Vehicle control method and system in emergency braking scene

Abstract

The invention provides a vehicle control method and system in an emergency braking scene. According to the method, the target slip rate range is dynamically adjusted by establishing an optimal slip rate data set under different road surface conditions and combining a digital model to simulate contact characteristics of tires and road surfaces; collecting vehicle motion state data, analyzing abrupt change characteristics and a tire stress change trend, and generating an early warning signal; inputting the target slip rate range and the early warning signal into a predictive control model, calculating the braking force of each wheel and generating a control instruction; simulating an expected slip state by using a digital tire model, and determining a pressure control value by referring to a friction force change rule; by comparing actual measurement and simulation results and combining with the pressure change rate of the brake hydraulic system, model parameters are corrected, and a pressure control value is adjusted, automatic adjustment of the brake force is achieved. According to the technical scheme, the stability and safety of the vehicle under the emergency braking condition are remarkably improved.

Description

Technical Field

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

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

[0003] At present, the more advanced solution is the ABS system based on model predictive control (MPC). This solution establishes a vehicle dynamics model, combines the wheel speed, vehicle speed and other parameters collected in real time, predicts the vehicle state changes in the future time domain, and calculates the optimal braking force distribution plan through rolling optimization. The system uses multiple sets of preset road parameter models to match the current road conditions in real time through the least squares method, thereby dynamically adjusting the control strategy.

[0004] However, this existing solution has significant shortcomings. First, the preset road parameter model is difficult to cover all possible actual road conditions, especially on complex and changeable mixed adhesion roads. Second, the real-time performance of model predictive control is limited by computing resources, and control delays may occur under extreme conditions. Finally, the existing solution does not have sufficient modeling accuracy for tire nonlinear characteristics, resulting in a decrease in 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 the present application provide a vehicle control method and system in an emergency braking scenario, so as to solve the problems of insufficient slip rate tracking accuracy and low yaw stability in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a vehicle control method in an emergency braking scenario, comprising: 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.

[0007] Optionally, 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.

[0008] Optionally, the combining of 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.

[0009] Optionally, the target slip ratio range and the warning signal are input into a 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.

[0010] Optionally, 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.

[0011] Optionally, the 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 with reference 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.

[0012] Optionally, the 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 to correct the physical characteristic parameters of the digital tire model 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.

[0013] Optionally, extracting a friction force variation feature matching the current road condition from the data set, performing matching analysis on the calculated expected wheel slip state and the friction force variation feature, 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.

[0014] Optionally, after generating the torque control instruction, the method further 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 torque control command is updated according to the adjusted model predictive control algorithm.

[0015] In a second aspect, an embodiment of the present application provides a vehicle control system in an emergency braking scenario, including: 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.

[0016] In a third aspect, an embodiment of the present application provides a computing device, comprising 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.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a vehicle control method in an emergency braking scenario as described in the first aspect.

[0018] In an embodiment of the present application, a data set including optimal wheel slip rates under different road conditions is established, and the contact characteristics between the tire and the road are simulated by a digital model to dynamically adjust the target slip rate range; vehicle motion state data is collected, the mutation characteristics and frequency distribution characteristics of the vehicle motion state data are analyzed, and the change trend of the tire force state is detected at the same time, the risk of vehicle instability is comprehensively judged and a warning signal is generated; the target slip rate range and the warning signal are input into a 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 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 using a digital tire model, and the pressure control value of each brake is determined by referring to the friction force change law in the data set; by comparing the actually 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 vehicle's braking force in emergency braking.

[0019] The technical solution of this application has the following beneficial effects: The present application establishes a data set containing the best wheel slip rate under different road conditions, and simulates the contact characteristics between tires and roads through digital models to achieve dynamic adjustment of the target slip rate range and ensure the best braking performance under different road conditions; collects vehicle motion state data and analyzes its mutation characteristics and frequency distribution characteristics, and detects the changing trend of tire force state at the same time, so as to achieve accurate judgment and early warning of vehicle instability risk; inputs the target slip rate range and early warning signal into the predictive control model, and generates accurate braking force control instructions in combination with the difference between the actual wheel speed and the target value and the vehicle steering state; uses the digital tire model to simulate and calculate the expected wheel slip state, and refers to the friction force change law in the best slip rate data set to determine the precise pressure control value of each brake; finally, by comparing the real-time measurement with the simulation results, combined with the pressure change rate of the brake hydraulic system, the model parameters are dynamically corrected and the control value is adjusted to achieve automatic optimization and adjustment of the braking force under emergency braking conditions. The method realizes adaptive braking control under different road conditions and significantly improves the braking efficiency and stability of the vehicle.

[0020] Furthermore, the vehicle's rotation and pitch rates are obtained through the angular velocity measurement device installed on the vehicle body, and its change characteristics are analyzed and the high-frequency and low-frequency components are separated. The coupling index is calculated by combining the difference and symmetry of the tire's travel direction and lateral force, and the vehicle's motion trend index and the tire force coupling index are combined to output the instability level signal. This method realizes the comprehensive monitoring of the vehicle's dynamic state and the accurate assessment of the instability risk, provides a reliable early warning signal for the braking control system, and effectively improves the safety and stability of the vehicle under emergency braking conditions.

[0021] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 A flow chart showing a vehicle control method in an emergency braking scenario provided by the present application is shown; Figure 2 A schematic diagram of the structure of a vehicle control system in an emergency braking scenario provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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.

[0025] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between 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 of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0027] Figure 1 A flow chart of a vehicle control method in an emergency braking scenario is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Establish a data set containing the best wheel slip rate under different road conditions, and simulate the contact characteristics between tire and road through digital model to dynamically adjust the target slip rate range; Optimal wheel slip rate data set: refers to the slip rate range that can achieve the best grip and braking effect when the tire contacts the road under different road conditions (such as wet, dry, icy and snowy, etc.).

[0028] Digital tire model: is a physics-based simulation tool used to simulate the contact behavior of the tire with the road, including characteristics such as friction, deformation and slip.

[0029] Target slip rate range: refers to the slip rate range that the system dynamically adjusts under different road conditions to achieve the best braking effect and vehicle stability.

