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 speed characteristic quantity at the current moment, and predicting the predicted parameter quantity of the vehicle at the next moment according to the two-degree-of-freedom model and based on the motion algorithm; performing a primary calibration on the predicted parameter quantity at the next moment to obtain the primary calibration parameter quantity at the next moment; obtaining the speed correlation parameter at the current moment detected by the sensor, and performing a secondary calibration based on the speed correlation parameter and the primary calibration parameter quantity at the next moment to obtain the secondary calibration parameter quantity at the next moment; determining the calibrated lateral speed of the vehicle at the next moment according to the secondary calibration parameter quantity at the next moment. By combining the speed correlation parameter detected by the sensor at the current moment and performing the secondary calibration process, the accuracy of the calibrated lateral speed at the next moment will be higher, and it can be adapted to both simple and complex road conditions.

Description

Technical Field

[0001] The present 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 conditions of the vehicle so as to achieve intelligent driving.

[0003] However, the currently estimated lateral speed of the vehicle is not accurate enough, which easily leads to inaccurate control of the vehicle during driving and affects the intelligent driving of the vehicle. Summary of the Invention

[0004] In view of this, the purpose of the present 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 of inaccurate estimation of the lateral speed of the current vehicle.

[0005] Based on the above purpose, the present 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 speed characteristic quantity at the current moment, and based on the two-degree-of-freedom model and the motion algorithm, predict the predicted parameter quantity of the vehicle at the next moment, where the predicted parameter quantity includes parameters related to the predicted speed;

[0008] Perform primary calibration on the predicted parameter quantity at the next moment to obtain the primary calibration parameter quantity at the next moment, where the primary calibration parameter quantity includes parameters related to the initially calibrated speed;

[0009] Obtain the speed correlation parameter detected by the sensor at the current moment, and perform secondary calibration based on the speed correlation parameter and the primary calibration parameter quantity at the next moment to obtain the secondary calibration parameter quantity at the next moment, where the secondary calibration parameter quantity includes parameters related to the re-calibrated speed;

[0010] Determine the calibrated lateral speed of the vehicle at the next moment according to the secondary calibration parameter quantity at the next moment.

[0011] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable by the processor, where the processor implements the above method when executing the computer program.

[0012] Based on the same inventive concept, the present application also provides a vehicle, including the above electronic device.

[0013] As can be seen from the above, the method, electronic device and vehicle for determining the lateral speed of a vehicle provided by the present application are based on a constructed two-degree-of-freedom model. According to the motion law, a motion algorithm is used to predict the prediction parameter related to speed at the next moment. In order to improve the accuracy, the prediction parameter at the next moment will be initially calibrated. However, the accuracy of the initially calibrated parameter at the next moment is still not high enough. In order to improve the accuracy, the speed correlation parameter at the current moment detected by the sensor will be combined to re-calibrate the initially calibrated parameter at the next moment, and a more accurate re-calibrated parameter at the next moment will be obtained. In this way, the accuracy of the calibrated lateral speed at the next moment determined according to the re-calibrated parameter at the next moment will be higher. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 Schematic flow chart of the method for determining the lateral speed of a vehicle according to an embodiment of the present application;

[0016] Figure 2 Schematic diagram of the two-degree-of-freedom model according to an embodiment of the present application;

[0017] Figure 3 Schematic logic diagram of the method for determining the lateral speed of a vehicle according to an embodiment of the present application;

[0018] Figure 4 Schematic structural diagram of the device for determining the lateral speed of a vehicle according to an embodiment of the present application;

[0019] Figure 5 Schematic structural diagram of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer and more understandable, the following will further describe the present application in detail with reference to specific embodiments and the accompanying drawings.

[0021] 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 meanings 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. The terms such as "include" or "comprise" 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. The terms such as "connect" or "be connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "up", "down", "left" and "right" are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0022] In the related art, generally, the lateral speed of a vehicle is estimated by means of Kalman filtering. 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 according to the change trend of the speed will be inaccurate.

[0023] When the vehicle is in an intelligent driving state, due to inaccurate estimation of the lateral speed, the driving state of the vehicle cannot be accurately determined, resulting in inaccurate next control strategies further determined according to the driving state of the vehicle, increasing driving risks and making intelligent driving unsafe.

[0024] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0025] The method for determining the lateral speed of a vehicle proposed in the embodiments of the present application, as Figure 1 shown, includes:

[0026] Step 101, construct a two-degree-of-freedom model of the vehicle.

[0027] Specifically, when implemented, the two-degree-of-freedom model of the vehicle is a two-dimensional model with the centroid of the vehicle as the origin, the longitudinal direction of the vehicle as the horizontal axis, and the lateral direction of the vehicle as the vertical axis (as Figure 2 shown). The two-degree-of-freedom model of the vehicle is the basis for subsequent various dynamic processes of the vehicle.

