Vehicle stability control method, system, computer device and storage medium

Abstract

An embodiment of the present invention provides a vehicle stability control method, system, computer device, and storage medium. The method includes acquiring driving operation input and the actual vehicle dynamic state in real time; calculating the vehicle target dynamic state based on the driving operation input and the actual vehicle dynamic state; identifying the risk of instability based on the difference between the actual vehicle dynamic state and the target vehicle dynamic state; and performing vehicle stability control based on the instability risk. Based on the accurate identification of extreme operating conditions, the method adopts reasonable and effective stability control, so that the vehicle stability control program assists the driver in controlling the vehicle to a stable state as soon as possible before conventional intervention. This method does not affect the driving comfort of the vehicle under normal operating conditions, and avoids the risk of complete vehicle instability and accident escalation caused by the vehicle stability control program's inability to perceive abnormalities and intervene in time under extreme operating conditions, thereby greatly improving the stability and safety of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safe driving, and in particular to a vehicle stability control method, system, computer equipment and storage medium based on extreme working conditions. Background Art

[0002] Vehicle stability refers to the vehicle's ability to maintain or quickly recover its original driving state when subjected to external factors while driving, or to avoid skidding or tipping over. It is a key performance characteristic of a vehicle. Vehicle stability directly impacts driving speed, safety, and maneuverability. With the continuous improvement of people's living standards, vehicles have become an indispensable means of transportation in daily life. Vehicle safety, in particular, is receiving increasing attention. Most existing vehicles utilize vehicle stability control programs to enhance stability and directional control during coasting, acceleration, or braking, as well as over- or under-turning. The program operates by using sensors and computational logic to identify the vehicle's desired motion state during driving. Whether the difference between the vehicle's dynamic state and the desired operating state exceeds a predetermined threshold is used as a basis for determining whether control intervention is necessary. Based on the degree of instability, feedback control is used to control and adjust the yaw torque acting on the wheels to assist the driver in quickly stabilizing the vehicle.

[0003] Existing technology has strict threshold conditions for the intervention of vehicle stability control programs. The vehicle stability control program will only intervene when the vehicle dynamics (yaw rate and sideslip angle) reach the program's set thresholds. During normal driving, the driver uses the steering wheel, brake, accelerator, and other inputs to ensure vehicle stability. Therefore, the threshold value must be set appropriately. If the threshold is set too low, the vehicle stability control program will intervene sensitively, causing comfort issues and possible driver complaints. If the threshold is set too high, the vehicle may become unstable and irreversible, defeating the purpose of controlling vehicle stability. However, the existing technology does not take into account that during high-speed driving, under certain extreme conditions, such as sudden side collisions, on-road conditions, etc., the vehicle may have unexpected yaw or lateral force input, causing the vehicle to become unstable and have a tendency to skid, and the vehicle stability control program may not meet the intervention conditions and may not assist in braking. However, since the accident is relatively sudden, the driver has no psychological expectations and no active steering wheel input, or panic may cause incorrect or excessive steering wheel input or fail to brake in time to slow down, which may cause the vehicle to become completely unstable and cause a secondary safety accident or personal injury. By the time the vehicle stability control program intervenes, it may be too late, resulting in an uncontrollable vehicle situation.

[0004] Therefore, it is very necessary to solve the problems of accurate identification of extreme working conditions and corresponding vehicle stability control based on existing technologies to further improve vehicle safety. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of accurate identification and corresponding vehicle stability control of extreme working conditions such as unexpected yaw or lateral force input during high-speed driving, which causes the vehicle to become unstable and have a tendency to drift, and the conditions for the vehicle stability control program to intervene are not met, so as to further improve the active safety of the vehicle.

[0006] In order to achieve the above objectives, it is necessary to provide a vehicle stability control method, system, computer device and storage medium in response to the above technical problems.

[0007] In a first aspect, an embodiment of the present invention provides a vehicle stability control method, the method comprising the following steps:

[0008] Obtain driving operation input and actual vehicle dynamics status in real time;

[0009] calculating a target vehicle dynamic state based on the driving operation input and the actual vehicle dynamic state;

[0010] identifying an instability risk based on a difference between the actual vehicle dynamics state and the target vehicle dynamics state;

[0011] Vehicle stability control is performed according to the instability risk.

[0012] Furthermore, the step of acquiring driving operation input and actual vehicle dynamic state in real time includes:

[0013] Identify the driving operation input through a sensor hard line or a CAN signal; the driving operation input includes an acceleration input, a braking input, a steering input, an emergency braking input, and a regenerative braking input;

[0014] The actual vehicle dynamic state is obtained through a vehicle stability control program; the actual vehicle dynamic state includes actual vehicle speed, actual yaw angular velocity, actual center of mass sideslip angle and actual center of mass lateral acceleration.

[0015] Furthermore, the step of identifying the driving operation input through a sensor hard line or a CAN signal includes:

[0016] Identify acceleration input based on throttle signal;

[0017] Identify brake input based on brake switch or master cylinder pressure signal;

[0018] Identify steering input based on steering wheel angle signals;

[0019] identifying emergency brake input based on vehicle stability control program switch status information;

[0020] Regenerative braking input is identified based on the motor regenerative torque signal.

[0021] Furthermore, the step of obtaining the actual dynamic state of the vehicle through a vehicle stability control program includes:

[0022] acquiring the actual yaw rate and the actual center-of-mass lateral acceleration via a yaw rate sensor of a vehicle stability control program;

[0023] The actual center of mass sideslip angle is estimated by a state observation algorithm.

[0024] Furthermore, the step of calculating the target vehicle dynamic state according to the driving operation input and the actual vehicle dynamic state includes:

[0025] Calculating a target yaw rate based on the driving operation input and vehicle data;

[0026] A target center-of-mass sideslip angle is calculated according to the target yaw rate, the actual center-of-mass lateral acceleration, and the actual vehicle speed.

