Vehicle motion state control method and device, and vehicle

Abstract

The application discloses a vehicle motion state control method and device and a vehicle. The method comprises the following steps: acquiring a future waypoint trajectory sequence of intelligent driving planning and a current motion state of the vehicle; taking the future waypoint trajectory sequence and the current motion state of the vehicle as inputs, and performing prediction through a vehicle dynamics prediction model to obtain a vehicle state prediction sequence in a future period; calculating an instability risk index according to the vehicle state prediction sequence; and when the instability risk index exceeds a preset monitoring result, correcting an initial state of the vehicle dynamics prediction model in a next control cycle. The application improves the stability and safety of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and more specifically, to a vehicle motion state control method, device, and vehicle. Background Technology

[0002] With the rapid development of intelligent driving technology, the collaboration between vehicle chassis control systems and intelligent driving systems has increasingly become a focus of industry attention.

[0003] Currently, for vehicle motion state control, the model predictive control (MPC) algorithm can be used for lateral steering control, and the additional yaw moment can be calculated based on sliding mode control theory to achieve trajectory tracking and stability control through four-wheel torque distribution.

[0004] However, the above methods result in poor vehicle motion state control performance due to the low prediction accuracy of the model predictive control algorithm. Summary of the Invention

[0005] The main objective of this application is to provide a vehicle motion state control method, device, and vehicle that can improve the vehicle motion state control effect.

[0006] To achieve the above objectives, in a first aspect, this application provides a vehicle motion state control method, comprising: Obtain the future waypoint trajectory sequence of intelligent driving planning, as well as the current motion state of the vehicle; Using the future waypoint trajectory sequence and the current motion state of the vehicle as input, a vehicle dynamics prediction model is used to predict the vehicle state prediction sequence for the future time period. Calculate the instability risk index based on the vehicle state prediction sequence; When the instability risk index exceeds the preset monitoring result, the initial state of the vehicle dynamics prediction model in the next control cycle is corrected.

[0007] In one embodiment, using the future waypoint trajectory sequence and the vehicle's current motion state as input, a vehicle dynamics prediction model is used to predict the vehicle state prediction sequence for the future time period, including: The future waypoint trajectory sequence is processed by reference trajectory transformation, and the predicted front wheel steering angle is calculated based on the position, curvature and expected vehicle speed of the waypoints at each time step. Using the vehicle's current motion state as the initial value for iteration, and combining it with the predicted value of the front wheel steering angle, the vehicle dynamics prediction model is used to perform forward iteration step by step along the future road points, outputting a vehicle state prediction sequence covering the entire prediction time domain.

[0008] In one embodiment, the vehicle dynamics prediction model employs a three-degree-of-freedom vehicle dynamics model; The state-space equations of the vehicle dynamics prediction model are as follows:

[0009] in, Represents the vehicle state vector. Represents the control input vector. Indicates time, This represents the rate of change of the vehicle's motion state. This represents a nonlinear function of vehicle dynamics.

[0010] In one embodiment, an instability risk index is calculated based on a vehicle state prediction sequence, including: Extract yaw rate, sideslip angle, lateral acceleration and tire force data at each prediction time in the vehicle state prediction sequence; The following factors were calculated: yaw rate deviation factor corresponding to yaw rate, center of mass sideslip angle risk factor corresponding to center of mass sideslip angle, and tire force utilization risk factor corresponding to lateral acceleration and tire force data. The yaw rate deviation factor was calculated by the deviation between the predicted yaw rate value and the expected yaw rate at each prediction time. The center of mass sideslip angle risk factor was calculated based on the absolute value of the center of mass sideslip angle and the corresponding rate of change. The tire force utilization risk factor was calculated based on the ratio of the combined force of the longitudinal force and lateral force of each tire to the road adhesion limit. Preset weights were assigned to the yaw rate deviation factor, the center of gravity sideslip angle risk factor, and the tire force utilization rate risk factor, and then weighted and summed to obtain the instability risk index.

[0011] In one embodiment, an instability risk index is calculated based on a vehicle state prediction sequence, including: Construct a phase plane, where the phase plane is the phase plane corresponding to the sideslip angle and the sideslip angular velocity of the center of mass; Different boundary levels are configured within the phase plane, wherein the different boundary levels include at least a stability boundary, an early warning boundary, and an intervention boundary; Map the state points at each time step in the vehicle state prediction sequence to the phase plane to generate the predicted state trajectory; Based on the boundary level touched by the predicted state trajectory, the corresponding level of instability risk index is output.

[0012] In one embodiment, when the instability risk indicator exceeds a preset warning threshold, active stabilization intervention is performed, including: Match the corresponding intervention intensity and intervention combination strategy according to the level of instability risk indicators; Based on the intervention intensity and intervention combination strategy, corresponding control commands are generated and sent to the vehicle actuators to achieve vehicle stability enhancement through at least one of the following methods: Differential braking force is applied to the left and right wheels to generate additional yaw moment, correct the front wheel steering angle, adjust the rear wheel steering angle, distribute the four-wheel drive torque, and limit the overall vehicle drive torque to reduce the target speed.

[0013] In one embodiment, when the instability risk index exceeds a preset monitoring result, the initial state of the vehicle dynamics prediction model for the next control cycle is corrected, including: Collect actual state data after the intervention is performed. The actual state data includes at least the actual yaw rate, actual center of gravity sideslip angle, and actual lateral acceleration state data of the vehicle. The actual state data is compared with the predicted state data at the corresponding time to obtain the state deviation. The initial values ​​of the vehicle dynamics prediction model in the next control cycle are updated based on the state deviation, and the model parameters are corrected simultaneously.

[0014] In one embodiment, the method further includes: Based on the vehicle state prediction sequence and instability risk index, the stability constraint information of the vehicle's maximum safe speed and allowable yaw rate envelope under the current working condition is calculated. Adjust the velocity planning or path curvature of future waypoint trajectories based on stability constraint information.

[0015] Secondly, embodiments of this application provide a vehicle motion state control device, comprising: The data acquisition module is used to acquire the future waypoint trajectory sequence of intelligent driving planning, as well as the current motion state of the vehicle; The state prediction module is used to take the future waypoint trajectory sequence and the current motion state of the vehicle as input, and make predictions through the vehicle dynamics prediction model to obtain the vehicle state prediction sequence for the future time period. The indicator calculation module is used to calculate the instability risk indicator based on the vehicle state prediction sequence. The state correction module is used to correct the initial state of the vehicle dynamics prediction model in the next control cycle when the instability risk index exceeds the preset monitoring result.