[0028] Step 102, determine the speed characteristic quantity at the current moment, and based on the two-degree-of-freedom model and a motion algorithm, predict the prediction parameter quantity of the vehicle at the next moment, where the prediction parameter quantity includes parameters related to the predicted speed.

[0029] In specific implementation, the velocity characteristic quantity at the current moment is some velocity-related characteristic quantities after prediction, initial calibration, and re-calibration at the previous moment. In this way, based on the two-degree-of-freedom model, prediction can be performed according to the motion law corresponding to the motion algorithm through the Jacobian matrix combined with the Kalman filter to determine the predicted parameter quantity at the next moment, and the predicted parameter quantity at the next moment can characterize the specific value of the lateral velocity at the next moment.

[0030] Step 103: Perform initial calibration on the predicted parameter quantity at the next moment to obtain the initially calibrated parameter quantity at the next moment, where the initially calibrated parameter quantity includes parameters related to velocity in the initial calibration.

[0031] 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 perform initial calibration on the predicted parameter quantity at the next moment through the Kalman filter, so that the initially calibrated parameter quantity obtained at the next moment is more accurate. The initially calibrated parameter quantity at the next moment can characterize the specific value of the lateral velocity at the next moment.

[0032] As Figure 3 shown, the processes of the above steps 102 and 103 are the execution processes of the dynamic extended Kalman filter.

[0033] Step 104: Obtain the velocity correlation parameter at the current moment detected by the sensor, and perform re-calibration based on the velocity correlation parameter and the initially calibrated parameter quantity at the next moment to obtain the re-calibrated parameter quantity at the next moment, where the re-calibrated parameter quantity includes parameters related to velocity in the re-calibration.

[0034] In specific implementation, the velocity correlation parameter at the current moment detected by the sensor includes: the detected lateral acceleration, the detected yaw rate, the detected longitudinal acceleration, and the detected longitudinal velocity.

[0035] Since the initially calibrated parameter quantity at the next moment is only the result of prediction and initial calibration according to the motion law of the vehicle, there may be a deviation from the actual driving situation of the vehicle, resulting in the initially calibrated parameter quantity at the next moment not being accurate enough. Therefore, in order to further improve the accuracy, the initially calibrated parameter quantity at the next moment will be re-calibrated according to the velocity correlation parameter at the current moment actually detected by the sensor, so that the re-calibrated parameter quantity at the next moment obtained can be combined with the actual driving situation of the vehicle and has higher accuracy.

[0036] Step 105: Determine the calibrated lateral velocity of the vehicle at the next moment according to the re-calibrated parameter quantity at the next moment.

[0037] During specific implementation, the calibrated lateral speed at the next moment can be retrieved or calculated based on the calibration parameter quantity at the next moment. Then, at the next moment, vehicle driving control can be performed according to the calibrated lateral speed at the next moment, such as controlling the steering angle of the steering wheel and the degree of vehicle acceleration or deceleration, etc.

[0038] As Figure 3 shown, the processes of steps 104 and 105 above are the execution processes of the kinematic Kalman filter.

[0039] Through the above solution, based on the constructed two-degree-of-freedom model, the prediction parameter quantity related to speed at the next moment is predicted using a motion algorithm according to the motion law. And to improve the accuracy, the prediction parameter quantity at the next moment is initially calibrated. However, the accuracy of the initially calibrated parameter quantity at the next moment is still not high enough. To improve the accuracy, the speed correlation parameter at the current moment detected by the sensor is combined to recalibrate the initially calibrated parameter quantity at the next moment, obtaining a more accurate recalibrated parameter quantity at the next moment. In this way, the accuracy of the calibrated lateral speed at the next moment determined according to the recalibrated parameter quantity at the next moment will be higher. Therefore, regardless of whether the vehicle is in a simple or complex road condition, the calibrated lateral speed at the next moment can be accurately determined.

[0040] In some embodiments, step 102 includes:

[0041] Step 1021, determining the tire slip angle at the current moment, and inputting the tire slip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment.

[0042] During specific implementation, 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 side slip are analyzed. The modeling mechanisms of the steady-state longitudinal slip brush model and the longitudinal slip and side slip brush model with slip in the footprint area are analyzed. The variation relationships of the longitudinal shear stress with the braking force and driving force respectively, the vector relationships between the total shear force during longitudinal slip and side slip and its longitudinal and lateral components respectively, and the internal relationships between the longitudinal slip and side slip brush model and the longitudinal slip brush model and the side slip brush model are discussed. These relationships are integrated to form the brush tire model.

[0043] Step 1022, based on the two-degree-of-freedom model, using the wheel lateral force at the current moment to determine the moment characteristic quantity at the current moment through moment balance.