[0027] Furthermore, the step of identifying the instability risk based on the difference between the actual vehicle dynamic state and the target vehicle dynamic state includes:

[0028] Presetting a set of yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds corresponding to different vehicle speeds;

[0029] Obtaining the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold according to the actual vehicle speed;

[0030] determining whether a yaw rate deviation, a center of mass sideslip angle deviation, and a weighted average of the yaw rate deviation and the center of mass sideslip angle deviation exceed corresponding yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds, respectively, and determining that an extreme operating condition exists when any one of the above exceeds the corresponding threshold;

[0031] Determine whether there is an instability risk based on the extreme operating condition and the driving operation input.

[0032] Furthermore, the step of determining whether the yaw rate deviation, the center of mass sideslip angle deviation, and the weighted average of the yaw rate deviation and the center of mass sideslip angle deviation respectively exceed the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold, and determining that an extreme operating condition exists when any one of them exceeds the corresponding threshold includes:

[0033] Use vehicle-mounted radar and cameras to obtain environmental data;

[0034] An auxiliary judgment is made on the extreme working condition based on the environmental data.

[0035] Furthermore, the step of determining whether there is an instability risk based on the extreme operating condition and the driving operation input includes:

[0036] When there are extreme operating conditions, whether the vehicle is controllable is determined based on the driving operation input, and if the vehicle is uncontrollable, it is determined that there is a risk of instability.

[0037] Furthermore, the step of performing vehicle stability control according to the instability risk includes:

[0038] establishing a vehicle dynamics model with specific degrees of freedom using the vehicle stability control program;

[0039] Calculating the vehicle model dynamic state in real time according to the vehicle dynamics model;

[0040] Stability control is performed according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state.

[0041] Furthermore, the step of performing stability control according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state includes:

[0042] performing feedforward control according to a difference between the vehicle model dynamic state and the vehicle target dynamic state;

[0043] Feedback control is performed according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle.

[0044] In a second aspect, an embodiment of the present invention provides a vehicle stability control system, the system comprising:

[0045] Data monitoring module, used to obtain driving operation input and actual vehicle dynamic status in real time;

[0046] a state acquisition module, configured to calculate a target vehicle dynamic state based on the driving operation input and the actual vehicle dynamic state;

[0047] a risk identification module, configured to identify an instability risk based on a difference between the actual vehicle dynamic state and the target vehicle dynamic state;

[0048] A stability control module is used to perform vehicle stability control according to the instability risk.

[0049] Furthermore, the data monitoring module includes:

[0050] a first data module for identifying the driving operation input through a sensor hard line or a CAN signal; the driving operation input includes an acceleration input, a braking input, a steering input, an emergency braking input, and a regenerative braking input;

[0051] The second data module is used to obtain the actual dynamic state of the vehicle through a vehicle stability control program; the actual dynamic state of the vehicle includes actual vehicle speed, actual yaw rate, actual center of mass sideslip angle and actual center of mass lateral acceleration.

[0052] Furthermore, the second data module includes:

[0053] a yaw rate module, configured to obtain the actual yaw rate and the actual center-of-mass lateral acceleration via a yaw rate sensor of a vehicle stability control program;

[0054] The center of mass sideslip angle module is used to estimate the actual center of mass sideslip angle through a state observation algorithm.

[0055] Furthermore, the status acquisition module includes:

[0056] a first calculation module, configured to calculate a target yaw rate based on the driving operation input and vehicle data;

[0057] The second calculation module is used to calculate a target center of mass sideslip angle according to the target yaw rate, the actual center of mass lateral acceleration and the actual vehicle speed.

[0058] Furthermore, the risk identification module includes:

[0059] A threshold setting module, used to pre-set a set of yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds and deviation weighted thresholds corresponding to different vehicle speeds;

[0060] a query threshold module, configured to obtain, according to the actual vehicle speed, the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold;

[0061] an operating condition identification module, configured to determine whether a yaw rate deviation, a center of mass sideslip angle deviation, and a weighted average of the yaw rate deviation and the center of mass sideslip angle deviation exceed corresponding yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds, respectively, and to determine that an extreme operating condition exists when any one of the above exceeds the corresponding threshold;

[0062] The risk determination module is used to determine whether there is an instability risk based on the extreme working condition and the driving operation input.

[0063] Furthermore, the operating condition identification module includes:

[0064] Environmental data module, used to obtain environmental data using vehicle-mounted radar and cameras;

[0065] An auxiliary judgment module is used to perform auxiliary judgment on the extreme working condition based on the environmental data.

[0066] Furthermore, the stability control module includes:

[0067] a model creation module for establishing a vehicle dynamics model with specific degrees of freedom using the vehicle stability control program;

[0068] A state calculation module, configured to calculate the vehicle model dynamic state in real time according to the vehicle dynamic model;

[0069] A state control module is used to perform stability control according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state.

[0070] Furthermore, the state control module includes:

[0071] a feedforward control module, configured to perform feedforward control according to a difference between the vehicle model dynamic state and the vehicle target dynamic state;

[0072] A feedback control module is used to perform feedback control according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle.

