Method for Determining Lateral Vehicle Speed, Electronic Device, and Vehicle

Abstract

The present application provides a method for determining the lateral speed of a vehicle, an electronic device, and a vehicle. The method includes: constructing a two-degree-of-freedom model of the vehicle; determining the tire sideslip angle at the current moment, inputting the tire sideslip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment; based on the two-degree-of-freedom model, using the wheel lateral force at the current moment to determine the torque characteristic quantity at the current moment through torque balance; determining the speed characteristic quantity at the current moment, and combining the torque characteristic quantity at the current moment, using a motion algorithm to predict the predicted parameter quantity at the next moment; calibrating the predicted parameter quantity at the next moment to obtain the calibrated parameter quantity at the next moment; determining the calibrated lateral speed of the vehicle at the next moment according to the calibrated parameter quantity at the next moment. Through the process of prediction and calibration by combining the two-degree-of-freedom model with the brush tire model, the final calibrated lateral speed is more accurate and can adapt to both simple and complex road conditions.

Description

Technical Field

[0001] This application relates to the technical field of vehicle control, and particularly to a method for determining the lateral speed of a vehicle, an electronic device, and a vehicle. Background Art

[0002] With the popularization of vehicle intelligence, it is necessary to accurately estimate the driving situation of the vehicle in order to achieve intelligent driving.

[0003] However, at present, for some complex road conditions, the prediction of the lateral speed of existing vehicles is not accurate enough. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a method for determining the lateral speed of a vehicle, an electronic device, and a vehicle, so as to solve the problem that the prediction of the lateral speed of the vehicle is inaccurate when facing complex road conditions.

[0005] Based on the above purpose, this application provides a method for determining the lateral speed of a vehicle, including:

[0006] Construct a two-degree-of-freedom model of the vehicle;

[0007] Determine the tire side slip angle at the current moment, and input the tire side slip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment;

[0008] Based on the two-degree-of-freedom model, use the wheel lateral force at the current moment to determine the torque characteristic quantity at the current moment through torque balance;

[0009] Determine the speed characteristic quantity at the current moment, and combine the torque characteristic quantity at the current moment, and use the motion algorithm to predict the prediction parameter quantity at the next moment, where the prediction parameter quantity includes the parameters related to the predicted speed;

[0010] Calibrate the prediction parameter quantity at the next moment to obtain the calibrated parameter quantity at the next moment, where the calibrated parameter quantity includes the calibrated parameters related to the speed;

[0011] Determine the calibrated lateral speed of the vehicle at the next moment according to the calibrated parameter quantity at the next moment.

[0012] Based on the same inventive concept, this application also provides a device for determining the lateral speed of a vehicle, including:

[0013] A two-degree-of-freedom model construction module configured to construct a two-degree-of-freedom model of the vehicle;

[0014] A lateral force determination module configured to determine the tire side slip angle at the current moment, and input the tire side slip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment;

[0015] A torque feature determination module, configured to determine a torque feature quantity at the current moment based on the two-degree-of-freedom model by using the lateral force of the wheel at the current moment through torque balance;

[0016] A prediction processing module, configured to determine a speed feature quantity at the current moment, and combine the torque feature quantity at the current moment, and use a motion algorithm to predict a predicted parameter quantity at the next moment, where the predicted parameter quantity includes parameters related to the predicted speed;

[0017] A calibration module, configured to calibrate the predicted parameter quantity at the next moment to obtain a calibrated parameter quantity at the next moment, where the calibrated parameter quantity includes calibrated parameters related to the speed;

[0018] A lateral speed determination module, configured to determine a calibrated lateral speed of the vehicle at the next moment according to the calibrated parameter quantity at the next moment.

[0019] Based on the same inventive concept, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable by the processor. When the processor executes the computer program, the method described above is implemented.

[0020] Based on the same inventive concept, the present application further provides a vehicle, including the electronic device described above.

[0021] As can be seen from the above, for the method, electronic device, and vehicle for determining the lateral speed of a vehicle provided by the present application, by constructing a two-degree-of-freedom model for the vehicle, and then on the basis of this two-degree-of-freedom model, the tire slip angle at the current moment determined is more accurate. In this way, using the brush tire model to determine the lateral force of the wheel at the current moment according to the tire slip angle at the current moment is more accurate; then combining torque balance to determine the torque feature quantity at the current moment according to the lateral force of the wheel at the current moment, making the obtained torque feature quantity at the current moment more in line with the principle of torque balance, and further being able to meet the torque balance characteristics of various road conditions; then according to the speed feature quantity at the current moment combined with the obtained torque feature quantity at the current moment, using a motion algorithm for prediction, making the predicted parameter quantity at the next moment obtained more in line with the motion law of the vehicle under various road conditions; finally, in order to further improve the accuracy of the predicted parameter quantity at the next moment, it is also calibrated, and then a calibrated parameter quantity at the next moment is obtained. In this way, according to the calibrated parameter quantity at the next moment, the calibrated lateral speed of the vehicle at the next moment determined will be more accurate. Therefore, whether the vehicle is in a simple or complex road condition, the calibrated lateral speed at the next moment can be accurately determined. Description of the Drawings

[0022] To more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the accompanying drawings required for use in the embodiments or related technology descriptions. Obviously, the accompanying drawings in the following descriptions are only embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic flowchart of the method for determining the lateral speed of a vehicle according to an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of a two-degree-of-freedom model according to an embodiment of the present application;

[0025] Figure 3 It is a schematic structural diagram of a device for determining the lateral speed of a vehicle according to an embodiment of the present application;

[0026] Figure 4 It is a schematic structural diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the following further details the present application in combination with specific embodiments and with reference to the accompanying drawings.