[0044] During specific implementation, the moment characteristic quantity is a vehicle driving parameter related to the lateral speed, including: the derivative of the sideslip angle of the center of mass and the derivative of the yaw rate, etc.

[0045] 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 carried out according to the moment balance to obtain the calculation formulas of various moment characteristic quantities at the current moment.

[0046] Step 1023: Determine the speed characteristic quantity at the current moment, and combine the moment characteristic quantity at the current moment to predict the predicted parameter quantity at the next moment related to speed by using the motion algorithm.

[0047] Through the above scheme, based on the two-degree-of-freedom model, using the brush tire model to determine the wheel side force at the current moment according to the tire side slip angle at the current moment is more accurate, and then combining the moment balance to determine the moment characteristic quantity at the current moment according to the wheel side force at the current moment, so that the moment characteristic quantity at the current moment obtained is more in line with the principle of moment balance, and further can meet the moment balance characteristics of various road conditions; then, according to the speed characteristic quantity at the current moment combined with the moment characteristic quantity at the current moment obtained, use the motion algorithm for prediction, so that the predicted parameter quantity at the next moment obtained is more in line with the motion law of the vehicle under various road conditions.

[0048] In some embodiments, step 1021 includes:

[0049] Step 10211: Determine the front wheel side slip angle at the current moment and the rear wheel side slip angle at the current moment.

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

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

[0052] The front wheel side slip angle at the current moment and the rear wheel side slip angle at the current moment have the following formulas:

[0053] , where is the center of gravity side slip angle at the current moment k, is the distance from the center of gravity to the front axle, is the distance from the center of gravity 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.

[0054] Step 10212: 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.

[0055] In specific implementation, since the function relationship between the lateral forces corresponding to various sideslip angles is stored in the brush tire model, inputting the front wheel sideslip angle at the current moment into the brush tire model can obtain the lateral force of the front axle at the current moment. The front wheels include the left front wheel and the right front wheel.

[0056] Step 10213: 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.

[0057] In specific implementation, since the function relationship between the lateral forces corresponding to various sideslip angles is stored in the brush tire model, 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.

[0058] The formula in the brush tire model is:

[0059] Among them, 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 for the lateral force of the front axle at the current moment k obtained after inputting into the brush tire model or the lateral force of the rear axle at the current moment k obtained after inputting into the brush tire model .

[0060] Through the above solution, directly use 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 determine 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.

[0061] In some embodiments, step 1022 includes:

[0062] Step 10221: 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.

[0063] In specific implementation, the component of the longitudinal force of the front axle in the lateral direction at the current moment k , the formula is:

[0064] , 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.

[0065] The compensation component of the longitudinal force of the front axle at the current moment k for the yaw moment , and the formula is:

[0066] 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.

[0067] Step 10222, 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.

[0068] Specifically, 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:

[0069] , 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.

[0070] Step 10223, input the component force and the compensation component into the moment balance algorithm in the lateral direction and the moment balance algorithm in the yaw direction to determine 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; 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.

[0071] Among them, the centroid is the coordinate center point of the two-degree-of-freedom model, the sideslip angle of the centroid 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.

[0072] The derivative of the sideslip angle of the centroid at the current moment k is:

[0073] ;

[0074] The derivative of the yaw angular velocity at the current moment k is:

[0075] .

[0076] Through the above scheme, the derivative of the sideslip angle of the centroid 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.

[0077] In some embodiments, a first state quantity X1 including the sideslip angle of the centroid, the yaw angular velocity, and the longitudinal velocity is preset. , where β is the sideslip angle of the centroid, is the yaw angular velocity, is the longitudinal velocity.

[0078] Step 1023 includes:

[0079] Step 10231, determining that the calibration parameter quantity at the current moment includes the calibration first state quantity at the current moment, determining the calibration longitudinal velocity at the current moment according to the calibration first state quantity at the current moment, and calculating the derivative of the calibration longitudinal velocity at the current moment, where the calibration first state quantity is the first state quantity after calibrating the predicted first state quantity.

[0080] Specifically, the calibration first state quantity at the current moment k is:

[0081] , is the sideslip angle of the centroid 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.

[0082] Substitute the determined calibrated longitudinal velocity at the current moment k 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:

[0083] .

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

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

[0086] Step 10232, determining 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 rate at the current moment .

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

[0088] .

[0089] Step 10233, combining the calibration state quantity at the current moment with the derivative of the calibrated 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 using the motion algorithm to determine the predicted first state quantity at the next moment , where the predicted parameter quantity at the next moment includes: the predicted first state quantity at the next moment.

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

[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 of the longitudinal force of the front axle in the lateral direction at the current moment k, the process noise belongs to the interval (0, ), where is the process covariance matrix.