[0073] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0074] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0075] The above-mentioned application provides a vehicle stability control method, system, computer device and storage medium. Through the method, the target dynamic state of the vehicle is calculated by real-time acquisition of driving operation input and the actual dynamic state of the vehicle, and whether the vehicle has an instability risk is determined according to whether the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle exceeds the threshold value at the corresponding vehicle speed. The instability risk is further confirmed by the environmental data collected by radar and camera, and when the instability risk exists, feedforward control is performed according to the difference between the vehicle model dynamic state and the target dynamic state of the vehicle, and feedback control is performed according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle to effectively control the instability risk of the vehicle. Compared with the existing technology, this method, on the basis of continuing the existing vehicle safety performance, further solves the problem of accurate identification of extreme working conditions and corresponding vehicle stability control, such as the situation that the vehicle has unexpected yaw or lateral force input during high-speed driving, causing the vehicle to be unstable and drifting, and the conditions for the vehicle stability control program to intervene are not met. The vehicle stability control program intervenes earlier than conventional control to assist the driver in controlling the vehicle to a stable state as soon as possible. Based on the accurate identification of extreme working conditions, reasonable and effective vehicle stability control is carried out, which neither affects the driving comfort of the vehicle nor avoids unnecessary accidents and casualties caused by the vehicle stability control program's inability to perceive and intervene in time under extreme working conditions, thereby greatly improving the stability and safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a schematic diagram of a reference model of a vehicle stability control program in an embodiment of the present invention;

[0077] Figure 2 1 is a flow chart of a vehicle stability control method under extreme working conditions according to an embodiment of the present invention;

[0078] Figure 3 yes Figure 1 A schematic diagram of a process for obtaining driving operation input;

[0079] Figure 4 yes Figure 1 Schematic diagram of a flow chart for identifying the instability risk according to the difference between the actual vehicle dynamic state and the target vehicle dynamic state in step S13;

[0080] Figure 5 yes Figure 4 A flow chart of assisting in determining the extreme operating condition in step S133;

[0081] Figure 6 yes Figure 1 Schematic diagram of the process of performing vehicle stability control according to the instability risk in step S14;

[0082] Figure 7 yes Figure 6 Flow chart of the stabilization control in step S143;

[0083] Figure 8 is a schematic structural diagram of a vehicle stability control system according to an embodiment of the present invention;

[0084] Figure 9 yes Figure 8 The structural diagram of the data monitoring module 1;

[0085] Figure 10 yes Figure 9 A schematic structural diagram of the second data module 12;

[0086] Figure 11 yes Figure 8 Schematic diagram of the structure of the state acquisition module 2;

[0087] Figure 12 yes Figure 8 Schematic diagram of the structure of the medium risk identification module 3;

[0088] Figure 13 yes Figure 12 A schematic diagram of the structure of the middle working condition identification module 33;

[0089] Figure 14 yes Figure 8 A schematic structural diagram of the stability control module 4;

[0090] Figure 15 yes Figure 14 A schematic structural diagram of the state control module 43;

[0091] Figure 16 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solutions and beneficial effects of this application more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0093] The vehicle stability control method provided by the present invention is based on the following Figure 1The improvements to the ESP (Vehicle Stability Control) program shown in the figure, while retaining the existing VSC processing logic, add intervention conditions for the VSC under extreme driving conditions, further enhancing vehicle safety and stability. The existing VSC model identifies vehicle dynamics through feedback from the vehicle body, suspension, and wheels. It then determines the driver's intended driving input and directly applies this information to the braking, powertrain, and steering systems. These systems then control the interaction between the wheels and the road surface to ensure stable driving. The basic principle of VSC vehicle stability control is to use sensors and computational logic to identify the driver's intended driving state (i.e., the target vehicle motion state), measure and estimate the actual vehicle motion state, and when the error between the two exceeds a given threshold, the VSC, according to a specific control logic, controls and adjusts the longitudinal force on the wheels accordingly, changing the yaw moment acting on the vehicle and bringing the actual vehicle motion state closer to the driver's desired state.

[0094] In one embodiment, Figure 2 As shown, a vehicle stability control method is provided, comprising the following steps:

[0095] S11, obtaining driving operation input and actual vehicle dynamics status in real time;

[0096] Among them, the driving operation input includes acceleration input, braking input, steering input, emergency braking input and regenerative braking input, and the actual vehicle dynamic state includes actual vehicle speed, actual yaw rate, actual center of mass side slip angle and actual center of mass lateral acceleration. The method of obtaining driving operation input and actual vehicle dynamic state also varies according to actual needs, such as Figure 3 As shown, the step S11 of acquiring the driving operation input and the actual vehicle dynamic state in real time includes:

[0097] S111. Identifying the driving operation input through a sensor hard line or a CAN signal;

[0098] The specific method for acquiring each driving input varies depending on the vehicle. For example, acceleration input can be identified based on the throttle signal, braking input can be identified based on the brake switch or master cylinder pressure signal, steering input can be identified based on the steering wheel angle signal, emergency braking input can be identified based on the vehicle stability control program switch status information, and regenerative braking input can be identified based on the motor regenerative torque signal. The presence or absence of driving input, as well as the content of the driving input, directly affects the functioning of the braking, powertrain, and steering systems, and thus the vehicle's dynamic state and corresponding stability.

[0099] S112: Acquire the actual dynamic state of the vehicle through a vehicle stability control program.

[0100] The actual vehicle speed, actual yaw rate, actual center of mass slip angle, and actual center of mass lateral acceleration in the vehicle's actual dynamic state can be generated by the vehicle stability control program using real-time feedback from the vehicle body, suspension, wheels, and other components. For example, after obtaining the actual yaw rate using the yaw rate sensor built into the vehicle stability control program, the actual center of mass slip angle can be estimated using conventional state observation algorithms. Vehicle speed includes longitudinal and lateral speeds, and the vehicle's actual driving state is primarily determined by these three factors. In the actual vehicle stability control program, the yaw angle is obtained by integrating the yaw rate. When the center of mass slip angle is small, the vehicle's stability is primarily determined by the yaw angle. However, when the vehicle experiences a tailspin or sideslip, the center of mass slip angle is generally large. In these cases, yaw cannot accurately represent the vehicle's stability, while the center of mass slip angle is a more accurate indicator of vehicle stability. Therefore, real-time driving input and the vehicle's actual dynamic state provide effective judgment criteria for subsequent vehicle instability risk identification.