[0016] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0018] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0019] Sixthly, embodiments of this application provide a vehicle, which includes the vehicle motion state control device described above.

[0020] This application provides a vehicle motion state control method, device, and vehicle, including: acquiring a future waypoint trajectory sequence planned by intelligent driving, and the current motion state of the vehicle; using the future waypoint trajectory sequence and the current motion state of the vehicle as input, making predictions through a vehicle dynamics prediction model to obtain a vehicle state prediction sequence for a future time period; calculating an instability risk index based on the vehicle state prediction sequence; and correcting the initial state of the vehicle dynamics prediction model for the next control cycle when the instability risk index exceeds a preset monitoring result. This application uses the future waypoint trajectory sequence of intelligent driving planning and the current motion state of the vehicle as joint inputs. It uses a vehicle dynamics prediction model to forward extrapolate the vehicle state in the future time period, obtaining a state prediction sequence including parameters such as yaw rate, center of gravity sideslip angle, and lateral acceleration. Based on this, an instability risk index is calculated, thereby extending the perception range of the control system to the next few seconds. This allows the vehicle to actively identify instability risks before actually entering an unstable state. When the instability risk index exceeds the preset monitoring result, the initial state of the prediction model in the next control cycle is corrected. This allows the prediction model to continuously receive real response feedback from the vehicle and continuously calibrate its own state, effectively avoiding the cumulative error and model mismatch problems caused by open-loop prediction, thereby improving vehicle stability and safety. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings: Figure 1 This is a schematic diagram of the structure of a vehicle motion state control system provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a vehicle motion state control method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a vehicle dynamics prediction model provided in an embodiment of this application; Figure 4 This is a flowchart illustrating an instability risk assessment method based on phase plane analysis provided in an embodiment of this application. Figure 5This is a schematic diagram of a predictive control timing relationship provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a vehicle motion state control device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the computer device provided in the embodiments of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0023] The terms first, second, third, fourth, etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein.

[0024] In the embodiments of this application, the term "module" or "unit" refers to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0025] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0026] It should be understood that in this application, inclusion and having, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product or device.

[0027] It should be understood that in this application, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, "and / or B" can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "Contains A, B, and C," "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0028] It should be understood that in this application, B corresponding to A, B corresponding to A, A and B being mutually related, or B and A being mutually related, indicates that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0029] Depending on the context, as used here, "if" can be interpreted as "when..." or "when...", or "in response to determination" or "in response to detection".

[0030] The data involved in this application may be data authorized by the tester or fully authorized by all parties. The collection, dissemination, and use of the data shall comply with the relevant laws, regulations and standards of the relevant countries and regions. The implementation methods / executives of this application may be combined with each other.

[0031] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0032] The present application will now be described in conjunction with the accompanying drawings and specific embodiments.

[0033] With the rapid development of intelligent driving technology, the collaboration between vehicle chassis control systems and intelligent driving systems has increasingly become a focus of industry attention. In a typical intelligent driving system architecture, the perception module is responsible for collecting environmental information, the planning module generates the desired driving trajectory based on this information, and the chassis domain controller is responsible for tracking this desired trajectory and executing corresponding steering, driving, and braking commands. Regarding chassis stability control, existing mass-produced vehicles are generally equipped with Electronic Stability Control (ESC / ESP) systems. This system monitors vehicle yaw rate, lateral acceleration, and steering wheel angle in real time using onboard sensors. Based on this current sensor data, it determines whether the vehicle exhibits signs of instability such as understeer or oversteer, and triggers braking or power intervention when instability is detected. Furthermore, at the academic research and patent application levels, various improved vehicle stability control schemes have emerged, such as using model predictive control (MPC) algorithms for trajectory tracking and stability co-control, using Bézier curves to piecewise fit the planned path to calculate road curvature in real time and combining it with predictive models for path tracking, and calculating stability margins based on real-time operating parameters and constructing an instability risk field for lateral and longitudinal co-control.

[0034] A search revealed that the closest existing technologies to this solution fall into three main categories. The first category, represented by Chinese patent application CN202512013105.1, acquires real-time vehicle operating parameters through a drive-by-wire chassis domain controller. Based on parameters such as current vehicle speed and yaw rate, it calculates the vehicle stability margin and constructs an instability risk field model to calculate the instability risk value. Then, it uses a dynamic efficiency ellipsoid and vehicle dynamics as constraints to solve for optimized control commands. This type of solution belongs to a real-time state risk assessment method, where the instability risk calculation relies entirely on current sensor feedback information. The controller only passively intervenes when the vehicle exhibits signs of instability. The second category, represented by Chinese patent application CN118387086A, employs a model predictive control (MPC) algorithm for lateral steering control and calculates additional yaw moment based on sliding mode control theory. It uses four-wheel torque distribution to achieve trajectory tracking and stability control. Its input is the desired trajectory tracking error, and its main control objectives are path tracking accuracy and yaw moment compensation. The third type of scheme is represented by Chinese patent application CN114435399A. It establishes a predictive model including a vehicle dynamics model and a tracking error model. It uses Bézier curves to piecewise fit the planned path to calculate road curvature in real time, and solves for the optimal control quantity using the error between the predicted and reference control output values ​​as the objective function. This type of scheme combines the predictive model with path planning, but its path planning is mainly used for local path smoothing and curvature calculation. Its control objective still focuses on path tracking accuracy, with stability only included as a constraint in the optimization problem. Although the aforementioned existing technologies all involve vehicle stability control, they differ fundamentally from this scheme in terms of information sources, prediction objectives, and inter-domain cooperation methods.