[0093]

[0094] Through the above solution, accurate prediction can be performed through the motion algorithm, and then the predicted first 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 a first measurement quantity are preset. The first measurement quantity is , where the lateral acceleration of the vehicle, is the yaw angular velocity, is the longitudinal velocity.

[0096] Step 103 includes:

[0097] Step 1031, determine the first Jacobian matrix at the current moment corresponding to the motion algorithm, and the first error covariance matrix at the current moment.

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

[0099]

[0100]

[0101] where is the calibrated first state quantity at the current moment k, is the sideslip angle of the center of mass after calibration at the current moment k, is the yaw angular velocity after calibration at the current moment k, is the longitudinal velocity after calibration at the current moment k, is the moment of inertia of the whole vehicle rotating about the center of mass in the vertical direction.

[0102] The first error covariance matrix at the current moment k is the retrieved first error covariance matrix after calibration at the current moment k.

[0103] Step 1032, predict the first error covariance matrix at the next moment according to the first Jacobian matrix and the first error covariance matrix at the current moment .

[0104] In specific implementation, the process covariance matrix also needs to be retrieved, so that the first error covariance matrix at the next moment k + 1 can be predicted, and the formula is: .

[0105] Step 1033, determine the second Jacobian matrix at the next moment corresponding to the measurement algorithm, and determine the first Kalman gain matrix at the next moment according to the first error covariance matrix and the second Jacobian matrix at the next moment.

[0106] In specific implementation, according to the formula:

[0107] , where is the first state quantity at the current moment k.

[0108] The formula for the second Jacobian matrix at the next moment:

[0109] , where is the predicted first state quantity at the next moment k + 1.

[0110]

[0111]

[0112] Among them, the predicted first state quantity at the next moment k + 1 has the formula: where .

[0113] Among them, according to the predicted first state quantity at the next moment k + 1 the predicted sideslip angle of the centroid at the next moment k + 1 is obtained , the predicted yaw rate at the next moment k + 1 , the predicted longitudinal speed at the next moment k + 1 .

[0114] Calculate the first Kalman gain matrix at the next moment k + 1 The formula is:

[0115] , where is the measurement covariance matrix.

[0116] Step 1034, determine the first measurement quantity at the next moment.

[0117] In specific implementation, the measurement quantity at the next moment k + 1 , the formula is:

[0118]

[0119] where is the measurement error, belongs to , is the measurement covariance matrix.

[0120] Step 1035, obtain the predicted first state quantity at the next moment , and combine the first measurement quantity at the next moment , the second Jacobian matrix and the first Kalman gain matrix at the next moment to predict the first state quantity at the next moment Perform initial calibration to obtain the first state quantity of the initial calibration at the next moment , where the initial calibration parameter quantity at the next moment includes the first state quantity of the initial calibration at the next moment.

[0121] Specifically, when implemented, the first state quantity of the initial calibration at the next moment k+1 is calculated by the formula:

[0122] . In this way, the longitudinal speed of the initial calibration at the next moment k+1 can be determined according to , as well as the sideslip angle of the center of mass at the next moment k+1 .

[0123] Then determine the calibration error covariance matrix at the next moment k+1 for subsequent continuous initial calibration at the next next moment k+2. The specific formula is:

[0124] , where is a 3×3 identity matrix.

[0125] Through the above solution, a more accurate first state quantity of the initial calibration can be obtained.

[0126] In some embodiments, the method further includes:

[0127] Step 103a, 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.

[0128] Specifically, when implemented, 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.

[0129] Through the above solution, when the vehicle is in a straight driving state, the clearing logic of 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; and when the vehicle is driving at a low speed, the clearing logic of the sideslip angle of the center of mass and the calibrated lateral speed can also be adopted to avoid the influence of the parameters of low-speed driving on subsequent driving control.

[0130] In some embodiments, a second state quantity X2 including longitudinal speed and lateral speed is preset. , where is the longitudinal speed, is the lateral velocity.

[0131] Step 104 includes:

[0132] Step 1041, retrieve the re - calibration parameter quantity X2 at the current moment k , and determine the calibrated longitudinal velocity and the calibrated lateral velocity at the current moment according to the re - calibration parameter quantity at the current moment.

[0133] In specific implementation, since the calibrated longitudinal velocity at the current moment can be directly determined and the calibrated lateral velocity at the current moment .

[0134] Step 1042, obtain the velocity - related parameter at the current moment detected by the sensor, and combine the velocity - related parameter at the current moment, the calibrated longitudinal velocity at the current moment, and the calibrated lateral velocity at the current moment to predict the second state quantity at the next moment.

[0135] In specific implementation, the velocity - related 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 angular velocity detected at the current moment k.

[0136] Predict the second state quantity at the next moment, and the formula is:

[0137] , where t is the sampling time, is the process noise belonging to the interval (0, ), and is the process covariance matrix.