[0028] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the ordinary meaning understood by those of ordinary skill in the art to which the present application belongs. The "first", "second", and similar terms used in the embodiments of the present application do not indicate any order, quantity, or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0029] In related technologies, generally, the lateral speed of a vehicle is estimated by means of Kalman filtering. For this method of estimating based on the change trend of speed, if the vehicle encounters complex road conditions (for example, various complex road conditions such as large-angle U-turns, accelerating lane changes, and straight driving alternate), the lateral speed of the vehicle changes greatly, and the lateral speed directly estimated based on the change trend of speed will be inaccurate.

[0030] When the vehicle is in the intelligent driving state, inaccurate estimation of the lateral speed may lead to inaccurate determination of the vehicle's driving state, resulting in inaccurate next control strategies determined based on the vehicle's driving state, increasing driving risks and making intelligent driving unsafe.

[0031] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0032] The method for determining the lateral speed of a vehicle proposed in the embodiments of the present application, as Figure 1 shown, includes:

[0033] Step 101, construct a two-degree-of-freedom model of the vehicle.

[0034] Specifically, the two-degree-of-freedom model of the vehicle is a two-dimensional model with the vehicle's center of mass as the origin, the vehicle's longitudinal direction as the horizontal axis, and the vehicle's lateral direction as the vertical axis (as Figure 2 shown). The two-degree-of-freedom model of the vehicle is the basis for subsequent various dynamic processing of the vehicle.

[0035] Step 102, determine the tire sideslip angle at the current moment, and input the tire sideslip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment.

[0036] Specifically, the brush tire model is a simplified theoretical model based on the assumption of "elastic tread and rigid carcass". Based on the assumption of elastic tread and rigid carcass, the longitudinal deformation of the footprint during longitudinal slip of the tire under steady-state conditions and the longitudinal and lateral deformations of the footprint during longitudinal slip and sideslip are analyzed. The modeling mechanisms of the steady-state longitudinal slip brush model and the longitudinal slip and sideslip brush model with slip in the footprint area are analyzed. The relationships between the longitudinal shear stress and the braking force and driving force respectively, the vector relationship between the total shear force during longitudinal slip and sideslip and its longitudinal and lateral components, and the internal relationship between the longitudinal slip and sideslip brush model and the longitudinal slip brush model and the sideslip brush model are discussed, and these relationships are integrated to form the brush tire model.

[0037] In this way, based on the brush tire model, the theoretical analysis of the acting force on the tire sideslip angle at the current moment can be carried out to determine the wheel lateral force at the current moment.

[0038] Step 103, based on the two-degree-of-freedom model, use the wheel lateral force at the current moment to determine the moment characteristic quantity at the current moment through moment balance.

[0039] Specifically, the moment characteristic quantity is a vehicle driving parameter related to the lateral speed, including: the derivative of the center of mass sideslip angle, the derivative of the yaw rate, etc.

[0040] Determine the distribution and values of various driving parameters of the vehicle based on the coordinate distribution corresponding to the two-degree-of-freedom model, so that calculation and processing can be performed according to torque balance to obtain the calculation formulas for various torque characteristic quantities at the current moment.

[0041] Step 104: Determine the speed characteristic quantity at the current moment, and combine the torque characteristic quantity at the current moment to predict the predicted parameter quantity at the next moment using a motion algorithm, where the predicted parameter quantity includes parameters related to the predicted speed.

[0042] In specific implementation, the speed characteristic quantity is some driving characteristic parameters related to speed. Combine the speed characteristic quantity at the current moment and the torque characteristic quantity at the current moment, and predict according to the motion law corresponding to the motion algorithm through the Jacobian matrix and Kalman filter to determine the predicted parameter quantity at the next moment. The predicted parameter quantity at the next moment can represent the specific value of the lateral speed at the next moment.

[0043] Step 105: Calibrate the predicted parameter quantity at the next moment to obtain the calibrated parameter quantity at the next moment, where the calibrated parameter quantity includes parameters related to the calibrated speed.

[0044] In specific implementation, since the predicted parameter quantity at the next moment may not be highly accurate, it needs to be calibrated. The specific calibration method can be to calibrate the predicted parameter quantity at the next moment through the Kalman filter method to make the finally obtained calibrated parameter quantity at the next moment more accurate. The calibrated parameter quantity at the next moment can also represent the specific value of the lateral speed at the next moment.

[0045] Step 106: Determine the calibrated lateral speed of the vehicle at the next moment according to the calibrated parameter quantity at the next moment.

[0046] In specific implementation, the calibrated lateral speed at the next moment can be retrieved or calculated according to the calibrated parameter quantity at the next moment. In this way, at the next moment, the driving of the vehicle can be controlled according to the calibrated lateral speed at the next moment, such as controlling the rotation angle of the steering wheel and the degree of acceleration or deceleration of the vehicle.

[0047] Through the above solution, a two-degree-of-freedom model can be constructed for the vehicle. Then, based on this two-degree-of-freedom model, the tire slip angle at the current moment is determined more accurately. In this way, using the brush tire model to determine the wheel lateral force at the current moment based on the tire slip angle at the current moment is more accurate. Then, combined with the moment balance, the moment characteristic quantity at the current moment is determined according to the wheel lateral force at the current moment, making the obtained moment characteristic quantity at the current moment more in line with the principle of moment balance, and thus being able to meet the moment balance characteristics of various road conditions. Then, according to the speed characteristic quantity at the current moment combined with the obtained moment characteristic quantity at the current moment, a motion algorithm is used for prediction, making the predicted parameter quantity at the next moment obtained by the prediction more in line with the motion law of the vehicle under various road conditions. Finally, in order to further improve the accuracy of the predicted parameter quantity at the next moment, it will be calibrated, and then the calibrated parameter quantity at the next moment is obtained. In this way, according to the calibrated parameter quantity at the next moment, the calibrated lateral speed of the vehicle at the next moment is determined more accurately. Therefore, no matter whether the vehicle is in a simple or complex road condition, the calibrated lateral speed at the next moment can be accurately determined.