[0030] In the embodiment of the present application, firstly, through experiments and actual road tests, wheel slip rate data under different road conditions are collected, and an optimal slip rate data set is established. Then, the contact characteristics between the tire and the road are simulated using a digital tire model, and the relationship between friction, slip rate and road conditions is analyzed. Then, the target slip rate range is dynamically adjusted according to the simulation results to ensure that it can achieve the best braking effect under various road conditions. Finally, the adjusted target slip rate range is stored in the system to provide a reference for subsequent braking control.

[0031] In a real case, a vehicle manufacturer established a data set of optimal slip rates under various road conditions through experiments and road tests. For example, on dry roads, the optimal slip rate range is 10%-15%; on wet roads, it is 5%-10%; on icy and snowy roads, it is 2%-5%. Through digital tire model simulation, it was found that the optimal slip rate range on icy and snowy roads is narrower, while the range on dry roads is wider. Based on this finding, the system dynamically adjusted the target slip rate range, significantly improving the vehicle's braking performance on icy and snowy roads.

[0032] 102. 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; Vehicle motion status data: including vehicle speed, acceleration, steering angle and other information.

[0033] Mutation characteristics: refers to the characteristics that these data change significantly in a short period of time.

[0034] Frequency distribution characteristics: reflects the regularity of data changes.

[0035] Tire stress state: refers to the longitudinal force, lateral force and vertical force applied to the tire during driving. Its changing trend can be used to evaluate the stability of the vehicle.

[0036] Warning signal: It is a prompt information generated based on the risk of vehicle instability, used to remind the driver or trigger braking system intervention.

[0037] In the embodiment of the present application, first, the vehicle motion state data and tire stress state data are collected in real time through vehicle-mounted sensors. Then, the mutation characteristics and frequency distribution characteristics of the motion state data are analyzed using signal processing technology to identify abnormal changes. At the same time, the changing trend of the tire stress state is detected to evaluate the stability of the vehicle. Then, combined with the above analysis results, the risk of vehicle instability 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.

[0038] Based on the data from the previous step, the system detected a sudden change in the vehicle's motion state data during a certain trip, such as a sudden drop in speed from 60km / h to 40km / h, and a significant increase in the lateral force of the tires, from 500N to 800N. After comprehensive judgment, the system generated a warning signal, prompting the driver to slow down and adjust the driving direction, successfully avoiding the occurrence of a vehicle skidding accident.

[0039] 103. Input the target slip ratio 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; The predictive control model is a control system based on a predictive algorithm, which is used to generate optimal control instructions based on input parameters.

[0040] The difference between the actual wheel speed and the target value reflects the deviation between the current slip rate and the target slip rate. The braking force refers to the braking force required to adjust the slip rate.

[0041] Vehicle steering status: including steering angle, steering speed and other information, used to optimize braking force distribution.

[0042] Braking force control command: It is a command generated by the system to adjust the braking force of each wheel.

[0043] In the embodiment 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, the braking force required for each wheel is calculated based on the difference between the actual wheel speed and the target value. Then, the braking force distribution is optimized in combination with the vehicle steering state, and a braking force control instruction is generated. Finally, the control instruction is transmitted to the brake actuator to achieve precise control of the braking force of each wheel.

[0044] In actual application, during an emergency braking, the system calculated and distributed the braking force of each wheel according to the target slip rate range (5%-10% on slippery roads) 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. Combined with the vehicle's steering state (steering angle is 15°), the braking force distribution is optimized to ensure that the vehicle remains stable during braking and avoid the risk of steering out of control.

[0045] 104. According to the braking force control instruction, the expected wheel slip state is simulated and calculated using the digital tire model, and the pressure control value of each brake is determined by referring to the friction force change law in the data set; Expected wheel slip state: refers to the wheel slip rate change trend simulated according to the braking force control command.

[0046] Friction force variation law: reflects the friction force relationship between tire and road surface under different slip rates.

[0047] Pressure control value: refers to the hydraulic pressure value required to adjust the braking force, which is used to accurately control the braking effect.

[0048] In the embodiment of the present application, first, according to the braking force control instruction, the digital tire model is used to simulate and calculate the expected wheel slip state and predict the change trend of the slip rate. Then, referring to the friction force change law in the optimal slip rate data set, the pressure control value of each brake is determined to ensure that the slip rate is within the target range. Finally, the pressure control value is transmitted to the brake hydraulic system to achieve precise adjustment of the braking force.

[0049] Through simulation calculation, the system predicted that the slip rate of a certain wheel would exceed the target range (5%-10% on slippery roads) during a certain braking operation. For example, the slip rate of the left front wheel was expected to be 12%. The pressure control value of the brake was immediately adjusted from 8MPa to 6.5MPa, successfully controlling the slip rate within the optimal range and improving the braking effect and vehicle stability.

[0050] 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.

[0051] The actual measured wheel slip state refers to the wheel slip rate data collected in real time by the sensor.

[0052] Simulation calculation results: refers to the expected slip state generated by the digital tire model.

[0053] The pressure change rate of the brake hydraulic system: reflects the response speed of the braking force regulation.

[0054] Physical property parameters: including the friction coefficient and stiffness of the tire, which are used to optimize the model accuracy.

[0055] Automatic braking force adjustment control: refers to the system dynamically adjusting the braking force according to real-time data to ensure the stability of the vehicle in emergency braking situations.

[0056] 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 deviations. Then, the physical characteristic parameters of the digital tire model are corrected in combination with the pressure change rate of the brake hydraulic system 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 emergency braking conditions.

[0057] In actual application, the system found deviations in model predictions by comparing the actual slip state with the simulation calculation results during an emergency braking. For example, the actual slip rate of the left front wheel was 11%, while the simulation result was 9%. The physical characteristic parameters of the digital tire model were immediately corrected (the friction coefficient was adjusted from 0.8 to 0.75), and the pressure control value was adjusted from 6.5MPa to 6MPa, successfully achieving automatic adjustment of the braking force and ensuring the stability and safety of the vehicle.