[0138] Step 1043, determine the calibration parameter according to the initial calibration parameter quantity at the next moment, and use the calibration parameter to re - calibrate the predicted second state quantity at the next moment to obtain the calibrated second state quantity at the next moment, where the re - calibration parameter quantity at the next moment includes the calibrated second state quantity at the next moment.

[0139] 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 re - calibration.

[0140] Through the above solution, it can ensure that the accuracy of the second state quantity at the next moment obtained after re - calibration is higher.

[0141] 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, and is the lateral measurement speed.

[0142] Step 1043 includes:

[0143] Step 10431, determining the third Jacobian matrix at the current moment, and the second error covariance matrix at the current moment.

[0144] In specific implementation, the formula of the third Jacobian matrix is:

[0145] , where is a 2×2 identity matrix, t is the sampling time, is the yaw angular velocity detected at the current moment k.

[0146] Step 10432, predicting the second error covariance matrix at the next moment according to 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 predicting the second error covariance matrix at the next moment is:

[0148] , where is the process covariance matrix.

[0149] Step 10433, determining the fourth Jacobian matrix H, and determining the second Kalman gain matrix at the next moment according to the second error covariance matrix at the next moment and the fourth Jacobian matrix H.

[0150] In specific implementation, the calculation formula for the second Kalman gain matrix at the next moment is: , where is the measurement covariance matrix.

[0151] Step 10434, determining the initially calibrated lateral speed and the initially calibrated longitudinal speed at the next moment based on the initially calibrated parameter quantity at the next moment.

[0152] In specific implementation, the initially calibrated parameter quantity at the next moment k+1 includes: the initially calibrated first state quantity at the next moment k+1 including the longitudinal speed of the initial calibration at the next moment k + 1 and the sideslip angle of the center of mass at the next moment k + 1 , calculate the lateral speed of the initial calibration at the next moment = .

[0153] In this way, it is possible to obtain .

[0154] Step 10435, according to the lateral speed of the initial calibration and the longitudinal speed of the initial calibration at the next moment, use the measurement algorithm to obtain the second measurement quantity at the next moment .

[0155] Among them, is the measurement error, belongs to , is the measurement covariance matrix.

[0156] Step 10436, combine the second measurement quantity at the next moment, the fourth Jacobian matrix H and the second Kalman gain matrix at the next moment, and re-calibrate the predicted second state quantity at the next moment to obtain the calibrated second state quantity at the next moment.

[0157] In some embodiments, the formula for the predicted second state quantity at the next moment is:[[]] Among them, t is the sampling time, is the longitudinal acceleration detected at the current moment k, is the yaw rate detected at the current moment k, is the calibrated longitudinal speed at the current moment k after re-calibration, is the calibrated lateral speed at the current moment k after re-calibration.

[0158] The formula for the calibrated second state quantity at the next moment is:[[]]

[0159] .

[0160] In this way, the calibrated longitudinal speed of the vehicle at the next moment can be directly determined according to .

[0161] Then determine the calibrated second error covariance matrix at the next moment k + 1, and the formula is:[[]]​ It is possible to perform arithmetic processing on the next next moment k+2 based on this For the next moment k+2.

[0162] Through the above solution, a more accurate calibrated lateral speed for the next moment can be obtained, which is convenient for vehicle driving control at the next moment according to the calibrated lateral speed of the next moment.

[0163] If the four wheels slip severely, then by increasing the matrix or decreasing the to increase the weight of the kinematic estimation.

[0164] If it is detected that the vehicle is stopped or driving straight, then by decreasing the matrix or increasing the to reduce the weight of the kinematic estimation and suppress the instability of the lateral speed when the small yaw rate or zero yaw rate occurs.

[0165] In this application, all parameters with "∧" represent the parameters in the calibration process, and all parameters with "∨" represent the parameters in the prediction process.

[0166] It should be noted that the method of the embodiment of this 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 a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiment of this application, and these multiple devices will interact with each other to complete the described method.

[0167] It should be noted that some embodiments of this 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 can be executed in a different order from that in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0168] Based on the same inventive concept, corresponding to the method of any of the above embodiments, this application also provides a device for determining the lateral speed of a vehicle.