[0101] S12, calculating a target vehicle dynamic state based on the driving operation input and the actual vehicle dynamic state;

[0102] The target vehicle dynamic state is the expected dynamic state. There are many existing estimation methods. In this embodiment, the target vehicle dynamic state is obtained based on the real-time driving operation input and the actual vehicle dynamic state, combined with vehicle theoretical calculations. The specific acquisition steps include:

[0103] S121, calculating a target yaw rate based on the driving operation input and vehicle data;

[0104] The vehicle data includes wheelbase, front axle cornering stiffness, rear axle cornering stiffness, distance from the center of mass to the front axle, distance from the center of mass to the rear axle, and vehicle mass. These can all be obtained using acquisition methods integrated into the vehicle control and stability program, such as sensor measurements. The theoretical calculation formula for the target yaw rate in this embodiment is as follows:

[0105]

[0106] Where ω is the yaw angular velocity, v x is the longitudinal speed, L is the wheelbase, and δ is the steering wheel angle, which is the available operational driving input. is the vehicle's stability factor, where m is the vehicle mass, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, k1 is the front axle cornering stiffness, and k2 is the rear axle cornering stiffness. The above vehicle speed and vehicle data can be obtained using existing methods and will not be further elaborated here.

[0107] S122: Calculate a target center-of-mass sideslip angle according to the target yaw rate, the actual center-of-mass lateral acceleration, and the actual vehicle speed.

[0108] The center of mass slip angle refers to the angle between the velocity direction of the vehicle's center of mass and the direction of the vehicle's front. It is one of the important variables describing the vehicle's lateral motion state, and it is also difficult to accurately estimate. Existing observation and estimation methods include the integral method based on kinematic theory, the extended Kalman filter estimation algorithm, the generalized Lomborg observer estimation algorithm, the robust observer estimation algorithm, the sliding mode observer estimation algorithm, the nonlinear observer estimation algorithm derived from Lyapunov theory, and the nonlinear tire model. In principle, all of the above estimation methods can be used. Since the integral method can more quickly describe the changing trend of the center of mass slip angle under extreme working conditions, this example introduces the integral method based on kinematic theory. The calculation of the target center of mass slip angle considers the following two situations:

[0109] (1) When the vehicle's lateral acceleration and sideslip angle are not large and the vehicle is in the linear range, the center of mass sideslip angle is calculated using the integral method as follows:

[0110]

[0111] Among them, β0 is the initial center of mass sideslip angle, a y is the lateral acceleration of the center of mass, v x is the longitudinal velocity, and t is the vehicle travel time. This method is simple and computationally inefficient. It is highly accurate when the signal quality is high, does not require vehicle body parameters, and has excellent robustness. Considering that the accumulation of sensor signal errors will increase the error in the sideslip angle obtained by integration, this method is only used for linear region processing. Nonlinear calibration is achieved using the following method (2).

[0112] (2) When the vehicle is in the nonlinear range, the center of mass sideslip angle is calculated using the general formula:

[0113]

[0114] Among them, v y is the lateral velocity, v x is the longitudinal speed.

[0115] In this embodiment, based on the characteristics of the vehicle's dynamic state under extreme operating conditions and combined with automobile theory, the method for calculating the vehicle's target dynamic state based on real-time driving operation input and the vehicle's actual dynamic state ensures the estimation accuracy of the yaw rate and center of mass sideslip angle under extreme operating conditions, thereby providing a reliable guarantee for subsequent comparison with the actual vehicle dynamic state to accurately identify whether the vehicle is at risk of instability.

[0116] S13, identifying an instability risk based on a difference between the actual vehicle dynamic state and the target vehicle dynamic state;

[0117] Among them, the idea of ​​judging whether the vehicle is running smoothly based on the deviation of the vehicle dynamics state is consistent with the design concept of the existing vehicle stability control program, such as Figure 4 As shown, the step S13 of identifying the instability risk based on the difference between the actual vehicle dynamic state and the target vehicle dynamic state includes:

[0118] S131, presetting a set of yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds corresponding to different vehicle speeds;

[0119] Among them, the yaw rate deviation threshold, center of mass sideslip angle deviation threshold and deviation weighting threshold at different vehicle speeds are all lower than the deviation threshold set by the normal vehicle stability control program, thereby ensuring early intervention when the conditions for intervention of the conventional vehicle stability control program have not been met to compensate for the vehicle stability control under unexpected effects, and assist the driver in stabilizing the unstable vehicle as soon as possible.

[0120] In this example, the yaw rate deviation threshold, center of mass sideslip angle deviation threshold, and deviation weighting threshold are all set through a calibration method. This method is based on testing the vehicle control effectiveness of an average driver at different speeds under extreme driving conditions, such as when driving on a two-way road. This method effectively ensures the rationality of the threshold settings for different speeds under extreme driving conditions, avoiding the possibility of reduced driving comfort due to improper threshold settings.

[0121] S132: acquiring the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold according to the actual vehicle speed;

[0122] Among them, the actual vehicle speed can be obtained using the method of estimating vehicle speed in the existing vehicle stability control program. Since the yaw rate deviation threshold, center of mass sideslip angle deviation threshold and deviation weighting threshold corresponding to different vehicle speeds are different, when comparing the target dynamic state and the actual dynamic state, it is necessary to first obtain the corresponding threshold based on the real-time vehicle speed, thereby ensuring the timeliness and necessity of the vehicle stability control program intervention.