[0035] The aforementioned existing technologies share three common drawbacks. First, the information sources of existing chassis stability enhancement systems are limited to current sensor data (such as yaw rate and lateral acceleration), failing to capture the future driving intentions of the driver or intelligent driving system. This results in the controller only passively intervening when the vehicle has already skidded or is about to skid in complex conditions such as high-speed cornering and continuous curves, lacking the information basis for early prediction. Second, even if the aforementioned technologies include predictive functions, their predictions are merely linear trend extrapolation based on the rate of change of the current state, or rolling optimization driven by tracking errors. Neither can reflect the impact of future path curvature changes, target speed changes, and other planning intentions on the vehicle's dynamic response. This leads to severely inaccurate predictions of the vehicle's state in extreme conditions such as curves with varying curvature and emergency lane changes, making it impossible to achieve true identification of instability precursors. Third, the interaction between the intelligent driving domain and the chassis domain is limited to a one-way command-execution level. The chassis domain only acts as a passive actuator to receive the desired trajectory command, without making any use of planning information to predict its own safety status, nor can it provide feedback on the stability limit to the intelligent driving domain. The information silos between the two domains prevent the formation of collaborative intelligence with safety as the core, resulting in the safety boundary of the entire system being fixed at the upper limit of the chassis domain's passive response capability. It is impossible to proactively avoid instability risks at the planning level through inter-domain collaboration.

[0036] Therefore, in order to solve the above problems, this application proposes a vehicle motion state control method.

[0037] Please see Figure 1 , Figure 1 A schematic diagram of a vehicle motion state control system provided in this application embodiment includes: like Figure 1 As shown, the system in this embodiment of the invention is divided into an intelligent driving domain (e.g., Figure 1 The system is divided into two parts: the intelligent driving domain and the chassis domain. The intelligent driving domain includes a perception module and a planning module. The perception module is responsible for collecting and understanding environmental information, while the planning module generates the expected driving trajectory for a future period based on the perception results. Unlike existing technologies that only use the planned trajectory for trajectory tracking control, this invention adds a feedforward information path from the intelligent driving domain to the chassis domain: the future waypoint trajectory sequence output by the planning module is directly injected into the vehicle dynamics prediction model in the chassis domain through this feedforward path. This waypoint trajectory sequence contains at least the expected position coordinates, expected vehicle speed, expected heading angle, and expected curvature for the next N time steps.

[0038] The chassis domain comprises a vehicle dynamics prediction model, an instability risk assessment module, a stability augmentation controller, and actuators. On one hand, the chassis domain receives future waypoint trajectory sequences from the intelligent driving domain via a feedforward information path. On the other hand, it collects real-time data on the vehicle's current motion state through onboard sensors, including longitudinal speed, yaw rate, sideslip angle, steering wheel angle, and lateral acceleration. The vehicle dynamics prediction model uses the aforementioned future waypoint trajectory sequences and the current vehicle state as joint inputs, iteratively calculating and outputting a predicted sequence of vehicle motion states for future time periods. The instability risk assessment module calculates an instability risk index based on this predicted sequence. When the instability risk index exceeds a preset warning threshold, the stability augmentation controller proactively triggers stability augmentation intervention before actual vehicle instability, issuing control commands to the actuators. The actuators execute corresponding stability augmentation actions according to the control commands, including braking, steering, and driving. Vehicle responses are collected in real-time by onboard sensors and fed back to the vehicle dynamics prediction model, forming a closed-loop control system.

[0039] Furthermore, the chassis domain also incorporates a stability constraint feedback path. The chassis domain feeds back the predicted stability constraint information, including the maximum safe speed and the allowable yaw rate envelope, to the planning module in the intelligent driving domain. This allows the planning module to adjust the speed planning or path curvature of future waypoint trajectories accordingly. Thus, the interaction between the intelligent driving domain and the chassis domain is elevated from a traditional one-way command-execution model to a deeply integrated two-way planning-prediction model.

[0040] Please see Figure 2 , Figure 2 A flowchart illustrating a vehicle motion state control method provided in this application embodiment includes: Step S201: Obtain the future waypoint trajectory sequence of the intelligent driving plan, as well as the current motion state of the vehicle.

[0041] Specifically, in this embodiment, the vehicle chassis domain controller obtains the future waypoint trajectory sequence output by the planning module of the intelligent driving domain through an inter-domain communication interface. This future waypoint trajectory sequence contains look-ahead information for the next N time steps, and the waypoint information for each time step includes at least the desired location coordinates. Expected speed Desired heading angle and expected curvature .

[0042] The aforementioned waypoint trajectory sequence is independent of the state feedback information provided by traditional chassis sensors, forming a new feedforward information path from the intelligent driving domain to the chassis domain. Simultaneously, the chassis domain controller collects the vehicle's current motion state in real time through onboard sensors, and this current motion state includes at least the longitudinal vehicle speed. yaw rate and centroid side slip angle It may also include steering wheel angle. and lateral acceleration The aforementioned current motion state serves two purposes: firstly, as feedback information for closed-loop control, and secondly, as the initial value for iteration of the dynamic prediction model.

[0043] Step S202: Using the future waypoint trajectory sequence and the current motion state of the vehicle as input, a vehicle dynamics prediction model is used to predict the vehicle state prediction sequence for the future time period.

[0044] In one embodiment, the vehicle state prediction sequence for the future time period is obtained by using the future waypoint trajectory sequence and the current motion state of the vehicle as input and predicting the vehicle state through a vehicle dynamics prediction model. This includes: performing reference trajectory transformation processing on the future waypoint trajectory sequence, and calculating the predicted front wheel angle value at the corresponding prediction time based on the position, curvature and expected vehicle speed of the waypoint at each time step; using the current motion state of the vehicle as the initial value of the iteration, and combining the predicted front wheel angle value, the vehicle dynamics prediction model is used to perform forward iteration along the future waypoints step by step to solve the problem and output a vehicle state prediction sequence covering the entire prediction time domain.

[0045] Among them, the vehicle dynamics prediction model adopts a three-degree-of-freedom vehicle dynamics model; The state-space equations of the vehicle dynamics prediction model are as follows:

[0046] in, Represents the vehicle state vector. Represents the control input vector. Indicates time, This represents the rate of change of the vehicle's motion state. This represents a nonlinear function of vehicle dynamics.

[0047] Specifically, the future waypoint trajectory sequence is first processed using a reference trajectory transformation. Based on the location coordinates and curvature information of each waypoint at each time step, and combined with the corresponding expected vehicle speed, the controller calculates the expected front wheel steering angle required by the vehicle at each predicted moment when following the planned trajectory. For example, based on vehicle kinematics, the desired front wheel steering angle can be determined by the vehicle's wheelbase. Waypoint curvature The calculation yielded:

[0048] Simultaneously, the predicted front wheel steering angle is adaptively adjusted based on the expected rate of change of vehicle speed, forming a sequence of expected front wheel steering angles covering the entire prediction time domain.