[0169] Referring to Figure 4 , the device includes:

[0170] A two-degree-of-freedom model construction module 201, configured to construct a two-degree-of-freedom model of the vehicle;

[0171] The prediction module 202 is configured to determine the speed characteristic quantity at the current moment, and based on the two-degree-of-freedom model and a motion algorithm, predict the predicted parameter quantity of the vehicle at the next moment, where the predicted parameter quantity includes parameters related to the predicted speed;

[0172] The initial calibration module 203 is configured to perform an initial calibration on the predicted parameter quantity at the next moment to obtain the initially calibrated parameter quantity at the next moment, where the initially calibrated parameter quantity includes parameters related to the initially calibrated speed;

[0173] The re-calibration module 204 is configured to obtain the speed correlation parameter at the current moment detected by the sensor, and perform a re-calibration based on the speed correlation parameter and the initially calibrated parameter quantity at the next moment to obtain the re-calibrated parameter quantity at the next moment, where the re-calibrated parameter quantity includes parameters related to the re-calibrated speed;

[0174] The lateral speed determination module 205 is configured to determine the calibrated lateral speed of the vehicle at the next moment according to the re-calibrated parameter quantity at the next moment.

[0175] In some embodiments, the prediction module 202 is specifically configured to:

[0176] Determine the tire sideslip angle at the current moment, input the tire sideslip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment;

[0177] 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;

[0178] Determine the speed characteristic quantity at the current moment, and in combination with the torque characteristic quantity at the current moment, use a motion algorithm to predict the predicted parameter quantity related to speed at the next moment.

[0179] In some embodiments, the prediction module 202 is further specifically configured to:

[0180] The determining the tire sideslip angle at the current moment and inputting the tire sideslip angle at the current moment into the brush tire model to obtain the wheel lateral force at the current moment includes:

[0181] Determine the front wheel sideslip angle and the rear wheel sideslip angle at the current moment;

[0182] Input the front wheel sideslip angle at the current moment into the brush tire model to obtain the front axle lateral force at the current moment;

[0183] Input the rear wheel slip 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.

[0184] In some embodiments, the prediction module 202 is further specifically configured to:

[0185] 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;

[0186] 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;

[0187] Input the component force and the compensation component into the moment balance algorithm in the lateral direction and the moment balance algorithm in the yaw direction to determine the derivative of the center-of-mass slip angle at the current moment and 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 slip angle at the current moment and the derivative of the yaw angular velocity at the current moment.

[0188] In some embodiments, a first state quantity including the center-of-mass slip angle, the yaw angular velocity, and the longitudinal velocity is preset;

[0189] The prediction module 202 is further specifically configured to:

[0190] Determine the velocity characteristic quantity at the current moment, and in combination with the moment characteristic quantity at the current moment, use the motion algorithm to predict the prediction parameter quantity at the next moment related to the velocity, including:

[0191] Determine the calibration parameter quantity at the current moment including the calibration first state quantity at the current moment, determine the calibration longitudinal velocity at the current moment according to the calibration first state quantity at the current moment, and calculate the derivative of the calibration longitudinal velocity at the current moment, where the calibration first state quantity is the first state quantity after calibrating the predicted first state quantity;

[0192] Determine the moment characteristic quantity at the current moment including: the derivative of the center-of-mass slip angle at the current moment and the derivative of the yaw angular velocity at the current moment;

[0193] Combine the calibration state quantity at the current moment with the derivative of the calibration longitudinal velocity at the current moment, the derivative of the center-of-mass slip angle 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 first state quantity at the next moment, where the prediction parameter quantity at the next moment includes: the predicted first state quantity at the next moment.

[0194] In some embodiments, the parameters of the first measurement quantity are preset;

[0195] The initial calibration module 203 is specifically configured to:

[0196] Determine the first Jacobian matrix at the current moment corresponding to the motion algorithm and the first error covariance matrix at the current moment;

[0197] Predict the first error covariance matrix at the next moment according to the first Jacobian matrix and the first error covariance matrix at the current moment;

[0198] Determine the second Jacobian matrix at the next moment corresponding to the measurement algorithm, and determine the first Kalman gain matrix at the next moment according to the first error covariance matrix and the second Jacobian matrix at the next moment;

[0199] Determine the first measurement quantity at the next moment;

[0200] Obtain the predicted first state quantity at the next moment, combine the first measurement quantity, the second Jacobian matrix, and the first Kalman gain matrix at the next moment, and perform initial calibration on the predicted first state quantity at the next moment to obtain the initially calibrated first state quantity at the next moment, where the initially calibrated parameter quantity at the next moment includes the initially calibrated first state quantity at the next moment.

[0201] In some embodiments, a second state quantity including a longitudinal velocity and a lateral velocity is preset;

[0202] The re - calibration module 204 is specifically configured to:

[0203] Retrieve the re - calibration parameter quantity at the current moment, and determine the calibrated longitudinal velocity and the calibrated lateral velocity at the current moment according to the re - calibration parameter quantity at the current moment;

[0204] Obtain the velocity correlation parameter detected by the sensor at the current moment, and combine the velocity correlation parameter at the current moment, the calibrated longitudinal velocity, and the calibrated lateral velocity at the current moment to predict the second state quantity at the next moment;

[0205] Determine the calibration parameter according to the initially calibrated parameter quantity at the next moment, and use the calibration parameter to perform re - calibration on the predicted second state quantity at the next moment to obtain the calibrated second state quantity at the next moment, where the re - calibration parameter quantity at the next moment includes the calibrated second state quantity at the next moment.

