking and providing reliable technical support for the driver and the vehicle stability control system.

[0200] In some embodiments, step 105 of correcting the physical property parameters of the digital tire model and adjusting the pressure control value by comparing the actually measured wheel slip state with the simulation calculation result and combining the pressure change rate of the braking hydraulic system includes:

[0201] 701. Calculate the deviation amount between the actually measured wheel slip state and the simulation calculation result within a preset time window, and simultaneously monitor the real-time pressure change rate of the braking hydraulic system. Based on the corresponding relationship between the deviation amount and the pressure change rate, dynamically correct the physical property parameters representing the tire stiffness and friction characteristics in the digital tire model;

[0202] Preset time window: The time range used to calculate and correct the deviation amount, usually several future control cycles.

[0203] Deviation amount: The difference value between the actually measured wheel slip state and the simulation calculation result, reflecting the accuracy of model prediction.

[0204] Pressure change rate: The real-time rate of pressure change in the braking hydraulic system, used to evaluate the response speed of braking force adjustment.

[0205] Tire stiffness and friction characteristics: Key parameters characterizing the physical properties of a tire in a digital tire model, which affect the prediction accuracy of the slip state.

[0206] In the embodiments of the present application, first, within a preset time window, the deviation between the actually measured wheel slip state and the simulation calculation result is calculated. For example, if the actual slip ratio is 18% and the simulation calculation result is 15%, the deviation is 3%. Then, the real-time pressure change rate of the brake hydraulic system is monitored, for example, the pressure change rate is 2 MPa / s. Then, based on the corresponding relationship between the deviation and the pressure change rate, the physical property parameters characterizing the tire stiffness and friction characteristics in the digital tire model are dynamically corrected. For example, the tire stiffness is corrected from 1000 N / m to 950 N / m, and the friction coefficient is corrected from 0.8 to 0.75. Finally, the corrected model parameters are stored in the system to provide a more accurate basis for subsequent slip state prediction.

[0207] 702. According to the corrected physical property parameters of the digital tire model, recalculate the expected wheel slip state, perform a secondary match between the recalculated slip state and the friction change law in the data set, generate an adjustment amount of the pressure control value based on the secondary match result, and update the final control instruction.

[0208] Secondary match: Re-contrast and analyze the recalculated slip state with the friction change law in the optimal slip ratio data set to optimize the braking force control strategy.

[0209] Adjustment amount of the pressure control value: The hydraulic pressure change value required to optimize the braking force control, which is used to dynamically adjust the braking effect.

[0210] Final control instruction: A set of instructions containing updated braking force control parameters, which is used to achieve precise braking force distribution.

[0211] In the embodiments of the present application, first, according to the corrected physical property parameters of the digital tire model, the expected wheel slip state is recalculated. For example, the expected slip ratio of the left front wheel is 16%, and that of the right front wheel is 15%. Then, the recalculated slip state is subjected to a secondary match with the friction change law in the optimal slip ratio data set to determine the adjustment amount of the pressure control value for each brake. For example, the adjustment amount of the pressure control value for the left front wheel is increased by 0.5 MPa, and that for the right front wheel is increased by 0.3 MPa. Finally, the final control instruction is updated and transmitted to the brake actuator to achieve precise adjustment of the braking force.

[0212] The following is a specific example:

[0213] During an emergency braking process, the vehicle is equipped with wheel speed sensors and a braking hydraulic system monitoring device. When the vehicle passes through a wet and slippery road surface, the system detects that the actual measured wheel slip state is 18%, the simulated calculation result is 15%, and the deviation is 3%. Through step 701, based on the deviation and the pressure change rate of the braking hydraulic system (2 MPa / s), the tire stiffness in the digital tire model is dynamically corrected (from 1000 N / m to 950 N / m) and the friction characteristics are corrected (from 0.8 to 0.75). Then, through step 702, the expected wheel slip state is recalculated according to the corrected model (16% for the left front wheel and 15% for the right front wheel), and it is secondarily matched with the friction change law in the optimal slip rate data set to generate the adjustment amount of the pressure control value (the left front wheel increases by 0.5 MPa and the right front wheel increases by 0.3 MPa). The braking actuator precisely adjusts the braking force of the four wheels according to the updated final control instruction, successfully adjusting the slip rate to 15% and ensuring the stability of the vehicle.