[0058] In summary, steps 101 to 105 realize the automatic adjustment and control of the braking force of the vehicle in emergency braking. By establishing an optimal slip rate data set, analyzing vehicle motion state data, generating warning signals, optimizing braking force distribution, simulating slip states, and dynamically adjusting model parameters, the present application provides an efficient and reliable vehicle braking control method. In practical applications, this method significantly improves the braking performance and stability of the vehicle under various road conditions, reduces the risk of accidents, and provides strong support for the development of intelligent driving technology.

[0059] In order to achieve high-precision braking force control and stability optimization of the vehicle under emergency braking, in some embodiments, the step 102 analyzes the mutation characteristics and frequency distribution characteristics of the vehicle motion state data, detects the change trend of the tire force state, comprehensively judges the vehicle instability risk and generates a warning signal, including: 201. Obtain the rotation rate of the vehicle around the vertical axis and the pitch change rate around the lateral axis through an angular velocity measurement device installed on the vehicle body, perform time series analysis on the rotation rate, extract the moment when its change amplitude exceeds a preset threshold and the corresponding change amount, and perform spectrum decomposition on the pitch change rate to separate the high-frequency component caused by road impact and the low-frequency component caused by vehicle body movement; Angular velocity measurement device: used to measure the vehicle's rotation rate around the vertical and lateral axes in real time, including devices such as gyroscopes or inertial measurement units (IMUs).

[0060] Time series analysis: Statistical analysis of the characteristics of rotation rate changes over time to extract key change points.

[0061] Spectral decomposition: Decompose the pitch change rate into different frequency components through Fourier transform or wavelet transform to distinguish the effects of road impact and vehicle body motion.

[0062] In the embodiment of the present application, the rotation rate of the vehicle around the vertical axis and the pitch change rate around the lateral axis are first collected in real time by an angular velocity measurement device. Next, a time series analysis is performed on the rotation rate to extract the moment when the change amplitude exceeds the preset threshold and the corresponding change amount, for example, the rotation rate suddenly increases from 10° / s to 30° / s within 0.5 seconds. Then, the pitch change rate is spectrally decomposed to separate high-frequency components (such as above 10Hz) and low-frequency components (such as below 1Hz), where the high-frequency components are mainly caused by road impact and the low-frequency components are caused by vehicle body movement. Finally, the extracted features are stored in the system to provide data support for subsequent analysis.

[0063] 202. Perform 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; Weighted fusion: The change in rotation rate and the amplitude of the low-frequency component of the pitch change rate are comprehensively calculated according to the preset weights.

[0064] Vehicle overall motion trend index: used to quantify the overall motion state of the vehicle during driving and reflect the stability of the vehicle.

[0065] In the embodiment of the present application, firstly, weights are assigned to the rotation rate change amount and the low-frequency component amplitude of the pitch change rate (e.g., the rotation rate weight is 0.6, and the pitch change rate weight is 0.4). Then, the overall motion trend index of the vehicle is calculated by the weighted fusion formula, for example, the index value = rotation rate change amount × 0.6 + low-frequency component amplitude × 0.4. Finally, the calculated index is stored in the system for subsequent instability risk assessment.

[0066] 203. Obtain the real-time force of each tire in the travel direction and lateral direction through the tire force detection module, calculate the difference of the force components of each tire in the travel direction and the symmetry of the lateral component at the same time, and calculate the tire force coupling index according to the difference of the force in the travel direction and the symmetry of the lateral force; Tire force detection module: used to measure the tire force in the travel direction and lateral direction in real time, including force sensors or tire pressure monitoring systems.

[0067] Difference of force in the direction of travel: reflects the difference in the force of each tire in the direction of travel.

[0068] Lateral force symmetry: reflects the symmetry of the lateral force of each tire.

[0069] Tire force coupling index: used to quantify the distribution of tire force and reflect the contact between the tire and the road surface.

[0070] In the embodiment of the present application, firstly, the force data of each tire in the travel direction and lateral direction are collected in real time through the tire force detection module. Then, the difference of the travel direction forces of each tire at the same time is calculated, for example, the difference = maximum travel direction force - minimum travel direction force. Then, the symmetry of the lateral force is calculated, for example, the symmetry = left front wheel lateral force - right front wheel lateral force. Finally, according to the difference and symmetry, the tire force coupling index is calculated by a preset formula, for example, the coupling index = difference × 0.5 + symmetry × 0.5.

[0071] 204. 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.

[0072] Preset rule combination: Set the instability level classification rules according to the numerical range of the vehicle's overall motion trend index and the tire force coupling index.

[0073] Instability level signal: a classification signal used to reflect the degree of vehicle instability, including low risk, medium risk and high risk levels.

[0074] In the embodiment of the present application, firstly, the instability level classification rules are set according to the numerical range of the vehicle's overall motion trend index and the tire force coupling index, 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 indicators are combined according to the preset rules to generate an instability level signal. Finally, the level signal is transmitted to the early warning system to prompt the driver or trigger the vehicle stability control system to intervene.

[0075] Here is a specific example: During a highway driving process, the vehicle was equipped with an angular velocity measurement device and a tire force detection module. When the vehicle passed a slippery road, the angular velocity measurement device detected that the rotation rate of the vehicle around the vertical axis suddenly increased from 10° / s to 30° / s within 0.5 seconds, and the amplitude of the low-frequency component of the pitch change rate was 0.8° / s. Through steps 201 and 202, the overall motion trend index of the vehicle was calculated to be 0.68. At the same time, the tire force detection module detected that the difference in the force of each tire in the direction of travel was 200N, and the symmetry of the lateral force was 150N. Through step 203, the tire force coupling index was calculated to be 0.75. In step 204, the overall motion trend index of the vehicle (0.68) and the tire force coupling index (0.75) were combined according to the preset rules to generate an instability level signal of high risk. The early warning system immediately issued an alarm, prompting the driver to slow down and adjust the driving direction, and triggered the vehicle stability control system to intervene, successfully avoiding the occurrence of a vehicle skidding accident.

[0076] In summary, through steps 201 to 204, the accuracy and real-time performance of vehicle instability detection during driving are achieved. This method can accurately identify the risk of vehicle instability and generate warning signals through the coordinated 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. In practical applications, this method significantly improves the safety and stability of vehicle driving and provides reliable decision support for drivers and vehicle stability control systems.