[0206] In some embodiments, a second measurement quantity including a longitudinal measured velocity and a lateral measured velocity is preset;

[0207] The re - calibration module 204 is further specifically configured to:

[0208] Determine the third Jacobian matrix at the current moment and the second error covariance matrix at the current moment;

[0209] Predict the second error covariance matrix at the next moment according to the third Jacobian matrix and the second error covariance matrix at the current moment;

[0210] Determine the fourth Jacobian matrix, and determine the second Kalman gain matrix at the next moment according to the second error covariance matrix at the next moment and the fourth Jacobian matrix;

[0211] Based on the initial calibration parameters at the next moment, determine the initial calibrated lateral velocity and the initial calibrated longitudinal velocity at the next moment;

[0212] 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 at the next moment;

[0213] Combine the second measurement at the next moment, the fourth Jacobian matrix, and the second Kalman gain matrix at the next moment to recalibrate the predicted second state at the next moment, and obtain the calibrated second state at the next moment.

[0214] For the convenience of description, when describing the above device, it is described by various modules according to functions. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0215] The device in the above embodiment 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 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 embodiments.

[0217] Figure 5 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 can be implemented in the form of 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 a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an 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 called by the processor 1010 for execution.

[0220] The input / output interface 1030 is used to connect to the input / output module to realize information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0221] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to realize the communication interaction between this device and other devices. Among them, the communication module can realize communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (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, this 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 do not necessarily include all the components shown in the figure.

[0224] The electronic device in the above embodiment 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 of any of the above embodiments, the present application further provides a non-transitory computer-readable storage medium storing computer instructions for causing 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 further provides a computer program product including computer program instructions that, when run 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 further 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, when responding to an active request from a 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 software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the technical solution of this application according to the prompt message.

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

[0233] It can be understood that the above process of notifying and obtaining user authorization is only illustrative and does not limit the implementation manner of this application. Other manners that meet relevant laws and regulations can also be applied to the implementation manner of this 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 this application (including the claims) is limited to these examples; under the idea of this 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 this application as described above, and they are not provided in detail for the sake of brevity.

[0235] In addition, for the sake of simplicity of description and discussion, and in order not to make the embodiments of this 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 can be shown in the form of a block diagram to avoid making the embodiments of this application difficult to understand, and this also takes into account the fact that the details of the implementation manner of these block diagram devices highly depend on the platform on which the embodiments of this application will be implemented (that is, these details should be completely within the understanding scope of those skilled in the art). In the case of elaborating specific details (such as circuits) to describe the exemplary embodiments of this application, it is obvious to those skilled in the art that the embodiments of this application can be implemented without these specific details or with changes to these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0236] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0237] 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. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principle 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: include: Construct a two-degree-of-freedom model of the vehicle; Determine the speed feature quantity at the current moment, and predict the predicted parameter quantity of the vehicle at the next moment based on the two-degree-of-freedom model and the motion algorithm, wherein the predicted parameter quantity includes the predicted speed-related parameters; Performing an initial calibration on the predicted parameter quantity at the next moment to obtain the initial calibration parameter quantity at the next moment, wherein the initial calibration parameter quantity includes the speed-related parameters of the initial calibration; Acquire the speed-related parameters detected by the sensor at the current moment, and recalibrate based on the speed-related parameters and the initial calibration parameter quantity at the next moment to obtain the recalibrated parameter quantity at the next moment, wherein the recalibrated parameter quantity includes the recalibrated speed-related parameters; Determining a calibrated lateral speed of the vehicle at the next moment according to the recalibrated parameter at the next moment; Presetting a second state quantity including a longitudinal speed and a lateral speed; acquiring a speed-related parameter detected by a sensor at a current moment, and recalibrating the initial calibration parameter quantity at the next moment based on the speed-related parameter to obtain a recalibrated parameter quantity at the next moment, including: Retrieving the recalibration parameter amount at the current moment, and determining the calibrated longitudinal speed at the current moment and the calibrated lateral speed at the current moment according to the recalibration parameter amount at the current moment; Obtaining the speed-related parameter detected by the sensor at the current moment, combining the speed-related 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; The calibration parameters are determined according to the initial calibration parameter quantity at the next moment, and the second state quantity at the next moment is predicted to be recalibrated using the calibration parameters to obtain the calibrated second state quantity at the next moment, wherein the recalibrated parameter quantity at the next moment includes the calibrated second state quantity at the next moment.