[0049] Then, the current motion state of the vehicle (including lateral speed) obtained in step S201 is used. yaw rate and centroid side slip angle Using the calculated expected front wheel steering angle sequence, driving torque, and braking torque as the control input sequence, the vehicle dynamics prediction model iterates forward step by step along the future waypoint trajectory sequence.

[0050] In this embodiment, the vehicle dynamics prediction model preferably adopts a three-degree-of-freedom vehicle dynamics model, including longitudinal, lateral, and yaw degrees of freedom. Its state-space equations are expressed as follows:

[0051] in, Represents the vehicle state vector. Represents the control input vector. Indicates time, This represents the rate of change of the vehicle's motion state. Represents the nonlinear function of vehicle dynamics, vehicle state vector At least lateral velocity yaw rate and centroid side slip angle Control input vector At least include front wheel steering angle Driving torque and braking torque .

[0052] The tire model can employ a linear tire model to reduce computational complexity, or a nonlinear tire model such as the Pacejka magic formula to improve prediction accuracy under extreme conditions. The road adhesion coefficient estimate μ is an optional input parameter for the model and can be obtained through visual perception or dynamic estimation to further improve prediction accuracy under low-adhesion road conditions.

[0053] The iterative solution described above can be achieved using numerical methods such as the Runge-Kutta method or the Euler method. Through iterative calculations step-by-step, the model output covers the entire prediction time domain. A vehicle state prediction sequence (e.g., 1 to 5 seconds, preferably 2 to 3 seconds), which includes at least a yaw rate prediction sequence. Centroid Lateral Slip Angle Prediction Sequence Lateral acceleration prediction sequence and the prediction sequence of tire force utilization rate The aforementioned predicted sequences form the data basis for subsequent instability risk assessments.

[0054] In another embodiment, the above prediction step can also be implemented alternatively based on the phase plane method. In this alternative, the controller directly uses the predicted state trajectory output by the vehicle dynamics prediction model to generate a predicted state trajectory curve in the phase plane of the center of mass sideslip angle - center of mass sideslip angular velocity. By judging the trend and landing area of ​​the trajectory in the phase plane, the state prediction and risk assessment are integrated. This will be detailed in the phase plane method embodiment of step S203.

[0055] like Figure 3 As shown, the vehicle dynamics prediction model consists of three parts: the input layer, the prediction model core, and the output layer.

[0056] The input layer receives three types of input information: the first type is the vehicle's current motion state, including lateral speed. yaw rate , centroid side slip angle Steering wheel angle and lateral acceleration The first category is obtained by direct measurement from onboard sensors or through state estimation; the second category is the future waypoint trajectory sequence provided by the intelligent driving domain, including the location coordinates of waypoints at each time step. Expected speed Desired heading angle and expected curvature The third category is the estimated value of the road surface adhesion coefficient. This information can be obtained through visual perception or dynamic estimation. These three types of information together constitute the input data foundation for the prediction model.

[0057] The core of the prediction model comprises a reference trajectory transformation module, a tire model module, and a vehicle dynamics equations module. The reference trajectory transformation module converts the future road point trajectory sequence into a desired front wheel steering angle sequence and a desired steering wheel steering angle sequence. The tire model can employ a linear tire model to reduce computational complexity, or a nonlinear tire model such as the Pacejka magic formula to meet the accuracy requirements of different operating conditions. The vehicle dynamics equations module preferably uses a three-degree-of-freedom vehicle dynamics model, including longitudinal, lateral, and yaw degrees of freedom, with the following state-space equations:

[0058] in, Represents the vehicle state vector. Represents the control input vector. Indicates time, This represents the rate of change of the vehicle's motion state. Represents the nonlinear function of vehicle dynamics, vehicle state vector At least lateral velocity yaw rate and centroid side slip angle Control input vector At least include front wheel steering angle Driving torque and braking torque The iterative solver uses the Runge-Kutta method or the Euler method for numerical solution.

[0059] The output layer outputs a vehicle state prediction sequence for future time periods, including a yaw rate prediction sequence. Centroid Lateral Slip Angle Prediction Sequence Lateral acceleration prediction sequence and tire force utilization prediction sequence The aforementioned predicted sequences form the data basis for the instability risk assessment.

[0060] In this embodiment, the chassis domain controller first transforms the future waypoint trajectory sequence through a reference trajectory transformation module. Specifically, based on the position coordinates and curvature information of each waypoint at each time step, and combined with the corresponding expected vehicle speed, the expected front wheel steering angle required by the vehicle at each predicted moment when following the planned trajectory is calculated. Based on vehicle kinematics, the desired front wheel steering angle can be determined by the vehicle's wheelbase. Waypoint curvature The calculation yielded:

[0061] Simultaneously, the predicted front wheel steering angle is adaptively adjusted based on the expected rate of change of vehicle speed, forming a sequence of expected front wheel steering angles covering the entire prediction time domain.

[0062] Then, using the vehicle's current motion state as the initial value for iteration, and the calculated desired front wheel steering angle sequence, driving torque, and braking torque as the control input sequence, the vehicle dynamics equation module performs forward iterative solutions step-by-step along the future waypoint trajectory sequence. The iterative solver can employ the Runge-Kutta method or the Euler method for numerical solution. Through time-step iterative calculations, the model output covers the entire prediction time domain. The vehicle state prediction sequence.

[0063] Step S203: Calculate the instability risk index based on the vehicle state prediction sequence.

[0064] In one embodiment, an instability risk index is calculated based on a vehicle state prediction sequence, including: extracting yaw rate, center of gravity sideslip angle, lateral acceleration, and tire force data at each prediction time in the vehicle state prediction sequence; calculating the yaw rate deviation factor corresponding to the yaw rate, the center of gravity sideslip angle risk factor corresponding to the center of gravity sideslip angle, and the tire force utilization risk factor corresponding to the lateral acceleration and tire force data, wherein the yaw rate deviation factor is calculated by the deviation between the predicted yaw rate value and the expected yaw rate at each prediction time, the center of gravity sideslip angle risk factor is calculated based on the absolute value of the center of gravity sideslip angle and the corresponding rate of change, and the tire force utilization risk factor is calculated based on the proportion of the combined force of the longitudinal and lateral forces of each tire to the road adhesion limit; assigning preset weights to the yaw rate deviation factor, the center of gravity sideslip angle risk factor, and the tire force utilization risk factor, and performing weighted summation to obtain the instability risk index.