[0077] In order to achieve high-precision instability detection and early warning of vehicles during driving, this study aims to accurately extract the vehicle motion trend and tire force coupling characteristics through the coordinated application of angular velocity measurement devices and tire force detection modules. The research and development idea is to combine the amplitude of the change in rotation rate and the amplitude of the low-frequency component of the pitch change rate, and obtain the vehicle's overall motion trend index by weighted fusion, and generate a tire force coupling index by calculating the difference in the components of each tire force in the direction of travel and the symmetry of the lateral components. Based on the preset rule combination of the vehicle's overall motion trend index and the tire force coupling index, a level signal reflecting the degree of vehicle instability is output to provide real-time early warning support for vehicle driving safety. In some embodiments, in step 204, the vehicle's overall motion trend index and the tire force coupling index are combined according to preset rules, and a level signal reflecting the degree of vehicle instability is output as a warning signal, including: 301. 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; Two-dimensional coordinate system: A plane coordinate system with the change in 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 movement trend of the vehicle.

[0078] Stable area, transition area and danger area: different vehicle motion state areas divided based on historical data, representing safety, potential risks and serious instability states respectively.

[0079] In an embodiment of the present application, first, a two-dimensional coordinate system is established based on the overall motion trend index of the vehicle, in which the horizontal axis is the change in rotation rate, and the vertical axis is the amplitude of the low-frequency component of the pitch change rate. Next, based on historical data, the boundary ranges of the stable area, transition area, and dangerous area are divided. For example, the range of the change in rotation rate in the stable area is 0~10° / s, and the range of the amplitude of the low-frequency component is 0~1° / s; the range of the change in rotation rate in the dangerous area 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.

[0080] 302. Calibrate the current vehicle position point in real time in the two-dimensional coordinate system, and calculate the Euclidean distance between the current vehicle position point and the nearest dangerous area boundary as the movement trend danger degree; Current vehicle position point: In a two-dimensional coordinate system, the vehicle status point calibrated according to the real-time rotation rate change and the low-frequency component amplitude.

[0081] Euclidean distance: used to calculate the geometric distance between the current vehicle position and the boundary of the danger zone, reflecting the degree to which the vehicle is close to an unstable state.

[0082] Movement trend hazard degree: used to quantify the degree to which the vehicle's movement trend approaches the danger zone. The smaller the value, the higher the risk.

[0083] 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 rotation rate change and the amplitude of the low-frequency component. Then, the Euclidean distance between the current vehicle position point and the nearest dangerous area boundary is calculated. For example, 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 motion trend hazard is stored in the system for subsequent instability level judgment.

[0084] 303. Decompose the tire force coupling index into a travel direction force difference component and a lateral force symmetry component, construct a tire force state plane with the two components as coordinate axes, and calculate 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; Tire force state plane: A plane coordinate system with the difference component of the force in the travel direction as the horizontal axis and the symmetry component of the lateral force as the vertical axis, used to describe the tire force distribution state.

[0085] Tire force coupling strength value: used to quantify the degree of deviation between the tire force distribution state and the ideal coupling state. The larger the value, the worse the coupling state.

[0086] In an embodiment of the present application, the tire force coupling index is first decomposed into a force difference component in the direction of travel and a symmetry component in the lateral force. Next, 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), 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 instability level judgment.

[0087] 304. Input the motion trend risk and the tire force coupling strength value into a preset grading decision table, output the corresponding grade signal according to the grading decision table, perform consistency detection on the change direction of the grade signal within three consecutive control cycles, if they are all increasing trends, increase the output grade, and determine the final grade signal as a warning signal; Grading decision table: a preset rule table for outputting corresponding instability level signals according to the motion trend risk and the tire force coupling strength value. The grading decision table is used to output the highest level signal when the motion trend risk is greater than the first threshold and the tire force abnormality coefficient is greater than the second threshold; output the middle level signal when the motion trend risk or the tire force abnormality coefficient exceeds the corresponding threshold; output the lowest level signal when neither the motion trend risk nor the tire force abnormality coefficient exceeds the threshold.

[0088] The first threshold and the second threshold are preset values ​​used to classify the instability levels. For example, the first threshold is 5.0 and the second threshold is 200N.

[0089] The highest level signal, the middle level signal and the lowest level signal: represent instability level signals of high risk, medium risk and low risk respectively.

[0090] Consistency detection: It is used to determine whether the changing trend of the level signal is consistent within three consecutive control cycles. If the trend is increasing, the output level is increased.

[0091] Warning signal: A classified signal used to reflect the degree of vehicle instability, including low risk, medium risk and high risk levels.

[0092] In an embodiment of the present application, the motion trend hazard degree and the tire force coupling strength value are first input into a grading decision table, and a corresponding level signal is output. For example, if the motion trend hazard degree is 5.1 and the tire force coupling strength value is 250N, a medium risk level signal is output. Next, the level signals within three consecutive control cycles are checked for consistency, and if they are all increasing trends, the output level is increased. For example, if the level signals for three consecutive control cycles are low risk, medium risk, and medium risk, respectively, the output level is increased to high risk. Finally, the final level signal is transmitted to the early warning system to prompt the driver or trigger the vehicle stability control system to intervene.

[0093] Specifically, it is possible to first determine whether the first threshold and the second threshold are exceeded based on the motion trend risk and the tire force coupling strength value. For example, if the motion trend risk is 5.1 (greater than the first threshold 5.0) and the tire force coupling strength value is 250N (greater than the second threshold 200N), the highest level signal is output. Then, the level signal is transmitted to the early warning system to prompt the driver or trigger the vehicle stability control system to intervene.