2. The method according to claim 1, characterized in that: The determining of the speed characteristic quantity at the current moment and predicting the prediction parameter quantity of the vehicle at the next moment based on the two-degree-of-freedom model and the motion algorithm include: 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; Based on the two-degree-of-freedom model, the moment characteristic quantity at the current moment is determined by using the wheel lateral force at the current moment through moment balance; The speed feature quantity at the current moment is determined, and combined with the torque feature quantity at the current moment, a prediction parameter quantity at the next moment related to the speed is predicted using a motion algorithm.

3. The method according to claim 2, characterized in that The determining of the tire slip angle at the current moment, inputting the tire slip angle at the current moment into the brush tire model, and obtaining the wheel lateral force at the current moment, comprises: Determine the front wheel sideslip angle at the current moment and the rear wheel sideslip angle at the current moment; Inputting the front wheel sideslip angle at the current moment into the brush tire model to obtain the front axle lateral force at the current moment; The rear wheel slip angle at the current moment is input into the brush tire model to obtain the rear axle lateral force at the current moment, wherein 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.

4. The method according to claim 2, characterized in that: The determining of the moment characteristic quantity at the current moment by moment balance based on the two-degree-of-freedom model includes: Determine the component of the front axle longitudinal force in the lateral direction at the current moment, and the compensation component of the front axle longitudinal force for the yaw moment at the current moment; Determine a moment balance algorithm in the lateral direction of the two-degree-of-freedom model and a moment balance algorithm in the yaw direction of the two-degree-of-freedom model; The component force and the compensation component are input into the moment balance algorithm in the lateral direction and the moment balance algorithm in the yaw direction to determine 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; wherein 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.

5. The method according to claim 2, characterized in that: Presetting a first state quantity including a sideslip angle of the center of mass, a yaw rate and a longitudinal rate; The determining of the speed feature quantity at the current moment, and combining the torque feature quantity at the current moment, using a motion algorithm to predict a prediction parameter quantity related to the speed at the next moment, includes: Determining the calibration parameter quantity at the current moment includes the calibration first state quantity at the current moment, determining the calibration longitudinal speed at the current moment according to the calibration first state quantity at the current moment, and calculating the derivative of the calibration longitudinal speed at the current moment, wherein the calibration first state quantity is the first state quantity after calibrating the predicted first state quantity; Determining 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; The calibration state quantity at the current moment is combined with the derivative of the calibration longitudinal speed at the current moment, 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, and a motion algorithm is used to determine the predicted first state quantity at the next moment, wherein the predicted parameter quantity at the next moment includes: the predicted first state quantity at the next moment.

6. The method according to claim 5, characterized in that Presetting parameters of a first measurement quantity; The first calibration of the prediction parameter quantity at the next moment to obtain the first calibration parameter quantity at the next moment includes: Determine the first Jacobian matrix corresponding to the motion algorithm at the current moment and the first error covariance matrix at the current moment; Predicting a first error covariance matrix at a next moment according to the first Jacobian matrix and a first error covariance matrix at a current moment; Determine a second Jacobian matrix at a next moment corresponding to the measurement algorithm, and determine a first Kalman gain matrix at a next moment according to the first error covariance matrix and the second Jacobian matrix at the next moment; Determine a first measurement quantity at a next moment; Obtain the predicted first state quantity at the next moment, combine the first measurement quantity at the next moment, the second Jacobian matrix and the first Kalman gain matrix at the next moment, perform an initial calibration on the predicted first state quantity at the next moment, and obtain the initial calibrated first state quantity at the next moment, wherein the initial calibration parameter quantity at the next moment includes the initial calibrated first state quantity at the next moment.

7. The method according to claim 1, characterized in that Presetting a second measurement variable including a longitudinal measurement speed and a lateral measurement speed; Determining calibration parameters according to the initial calibration parameter quantity at the next moment, and recalibrating the predicted second state quantity at the next moment by using the calibration parameters to obtain the calibrated second state quantity at the next moment, including: Determine the third Jacobian matrix at the current moment and the second error covariance matrix at the current moment; Predicting the second error covariance matrix at the next moment according to the third Jacobian matrix and the second error covariance matrix at the current moment; Determine a fourth Jacobian matrix, and determine a second Kalman gain matrix at the next moment according to the second error covariance matrix at the next moment and the fourth Jacobian matrix; Based on the first calibration parameter at the next moment, determining the first calibration lateral speed and the first calibration longitudinal speed at the next moment; Obtaining a second measurement quantity at the next moment by using a measurement algorithm according to the initially calibrated lateral velocity and the initially calibrated longitudinal velocity at the next moment; The second measurement quantity at the next moment, the fourth Jacobian matrix and the second Kalman gain matrix at the next moment are combined, and the predicted second state quantity at the next moment is recalibrated to obtain a calibrated second state quantity at the next moment.

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

9. A vehicle, characterized in that: An electronic device comprising the electronic device described in claim 8.

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