[0214] In summary, through steps 701 to 702, high-precision braking force control and dynamic optimization of the vehicle in an emergency braking situation are achieved. By dynamically correcting the physical characteristic parameters in the digital tire model and performing secondary matching in combination with the optimal slip rate data set, the accuracy and stability of the braking force control are significantly improved, providing reliable technical support for the driver and the vehicle stability control system.

[0215] In some embodiments, extracting the friction change characteristics matching the current road surface condition from the data set in step 602, performing a matching analysis on the calculated expected wheel slip state and the friction change characteristics, and determining the pressure control value of each brake based on the matching result includes:

[0216] 801. According to the current road surface condition, extract the friction change characteristics corresponding to this road surface condition from the data set, and the friction change characteristics include the friction force values and their change laws at different slip rates;

[0217] Optimal slip rate data set: A data set containing the optimal slip rate ranges and their corresponding friction change characteristics under different road surface conditions.

[0218] Friction change characteristics: Characteristic parameters reflecting the friction change law between the tire and the road surface at different slip rates.

[0219] In the embodiments of the present application, first, according to the current road surface conditions (such as a slippery road surface), the corresponding friction force change characteristics are extracted from the optimal slip ratio data set. For example, on a slippery road surface, when the slip ratio is 10%, the friction force is 2000N, and when the slip ratio is 20%, the friction force is 2500N. Then, the extracted friction force change characteristics are stored in the system to provide a reference for subsequent braking force optimization.

[0220] 802. Based on the expected wheel slip state, determine the slip ratio value corresponding to the expected wheel slip state, and according to the slip ratio value, find the corresponding friction force value from the friction force change characteristics;

[0221] Expected wheel slip state: The future slip ratio state predicted based on the vehicle dynamics model and the current state.

[0222] Slip ratio value: The specific slip ratio value corresponding to the expected wheel slip state.

[0223] In the embodiments of the present application, first, based on the expected wheel slip state, determine the corresponding slip ratio value. For example, if the expected slip state is 15%, the slip ratio value is 15%. Then, find the corresponding friction force value from the friction force change characteristics. For example, when the slip ratio is 15%, the friction force is 2200N. Finally, store the search result in the system for subsequent braking force optimization.

[0224] 803. Compare the tire force state under the expected wheel slip state with the friction force value, calculate the difference degree between the two, and according to the difference degree, adjust the magnitude of the independent control quantity so that the difference degree between the tire force state under the expected wheel slip state and the friction force value is minimized;

[0225] Tire force state: The longitudinal force, lateral force, vertical force, etc. that the tire receives under the expected slip state.

[0226] Difference degree: The deviation value between the tire force state and the friction force value, reflecting the optimization space of the braking force distribution.

[0227] In the embodiments of the present application, first, compare the tire force state under the expected wheel slip state with the friction force value and calculate the difference degree. For example, if the tire force state is 2100N and the friction force value is 2200N, the difference degree is 100N. Then, adjust the magnitude of the independent control quantity according to the difference degree. For example, increase the braking force of the left front wheel by 100N and the braking force of the right front wheel by 80N. Finally, ensure that the difference degree between the tire force state and the friction force value is minimized to optimize the braking force distribution.

[0228] 804. Calculate the braking force distribution required for each wheel based on the adjusted independent control quantity, and determine the pressure control values for each brake.

[0229] Braking force distribution: The distribution of braking forces for each wheel under the adjusted independent control quantity.

[0230] Pressure control value: The hydraulic pressure value required to adjust the braking force, used to precisely control the braking effect.

[0231] In the embodiments of the present application, first, calculate the braking force distribution required for each wheel based on the adjusted independent control quantity. For example, the braking force of the left front wheel is 6200 N, and that of the right front wheel is 5800 N. Then, determine the pressure control values for each brake. For example, the pressure control value of the left front wheel is 8 MPa, and that of the right front wheel is 7.5 MPa. Finally, transmit the pressure control values to the brake actuator to achieve precise adjustment of the braking force.