[0048] In some embodiments, step 102 includes:

[0049] Step 1021, determine the front-wheel slip angle at the current moment and the rear-wheel slip angle at the current moment.

[0050] Specifically in implementation, the front-wheel slip angle is the angle at which the front wheel deflects in the lateral direction, and the rear-wheel slip angle is the angle at which the rear wheel deflects in the lateral direction.

[0051] Since the lateral speed is related to the slip angles of the front and rear wheels, determining the front-wheel slip angle at the current moment and the rear-wheel slip angle at the current moment helps to determine the lateral force and the lateral speed.

[0052] Step 1022, input the front-wheel slip angle at the current moment into the brush tire model to obtain the front-axle lateral force at the current moment.

[0053] Specifically in implementation, since the functional relationship between the lateral forces corresponding to various slip angles is stored in the brush tire model, inputting the front-wheel slip angle at the current moment into the brush tire model can obtain the front-axle lateral force at the current moment. The front wheels include: the left front wheel and the right front wheel.

[0054] Step 1023, input the rear-wheel slip angle at the current moment into the brush tire model to obtain the rear-axle lateral force at the current moment, where the wheel lateral force at the current moment includes: the front-axle lateral force at the current moment and the rear-axle lateral force at the current moment.

[0055] During specific implementation, since the brush tire model stores the functional relationship of lateral forces corresponding to various sideslip angles, inputting the rear wheel sideslip angle at the current moment into the brush tire model can obtain the lateral force of the rear axle at the current moment. The rear wheels include: the left rear wheel and the right rear wheel.

[0056] Through the above solution, directly using the brush tire model to determine the lateral force of the front axle corresponding to the front wheel sideslip angle at the current moment and the lateral force of the rear axle corresponding to the rear wheel sideslip angle at the current moment, the determination using the brush tire model will be more accurate.

[0057] In some embodiments, the front wheel sideslip angle at the current moment and the rear wheel sideslip angle at the current moment have the following formula:

[0058] , where is the center of mass sideslip angle at the current moment k, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the yaw angular velocity at the current moment k, is the longitudinal velocity at the current moment k, is the front wheel steering angle at the current moment k;

[0059] The formula in the brush tire model is:

[0060] where is the equivalent sideslip stiffness of the wheel axle, μ is the road surface adhesion coefficient, is the vertical force of the ground received by the wheel, is the front wheel sideslip angle at the current moment k or the rear wheel sideslip angle at the current moment k , is the lateral force of the front axle at the current moment k obtained after inputting or the lateral force of the rear axle at the current moment k obtained after inputting .

[0061] Through the above solution, substituting the formulas of the above front wheel sideslip angle and rear wheel sideslip angle into the formula of the brush tire model can accurately calculate the lateral force of the front axle and the lateral force of the rear axle.

[0062] In some embodiments, step 103 includes:

[0063] Step 1031, determining the component of the longitudinal force of the front axle in the lateral direction at the current moment and the compensation component of the longitudinal force of the front axle for the yaw moment.

[0064] Step 1032: Determine the moment balance algorithm in the lateral direction of the two-degree-of-freedom model and the moment balance algorithm in the yaw direction of the two-degree-of-freedom model.

[0065] Step 1033: Input the component force into the moment balance algorithm in the lateral direction to determine the derivative of the sideslip angle of the center of mass at the current moment, and input the compensation component into the moment balance algorithm in the yaw direction to determine the derivative of the yaw angular velocity at the current moment; wherein, the moment characteristic quantity at the current moment includes: the derivative of the sideslip angle of the center of mass at the current moment and the derivative of the yaw angular velocity at the current moment.

[0066] Wherein, the center of mass is the coordinate center point of the two-degree-of-freedom model, the sideslip angle of the center of mass is the deviation angle in the lateral direction, and the yaw angle is the rotation angle of the vehicle around the vertical axis of the two-degree-of-freedom model.

[0067] Through the above solution, the derivative of the sideslip angle of the center of mass at the current moment and the derivative of the yaw angular velocity at the current moment can be accurately calculated, and the calculation process is convenient and fast.

[0068] In some embodiments, the component force of the longitudinal force of the front axle at the current moment k in the lateral direction , the formula is:

[0069] , wherein, is the longitudinal force of the front axle at the current moment k, is the front wheel steering angle at the current moment k;

[0070] The compensation component of the longitudinal force of the front axle at the current moment k for the yaw moment , the formula is:

[0071] Wherein; is the distance from the center of mass to the front axle, is the longitudinal force of the left wheel of the front axle at the current moment k, is the longitudinal force of the right wheel of the front axle at the current moment k, is the front axle track, is the longitudinal force of the left wheel of the rear axle at the current moment k, is the longitudinal force of the right wheel of the rear axle at the current moment k, is the rear axle track;

[0072] The formulas of the moment balance algorithm in the lateral direction of the two-degree-of-freedom model and the moment balance algorithm in the yaw direction of the two-degree-of-freedom model are:

[0073] , wherein, is the lateral force of the front axle at the current moment k, is the lateral force on the rear axle at the current moment k, m is the vehicle mass, is the lateral acceleration of the vehicle at the current moment k, is the longitudinal speed at the current moment k, is the derivative of the sideslip angle of the center of mass at the current moment k, is the yaw rate at the current moment k, is the distance from the center of mass to the rear axle, is the moment of inertia of the whole vehicle about the center of mass in the vertical direction at the current moment k, is the derivative of the yaw rate at the current moment k;

[0074] The derivative of the sideslip angle of the center of mass at the current moment k is:

[0075] ;

[0076] The derivative of the yaw rate at the current moment k is:

[0077] .

[0078] Through the above scheme, the specific formulas of the component force, compensation component, moment balance algorithm in the lateral direction and moment balance algorithm in the yaw direction are specifically given, so that the derivative of the sideslip angle of the center of mass at the corresponding current moment and the derivative of the yaw rate at the current moment can be accurately calculated.