[0094] Here is a specific example: During a highway driving process, the vehicle was equipped with an angular velocity measurement device and a tire force detection module. When the vehicle passed a slippery road, the angular velocity measurement device detected that the rotation rate of the vehicle around the vertical axis suddenly increased from 10° / s to 30° / s within 0.5 seconds, and the amplitude of the low-frequency component of the pitch change rate was 1.5° / s. Through steps 301 and 302, the motion trend hazard was calculated to be 5.1. At the same time, the tire force detection module detected that the difference in the force of each tire in the direction of travel was 200N, and the symmetry of the lateral force was 150N. Through step 303, the tire force coupling strength value was calculated to be 250N. In steps 304 and 305, the motion trend hazard (5.1) and the tire force coupling strength value (250N) were input into the hierarchical decision table, and the highest level signal was output. The early warning system immediately issued an alarm, prompting the driver to slow down and adjust the driving direction, and triggered the vehicle stability control system to intervene, successfully avoiding the occurrence of a vehicle skidding accident.

[0095] In summary, through steps 301 to 305, the hierarchical warning and dynamic optimization of vehicle instability detection during driving are realized. This method can accurately identify the risk of vehicle instability and generate a warning signal by establishing a two-dimensional coordinate system, dividing regional boundaries, calculating the motion trend danger and tire force coupling strength value, and combining the hierarchical decision table and consistency detection technology. In practical applications, this method significantly improves the safety and stability of vehicle driving and provides reliable decision support for drivers and vehicle stability control systems.

[0096] In order to achieve hierarchical 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, 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, including: 401. In the prediction time window, establish an allowable control interval including the upper and lower limits of the target slip rate, and dynamically shrink the boundary range of the allowable control interval according to the level signal corresponding to the warning signal, wherein the shrinkage amplitude is positively correlated with the level corresponding to the level signal; Prediction time window: The time range used to predict and control the slip rate, usually several control cycles in the future.

[0097] Allowable control range: The upper and lower limits of the target slip rate, used to limit the adjustment range of the braking force.

[0098] Dynamic contraction: According to the level of the early warning signal, the boundary range of the allowable control interval is gradually narrowed to improve the control accuracy.

[0099] In an embodiment of the present application, first, within the prediction time window, an allowable control interval including the upper and lower limits of the target slip rate is established, for example, the target slip rate range is 10% to 20%. Next, the boundary range of the allowable control interval is dynamically shrunk according to the level signal corresponding to the early warning signal. For example, the shrinkage range corresponding to the low-risk level signal is 5%, and the shrinkage range 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.

[0100] 402. After collecting the rotation number signal of each wheel in real time and converting it 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; Rotational speed signal: The number of wheel rotations collected in real time by the wheel speed sensor is used to calculate the actual slip rate.

[0101] Vehicle dynamics equation: A mathematical model used to describe the motion state of a vehicle, including the relationship between slip rate and braking force.

[0102] Braking force adjustment: The braking force change required to adjust the predicted slip ratio to the target slip ratio.

[0103] In the embodiment of the present application, the number of rotations of each wheel is first collected in real time by the wheel speed sensor and converted into the current actual slip rate. Then, based on the vehicle dynamics equation in the predictive control model, the predicted slip rate in the next three control cycles is calculated. Then, the braking force adjustment amount is determined based on the difference between the predicted slip rate and the target slip rate. For example, if the predicted slip rate is 25% and the target slip rate is 15%, the braking force adjustment amount is to increase by 1000N.

[0104] 403. The vehicle 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; Steering wheel angle sensor: used to detect the steering wheel angle in real time and reflect the driver's steering intention.

[0105] Steering dynamics relationship: A mathematical model used to describe the distribution of braking force to each wheel during steering.

[0106] Braking force correction weight: used to adjust the proportion of braking force distribution to each wheel to optimize steering stability.

[0107] In the embodiment of the present application, the steering intention of the vehicle is first obtained through the steering wheel angle sensor, for example, the steering wheel angle is 30°. Then, 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. 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.

[0108] 404. 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; Rolling Optimization Module: A calculation module used to dynamically optimize the braking force distribution within the prediction time window.

[0109] Braking force change value: the braking force adjustment amount of each wheel within the predicted time window, which is used to achieve independent control.

[0110] In the embodiment of the present application, the braking force adjustment amount is first multiplied by the braking force correction weight to obtain the braking force change value of each wheel. For example, the braking force change value of the left front wheel is 1000N×1.2=1200N, and the braking force change value of the right front wheel is 1000N×0.8=800N. Then, the rolling optimization module of the predictive control model is used to dynamically optimize the braking force change value of each wheel to ensure that it meets the requirements of the allowable control range.

[0111] 405. 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.

[0112] Braking force instruction set: Contains an instruction set for independent braking force control of the four wheels, used to achieve precise braking force distribution.

[0113] In the embodiment of the present application, the braking force change value is first superimposed on the current braking force to generate the braking force command for each wheel. For example, the braking force of the left front wheel is 5000N+1200N=6200N, and the braking force of the right front wheel is 5000N+800N=5800N. Then, the braking force commands of each wheel are summarized into a braking force command set and transmitted to the brake actuator to achieve independent control of the four wheels.

[0114] Here is a specific example: During an emergency braking process, the vehicle is equipped with a wheel speed sensor and a steering wheel angle sensor. When the vehicle passes through a slippery road, the system detects that the current actual slip rate is 25% and the target slip rate 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 rate in the next three control cycles is calculated to be 28%, and the braking force adjustment amount is determined to increase by 1000N. At the same time, through step 403, according to the steering wheel angle of 30°, the braking force correction weight of each wheel is calculated to be 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 value of 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, the braking force change value is superimposed on the current braking force 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 brake actuator independently controls the four wheels according to the instruction set, successfully adjusting the slip rate to 15%, ensuring the stability of the vehicle.

[0115] In summary, high-precision braking force control and stability optimization of the vehicle in emergency braking are achieved through steps 401 to 405. The method significantly improves the safety and stability of vehicle braking by dynamically shrinking the allowable control range, calculating the predicted slip rate, optimizing the braking force distribution, and generating independent control instructions, providing reliable technical support for the driver and the vehicle stability control system.

[0116] In some embodiments, step 402 determines the braking force adjustment amount based on the predicted slip rate, including: when the predicted slip rate is lower than the lower limit of the target interval, calculating the braking force reduction of each wheel according to a first functional relationship; or, when the predicted slip rate is higher than the upper limit of the target interval, calculating the braking force increase of each wheel according to a second functional relationship; or, when the predicted slip rate 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.