[0232] The following is a specific example:

[0233] During an emergency braking process, the vehicle is equipped with wheel speed sensors and a braking force control system. When the vehicle passes through a wet road surface, the system detects that the expected wheel slip state is 15%. Through step 801, extract the friction force change characteristics under wet road surface conditions from the optimal slip rate data set (the friction force is 2200 N when the slip rate is 15%). Then, through step 802, determine that the slip rate value is 15% and find the corresponding friction force value of 2200 N. Through step 803, compare the tire force state (2100 N) under the expected wheel slip state with the friction force value (2200 N), calculate the difference degree of 100 N, and adjust the independent control quantity (increase the left front wheel by 100 N and the right front wheel by 80 N). Finally, through step 804, based on the adjusted independent control quantity, calculate the braking force distribution for each wheel (left front wheel 6200 N, right front wheel 5800 N), and determine the pressure control values for each brake (left front wheel 8 MPa, right front wheel 7.5 MPa). The brake actuator precisely adjusts the braking forces of the four wheels according to the pressure control values, successfully adjusts the slip rate to 15%, and ensures the stability of the vehicle.

[0234] In summary, through steps 801 to 804, high-precision braking force control and dynamic optimization of the vehicle in emergency braking situations are achieved. By extracting the friction force change characteristics, optimizing the independent control quantity, and determining the pressure control values, the accuracy and stability of the braking force control are significantly improved, providing reliable technical support for the driver and the vehicle stability control system.

[0235] In some embodiments, after generating the braking force control instruction in step 103, it further includes:

[0236] Adjust the time-domain window length of the model predictive control algorithm according to the interaction intensity between the virtual tread and the road surface particles reconstructed by the digital twin technology;

[0237] Digital twin technology: It can simulate the interaction behavior between the tire and the road surface in real time through a virtual model, reflecting the dynamic characteristics of the actual physical system.

[0238] Interaction intensity between the virtual tread and the road surface particles: A parameter in the digital twin model that characterizes the contact intensity between the tire and the road surface, affecting the prediction accuracy of the slip state.

[0239] Time-domain window length: The time range used for predicting and optimizing the braking force distribution in the model predictive control algorithm.

[0240] In the embodiments of the present application, first, reconstruct the interaction intensity between the virtual tread and the road surface particles through digital twin technology. For example, the interaction intensity is lower on a wet road surface and higher on a dry road surface. Then, adjust the time-domain window length of the model predictive control algorithm according to the interaction intensity. For example, when the interaction intensity is lower, shorten the time-domain window length from 0.5 seconds to 0.3 seconds to improve the real-time performance of the prediction. Finally, apply the adjusted time-domain window length to the model predictive control algorithm to optimize the braking force control strategy.

[0241] 902. Update the braking force control instruction according to the adjusted model predictive control algorithm.

[0242] Model predictive control algorithm: A control algorithm that optimizes the braking force distribution based on a prediction model and realizes dynamic adjustment through rolling optimization.

[0243] Braking force control instruction: A set of instructions containing the braking force control parameters of each wheel, used to achieve precise braking force distribution.

[0244] In the embodiments of the present application, first, recalculate the braking force distribution of each wheel according to the adjusted model predictive control algorithm. For example, the braking force of the left front wheel is 6200 N, and that of the right front wheel is 5800 N. Then, update the braking force control instruction and transmit it to the braking actuator to achieve precise adjustment of the braking force. Finally, monitor the braking force control effect in real time to ensure the stability of the vehicle in an emergency braking situation.

[0245] The following is a specific example:

[0246] During an emergency braking process, the vehicle is equipped with a digital twin system and a braking force control system. When the vehicle passes through a wet and slippery road surface, the digital twin system detects that the interaction intensity between the virtual tire tread and the road surface particles is low. Through step 901, the time-domain window length of the model predictive control algorithm is shortened from 0.5 seconds to 0.3 seconds to improve the real-time performance of prediction. Then, through step 902, the braking force control command (left front wheel 6200N, right front wheel 5800N) is updated according to the adjusted model predictive control algorithm. The braking actuator precisely adjusts the braking forces of the four wheels according to the updated command, successfully adjusting the slip ratio to 15% and ensuring the stability of the vehicle.