[0065] Specifically, the risk assessment module first extracts the yaw rate at each prediction moment in the vehicle state prediction sequence. and centroid side slip angle Lateral acceleration Based on tire force data, risk factors are calculated in the following three dimensions: (1) Yaw rate deviation factor: Calculated based on the deviation between the predicted yaw rate and the expected yaw rate at each prediction time. The expected yaw rate is calculated based on the future waypoint curvature and the expected vehicle speed. Specifically, the expected yaw rate... ,in, For the desired vehicle speed, Waypoint curvature. Yaw rate deviation. The larger the deviation, the more severe the conflict between vehicle trajectory tracking accuracy and yaw stability.

[0066] (2) Centroid sideslip angle risk factor: based on the absolute value of the predicted centroid sideslip angle. and its rate of change The calculation shows that when the absolute value of the predicted centroid sideslip angle or its rate of change exceeds a preset threshold, it indicates that the vehicle has approached or entered a nonlinear unstable region.

[0067] (3) Tire force utilization risk factor: based on the longitudinal force of each tire and lateral force The ratio of the combined force to the road adhesion limit is calculated. The single-wheel tire force utilization rate is defined as:

[0068] in, The road surface adhesion coefficient, This represents the vertical force of each wheel. When the tire force utilization rate approaches 1, it indicates that the tire is nearing its adhesion limit, and the vehicle's available stability margin decreases sharply.

[0069] Then, pre-defined weights are assigned to the three risk factors mentioned above. , , The weighted summation is then performed to construct the comprehensive instability risk function as follows:

[0070] in, The risk mapping function for the yaw rate deviation factor. The risk mapping function for the centroid sideslip angle risk factor. This refers to the risk mapping function for tire force utilization risk factors. The aforementioned risk mapping functions can be implemented using piecewise linear functions or S-shaped mapping curves, mapping each physical quantity to a unified risk measurement interval (such as the 0 to 1 interval).

[0071] In another embodiment, the instability risk index is calculated based on the vehicle state prediction sequence, including: constructing a phase plane, wherein the phase plane is the phase plane corresponding to the centroid sideslip angle and the centroid sideslip angular velocity; configuring different boundary levels within the phase plane, wherein the different boundary levels include at least a stability boundary, a warning boundary, and an intervention boundary; mapping the state points at each moment in the vehicle state prediction sequence to the phase plane to generate a predicted state trajectory; and outputting the corresponding level of instability risk index based on the boundary level touched by the predicted state trajectory.

[0072] Specifically, the phase plane analysis method can be used to replace the weighted summation method mentioned above for instability risk assessment. For example... Figure 4 As shown, construct the centroid sideslip angle. angular velocity of the center of mass deflection The corresponding phase plane is defined, and different boundary levels are configured within the phase plane. The phase plane is divided into a stable domain, a warning domain, and an intervention domain, corresponding to the stable boundary, the warning boundary, and the intervention boundary, respectively. The state points at each prediction time in the vehicle state prediction sequence in step S202 are... , This is mapped onto the phase plane to generate a predicted state trajectory. Based on the boundary levels touched by the predicted state trajectory, a corresponding level of instability risk index is output. For example, when the predicted state trajectory touches a warning boundary, a judgment is made... Exceeding the warning threshold If the predicted trajectory further approaches and touches the intervention boundary, then it is determined that... Exceeding the intervention threshold This phase plane method can intuitively reflect the evolution trend of vehicle state in the nonlinear region, and is especially suitable for risk assessment under extreme conditions.

[0073] In one embodiment, when the instability risk index exceeds a preset warning threshold, active stabilization intervention is performed, including: matching the corresponding intervention intensity and intervention combination strategy according to the level of the instability risk index; generating corresponding control commands based on the intervention intensity and intervention combination strategy, and sending them to the vehicle actuators to achieve vehicle stabilization through at least one of the following methods: applying differential braking force to the left and right wheels to generate additional yaw moment, correcting the front wheel steering angle, adjusting the rear wheel steering angle, distributing the four-wheel drive torque, and limiting the overall vehicle drive torque to reduce the target vehicle speed.

[0074] Specifically, the stabilization controller first matches the corresponding intervention intensity and intervention combination strategy based on the level of the instability risk indicator. For example, when At the warning threshold With intervention threshold In between, the system implements a mild intervention strategy, applying corrections in a gentle manner; when Exceeding the intervention threshold At this time, the system executes a forced intervention strategy to intervene with stronger control and ensure vehicle stability.

[0075] Then, the stability augmentation controller generates corresponding control commands based on the determined intervention intensity and intervention combination strategy, and sends the control commands to the vehicle actuators. Intervention methods include one or more combinations of the following: (1) By applying differential braking or driving force to the left and right wheels through direct yaw moment control (DYC) to generate additional yaw moment, the yaw response of the vehicle is corrected. (2) By correcting the front wheel steering angle through active front wheel steering (AFS), the front wheel sideslip characteristics of the vehicle are changed to enhance path tracking stability; (3) Improve vehicle yaw stability at high speeds by adjusting the rear wheel steering angle (RWS) through the rear wheel steering system; (4) Distribute the driving torque of the four wheels and realize the fine distribution of driving force of each wheel through torque vector control, and actively adjust the vehicle yaw response under driving conditions; (5) Limit the driving torque of the whole vehicle to reduce the target speed and reduce the risk of instability caused by excessive speed.

[0076] The above intervention methods can be used individually or in combination depending on the risk level and the current vehicle status to achieve the best stability enhancement effect.

[0077] Prediction Time Domain Typically 1 to 5 seconds, preferably 2 to 3 seconds; control time domain Less than or equal to the prediction time domain The system solves the problem in the control time domain and applies active stabilization intervention. After the intervention is executed, it proceeds to step S204 for feedback correction.

[0078] like Figure 5 As shown, in the predictive control timing relationship of this embodiment of the invention, the horizontal axis represents actual time, and the vertical axis indicates the trend of vehicle state variables changing over time. At the beginning of each control cycle (current time) The system acquires the vehicle's current motion state and the future waypoint trajectory provided by the intelligent driving domain. (Prediction time domain) from The moment begins to extend to + The time domain is typically 1 to 5 seconds, but preferably 2 to 3 seconds. from The moment begins to extend to + Less than or equal to the prediction time domain .