[0117] Predicted slip rate: The future slip rate value calculated based on the vehicle dynamics equation is used to predict the vehicle's motion state.

[0118] Target range: The upper and lower limits of the slip rate, used to limit the adjustment range of the braking force.

[0119] First functional relationship: a mathematical model used to calculate the reduction in braking force, usually a linear or nonlinear function.

[0120] Second functional relationship: a mathematical model used to calculate the increase in braking force, usually a linear or nonlinear function.

[0121] Braking force adjustment: The braking force change required to adjust the slip ratio to the target range.

[0122] In the embodiment of the present application, first, based on the relationship between the predicted slip rate and the target interval, it is determined whether the slip rate is lower than the lower limit, higher than the upper limit, or within the interval. When the predicted slip rate is lower than the lower limit of the target interval, the reduction in braking force of each wheel is calculated according to the first functional relationship. For example, if the predicted slip rate is 8% and the lower limit of the target interval is 10%, the reduction in braking force is a reduction of 500N. When the predicted slip rate is higher than the upper limit of the target interval, the increase in braking force of each wheel is calculated according to the second functional relationship. For example, if the predicted slip rate is 22% and the upper limit of the target interval is 20%, the increase in braking force is an increase of 800N. When the predicted slip rate is within the target interval, the braking force of each vehicle is optimized to maintain the current change trend, such as keeping the braking force unchanged or fine-tuning it. Finally, the calculated braking force adjustment amount is stored in the system to provide a reference for subsequent braking force control.

[0123] Here is a specific example: During an emergency braking process, the vehicle was equipped with wheel speed sensors and a braking force control system. When the vehicle passed a slippery road, the system detected that the predicted slip rate was 22% and the target range was 10% to 20%. Through step 501, it was determined that the predicted slip rate was higher than the upper limit of the target range, and the increase in braking force for each wheel was calculated to be 800N according to the second functional relationship. The brake actuator increased the braking force of the four wheels based on the calculation results, successfully adjusting the slip rate to 18%, ensuring the stability of the vehicle.

[0124] In summary, the above steps achieve high-precision braking force adjustment and stability optimization of the vehicle in emergency braking. This method determines the relationship between the predicted slip rate and the target range, dynamically calculates the braking force adjustment, significantly improves the safety and stability of vehicle braking, and provides reliable technical support for drivers and vehicle stability control systems.

[0125] In some embodiments, the step 104 of calculating the expected wheel slip state by simulating using a 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: 601. Based on the independent control amount of each wheel in the braking force control instruction, the deformation characteristics of the contact area between the tire and the road are calculated through the 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; Digital tire model: A mathematical model used to simulate the contact behavior of the tire with the road, including characteristics such as deformation, slip and friction.

[0126] Contact area deformation characteristics: The degree and distribution of deformation in the contact area between the tire and the road reflect the tire's grip and stability.

[0127] Expected wheel slip state: The predicted future slip state based on the independent control variables and the current wheel speed.

[0128] In the embodiment of the present application, first, the deformation characteristics of the contact area between the tire and the road are calculated through the digital tire model according to the independent control amount of each wheel in the braking force control command. For example, the independent control amount of the left front wheel is 6200N, and that of the right front wheel is 5800N. Then, based on 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. For example, the expected slip rate of the left front wheel is 15%, and that of the right front wheel is 14%. Finally, the calculation results are stored in the system to provide a reference for subsequent braking force optimization.

[0129] 602. Extract the friction force variation characteristics that match the current road conditions from the data set, match and analyze the calculated expected wheel slip state with the friction force variation characteristics, and determine the pressure control value of each brake based on the matching result.

[0130] Optimal slip ratio data set: A data set containing the optimal slip ratio range under different road conditions, used to optimize braking force control.

[0131] Friction force variation characteristics: characteristic parameters that reflect the variation law of friction force between tire and road surface under different slip rates.

[0132] Pressure control value: The hydraulic pressure value required to adjust the braking force, used to accurately control the braking effect.

[0133] In the embodiment of the present application, the friction force variation characteristics that match the current road conditions are first extracted from the optimal slip rate data set. For example, on a slippery road, the optimal slip rate range is 10% to 20%. Next, the calculated expected wheel slip state is matched with the friction force variation 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, the pressure control value is transmitted to the brake actuator to achieve precise adjustment of the braking force.

[0134] Here is a specific example: During an emergency braking process, the vehicle was equipped with a wheel speed sensor and a braking force control system. When the vehicle passed a slippery road, the system detected that the current actual slip rate was 25%, and the target slip rate range was 10%~20%. Through step 601, based on the independent control amount in the braking force control command (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 are calculated through the digital tire model, and the expected slip state of each wheel is 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 slippery road surface are extracted from the optimal slip rate data set, and the expected slip state is matched and analyzed with the friction force change characteristics to determine the pressure control value of each brake (8MPa for the left front wheel and 7.5MPa for the right front wheel). The brake actuator accurately adjusts the braking force of the four wheels according to the pressure control value, successfully adjusting the slip rate to 15%, and ensuring the stability of the vehicle.

[0135] In summary, through steps 601 to 602, high-precision braking force control and stability optimization of the vehicle in emergency braking are achieved. This method simulates the contact characteristics between the tire and the road through a digital tire model, combines the optimal slip rate data set and the friction force change characteristics, and dynamically adjusts the pressure control value of each brake, which significantly improves the safety and stability of vehicle braking and provides reliable technical support for drivers and vehicle stability control systems.

[0136] In some embodiments, the step 105 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: 701. Calculate the deviation between the actual measured wheel slip state and the simulation calculation result within a preset time window, and monitor the real-time pressure change rate of the brake hydraulic system at the same time, 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; Preset time window: The time range used to calculate and correct the deviation, usually several control cycles in the future.

[0137] Deviation: The difference between the actual measured wheel slip state and the simulation calculation result, reflecting the accuracy of the model prediction.

[0138] Pressure Change Rate: The real-time rate of change of pressure in the brake hydraulic system, used to evaluate the response speed of brake force regulation.