[0247] In summary, through steps 901 to 902, high-precision braking force control and dynamic optimization of the vehicle in an emergency braking situation are achieved. This method reconstructs the interaction intensity between the virtual tire tread and the road surface particles through digital twin technology, dynamically adjusts the time-domain window length of the model predictive control algorithm, and updates the braking force control command, significantly improving the real-time performance and accuracy of braking force control, and providing reliable technical support for the driver and the vehicle stability control system.

[0248] Figure 2 The following is a schematic structural diagram of a vehicle control system in an emergency braking scenario provided by an embodiment of the present application, as Figure 2 shown, the system includes:

[0249] An adjustment module 21 that establishes a data set containing the optimal wheel slip ratio under different road surface conditions, and dynamically adjusts the target slip ratio range by simulating the contact characteristics between the tire and the road surface through a digital model;

[0250] An acquisition module 22 that acquires vehicle motion state data, analyzes the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, simultaneously detects the change trend of the tire force state, comprehensively judges the vehicle instability risk, and generates a warning signal;

[0251] A calculation module 23 that inputs the target slip ratio range and the warning signal into a predictive control model, calculates the required braking force magnitude of each wheel according to the difference between the actual wheel speed and the target value, and generates a braking force control command in combination with the vehicle steering state;

[0252] A construction module 24 that, according to the braking force control command, uses a digital tire model to simulate and calculate the expected wheel slip state, and determines the pressure control value of each brake with reference to the friction change law in the data set;

[0253] The correction module 25 corrects the physical property parameters of the digital tire model by comparing the actually measured wheel slip state with the simulation calculation result, combines the pressure change rate of the brake hydraulic system, and adjusts the pressure control value to achieve automatic adjustment and control of the braking force of the vehicle in an emergency braking situation.

[0254] Figure 2 The vehicle control system in an emergency braking scenario described above can execute Figure 1 The vehicle control method in an emergency braking scenario described in the illustrated embodiment, the implementation principle and technical effects will not be elaborated. For the vehicle control system in an emergency braking scenario in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0255] In a possible design, Figure 2 The vehicle control device in an emergency braking scenario of the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32;

[0256] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0257] The processing component 32 is used for the Figure 1 Vehicle control method in an emergency braking scenario described in the above embodiment.

[0258] Among them, the processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0259] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0260] Of course, the computing device necessarily may further include other components, such as an input / output interface, a display component, a communication component, etc.

[0261] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module may be an output device, an input device, etc.

[0262] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0263] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0264] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 vehicle control method in an emergency braking scenario shown in the embodiment.

[0265] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0266] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0267] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0268] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle control method in an emergency braking scenario, characterized in that: include: Establish a data set containing the optimal wheel slip rate under different road conditions, and simulate the contact characteristics between tires and roads through digital models to dynamically adjust the target slip rate range; Collect vehicle motion state data, analyze the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, detect the change trend of the tire stress state, comprehensively judge the vehicle instability risk and generate a warning signal; The target slip ratio range and the warning signal are input into the predictive control model, the braking force required for each wheel is calculated according to the difference between the actual wheel speed and the target value, and the braking force control command is generated in combination with the vehicle steering state; According to the braking force control instruction, the expected wheel slip state is calculated by using a digital tire model, and the pressure control value of each brake is determined by referring to the friction force variation law in the data set; By comparing the actual measured wheel slip state with the simulation calculation result, combined with the pressure change rate of the brake hydraulic system, the physical characteristic parameters of the digital tire model are corrected, and the pressure control value is adjusted to achieve automatic adjustment and control of the braking force of the vehicle in emergency braking conditions.