[0079] exist to + Throughout the entire prediction time domain, the vehicle dynamics prediction model iteratively calculates along the future waypoint trajectory sequence to generate a vehicle state prediction sequence for future time periods. Meanwhile, to + Within the control time domain, the system solves for the optimal control variable based on the prediction results and applies active stability augmentation intervention to the vehicle. After the control time domain ends, i.e. + At any given moment, the system monitors the vehicle's response status and adjusts the prediction model accordingly. The status update and feedback for the next control cycle occur during... + At any given moment, the system reacquires the updated vehicle status and planned trajectory, repeating the above process to form a closed-loop control with rolling time-domain optimization. + The subsequent vehicle response curves reflect the actual effect of the stability enhancement intervention.

[0080] The aforementioned temporal relationship ensures the orderly connection of each stage—prediction, decision-making, execution, and feedback correction—on the timeline, achieving an upgrade in control mode from passive response to proactive prediction.

[0081] Step S204: When the instability risk index exceeds the preset monitoring result, correct the initial state of the vehicle dynamics prediction model in the next control cycle.

[0082] In one embodiment, when the instability risk index exceeds the preset monitoring result, the initial state of the vehicle dynamics prediction model in the next control cycle is corrected, including: collecting actual state data after the intervention is executed, wherein the actual state data includes at least the actual yaw rate, actual center of gravity sideslip angle, and actual lateral acceleration state data of the vehicle; comparing the actual state data with the predicted state data at the corresponding time to obtain the state deviation; updating the iterative initial value of the vehicle dynamics prediction model in the next control cycle based on the state deviation, and simultaneously correcting the model parameters.

[0083] Specifically, the first step is to collect actual state data after the intervention is implemented. This actual state data should include at least the vehicle's actual yaw rate. Actual centroid sideslip angle Actual lateral acceleration Status data, etc. This data is acquired in real time through onboard sensors.

[0084] Then, the actual state data is compared with the predicted state data at the corresponding time in step S202 to calculate the state deviation. For example, the yaw rate deviation. Centroid side slip angle deviation .

[0085] Finally, based on the calculated state deviation, the initial values ​​for the vehicle dynamics prediction model in the next control cycle are updated, and the model parameters are corrected simultaneously. Specifically, in the next execution of step S202, the corrected actual state values ​​are used as the new initial values ​​for the vehicle dynamics prediction model, replacing the original predicted values, thereby avoiding the cumulative propagation of prediction deviations along the control link. The correction of model parameters may include online adjustment of key parameters such as tire stiffness coefficient and road adhesion coefficient to further improve the model's prediction accuracy under time-varying conditions.

[0086] Through the aforementioned closed-loop feedback mechanism, even during intervention, the system can continuously assess the evolution trend of the vehicle's state and dynamically adjust the control strategy, effectively avoiding the problems of insufficient or excessive control in traditional ESC systems, and significantly improving the system's robustness and adaptability. Simultaneously, after each control cycle, the system returns to step S201 to re-acquire the updated vehicle state and planned trajectory, forming a rolling time-domain optimized closed-loop control.

[0087] In one embodiment, the method further includes: Based on the vehicle state prediction sequence and instability risk index, the stability constraint information of the vehicle's maximum safe speed and allowable yaw rate envelope under the current operating conditions is calculated; the speed planning or path curvature of the future waypoint trajectory is adjusted according to the stability constraint information.

[0088] Specifically, based on the vehicle state prediction sequence output in step S202 and the instability risk index output in step S203, the chassis domain controller calculates the vehicle's stability constraint information under the current operating condition. This stability constraint information includes at least the vehicle's maximum safe speed under the current operating condition. and the allowable yaw rate envelope The maximum safe speed can be calculated by comprehensively considering the curvature of each road point in the predicted trajectory, the road surface adhesion coefficient, and the vehicle's roll stability conditions.

[0089] in, The road surface adhesion coefficient, It is the acceleration due to gravity. To predict the maximum curvature in the trajectory, the allowable yaw rate envelope is determined based on the vehicle dynamics boundaries to ensure that both the vehicle's center of gravity sideslip angle and yaw rate are within the stability region.

[0090] The aforementioned stability constraint information is fed back to the planning module of the intelligent driving domain via the inter-domain communication interface, allowing the intelligent driving planning module to adjust the speed planning or path curvature of future waypoint trajectories accordingly. For example, when the chassis domain predicts that the current planned vehicle speed exceeds the maximum safe speed on a certain curve, the chassis domain feeds this constraint back to the intelligent driving domain. The intelligent driving domain then reduces the target vehicle speed for that section of the trajectory or, if there is a speed adjustment margin, replans the path curvature, forming a two-way deep fusion closed loop of planning, prediction, constraint, and replanning.

[0091] The aforementioned reverse feedback steps enable the chassis domain to upgrade from a passive trajectory executor to an active safety guardian, realizing a leap from the traditional one-way command-execution mode to a two-way planning-prediction deep fusion mode between the intelligent driving domain and the chassis domain, providing architectural support for the safety closed loop of high-level autonomous driving systems.

[0092] This application provides a vehicle motion state control method, including: acquiring a future waypoint trajectory sequence of intelligent driving planning and the current motion state of the vehicle; using the future waypoint trajectory sequence and the current motion state of the vehicle as input, making predictions through a vehicle dynamics prediction model to obtain a vehicle state prediction sequence for a future time period; calculating an instability risk index based on the vehicle state prediction sequence; and correcting the initial state of the vehicle dynamics prediction model in the next control cycle when the instability risk index exceeds a preset monitoring result. This application uses the future waypoint trajectory sequence of intelligent driving planning and the current motion state of the vehicle as joint inputs. It uses a vehicle dynamics prediction model to forward extrapolate the vehicle state in the future time period, obtaining a state prediction sequence including parameters such as yaw rate, center of gravity sideslip angle, and lateral acceleration. Based on this, an instability risk index is calculated, thereby extending the perception range of the control system to the next few seconds. This allows the vehicle to actively identify instability risks before actually entering an unstable state. When the instability risk index exceeds the preset monitoring result, the initial state of the prediction model in the next control cycle is corrected. This allows the prediction model to continuously receive real response feedback from the vehicle and continuously calibrate its own state, effectively avoiding the cumulative error and model mismatch problems caused by open-loop prediction, thereby improving vehicle stability and safety.