[0139] Tire stiffness and friction characteristics: Key parameters in the digital tire model that characterize the physical properties of the tire and affect the prediction accuracy of the slip state.

[0140] In an embodiment 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 rate is 18% and the simulation calculation result is 15%, the deviation is 3%. Next, the real-time pressure change rate of the brake hydraulic system is monitored, for example, the pressure change rate is 2MPa / s. Then, based on the correspondence between the deviation and the pressure change rate, the physical characteristic parameters characterizing the tire stiffness and friction characteristics in the digital tire model are dynamically corrected. For example, the tire stiffness is corrected from 1000N / m to 950N / 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 predictions.

[0141] 702. According to the corrected physical characteristic parameters of the digital tire model, the expected wheel slip state is recalculated, and 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.

[0142] Secondary matching: The recalculated slip state is compared and analyzed again with the friction force variation law in the optimal slip rate data set to optimize the braking force control strategy.

[0143] Pressure control value adjustment: The hydraulic pressure change required to optimize braking force control, used to dynamically adjust the braking effect.

[0144] Final control instructions: A set of instructions containing updated braking force control parameters for achieving precise braking force distribution.

[0145] In the embodiment of the present application, the expected wheel slip state is first recalculated based on the corrected physical characteristic parameters of the digital tire model. For example, the expected slip rate of the left front wheel is 16%, and that of the right front wheel is 15%. Next, the recalculated slip state is matched twice with the friction force variation law in the optimal slip rate data set to determine the pressure control value adjustment amount of each brake. For example, the pressure control value adjustment amount of the left front wheel is an increase of 0.5MPa, and that of the right front wheel is an increase of 0.3MPa. Finally, the final control instruction is updated and transmitted to the brake actuator to achieve precise adjustment of the braking force.

[0146] Here is a specific example: During an emergency braking process, the vehicle was equipped with a wheel speed sensor and a brake hydraulic system monitoring device. When the vehicle passed a slippery road, the system detected that the actual measured wheel slip state was 18%, the simulated calculation result was 15%, and the deviation was 3%. Through step 701, based on the deviation and the pressure change rate of the brake hydraulic system (2MPa / s), the tire stiffness (from 1000N / m to 950N / m) and friction characteristics (from 0.8 to 0.75) in the digital tire model were dynamically corrected. Then, through step 702, the expected wheel slip state (16% for the left front wheel and 15% for the right front wheel) was recalculated according to the corrected model, and the friction force change law in the optimal slip rate data set was matched twice to generate the pressure control value adjustment (the left front wheel increased by 0.5MPa and the right front wheel increased by 0.3MPa). The brake actuator accurately adjusted the braking force of the four wheels according to the updated final control instruction, successfully adjusted the slip rate to 15%, and ensured the stability of the vehicle.

[0147] In summary, through steps 701 to 702, high-precision braking force control and dynamic optimization of the vehicle in emergency braking are achieved. This method significantly improves the accuracy and stability of braking force control 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, providing reliable technical support for drivers and vehicle stability control systems.

[0148] In some embodiments, step 602 extracts friction force variation characteristics matching the current road condition from the data set, performs matching analysis on the calculated expected wheel slip state and the friction force variation characteristics, and determines the pressure control value of each brake based on the matching result, including: 801. According to the current road condition, extract the friction force change characteristics corresponding to the road condition from the data set, wherein the friction force change characteristics include the friction force values ​​and their change rules under different slip rates; Optimal slip rate data set: a data set containing the optimal slip rate range and its corresponding friction force change characteristics under different road conditions.

[0149] Friction force variation characteristics: characteristic parameters that reflect the variation law of friction force between tire and road surface under different slip rates.

[0150] In the embodiment of the present application, firstly, the corresponding friction force variation characteristics are extracted from the optimal slip rate data set according to the current road conditions (such as a slippery road surface). For example, on a slippery road surface, the friction force is 2000N when the slip rate is 10%, and the friction force is 2500N when the slip rate is 20%. Then, the extracted friction force variation characteristics are stored in the system to provide a reference for subsequent braking force optimization.

[0151] 802. Based on the expected wheel slip state, determine a slip ratio value corresponding to the expected wheel slip state, and according to the slip ratio value, find a corresponding friction force value from the friction force variation characteristics; Expected wheel slip state: The future slip state predicted based on the vehicle dynamics model and the current state.

[0152] Slip ratio value: The specific slip ratio value corresponding to the expected wheel slip state.

[0153] In the embodiment of the present application, firstly, the corresponding slip ratio value is determined based on the expected wheel slip state. For example, if the expected slip state is 15%, the slip ratio value is 15%. Then, the corresponding friction force value is searched from the friction force variation characteristics. For example, when the slip ratio is 15%, the friction force is 2200N. Finally, the search result is stored in the system for subsequent braking force optimization.

[0154] 803. Compare the tire force state under the expected wheel slip state with the friction force value, calculate the difference between the two, and adjust 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; Tire stress state: longitudinal force, lateral force and vertical force etc. that the tire is subjected to under the expected slip state.

[0155] Difference degree: The deviation between the tire stress state and the friction value reflects the optimization space for braking force distribution.

[0156] In the embodiment of the present application, the tire force state under the expected wheel slip state is first compared with the friction value to calculate the degree of difference. For example, if the tire force state is 2100N and the friction value is 2200N, the degree of difference is 100N. Then, the size of the independent control amount is adjusted according to the degree of difference. For example, increase the left front wheel braking force by 100N and the right front wheel braking force by 80N. Finally, ensure that the degree of difference between the tire force state and the friction value is minimized to optimize the braking force distribution.

[0157] 804. Based on the adjusted independent control quantity, calculate the braking force distribution required for each wheel and determine the pressure control value of each brake.

[0158] Braking force distribution: the braking force distribution of each wheel under the adjusted independent control amount.

[0159] Pressure control value: The hydraulic pressure value required to adjust the braking force, used to accurately control the braking effect.