2. The method according to claim 1, characterized in that The analyzing the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, detecting the change trend of the tire stress state, comprehensively judging the vehicle instability risk and generating a warning signal, includes: The rotation rate of the vehicle around the vertical axis and the pitch change rate around the lateral axis are obtained by an angular velocity measuring device installed on the vehicle body, a time series analysis is performed on the rotation rate to extract the moment when the change amplitude exceeds a preset threshold and the corresponding change amount, and a spectrum decomposition is performed on the pitch change rate to separate the high-frequency component caused by the road impact and the low-frequency component caused by the vehicle body movement; Performing weighted fusion on the change in the rotation rate and the amplitude of the low-frequency component of the pitch change rate to obtain an overall motion trend index of the vehicle; The real-time force of each tire in the travel direction and lateral direction is obtained through the tire force detection module, and the difference of the force components of each tire in the travel direction and the symmetry of the lateral component at the same time are calculated, and the tire force coupling index is calculated according to the difference of the force in the travel direction and the symmetry of the lateral force; The vehicle overall motion trend index and the tire force coupling index are combined according to a preset rule, and a level signal reflecting the degree of vehicle instability is output as a warning signal.

3. The method according to claim 2, characterized in that The step of combining the vehicle overall motion trend index and the tire force coupling index according to a preset rule and outputting a level signal reflecting the degree of vehicle instability as a warning signal includes: According to the overall motion trend index of the vehicle, a two-dimensional coordinate system including the change amount of the rotation rate and the amplitude of the low-frequency component of the pitch change rate is established, and the boundary range of the stable area, the transition area and the dangerous area is divided according to the historical data; In the two-dimensional coordinate system, the current vehicle position point is calibrated in real time, and the Euclidean distance between the current vehicle position point and the nearest dangerous area boundary is calculated as the movement trend danger degree; Decomposing the tire force coupling index into a travel direction force difference component and a lateral force symmetry component, constructing a tire force state plane with the two components as coordinate axes, and calculating the distance between the current state point and the coordinate origin in the tire force state plane as the tire force coupling strength value, wherein the coordinate origin represents an ideal coupling state; The motion trend risk and the tire force coupling strength value are input into a preset grading decision table, and a corresponding grade signal is output according to the grading decision table. The change direction of the grade signal in three consecutive control cycles is tested for consistency. If the trend is increasing, the output grade is increased, and the final grade signal is determined as a warning signal; Among them, the grading decision table is used to output the highest level signal when the movement trend risk is greater than a first threshold and the tire force abnormality coefficient is greater than a second threshold; output an intermediate level signal when the movement trend risk or the tire force abnormality coefficient exceeds the corresponding threshold; output the lowest level signal when neither the movement trend risk nor the tire force abnormality coefficient exceeds the threshold.

4. The method according to claim 1, characterized in that The target slip ratio range and the warning signal are input into the predictive control model, the braking force required for each wheel is calculated according to the difference between the actual wheel speed and the target value, and the braking force control instruction is generated in combination with the vehicle steering state, including: In the prediction time window, an allowable control interval including the upper and lower limits of the target slip rate is established, and the boundary range of the allowable control interval is dynamically contracted according to the level signal corresponding to the early warning signal, wherein the contraction amplitude is positively correlated with the level corresponding to the level signal; After real-time acquisition of the rotation number signal of each wheel and conversion into the current actual slip rate, the predicted slip rate in the next three control cycles is calculated based on the vehicle dynamics equation in the predictive control model, and the braking force adjustment amount is determined according to the predicted slip rate; The vehicle's steering intention is obtained through the steering wheel angle sensor, and the steering dynamics relationship in the predictive control model is used to calculate the braking force correction weight of each wheel within the prediction time window; The braking force adjustment amount is multiplied by the braking force correction weight, and a braking force change value of each wheel is obtained through a rolling optimization module of a predictive control model; The braking force change value is superimposed on the current braking force to generate a braking force instruction set including independent control quantities of four wheels.

5. The method according to claim 4, characterized in that Determining the braking force adjustment amount according to the predicted slip rate includes: When the predicted slip rate is lower than the lower limit of the target range, the reduction in braking force of each wheel is calculated according to the first functional relationship; or, when the predicted slip rate is higher than the upper limit of the target range, the increase in braking force of each wheel is calculated according to the second functional relationship; or, when the predicted slip rate is within the target range, the braking force of each vehicle is optimized to maintain the current change trend to determine the braking force adjustment amount corresponding to each vehicle.