[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0095] Figure 6 This diagram illustrates the structure of a vehicle motion state control device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. The vehicle motion state control device includes: The data acquisition module 601 is used to acquire the future waypoint trajectory sequence of intelligent driving planning, as well as the current motion state of the vehicle; The state prediction module 602 is used to take the future waypoint trajectory sequence and the current motion state of the vehicle as input, and make predictions through the vehicle dynamics prediction model to obtain the vehicle state prediction sequence for the future time period. The indicator calculation module 603 is used to calculate the instability risk indicator based on the vehicle state prediction sequence. The state correction module 604 is used to correct the initial state of the vehicle dynamics prediction model in the next control cycle when the instability risk index exceeds the preset monitoring result.

[0096] In one embodiment, the state prediction module 602 is further configured to perform reference trajectory transformation processing on the future waypoint trajectory sequence, and calculate the predicted value of the front wheel steering angle at the corresponding prediction time based on the position, curvature and expected vehicle speed of the waypoint at each time step. Using the vehicle's current motion state as the initial value for iteration, and combining it with the predicted value of the front wheel steering angle, the vehicle dynamics prediction model is used to perform forward iteration step by step along the future road points, outputting a vehicle state prediction sequence covering the entire prediction time domain.

[0097] In one embodiment, the vehicle dynamics prediction model employs a three-degree-of-freedom vehicle dynamics model; The state-space equations of the vehicle dynamics prediction model are as follows:

[0098] in, Represents the vehicle state vector. Represents the control input vector. Indicates time, This represents the rate of change of the vehicle's motion state. This represents a nonlinear function of vehicle dynamics.

[0099] In one embodiment, the index calculation module 603 is also used to extract yaw rate, center of gravity sideslip angle, lateral acceleration and tire force data at each prediction time in the vehicle state prediction sequence; The following factors were calculated: yaw rate deviation factor corresponding to yaw rate, center of mass sideslip angle risk factor corresponding to center of mass sideslip angle, and tire force utilization risk factor corresponding to lateral acceleration and tire force data. The yaw rate deviation factor was calculated by the deviation between the predicted yaw rate value and the expected yaw rate at each prediction time. The center of mass sideslip angle risk factor was calculated based on the absolute value of the center of mass sideslip angle and the corresponding rate of change. The tire force utilization risk factor was calculated based on the ratio of the combined force of the longitudinal force and lateral force of each tire to the road adhesion limit. Preset weights were assigned to the yaw rate deviation factor, the center of gravity sideslip angle risk factor, and the tire force utilization rate risk factor, and then weighted and summed to obtain the instability risk index.

[0100] In one embodiment, the index calculation module 603 is further configured to construct a phase plane, wherein the phase plane is the phase plane corresponding to the centroid sideslip angle and the centroid sideslip angular velocity; Different boundary levels are configured within the phase plane, wherein the different boundary levels include at least a stability boundary, an early warning boundary, and an intervention boundary; Map the state points at each time step in the vehicle state prediction sequence to the phase plane to generate the predicted state trajectory; Based on the boundary level touched by the predicted state trajectory, the corresponding level of instability risk index is output.

[0101] In one embodiment, when the instability risk indicator exceeds a preset warning threshold, active stabilization intervention is performed, including: Match the corresponding intervention intensity and intervention combination strategy according to the level of instability risk indicators; Based on the intervention intensity and intervention combination strategy, corresponding control commands are generated and sent to the vehicle actuators to achieve vehicle stability enhancement through at least one of the following methods: Differential braking force is applied to the left and right wheels to generate additional yaw moment, correct the front wheel steering angle, adjust the rear wheel steering angle, distribute the four-wheel drive torque, and limit the overall vehicle drive torque to reduce the target speed.

[0102] In one embodiment, the state correction module 604 is further used to collect actual state data after the intervention is performed, wherein the actual state data includes at least the actual yaw rate of the vehicle, the actual center of gravity sideslip angle, and the actual lateral acceleration state data. The actual state data is compared with the predicted state data at the corresponding time to obtain the state deviation. The initial values ​​of the vehicle dynamics prediction model in the next control cycle are updated based on the state deviation, and the model parameters are corrected simultaneously.

[0103] In one embodiment, the device further includes: a trajectory adjustment module, which is used to calculate the stability constraint information of the vehicle's maximum safe speed and allowable yaw rate envelope under the current operating conditions based on the vehicle state prediction sequence and instability risk index; Adjust the velocity planning or path curvature of future waypoint trajectories based on stability constraint information.

[0104] This application provides a vehicle motion state control device, specifically used for: acquiring the future waypoint trajectory sequence of intelligent driving planning, and the current motion state of the vehicle; using the future waypoint trajectory sequence and the current motion state of the vehicle as input, making predictions through a vehicle dynamics prediction model to obtain a vehicle state prediction sequence for the future time period; calculating an instability risk index based on the vehicle state prediction sequence; and correcting the initial state of the vehicle dynamics prediction model in the next control cycle when the instability risk index exceeds a preset monitoring result. This application uses the future waypoint trajectory sequence of intelligent driving planning and the current motion state of the vehicle as joint inputs. It uses a vehicle dynamics prediction model to forward extrapolate the vehicle state in the future time period, obtaining a state prediction sequence including parameters such as yaw rate, center of gravity sideslip angle, and lateral acceleration. Based on this, an instability risk index is calculated, thereby extending the perception range of the control system to the next few seconds. This allows the vehicle to actively identify instability risks before actually entering an unstable state. When the instability risk index exceeds the preset monitoring result, the initial state of the prediction model in the next control cycle is corrected. This allows the prediction model to continuously receive real response feedback from the vehicle and continuously calibrate its own state, effectively avoiding the cumulative error and model mismatch problems caused by open-loop prediction, thereby improving vehicle stability and safety.

[0105] This application Figure 7 A schematic diagram of a computer device is provided. (Example) Figure 7 As shown, the computer device 7 in this embodiment includes a processor 701, a memory 702, and a computer program 703 stored in the memory 702 and executable on the processor 701. When the processor 701 executes the computer program 703, it implements the steps in the various vehicle motion state control method embodiments described above, for example... Figure 2 Steps 201 to 204 are shown. Alternatively, when processor 701 executes computer program 703, it implements the functions of each module / unit in the above-described vehicle motion state control device embodiments, for example... Figure 6 The functions of modules / units 601 to 604 shown.