[0079] In some embodiments, a state quantity X including the sideslip angle of the center of mass, yaw rate and longitudinal speed is preset. The specific formula is: . Where β is the sideslip angle of the center of mass, is the yaw rate, is the longitudinal speed.

[0080] Step 104 includes:

[0081] Step 1041, determining that the calibration parameter quantity at the current moment includes the calibration state quantity at the current moment, determining the calibration longitudinal speed at the current moment according to the calibration state quantity at the current moment, and calculating the derivative of the longitudinal speed at the current moment, where the calibration state quantity is the state quantity after calibrating the predicted state quantity.

[0082] Specifically in implementation, the calibration state quantity at the current moment k is , is the calibrated sideslip angle of the center of mass at the current moment k, is the calibrated yaw rate at the current moment k, is the calibrated longitudinal speed at the current moment k.

[0083] The determined calibrated longitudinal speed at the current moment k Substitute it into the formula of the moment balance algorithm in the lateral direction to obtain the derivative of the longitudinal velocity at the current moment , and the formula is .

[0084] The derivative of the longitudinal velocity at the current moment k is:[[]]END]]

[0085] , where is the longitudinal acceleration at the current moment, is the yaw angular velocity at the current moment k.

[0086] Step 1042, determine that the moment characteristic quantity at the current moment includes: the derivative of the sideslip angle of the center of mass at the current moment and the derivative of the yaw angular velocity at the current moment .

[0087] Specifically, when implemented, the formula for the derivative of the yaw angular velocity at the current moment k is:[[]]END]]

[0088] .

[0089] Step 1043, combine the calibration state quantity at the current moment with the derivative of the longitudinal velocity at the current moment, the derivative of the sideslip angle of the center of mass at the current moment, and the derivative of the yaw angular velocity at the current moment, and use the motion algorithm to determine the predicted state quantity at the next moment , where the predicted parameter quantity at the next moment includes: the predicted state quantity at the next moment.

[0090] Specifically, when implemented, the formula of the motion algorithm:[[]]END]]

[0091]

[0092] where t is the sampling time, is the lateral force of the front axle at the current moment k, is the lateral force of the rear axle at the current moment k, is the distance from the center of mass to the rear axle, is the distance from the center of mass to the front axle, is the front wheel steering angle at the current moment k, is the longitudinal force of the front axle at the current moment k, is the compensation component of the yaw moment at the current moment k, is the component force of the longitudinal force of the front axle at the current moment k in the lateral direction, is the process noise belonging to the interval (0, ) where is the process covariance matrix.

[0093] =

[0094] Through the above solution, accurate prediction can be carried out through the motion algorithm, and then the predicted state quantity at the next moment can be obtained, ensuring the accuracy of the prediction and improving the efficiency.

[0095] In some embodiments, parameters of the measurement quantity Y are preset. The measurement quantity is wherein, the lateral acceleration of the vehicle, is the yaw angular velocity, is the longitudinal velocity.

[0096] Step 105 includes:

[0097] Step 1051, determining the first Jacobian matrix at the current moment corresponding to the motion algorithm and the error covariance matrix at the current moment.

[0098] The first Jacobian matrix at the current moment k The calculation formula is:

[0099] , wherein, is the calibrated state quantity at the current moment k.

[0100]

[0101]

[0102] wherein, is the sideslip angle of the center of mass at the calibrated current moment k, is the yaw angular velocity at the calibrated current moment k, is the longitudinal velocity at the calibrated current moment k, is the moment of inertia of the whole vehicle rotating about the center of mass in the vertical direction.

[0103] The error covariance matrix at the current moment k is the retrieved error covariance matrix calibrated at the current moment k.

[0104] Step 1052, predicting the error covariance matrix at the next moment according to the first Jacobian matrix and the error covariance matrix at the current moment .

[0105] In specific implementation, it is also necessary to retrieve the process covariance matrix , so that the error covariance matrix at the next moment k + 1 can be predicted. The formula is: .

[0106] Step 1053: Determine the second Jacobian matrix at the next moment corresponding to the measurement algorithm, and determine the Kalman gain matrix at the next moment based on the error covariance matrix and the second Jacobian matrix at the next moment.

[0107] During specific implementation, according to the formula:

[0108] , where is the state quantity at the current moment k.

[0109] The formula for the second Jacobian matrix at the next moment:

[0110] , where is the predicted state quantity at the next moment k + 1.

[0111]

[0112]

[0113] Among them, the predicted state quantity at the next moment k + 1 has the formula: where .

[0114] Among them, according to the predicted state quantity at the next moment k + 1, the predicted centroid sideslip angle at the next moment k + 1 is obtained, the predicted yaw rate at the next moment k + 1, and the predicted longitudinal velocity at the next moment k + 1.

[0115] Calculate the Kalman gain matrix at the next moment k + 1. The formula is:

[0116] , where is the measurement covariance matrix.

[0117] Step 1054: Determine the measurement quantity at the next moment.

[0118] During specific implementation, the measurement quantity at the next moment k + 1 has the calculation formula:

[0119]

[0120] where is the measurement error, belongs to , is the measurement covariance matrix.

[0121] Step 1055, obtain the predicted state quantity at the next moment , combine the measurement quantity at the next moment , the second Jacobian matrix and the Kalman gain matrix at the next moment , and calibrate the predicted state quantity at the next moment to obtain the calibrated state quantity at the next moment , where the calibrated parameter quantity at the next moment includes the calibrated state quantity at the next moment .

[0122] Specifically, the formula for the calibrated state quantity at the next moment k+1 is:

[0123] . In this way, the calibrated longitudinal speed at the next moment k+1 can be determined according to , and the sideslip angle of the center of mass at the next moment k+1, so that the calibrated lateral speed = multiplied by can be obtained at the next moment k+1.

[0124] Then determine the calibrated error covariance matrix at the next moment k+1, and the formula is:

[0125] , where is a 3×3 identity matrix.