[0123] S133, determining whether the yaw rate deviation, the center of mass sideslip angle deviation, and the weighted average of the yaw rate deviation and the center of mass sideslip angle deviation exceed the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold, respectively, and determining that an extreme operating condition exists when any one of them exceeds the corresponding threshold;

[0124] Yaw rate deviation and center of mass slip angle deviation are important factors affecting vehicle stability. When comparing the target dynamic state with the actual dynamic state, the primary focus is on whether these deviations exceed corresponding thresholds. The impact of the vehicle's yaw rate and center of mass slip angle on stability varies under different operating conditions. For example, when the center of mass slip angle is small or even negligible, vehicle stability is primarily determined by the yaw angle. However, when the vehicle is skidding or sideways, the center of mass slip angle is larger, and the yaw angle cannot accurately reflect vehicle stability. The center of mass slip angle is a more robust indicator of vehicle stability. In principle, an extreme operating condition is determined as long as either the yaw rate deviation or the center of mass slip angle deviation exceeds a preset threshold. However, considering that certain special operating conditions may be more subtle and sensitive to vehicle stability checks, this embodiment, when neither the yaw rate deviation nor the center of mass slip angle deviation exceeds the corresponding thresholds, adds a step to determine whether the weighted average of the yaw rate deviation and the center of mass slip angle deviation exceeds a weighted deviation threshold. Specifically, an extreme operating condition is determined when either the yaw rate deviation exceeds the yaw rate deviation threshold, the center of mass slip angle deviation exceeds the center of mass slip angle deviation threshold, or the weighted average of the yaw rate deviation and the center of mass slip angle deviation exceeds the weighted deviation threshold. Other conditions are not considered extreme operating conditions. It should be noted that when a vehicle brakes, the braking force applied to the wheels is related to the road adhesion coefficient. When the wheels are in a semi-slipping, semi-rolling state, the ground adhesion coefficient is maximized, meaning that braking force is high and lateral stability is also good. When the wheels are fully locked and rolling, ground adhesion decreases and lateral stability reaches zero, making sideslip and tailspin very likely to occur, potentially leading to accidents. Therefore, while the yaw rate deviation threshold and center of mass sideslip angle deviation threshold in this embodiment are derived using a calibration method, the impact of the vehicle's actual adhesion coefficient will be considered when detecting extreme operating conditions. The pre-calibrated yaw rate deviation threshold and center of mass sideslip angle deviation threshold will be adjusted to further improve the accuracy of extreme operating condition detection through comprehensive assessment.

[0125] Preferably, when determining the existence of an extreme operating condition based on the yaw rate deviation, the center of mass sideslip angle deviation, and the weighted average of the yaw rate deviation and the center of mass sideslip angle deviation, an auxiliary judgment method is used to further determine the existence of the extreme operating condition, such as Figure 5 As shown, the step S133 of determining whether the yaw rate deviation, the center of mass sideslip angle deviation, and the weighted average of the yaw rate deviation and the center of mass sideslip angle deviation respectively exceed the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold, and determining that an extreme operating condition exists when any one of them exceeds the corresponding threshold includes:

[0126] S1331, using vehicle-mounted radar and cameras to obtain environmental data;

[0127] S1332. Perform auxiliary judgment on the special working condition based on the environmental data.

[0128] Environmental data includes real-time position information of the vehicle under test on a lane perpendicular to the vehicle's lane, measured by the vehicle's onboard radar system at different times, which can be used to assist in determining conditions such as a side collision. Furthermore, actual road surface images taken using a camera can be used to determine conditions such as driving on a split-road surface or a slippery road on one side. This embodiment utilizes the vehicle's existing radar and camera to assist in identifying extreme conditions. This improves the accuracy of extreme condition identification without increasing vehicle costs, effectively avoiding unnecessary intervention in the vehicle stability control program while ensuring its stability.

[0129] S134. Determine whether there is an instability risk based on the extreme operating condition and the driving operation input.

[0130] Under extreme operating conditions, the vehicle will inevitably experience unexpected yaw or lateral force input, causing it to become unstable or drift. If the driver can remain calm and make appropriate driving inputs, the vehicle may be stabilized in a timely manner, and the vehicle stability control program may not be required to intervene. However, if there is no sudden accident and no psychological expectation, the driver does not actively input driving operations, or panic causes incorrect or excessive driving operations. At this time, without the timely intervention of the vehicle stability control system, it is very likely that the vehicle will become completely unstable, resulting in a secondary safety accident or personal injury. Therefore, based on a comprehensive consideration of the driver's possible response strategies under the above extreme operating conditions, before identifying the existence of extreme operating conditions and allowing the vehicle stability control program to intervene, it is necessary to determine whether the vehicle is controllable based on the driving inputs and determine the risk of instability if the vehicle is uncontrollable. In this embodiment, when an extreme operating condition is identified, the current driving operation input is promptly obtained. Based on the driver's actual steering, braking, throttle, and vehicle speed inputs, a real-time determination is made as to whether the difference between the actual vehicle dynamics state and the target dynamics state has been adjusted, and whether the adjustment is in a direction below a preset yaw rate deviation threshold and a center of mass sideslip angle deviation threshold, that is, whether the correction is in a direction conducive to vehicle stability, thereby ensuring that the vehicle dynamics state is within a stable and controllable range. If the result of the real-time determination still shows that one of the thresholds exceeds the predetermined threshold, it is determined that the vehicle is at risk of instability and requires the timely intervention of the vehicle stability control program to enter the extreme operating condition mode. Otherwise, execution is performed according to the conventional vehicle stability control logic.

[0131] This embodiment identifies the presence of extreme operating conditions by real-time monitoring of the difference between the vehicle's actual dynamic state and its target dynamic state to see if it exceeds a predetermined threshold. The on-board radar and camera are used to assist in determining the identification results of the extreme operating conditions. After further confirming the presence of the extreme operating conditions, the method combines the driver's real-time driving operation input to comprehensively determine whether the vehicle is at risk of instability under extreme operating conditions. This method ensures both accurate identification of extreme operating conditions and timely, reasonable, and effective intervention of the vehicle stability control program under extreme conditions.