[0106] This application also provides a readable storage medium storing a computer program, which, when executed by a processor, is used to implement the vehicle motion state control method provided in the various embodiments described above.

[0107] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0108] This application also provides a program product including execution instructions stored in a readable storage medium. At least one processor of the device can read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the vehicle motion state control methods provided in the various embodiments described above.

[0109] This application also provides a vehicle that includes the vehicle motion state control device described above.

[0110] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0111] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling the motion state of a vehicle, characterized in that, include: Obtain the future waypoint trajectory sequence of the intelligent driving plan, as well as the current motion state of the vehicle; Using the future waypoint trajectory sequence and the current motion state of the vehicle as input, a vehicle dynamics prediction model is used to predict the vehicle state prediction sequence for the future time period. Calculate the instability risk index based on the vehicle state prediction sequence; When the instability risk index exceeds the preset monitoring result, the initial state of the vehicle dynamics prediction model in the next control cycle is corrected; The step of correcting the initial state of the vehicle dynamics prediction model in the next control cycle includes: collecting actual state data after the intervention is executed, comparing the actual state data with the predicted state data at the corresponding time to obtain the state deviation, and updating the iterative initial value of the vehicle dynamics prediction model in the next control cycle based on the state deviation.

2. The method according to claim 1, characterized in that, The process of using the future waypoint trajectory sequence and the current vehicle motion state as input, and predicting using a vehicle dynamics prediction model to obtain a vehicle state prediction sequence for the future time period includes: The future waypoint trajectory sequence is processed by reference trajectory transformation, and the predicted front wheel steering angle at the corresponding prediction time is calculated based on the position, curvature and expected vehicle speed of the waypoint at each time step. Using the current motion state of the vehicle as the initial value for iteration, and combining it with the predicted value of the front wheel steering angle, the vehicle dynamics prediction model is used to perform forward iteration step by step along the future road points, outputting a vehicle state prediction sequence covering the entire prediction time domain.

3. The method according to claim 2, characterized in that, The vehicle dynamics prediction model adopts a three-degree-of-freedom vehicle dynamics model; The state-space equations of the vehicle dynamics prediction model are as follows: in, Represents the vehicle state vector. Represents the control input vector. Indicates time, This represents the rate of change of the vehicle's motion state. This represents a nonlinear function of vehicle dynamics.

4. The method according to claim 1, characterized in that, The step of calculating the instability risk index based on the vehicle state prediction sequence includes: Extract the yaw rate, center of gravity sideslip angle, lateral acceleration and tire force data at each prediction time in the vehicle state prediction sequence; The following factors are calculated: yaw rate deviation factor corresponding to the yaw rate, center of mass sideslip angle risk factor corresponding to the center of mass sideslip angle, and tire force utilization risk factor corresponding to the lateral acceleration and tire force data. The yaw rate deviation factor is calculated by the deviation between the predicted yaw rate value and the expected yaw rate at each prediction time. The center of mass sideslip angle risk factor is calculated based on the absolute value of the center of mass sideslip angle and the corresponding rate of change. The tire force utilization risk factor is calculated based on the ratio of the combined force of the longitudinal force and lateral force of each tire to the road adhesion limit. The yaw rate deviation factor, the center of gravity sideslip angle risk factor, and the tire force utilization rate risk factor are assigned preset weights and then weighted and summed to obtain the instability risk index.

5. The method according to claim 1, characterized in that, The step of calculating the instability risk index based on the vehicle state prediction sequence includes: Construct a phase plane, wherein the phase plane is the phase plane corresponding to the sideslip angle and the sideslip angular velocity of the center of mass; Different boundary levels are configured within the phase plane, wherein the different boundary levels include at least a stability boundary, an early warning boundary, and an intervention boundary; Map the state points at each moment in the vehicle state prediction sequence to the phase plane to generate the predicted state trajectory; Based on the boundary level touched by the predicted state trajectory, the corresponding level of instability risk index is output.

6. The method according to claim 1, characterized in that, When the instability risk indicator exceeds a preset early warning threshold, proactive stabilization intervention is executed, including: The corresponding intervention intensity and intervention combination strategy are matched according to the level of the instability risk index; Based on the intervention intensity and intervention combination strategy, corresponding control commands are generated and sent to the vehicle actuators to achieve vehicle stability enhancement through at least one of the following methods: Differential braking force is applied to the left and right wheels to generate additional yaw moment, correct the front wheel steering angle, adjust the rear wheel steering angle, distribute the four-wheel drive torque, and limit the overall vehicle drive torque to reduce the target speed.

7. The method according to claim 1, characterized in that, The step of correcting the initial state of the vehicle dynamics prediction model in the next control cycle when the instability risk index exceeds the preset monitoring result includes: Collect actual state data after the intervention is performed, wherein the actual state data includes at least the actual yaw rate, actual center of gravity sideslip angle, and actual lateral acceleration state data of the vehicle; The actual state data is compared with the predicted state data at the corresponding time to obtain the state deviation. The initial values ​​of the vehicle dynamics prediction model in the next control cycle are updated based on the state deviation, and the model parameters are corrected simultaneously.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: Based on the vehicle state prediction sequence and instability risk index, calculate the stability constraint information of the vehicle's maximum safe speed and allowable yaw rate envelope under the current operating conditions; Adjust the velocity planning or path curvature of future waypoint trajectories based on the stability constraint information.

9. A vehicle motion state control device, characterized in that, include: The data acquisition module is used to acquire the future waypoint trajectory sequence of intelligent driving planning, as well as the current motion state of the vehicle; The state prediction module is used to take the future waypoint trajectory sequence and the current motion state of the vehicle as input, and make predictions through the vehicle dynamics prediction model to obtain the vehicle state prediction sequence for the future time period. The index calculation module is used to calculate the instability risk index based on the vehicle state prediction sequence. The state correction module is used to correct the initial state of the vehicle dynamics prediction model in the next control cycle when the instability risk index exceeds the preset monitoring result. The step of correcting the initial state of the vehicle dynamics prediction model in the next control cycle includes: collecting actual state data after the intervention is executed, comparing the actual state data with the predicted state data at the corresponding time to obtain the state deviation, and updating the iterative initial value of the vehicle dynamics prediction model in the next control cycle based on the state deviation.

10. A vehicle, characterized in that, The vehicle includes the vehicle motion state control device as described in claim 9.

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