[0126] Through the above solution, a more accurate calibrated lateral speed at the next moment can be obtained, which is convenient for vehicle driving control at the next moment according to the calibrated lateral speed at the next moment.

[0127] In some embodiments, the method further includes:

[0128] Step 107, in response to determining that the vehicle is in a straight driving state or the vehicle speed is lower than the low speed threshold (for example, 2 kph or 1 kph), clear the sideslip angle of the center of mass at the current moment, and clear the calibrated lateral speed at the current moment.

[0129] Specifically, the way to detect the straight driving of the vehicle is to take the average of a section of historical steering wheel angle signals to obtain the average steering wheel angle. If the average steering wheel angle is less than a predetermined threshold (which can be calibrated), it is considered that the vehicle is in a straight driving state. The vehicle speed can be directly determined according to the speed displayed on the dashboard.

[0130] With the above solution, when the vehicle is driving straight, the logic of zeroing the sideslip angle of the center of mass and the calibrated lateral speed can be adopted to ensure that the vehicle can quickly enter the straight driving state and perform driving control in a timely manner according to the straight driving state. Moreover, when the vehicle is driving at a low speed, the logic of zeroing the sideslip angle of the center of mass and the calibrated lateral speed can be adopted to avoid the influence of the parameters of low-speed driving on subsequent driving control.

[0131] Among them, step 107 has no sequence relationship with the above steps. And in this application, all parameters with "∧" indicating the calibration process and all parameters with "∨" indicating the prediction process.

[0132] The process of specifically determining the lateral speed at the next moment in step 106 can also be:

[0133] In some embodiments, a second state quantity X2 including the longitudinal speed and the lateral speed is preset. , where is the longitudinal speed, is the lateral speed.

[0134] Step 106 includes:

[0135] Step 1061, retrieve the re-calibration parameter quantity X2 at the current moment k , and determine the calibrated longitudinal speed and the calibrated lateral speed at the current moment according to the re-calibration parameter quantity at the current moment.

[0136] During specific implementation, since the calibrated longitudinal speed at the current moment can be directly determined and the calibrated lateral speed at the current moment .

[0137] Step 1062, obtain the speed correlation parameter at the current moment detected by the sensor, and combine the speed correlation parameter at the current moment, the calibrated longitudinal speed at the current moment, and the calibrated lateral speed at the current moment to predict the second state quantity at the next moment.

[0138] During specific implementation, the speed correlation parameter u at the current moment k is where is the longitudinal acceleration detected at the current moment k, is the lateral acceleration detected at the current moment k, is the yaw rate detected at the current moment k.

[0139] Predict the second state quantity at the next moment The formula for is:

[0140] , where t is the sampling time, The process noise belongs to the interval (0, ), where is the process covariance matrix.

[0141] Step 1063: Determine the calibration parameter according to the initial calibration parameter quantity at the next moment, and use the calibration parameter to recalibrate the predicted second state quantity at the next moment to obtain the calibrated second state quantity at the next moment, where the recalibration parameter quantity at the next moment includes the calibrated second state quantity at the next moment.

[0142] In specific implementation, to ensure the accuracy of the second state quantity at the next moment, the calibration parameter will be determined according to the initial calibration parameter quantity at the next moment obtained in the above steps for recalibration.

[0143] Through the above solution, it can be ensured that the accuracy of the second state quantity at the next moment obtained after recalibration is higher.

[0144] In some embodiments, a second measurement quantity Y2 including a longitudinal measurement speed and a lateral measurement speed is preset. , is the longitudinal measurement speed, is the lateral measurement speed.

[0145] Step 1063 includes:

[0146] Step 10631: Determine the third Jacobian matrix at the current moment, and the second error covariance matrix at the current moment.

[0147] In specific implementation, the formula for the third Jacobian matrix is:

[0148] , where is a 2×2 standard matrix, t is the sampling time, is the yaw angular velocity detected at the current moment k.

[0149] Step 10632: According to the third Jacobian matrix and the second error covariance matrix at the current moment, predict the second error covariance matrix at the next moment.

[0150] In specific implementation, the formula for predicting the second error covariance matrix at the next moment is:

[0151] , where is the process covariance matrix.

[0152] Step 10633: Determine the fourth Jacobian matrix H. Based on the second error covariance matrix at the next moment and the fourth Jacobian matrix H, determine the second Kalman gain matrix at the next moment .

[0153] In specific implementation, the calculation formula for the second Kalman gain matrix at the next moment is: , where is the measurement covariance matrix.

[0154] Step 10634: Based on the initial calibration parameters at the next moment obtained in the above steps (i.e., ), determine the initial calibrated lateral velocity and the initial calibrated longitudinal velocity at the next moment.

[0155] In specific implementation, the initial calibration parameters at the next moment k + 1 include: the calibrated first state quantity at the next moment k + 1, which includes the initial calibrated longitudinal velocity at the next moment k + 1 and the centroid side slip angle at the next moment k + 1. Calculate the initial calibrated lateral velocity = .

[0156] In this way, can be obtained.

[0157] Step 10635: According to the initial calibrated lateral velocity and the initial calibrated longitudinal velocity at the next moment, use the measurement algorithm to obtain the second measurement quantity at the next moment.

[0158] Where is the measurement error, belongs to , is the measurement covariance matrix.

[0159] Step 10636: Combine the second measurement quantity at the next moment, the fourth Jacobian matrix H, and the second Kalman gain matrix at the next moment to recalibrate the predicted second state quantity at the next moment, and obtain the calibrated second state quantity at the next moment.

[0160] In some embodiments, the formula for the predicted second state quantity at the next moment is: where t is the sampling time, The longitudinal acceleration detected at the current moment k The yaw rate detected at the current moment k The calibrated longitudinal speed at the current moment k after re - calibration The calibrated lateral speed at the current moment k after re - calibration

[0161] The second state quantity at the next moment after calibration The formula is:

[0162] .