[0132] S14: Perform vehicle stability control according to the instability risk.

[0133] Among them, vehicle stability control is implemented based on existing vehicle stability programs, such as Figure 6 As shown, the vehicle stability control according to the instability risk S14 includes:

[0134] S141. Establishing a vehicle dynamics model with a specific degree of freedom using the vehicle stability control program;

[0135] The vehicle dynamics model is generally used to analyze vehicle ride comfort and handling stability. The vehicle stability control program can establish a vehicle dynamics model with specific degrees of freedom as needed, providing real-time feedback on the vehicle dynamics state. In this embodiment, the number of specific degrees of freedom is preferably 7 or 15.

[0136] S142, calculating the vehicle model dynamic state in real time according to the vehicle dynamics model;

[0137] The method for calculating the vehicle dynamics state including the yaw rate and the sideslip angle of the center of mass according to the specific free vehicle dynamics model can be implemented by the existing vehicle stability control program and will not be described in detail here.

[0138] S143: Perform stability control according to the vehicle model dynamic state, the vehicle target dynamic state, and the vehicle actual dynamic state.

[0139] Among them, the stability control is achieved by combining feedforward control with feedback control, such as Figure 7 As shown, the step S143 of performing stability control according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state includes:

[0140] S1431, performing feedforward control according to the difference between the vehicle model dynamic state and the vehicle target dynamic state;

[0141] The target vehicle dynamic state is calculated based on the driving input and the actual vehicle dynamic state, as previously described, in conjunction with vehicle theory. The difference between the vehicle model dynamic state and the target vehicle dynamic state is determined primarily based on whether the deviation values ​​of the corresponding yaw rate and center of mass slip angle exceed preset yaw rate feedforward control thresholds and center of mass slip angle feedforward control thresholds, respectively, or whether the weighted average of the deviation values ​​of the corresponding yaw rate and center of mass slip angle exceeds a deviation-weighted feedforward control threshold. If either of these exceeds the corresponding preset threshold, feedforward control is determined to be necessary, and a feedforward control variable for early decision-making is generated and handed over to the controller for subsequent stabilization control. The yaw rate feedforward control threshold, center of mass slip angle feedforward control threshold, and deviation-weighted feedforward control threshold in this embodiment are also determined using the aforementioned calibration method to ensure effective and rational feedforward control.

[0142] S1432: Perform feedback control according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle.

[0143] The actual vehicle dynamic state is obtained using the aforementioned acquisition method based on existing vehicle stability control programs, such as sensor measurement or observational estimation. The difference between the target vehicle dynamic state and the actual vehicle dynamic state is determined primarily based on whether the corresponding deviations of the yaw rate and center of mass sideslip angle exceed preset yaw rate feedback control thresholds and center of mass sideslip angle feedback control thresholds, respectively, or whether the weighted average of the corresponding yaw rate and center of mass sideslip angle deviations exceeds a deviation-weighted feedback control threshold. If either exceeds the corresponding preset threshold, feedback control is determined to be necessary, and a corrective feedback control variable is generated and submitted to the controller for processing. Similarly, the yaw rate feedback control threshold, center of mass sideslip angle feedback control threshold, and deviation-weighted feedback control threshold are also determined using the aforementioned calibration method to ensure effective and reasonable feedback control. In this embodiment, the controller sums the generated feedforward control variable and feedback control variable to obtain a final control variable, which is then applied to vehicle stability control. This makes vehicle stability control more robust and precise, significantly improving the reliability of vehicle stability control.

[0144] This embodiment calculates the target vehicle dynamic state by real-time monitoring of driving operation input and the actual vehicle dynamic state. After identifying extreme operating conditions based on the difference between the actual vehicle dynamic state and the target vehicle dynamic state in combination with the on-board radar and camera, it determines whether there is an instability risk based on the real-time driving operation input under the extreme operating conditions. When there is an instability risk, the vehicle stability control system uses a method that combines feedforward control and feedback control to preemptively stabilize the unstable vehicle. Based on the accurate identification of extreme operating conditions, reasonable and effective vehicle stability control is performed, which neither affects vehicle driving comfort nor avoids unnecessary accidents and casualties caused by the vehicle stability control program's inability to perceive and intervene in time under extreme operating conditions, thereby greatly improving vehicle stability and safety.

[0145] It should be noted that, although the various steps in the above flow chart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0146] In one embodiment, Figure 8 As shown, a vehicle stability control system is provided, the system comprising:

[0147] Data monitoring module 1, used to obtain driving operation input and actual vehicle dynamics status in real time;

[0148] A state acquisition module 2 is configured to calculate a target vehicle dynamic state based on the driving operation input and the actual vehicle dynamic state;

[0149] a risk identification module 3, configured to identify an instability risk based on a difference between the actual vehicle dynamic state and the target vehicle dynamic state;

[0150] The stability control module 4 is configured to perform vehicle stability control according to the instability risk.

[0151] In one embodiment, Figure 9 As shown, the data monitoring module 1 includes:

[0152] A first data module 11 is configured to identify the driving operation input through a sensor hard line or a CAN signal; the driving operation input includes an acceleration input, a braking input, a steering input, an emergency braking input, and a regenerative braking input;

[0153] The second data module 12 is used to obtain the actual dynamic state of the vehicle through a vehicle stability control program; the actual dynamic state of the vehicle includes actual vehicle speed, actual yaw rate, actual center of mass sideslip angle and actual center of mass lateral acceleration.

[0154] In one embodiment, Figure 10 As shown, the second data module 12 includes:

[0155] The yaw rate module 121 is configured to obtain the actual yaw rate and the actual center-of-mass lateral acceleration via a yaw rate sensor of a vehicle stability control program;

[0156] The center of mass sideslip angle module 122 is configured to estimate the actual center of mass sideslip angle using a state observation algorithm.