6. The method according to claim 1, characterized in that The method of calculating the expected wheel slip state by simulating the digital tire model according to the braking force control instruction and determining the pressure control value of each brake by referring to the friction force variation law in the data set includes: Based on the independent control amount of each wheel in the braking force control command, the deformation characteristics of the contact area between the tire and the road are calculated through a digital tire model, and according to the deformation characteristics of the contact area and the current wheel speed, the expected wheel slip state of each wheel after the independent control amount is applied is simulated and calculated; A friction force variation feature matching the current road condition is extracted from the data set, the calculated expected wheel slip state is matched and analyzed with the friction force variation feature, and the pressure control value of each brake is determined based on the matching result.

7. The method according to claim 1, characterized in that The method of comparing the actually measured wheel slip state with the simulation calculation result, combining the pressure change rate of the brake hydraulic system, correcting the physical characteristic parameters of the digital tire model, and adjusting the pressure control value includes: Calculate the deviation between the actual measured wheel slip state and the simulated calculation result within a preset time window, monitor the real-time pressure change rate of the brake hydraulic system, and dynamically correct the physical characteristic parameters representing the tire stiffness and friction characteristics in the digital tire model based on the corresponding relationship between the deviation and the pressure change rate; According to the corrected physical characteristic parameters of the digital tire model, the expected wheel slip state is recalculated, the recalculated slip state is secondarily matched with the friction force variation law in the data set, and the adjustment amount of the pressure control value is generated based on the second matching result and the final control instruction is updated.

8. The method according to claim 6, characterized in that The extracting friction force variation characteristics matching the current road condition from the data set, matching and analyzing the calculated expected wheel slip state with the friction force variation characteristics, and determining the pressure control value of each brake based on the matching result, includes: According to the current road condition, extracting the friction force variation characteristics corresponding to the road condition from the data set, the friction force variation characteristics including the friction force values ​​and their variation rules under different slip rates; Based on the expected wheel slip state, determining a slip ratio value corresponding to the expected wheel slip state, and searching for a corresponding friction force value from the friction force variation characteristics according to the slip ratio value; Comparing the tire force state under the expected wheel slip state with the friction force value, calculating the difference between the two, and adjusting the size of the independent control amount according to the difference, so as to minimize the difference between the tire force state under the expected wheel slip state and the friction force value; Based on the adjusted independent control amount, the braking force distribution required for each wheel is calculated and the pressure control value of each brake is determined.

9. The method according to claim 1, characterized in that: After generating the braking force control command, it also includes: According to the interaction intensity between the virtual tread reconstructed by digital twin technology and the road particles, the time domain window length of the model predictive control algorithm is adjusted; The braking force control instruction is updated according to the adjusted model predictive control algorithm.

10. A vehicle control system in an emergency braking scenario, characterized in that: include: Adjust the plate to establish a data set containing the best wheel slip rate under different road conditions, and simulate the contact characteristics between the tire and the road through a digital model to dynamically adjust the target slip rate range; The acquisition section collects vehicle motion state data, analyzes the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, detects the change trend of the tire stress state, comprehensively judges the vehicle instability risk and generates a warning signal; A calculation section, which inputs the target slip rate range and the warning signal into the predictive control model, calculates the braking force required for each wheel according to the difference between the actual wheel speed and the target value, and generates a braking force control instruction in combination with the vehicle steering state; A construction section is provided to simulate and calculate the expected wheel slip state using a digital tire model according to the braking force control instruction, and to determine the pressure control value of each brake by referring to the friction force variation law in the data set; The correction section corrects the physical characteristic parameters of the digital tire model by comparing the actually measured wheel slip state with the simulation calculation result, combining the pressure change rate of the brake hydraulic system, and adjusting the pressure control value to achieve automatic adjustment and control of the braking force of the vehicle in emergency braking conditions.

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