[0163] In this way, based on the calibrated longitudinal speed of the vehicle at the next moment can be directly determined .

[0164] Then, the calibrated second error covariance matrix at the next moment k + 1 is determined , and the formula is: . Based on this operations can be performed on the moment k + 2

[0165] Through the above - mentioned scheme, a more accurate calibrated lateral speed at the next moment can be obtained, which is convenient for vehicle driving control at the next moment according to the calibrated lateral speed at the next moment

[0166] If the four wheels slip severely, then by increasing the matrix or decreasing , the weight of kinematic estimation is increased

[0167] If parking or straight - line driving is detected, then by decreasing the matrix or increasing , the weight of kinematic estimation is decreased to suppress the instability of the lateral speed when the yaw rate is small or zero

[0168] It should be noted that the method of the embodiment of the present application can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario, and completed by multiple devices cooperating with each other. In this case of the distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiment of the present application, and these multiple devices will interact with each other to complete the described method

[0169] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0170] Based on the same inventive concept, corresponding to any of the above method embodiments, the present application further provides a device for determining the lateral speed of a vehicle.

[0171] Referring to Figure 3 , the device includes:

[0172] A model construction module 201, configured to construct a two-degree-of-freedom model of the vehicle;

[0173] A lateral force determination module 202, configured to determine the tire slip angle at the current moment, input the tire slip angle at the current moment into the brush tire model, and obtain the wheel lateral force at the current moment;

[0174] A moment balance module 203, configured to determine the moment characteristic quantity at the current moment based on the two-degree-of-freedom model and using the wheel lateral force at the current moment through moment balance;

[0175] A prediction module 204, configured to determine the speed characteristic quantity at the current moment, and in combination with the moment characteristic quantity at the current moment, use a motion algorithm to predict the prediction parameter quantity at the next moment, where the prediction parameter quantity includes parameters related to the predicted speed;

[0176] A calibration module 205, configured to calibrate the prediction parameter quantity at the next moment to obtain the calibrated parameter quantity at the next moment, where the calibrated parameter quantity includes parameters related to the calibrated speed;

[0177] A lateral speed determination module 206, configured to determine the calibrated lateral speed of the vehicle at the next moment according to the calibrated parameter quantity at the next moment.

[0178] In some embodiments, the lateral force determination module 202 is specifically configured to:

[0179] Determine the front wheel slip angle at the current moment and the rear wheel slip angle at the current moment;

[0180] Input the front wheel slip angle at the current moment into the brush tire model to obtain the front axle lateral force at the current moment;

[0181] Input the rear wheel sideslip angle at the current moment into the brush tire model to obtain the lateral force of the rear axle at the current moment. Among them, the wheel lateral force at the current moment includes: the lateral force of the front axle at the current moment and the lateral force of the rear axle at the current moment.

[0182] In some embodiments, the front wheel sideslip angle at the current moment and the rear wheel sideslip angle at the current moment are calculated by the formula:

[0183] , where is the sideslip angle of the center of mass at the current moment k, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the yaw rate at the current moment k, is the longitudinal speed at the current moment k, is the front wheel steering angle at the current moment k;

[0184] The formula in the brush tire model is:

[0185] where is the equivalent sideslip stiffness of the wheel axle, μ is the road adhesion coefficient, is the vertical force of the ground on the wheel, is the front wheel sideslip angle at the current moment k or the rear wheel sideslip angle at the current moment k , is the lateral force of the front axle at the current moment k obtained by inputting into the brush tire model or the lateral force of the rear axle at the current moment k obtained by inputting .

[0186] In some embodiments, the moment balance module 203 is specifically configured to:

[0187] Determine the component of the longitudinal force of the front axle in the lateral direction at the current moment and the compensation component of the longitudinal force of the front axle for the yaw moment;

[0188] Determine the moment balance algorithm in the lateral direction of the two-degree-of-freedom model and the moment balance algorithm in the yaw direction of the two-degree-of-freedom model;

[0189] Input the component force into the moment balance algorithm in the lateral direction to determine the derivative of the sideslip angle of the center of mass at the current moment, and input the compensation component into the moment balance algorithm in the yaw direction to determine the derivative of the yaw angular velocity at the current moment; wherein, the moment characteristic quantity at the current moment includes: the derivative of the sideslip angle of the center of mass at the current moment and the derivative of the yaw angular velocity at the current moment.

[0190] In some embodiments, the component force of the longitudinal force of the front axle at the current moment k in the lateral direction , the formula is:

[0191] , where is the longitudinal force of the front axle at the current moment k, is the front wheel steering angle at the current moment k;

[0192] The compensation component of the longitudinal force of the front axle at the current moment k for the yaw moment , the formula is:

[0193] where; is the distance from the center of mass to the front axle, is the longitudinal force of the left wheel of the front axle at the current moment k, is the longitudinal force of the right wheel of the front axle at the current moment k, is the front axle track, is the longitudinal force of the left wheel of the rear axle at the current moment k, is the longitudinal force of the right wheel of the rear axle at the current moment k, is the rear axle track;

[0194] The formulas of the moment balance algorithm in the lateral direction and the moment balance algorithm in the yaw direction of the two-degree-of-freedom model are:

[0195] , where is the lateral force of the front axle at the current moment k, is the lateral force of the rear axle at the current moment k, m is the vehicle mass, is the vehicle lateral acceleration at the current moment k, is the longitudinal speed at the current moment k, is the derivative of the sideslip angle of the center of mass at the current moment k, is the yaw angular velocity at the current moment k, is the distance from the center of mass to the rear axle, is the moment of inertia of the whole vehicle about the center of mass in the vertical direction at the current moment k, is the derivative of the yaw angular velocity at the current moment k;

[0196] The derivative of the sideslip angle of the center of mass at the current moment k is:

[0197] ;

[0198] The derivative of the yaw rate at the current moment k is:

[0199] .