[0157] In one embodiment, Figure 11 As shown, the state acquisition module 2 includes:

[0158] The first calculation module 21 is used to calculate the target yaw rate based on the driving operation input and vehicle data; the vehicle data includes wheelbase, front axle lateral stiffness, rear axle lateral stiffness, center of mass to front axle distance, center of mass to rear axle distance, and vehicle mass.

[0159] The second calculation module 22 is configured to calculate a target center-of-mass sideslip angle according to the target yaw rate, the actual center-of-mass lateral acceleration, and the actual vehicle speed.

[0160] In one embodiment, Figure 12 As shown, the risk identification module 3 includes:

[0161] A threshold setting module 31 is used to pre-set a set of yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds and deviation weighted thresholds corresponding to different vehicle speeds;

[0162] a threshold query module 32 for acquiring the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold according to the actual vehicle speed;

[0163] an operating condition identification module 33 for determining whether the yaw rate deviation, the center of mass sideslip angle deviation, and the weighted average of the yaw rate deviation and the center of mass sideslip angle deviation exceed the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold, respectively, and determining that an extreme operating condition exists when any one of the above exceeds the corresponding threshold;

[0164] The risk determination module 34 is configured to determine whether there is an instability risk based on the special operating conditions and the driving operation input.

[0165] In one embodiment, Figure 13 As shown, the operating condition identification module 33 includes:

[0166] Environmental data module 331, used to obtain environmental data using vehicle-mounted radar and camera;

[0167] The auxiliary judgment module 332 is used to perform auxiliary judgment on the special working condition based on the environmental data.

[0168] In one embodiment, Figure 14 As shown, the stability control module 4 includes:

[0169] A model creation module 41 is used to establish a vehicle dynamics model with a specific degree of freedom using the vehicle stability control program;

[0170] A state calculation module 42 is used to calculate the vehicle model dynamic state in real time according to the vehicle dynamic model;

[0171] The state control module 43 is configured to perform stability control according to the vehicle model dynamic state, the vehicle target dynamic state, and the vehicle actual dynamic state.

[0172] In one embodiment, Figure 15 As shown, the state control module 43 includes:

[0173] a feedforward control module 431 for performing feedforward control according to a difference between the vehicle model dynamic state and the vehicle target dynamic state;

[0174] The feedback control module 432 is configured to perform feedback control according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle.

[0175] The specific definition of the vehicle stability control system can be found in the definition of the vehicle stability control method above and will not be repeated here. Each module in the aforementioned vehicle stability control system may be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0176] Figure 16 FIG. 1 shows an internal structure diagram of a computer device in one embodiment, which may be a terminal or a server. Figure 16 As shown, the computer device includes a processor, memory, network interface, display and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a vehicle stability control method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0177] It can be understood by those skilled in the art that Figure 16 The structure shown in the figure is merely a block diagram of a portion of the structure related to the present application solution, and does not constitute a limitation on the computer device to which the present application solution is applied. The specific computing device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0178] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0179] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0180] In summary, embodiments of the present invention provide a vehicle stability control method, system, computer device, and storage medium. These methods calculate a target vehicle dynamic state by real-time monitoring of driving input and the actual vehicle dynamic state. Based on the difference between the actual and target vehicle dynamic states, combined with onboard radar and cameras, they identify extreme operating conditions. The method then determines whether there is a risk of instability based on the real-time driving input under these extreme conditions. When this risk exists, the method employs a combined feedforward and feedback control method to proactively stabilize the unstable vehicle through the vehicle stability control system. This method addresses the issues of accurately identifying and controlling extreme operating conditions, such as those during high-speed driving, where the vehicle is experiencing instability or drifting and has not met the conditions for the vehicle stability control program to intervene. This method enables the vehicle stability control program to intervene earlier than conventional control programs to assist the driver in quickly stabilizing the vehicle. Based on accurate identification of extreme operating conditions, reasonable and effective vehicle stability control is implemented, without compromising driving comfort. This method also avoids unnecessary accidents and casualties caused by the vehicle stability control program's inability to detect anomalies and intervene in a timely manner under extreme operating conditions, significantly improving vehicle stability and safety.

[0181] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0182] Each embodiment in this specification is described in a progressive manner, and the parts that are directly the same or similar in each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the system, computer equipment and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A vehicle stability control method, characterized in that: The method comprises the following steps: Acquiring driving operation input and actual vehicle dynamics in real time; the actual vehicle dynamics includes actual vehicle speed, actual yaw rate, actual center of mass sideslip angle, and actual center of mass lateral acceleration; the actual center of mass lateral acceleration is used to calculate a target center of mass sideslip angle; calculating a target vehicle dynamic state based on the driving operation input and the actual vehicle dynamic state; identifying an instability risk based on a difference between the actual vehicle dynamics state and the target vehicle dynamics state; performing vehicle stability control according to the instability risk; The step of identifying the instability risk according to the difference between the actual vehicle dynamic state and the target vehicle dynamic state includes: Presetting a set of yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds corresponding to different vehicle speeds; Obtaining the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold according to the actual vehicle speed; determining whether a yaw rate deviation, a center of mass sideslip angle deviation, and a weighted average of the yaw rate deviation and the center of mass sideslip angle deviation exceed corresponding yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds, respectively, and determining that an extreme operating condition exists when any one of them exceeds the corresponding threshold; determining that an extreme operating condition exists includes: acquiring environmental data using an on-board radar and a camera, and assisting in determining the extreme operating condition based on the environmental data; the extreme operating conditions include a vehicle side collision condition, a vehicle driving on a split road condition, and a vehicle driving on a slippery road on one side; Determine whether there is an instability risk based on the extreme operating condition and the driving operation input.