[0160] In the embodiment of the present application, firstly, the braking force distribution required for each wheel is calculated based on the adjusted independent control amount. For example, the braking force of the left front wheel is 6200N and that of the right front wheel is 5800N. Then, the pressure control value of each brake is determined. For example, the pressure control value of the left front wheel is 8MPa and that of the right front wheel is 7.5MPa. Finally, the pressure control value is transmitted to the brake actuator to achieve precise adjustment of the braking force.

[0161] Here is a specific example: During an emergency braking process, the vehicle is equipped with a wheel speed sensor and a braking force control system. When the vehicle passes through a slippery road, the system detects that the expected wheel slip state is 15%. Through step 801, the friction force change characteristics under slippery road conditions are extracted from the optimal slip rate data set (the friction force is 2200N when the slip rate is 15%). Then, through step 802, the slip rate value is determined to be 15%, and the corresponding friction force value is 2200N. Through step 803, the tire force state (2100N) under the expected wheel slip state is compared with the friction force value (2200N), the difference is calculated to be 100N, and the independent control amount is adjusted (the left front wheel increases by 100N and the right front wheel increases by 80N). Finally, through step 804, based on the adjusted independent control amount, the braking force distribution of each wheel is calculated (6200N for the left front wheel and 5800N for the right front wheel), and the pressure control value of each brake is determined (8MPa for the left front wheel and 7.5MPa for the right front wheel). The brake actuator precisely adjusts the braking force of the four wheels according to the pressure control value, successfully adjusting the slip rate to 15%, ensuring the stability of the vehicle.

[0162] In summary, high-precision braking force control and dynamic optimization of the vehicle in emergency braking are achieved through steps 801 to 804. This method significantly improves the accuracy and stability of braking force control by extracting friction force variation characteristics, optimizing independent control quantities, and determining pressure control values, providing reliable technical support for drivers and vehicle stability control systems.

[0163] In some embodiments, after generating the braking force control instruction in step 103, the method further includes: 901. According to the interaction strength between the virtual tread reconstructed by the digital twin technology and the road particles, adjust the time domain window length of the model predictive control algorithm; Digital twin technology: Use virtual models to simulate the interaction between tires and roads in real time, reflecting the dynamic characteristics of the actual physical system.

[0164] Interaction strength between virtual tread and road particles: A parameter in the digital twin model that characterizes the contact strength between the tire and the road, which affects the prediction accuracy of the slip state.

[0165] Time domain window length: The time range used to predict and optimize braking force distribution in the model predictive control algorithm.

[0166] In the embodiment of the present application, the interaction intensity between the virtual tread and the road particles is first reconstructed by digital twin technology. For example, the interaction intensity is low on wet roads and high on dry roads. Then, the time domain window length of the model predictive control algorithm is adjusted according to the interaction intensity. For example, when the interaction intensity is low, the time domain window length is shortened from 0.5 seconds to 0.3 seconds to improve the real-time performance of the prediction. Finally, the adjusted time domain window length is applied to the model predictive control algorithm to optimize the braking force control strategy.

[0167] 902. Update the braking force control instruction according to the adjusted model predictive control algorithm.

[0168] Model predictive control algorithm: A control algorithm that optimizes braking force distribution based on a predictive model and achieves dynamic adjustment through rolling optimization.

[0169] Braking force control instructions: A set of instructions containing the braking force control parameters of each wheel, used to achieve accurate braking force distribution.

[0170] In the embodiment of the present application, the braking force distribution of each wheel is first recalculated according to the adjusted model predictive control algorithm. For example, the braking force of the left front wheel is 6200N and the right front wheel is 5800N. Then, the braking force control command is updated and transmitted to the brake actuator to achieve precise adjustment of the braking force. Finally, the braking force control effect is monitored in real time to ensure the stability of the vehicle in emergency braking situations.

[0171] Here is a specific example: During an emergency braking process, the vehicle was equipped with a digital twin system and a braking force control system. When the vehicle passed a slippery road, the digital twin system detected that the interaction intensity between the virtual tread and the road particles was low. Through step 901, the time domain window length of the model predictive control algorithm was shortened from 0.5 seconds to 0.3 seconds to improve the real-time performance of the prediction. Then, through step 902, the braking force control command (6200N for the left front wheel and 5800N for the right front wheel) was updated according to the adjusted model predictive control algorithm. The brake actuator accurately adjusted the braking force of the four wheels according to the updated command, successfully adjusted the slip rate to 15%, and ensured the stability of the vehicle.

[0172] In summary, through steps 901 to 902, high-precision braking force control and dynamic optimization of the vehicle in emergency braking are achieved. This method reconstructs the interaction strength between the virtual tread and road 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, which significantly improves the real-time and accuracy of the braking force control and provides reliable technical support for drivers and vehicle stability control systems.

[0173] Figure 2 A schematic diagram of the structure of a vehicle control system in an emergency braking scenario is provided for an embodiment of the present application. Figure 2 As shown, the system includes: An adjustment module 21 establishes a data set including optimal wheel slip rates under different road conditions, simulates the contact characteristics between the tire and the road through a digital model, and dynamically adjusts the target slip rate range; The acquisition module 22 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 determines the risk of vehicle instability and generates a warning signal; The calculation module 23 inputs the target slip ratio 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 module 24 is used to simulate and calculate the expected wheel slip state using a digital tire model according to the braking force control instruction, and determine the pressure control value of each brake by referring to the friction force variation law in the data set; The correction module 25 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.

[0174] Figure 2 The vehicle control system in an emergency braking scenario can execute Figure 1 The implementation principle and technical effect of the vehicle control method in an emergency braking scenario described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in a vehicle control system in an emergency braking scenario in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0175] In one possible design, Figure 2 The vehicle control device in an emergency braking scenario of the embodiment shown can be implemented as a computing device, such as Figure 3As shown, the computing device may include a storage component 31 and a processing component 32; 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 .

[0176] The processing component 32 is used for the above Figure 1 The embodiment provides a vehicle control method in an emergency braking scenario.

[0177] The processing component 32 may 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 may 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 to perform the above method.

[0178] The storage component 31 is configured to store various types of data to support operations at 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.

[0179] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0180] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0181] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0182] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0183] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A vehicle control method in an emergency braking scenario in the illustrated embodiment.

[0184] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0185] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0186] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can 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.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present 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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