[0200] In some embodiments, state variables including the sideslip angle of the center of mass, the yaw rate, and the longitudinal speed are preset;

[0201] The prediction module 204 is specifically configured to:

[0202] Determine that the calibration parameter quantity at the current moment includes the calibration state quantity at the current moment, determine the calibration longitudinal speed at the current moment according to the calibration state quantity at the current moment, and calculate the derivative of the longitudinal speed at the current moment, where the speed characteristic quantity includes the derivative of the longitudinal speed, and the calibration state quantity is the state quantity after calibrating the predicted state quantity;

[0203] Determine that the torque characteristic quantity at the current moment includes: the derivative of the sideslip angle of the center of mass at the current moment and the derivative of the yaw rate at the current moment;

[0204] Combine the calibration state quantity at the current moment with the derivative of the longitudinal speed at the current moment, the derivative of the sideslip angle of the center of mass at the current moment, and the derivative of the yaw rate at the current moment, and use the motion algorithm to determine the predicted state quantity at the next moment, where the predicted parameter quantity at the next moment includes: the predicted state quantity at the next moment.

[0205] In some embodiments, the parameters of the measured quantity are preset;

[0206] The calibration module 205 is configured to:

[0207] Determine the first Jacobian matrix at the current moment corresponding to the motion algorithm and the error covariance matrix at the current moment;

[0208] Predict the error covariance matrix at the next moment according to the first Jacobian matrix and the error covariance matrix at the current moment;

[0209] Determine the second Jacobian matrix at the next moment corresponding to the measurement algorithm, and determine the Kalman gain matrix at the next moment according to the error covariance matrix at the next moment and the second Jacobian matrix;

[0210] Determine the measured quantity at the next moment;

[0211] Obtain the predicted state quantity at the next moment, combine the measurement quantity at the next moment, the second Jacobian matrix, and the Kalman gain matrix at the next moment, and calibrate the predicted state quantity at the next moment to obtain the calibrated state quantity at the next moment, where the calibration parameter quantity at the next moment includes the calibrated state quantity at the next moment.

[0212] In some embodiments, the apparatus further includes: a clearing module configured to:

[0213] In response to determining that the vehicle is in a straight - line driving state or the vehicle speed is lower than the low - speed threshold, clear the sideslip angle of the center of mass at the current moment and clear the calibrated lateral speed at the current moment.

[0214] For convenience of description, when describing the above apparatus, various modules are described separately according to their functions. Of course, when implementing the present application, the functions of each module can be implemented in one or more software and / or hardware.

[0215] The apparatus in the above - mentioned embodiments is used to implement the corresponding method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0216] Based on the same inventive concept, corresponding to the method in any of the above - mentioned embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method in any of the above - mentioned embodiments.

[0217] Figure 4 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0218] The processor 1010 may be implemented in a general - purpose CPU (Central Processing Unit), a microprocessor, an application - specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0219] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store the operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0220] The input / output interface 1030 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Among them, the input devices can include keyboards, mice, touchscreens, microphones, various sensors, etc., and the output devices can include displays, speakers, vibrators, indicator lights, etc.

[0221] The communication interface 1040 is used to connect to the communication module (not shown in the figure) to achieve communication interaction between this device and other devices. Among them, the communication module can achieve communication through wired means (such as USB, network cable, etc.) or can also achieve communication through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0222] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0223] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solutions of the embodiments of this specification and does not necessarily include all the components shown in the figure.

[0224] The electronic device in the above embodiments is used to implement the corresponding method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0225] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method described in any of the above embodiments.

[0226] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0227] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0228] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a computer program product, including computer program instructions, which, when running on a computer, cause the computer to execute the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0229] Based on the same inventive concept, the present application also provides a vehicle, including the device described in the above embodiment or the electronic device described in the above embodiment. It has the beneficial effects of the corresponding device or electronic device embodiments, which will not be elaborated here.

[0230] It can be understood that before using the technical solutions of the various embodiments of the present application, the types, usage scopes, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0231] For example, in response to receiving an active request from the user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to the software or hardware such as an electronic device, application program, server, or storage medium that executes the technical solution of the present application according to the prompt message.

[0232] As an optional but non-limiting implementation manner, in response to receiving an active request from a user, the manner of sending a prompt message to the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry selection controls for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0233] It can be understood that the above notification and user authorization acquisition process is only illustrative and does not limit the implementation manner of the present application. Other manners that comply with relevant laws and regulations can also be applied to the implementation manner of the present application.

[0234] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0235] In addition, for simplicity of explanation and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device may be shown in block diagram form to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation manner of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be fully within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0236] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) can be used with the embodiments discussed.

[0237] The embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A method for determining the lateral speed of a vehicle, characterized in that, Including: Construct a two-degree-of-freedom model of the vehicle; Determine the tire sideslip angle at the current moment, input the tire sideslip angle at the current moment into the brush tire model, and obtain the wheel lateral force at the current moment; Based on the two-degree-of-freedom model, use the wheel lateral force at the current moment to determine the moment characteristic quantity at the current moment through moment balance; Determine the speed characteristic quantity at the current moment, and combine the moment characteristic quantity at the current moment, and use the motion algorithm to predict the predicted parameter quantity at the next moment, where the predicted parameter quantity includes parameters related to the predicted speed, and the speed characteristic quantity includes the derivative of the longitudinal speed; Calibrate the predicted parameter quantity at the next moment to obtain the calibrated parameter quantity at the next moment, where the calibrated parameter quantity includes calibrated parameters related to the speed, and the calibrated parameters related to the speed include calibrated longitudinal speed; Determine the calibrated lateral speed of the vehicle at the next moment according to the calibrated parameter quantity at the next moment; The determining the moment characteristic quantity at the current moment based on the two-degree-of-freedom model through moment balance includes: Determine the component of the longitudinal force of the front axle at the current moment in the lateral direction and the compensation component of the longitudinal force of the front axle at the current moment for the yaw moment; Determine the moment balance algorithm in the lateral direction of the two-degree-of-freedom model and the moment balance algorithm in the yaw direction of the two-degree-of-freedom model; Input the component force into the moment balance algorithm in the lateral direction to determine the derivative of the center-of-mass sideslip angle at the current moment, and input the compensation component into the moment balance algorithm in the yaw direction to determine the derivative of the yaw angular velocity at the current moment; where the moment characteristic quantity at the current moment includes: the derivative of the center-of-mass sideslip angle at the current moment and the derivative of the yaw angular velocity at the current moment.