2. The vehicle stability control method according to claim 1, wherein: The step of acquiring the driving operation input and the actual vehicle dynamic state in real time includes: Identify the driving operation input through a sensor hard line or a CAN signal; the driving operation input includes an acceleration input, a braking input, a steering input, an emergency braking input, and a regenerative braking input; The actual dynamic state of the vehicle is obtained through a vehicle stability control program.

3. The vehicle stability control method according to claim 2, wherein: The step of identifying the driving operation input through a sensor hard line or a CAN signal includes: Identify acceleration input based on throttle signal; Identify brake input based on brake switch or master cylinder pressure signal; Identify steering input based on steering wheel angle signals; identifying emergency brake input based on vehicle stability control program switch status information; Regenerative braking input is identified based on the motor regenerative torque signal.

4. The vehicle stability control method according to claim 2, wherein: The step of obtaining the actual dynamic state of the vehicle through a vehicle stability control program includes: acquiring the actual yaw rate and the actual center-of-mass lateral acceleration via a yaw rate sensor of a vehicle stability control program; The actual center of mass sideslip angle is estimated by a state observation algorithm.

5. The vehicle stability control method according to claim 4, wherein: The step of calculating the target vehicle dynamic state according to the driving operation input and the actual vehicle dynamic state comprises: Calculating a target yaw rate based on the driving operation input and vehicle data; A target center-of-mass sideslip angle is calculated according to the target yaw rate, the actual center-of-mass lateral acceleration, and the actual vehicle speed.

6. The vehicle stability control method according to claim 1, wherein: The step of determining whether there is an instability risk based on the extreme working condition and the driving operation input includes: When there are extreme operating conditions, whether the vehicle is controllable is determined based on the driving operation input, and if the vehicle is uncontrollable, it is determined that there is a risk of instability.

7. The vehicle stability control method according to claim 2, wherein: The step of performing vehicle stability control according to the instability risk includes: establishing a vehicle dynamics model with specific degrees of freedom using the vehicle stability control program; Calculating the vehicle model dynamic state in real time according to the vehicle dynamics model; Stability control is performed according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state.

8. The vehicle stability control method according to claim 7, wherein: The step of performing stability control according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state comprises: performing feedforward control according to a difference between the vehicle model dynamic state and the vehicle target dynamic state; Feedback control is performed according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle.

9. A vehicle stability control system, characterized in that: The system comprises: a data monitoring module for acquiring driving operation input and actual vehicle dynamics in real time; the actual vehicle dynamics includes actual vehicle speed, actual yaw rate, actual center of mass sideslip angle, and actual center of mass lateral acceleration; the actual center of mass lateral acceleration is used to calculate a target center of mass sideslip angle; a state acquisition module, configured to calculate a target vehicle dynamic state based on the driving operation input and the actual vehicle dynamic state; a risk identification module, configured to identify an instability risk based on a difference between the actual vehicle dynamic state and the target vehicle dynamic state; a stability control module, configured to perform vehicle stability control according to the instability risk; The risk identification module includes: A threshold setting module, used to pre-set a set of yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds and deviation weighted thresholds corresponding to different vehicle speeds; a query threshold module, configured to obtain, according to the actual vehicle speed, the corresponding yaw rate deviation threshold, the center of mass sideslip angle deviation threshold, and the deviation weighted threshold; an operating condition identification module, configured to determine whether a yaw rate deviation, a center of mass sideslip angle deviation, and a weighted average of the yaw rate deviation and the center of mass sideslip angle deviation exceed corresponding yaw rate deviation thresholds, center of mass sideslip angle deviation thresholds, and deviation weighted thresholds, respectively, and to determine that an extreme operating condition exists when any one of the above exceeds the corresponding threshold; a risk determination module, configured to determine whether there is an instability risk based on the extreme operating condition and the driving operation input; The operating condition identification module includes: Environmental data module, used to obtain environmental data using vehicle-mounted radar and cameras; An auxiliary judgment module is used to assist in judging the extreme working conditions based on the environmental data; the extreme working conditions include the vehicle side collision condition, the vehicle driving on a split road condition and the vehicle driving on a slippery road on one side.

10. The vehicle stability control system according to claim 9, wherein: The data monitoring module includes: a first data module for identifying the driving operation input through a sensor hard line or a CAN signal; the driving operation input includes an acceleration input, a braking input, a steering input, an emergency braking input, and a regenerative braking input; The second data module is used to obtain the actual dynamic state of the vehicle through a vehicle stability control program.

11. The vehicle stability control system according to claim 10, wherein: The second data module includes: a yaw rate module, configured to obtain the actual yaw rate and the actual center-of-mass lateral acceleration via a yaw rate sensor of a vehicle stability control program; The center of mass sideslip angle module is used to estimate the actual center of mass sideslip angle through a state observation algorithm.

12. The vehicle stability control system according to claim 9, wherein: The status acquisition module includes: a first calculation module, configured to calculate a target yaw rate based on the driving operation input and vehicle data; The second calculation module is used to calculate a target center of mass sideslip angle according to the target yaw rate, the actual center of mass lateral acceleration and the actual vehicle speed.

13. The vehicle stability control system according to claim 9, wherein: The stability control module includes: A model creation module for building a vehicle dynamics model with specific degrees of freedom using a vehicle stability control program; A state calculation module, configured to calculate the vehicle model dynamic state in real time according to the vehicle dynamic model; A state control module is used to perform stability control according to the vehicle model dynamic state, the vehicle target dynamic state and the vehicle actual dynamic state.

14. The vehicle stability control system according to claim 13, wherein: The state control module includes: a feedforward control module, configured to perform feedforward control according to a difference between the vehicle model dynamic state and the vehicle target dynamic state; A feedback control module is used to perform feedback control according to the difference between the target dynamic state of the vehicle and the actual dynamic state of the vehicle.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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