2. The method according to claim 1, wherein The determining the tire sideslip angle at the current moment, inputting the tire sideslip angle at the current moment into the brush tire model, and obtaining the wheel lateral force at the current moment includes: Determine the front-wheel sideslip angle at the current moment and the rear-wheel sideslip angle at the current moment; Input the front-wheel sideslip angle at the current moment into the brush tire model to obtain the lateral force of the front axle at the current moment; Input the rear-wheel sideslip angle at the current moment into the brush tire model to obtain the lateral force of the rear axle at the current moment, where the wheel lateral force at the current moment includes: the lateral force of the front axle at the current moment and the lateral force of the rear axle at the current moment.

3. The method according to claim 2, wherein The front wheel sideslip angle at the current moment and the rear wheel sideslip angle at the current moment are calculated by the following formula: , where is the centroid sideslip angle at the current moment k, is the distance from the centroid to the front axle, is the distance from the centroid to the rear axle, is the yaw rate at the current moment k, is the longitudinal speed at the current moment k, is the front wheel steering angle at the current moment k; The formula in the brush tire model is: , where is the equivalent cornering stiffness of the wheel axle, μ is the road surface adhesion coefficient, is the vertical ground force received by the wheel, is the front wheel cornering angle at the current time k or the rear wheel cornering angle at the current time k , is the lateral force of the front axle at the current time k obtained after inputting into the brush tire model or the lateral force of the rear axle at the current time k obtained after inputting .

4. The method according to claim 1, wherein The lateral component of the longitudinal force on the front axle at the current moment k , and the formula is: , where is the longitudinal force of the front axle at the current moment k, is the steering angle of the front wheel at the current moment k; Compensation component of the longitudinal force of the front axle at the current moment k for the yaw moment , and the formula is: Wherein; is the distance from the centroid to the front axle, is the longitudinal force of the left front wheel of the front axle at the current moment k, is the longitudinal force of the right front wheel of the front axle at the current moment k, is the front axle track, is the longitudinal force of the left rear wheel of the rear axle at the current moment k, is the longitudinal force of the right rear wheel of the rear axle at the current moment k, is the rear axle track; The formulas of the moment balance algorithm in the lateral direction of the two-degree-of-freedom model and the moment balance algorithm in the yaw direction of the two-degree-of-freedom model are: , where is the lateral force of the front axle at the current moment k, is the lateral force of the rear axle at the current moment k, m is the vehicle mass, is the lateral acceleration of the vehicle at the current moment k, is the longitudinal speed at the current moment k, is the derivative of the sideslip angle of the center of mass at the current moment k, is the yaw rate at the current moment k, is the distance from the center of mass to the rear axle, is the moment of inertia of the whole vehicle about the center of mass in the vertical direction at the current moment k, is the derivative of the yaw rate at the current moment k; The derivative of the center-of-mass sideslip angle at the current moment k is: ； The derivative of the yaw angular velocity at the current moment k is: 。 5. The method according to claim 1, wherein Preset state quantities including the center-of-mass sideslip angle, the yaw angular velocity, and the longitudinal speed; The determining the speed characteristic quantity at the current moment, and combining the moment characteristic quantity at the current moment, and using the motion algorithm to predict the predicted parameter quantity at the next moment includes: Determining the calibration parameter quantity at the current moment includes the calibration state quantity at the current moment. Determine the calibration longitudinal speed at the current moment according to the calibration state quantity at the current moment, and calculate the derivative of the longitudinal speed at the current moment. Among them, the calibration state quantity is the state quantity after calibrating the predicted state quantity; Determining the torque characteristic quantity at the current moment includes: the derivative of the sideslip angle of the center of mass at the current moment and the derivative of the yaw rate at the current moment; Combine the calibration state quantity at the current moment with the derivative of the longitudinal speed at the current moment, the derivative of the sideslip angle of the center of mass at the current moment, and the derivative of the yaw rate at the current moment, and use the motion algorithm to determine the predicted state quantity at the next moment. Among them, the predicted parameter quantity at the next moment includes: the predicted state quantity at the next moment.

6. The method according to claim 5, wherein Preset the parameters of the measurement quantity in advance; Calibrating the predicted parameter quantity at the next moment to obtain the calibration parameter quantity at the next moment includes: Determine the first Jacobian matrix at the current moment corresponding to the motion algorithm and the error covariance matrix at the current moment; Predict the error covariance matrix at the next moment according to the first Jacobian matrix and the error covariance matrix at the current moment; Determine the second Jacobian matrix at the next moment corresponding to the measurement algorithm, and determine the Kalman gain matrix at the next moment according to the error covariance matrix at the next moment and the second Jacobian matrix; Determine the measurement quantity at the next moment; Obtain the predicted state quantity at the next moment, combine the measurement quantity at the next moment, the second Jacobian matrix, and the Kalman gain matrix at the next moment, and calibrate the predicted state quantity at the next moment to obtain the calibration state quantity at the next moment. Among them, the calibration parameter quantity at the next moment includes the calibration state quantity at the next moment.

7. The method according to claim 3 or 5, characterized in that It also includes: In response to determining that the vehicle is in a straight driving state or the vehicle speed is lower than the low-speed threshold, clear the sideslip angle of the center of mass at the current moment and clear the calibrated lateral speed at the current moment.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method according to any one of claims 1 to 7.

9. A vehicle, characterized in that, An electronic device including the one described in claim 8.

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