Vehicle lateral speed determination method, electronic equipment and vehicle

Abstract

The invention provides a vehicle lateral speed determination method, electronic equipment and a vehicle. The method comprises the following steps: constructing a two-degree-of-freedom model of a 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 a motion algorithm; carrying out primary calibration on the prediction parameter quantity at the next moment to obtain a primary calibration parameter quantity at the next moment; acquiring a speed correlation parameter detected by the sensor at the current moment, and performing re-calibration based on the speed correlation parameter and the initial calibration parameter quantity at the next moment to obtain a re-calibration parameter quantity at the next moment; and determining the calibrated lateral speed of the vehicle at the next moment according to the recalibration parameter quantity at the next moment. According to the method, the speed correlation parameters, detected by the sensor, of the current moment are combined, the process of re-calibration is carried out, the accuracy of the obtained calibration lateral speed of the next moment is higher, and the calibration lateral speed can be adapted regardless of simple or complex road conditions.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a method for determining a vehicle lateral speed, an electronic device and a vehicle. Background Art

[0002] With the popularization of vehicle intelligence, the driving conditions of the vehicle need to be accurately estimated so that intelligent driving can be achieved.

[0003] However, the current estimated lateral speed of the vehicle is not accurate enough, which may easily lead to inaccurate control of the vehicle's driving, affecting the intelligent driving of the vehicle. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide 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 vehicle at present.

[0005] Based on the above purpose, the present application provides a method for determining the lateral speed of a vehicle, comprising: 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; The calibrated lateral speed of the vehicle at the next moment is determined according to the recalibrated parameter amount at the next moment.

[0006] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0007] Based on the same inventive concept, the present application also provides a vehicle, comprising the electronic device as described above.

[0008] From the above, it can be seen that the method for determining the vehicle lateral speed, the electronic device and the vehicle provided by the present application are based on the constructed two-degree-of-freedom model, and use the motion algorithm to predict the predicted parameter quantity related to the speed at the next moment according to the motion law. In order to improve the accuracy, the predicted parameter quantity at the next moment will be initially calibrated. However, the accuracy of the initial calibration parameter quantity at the next moment is still not high enough. In order to improve the accuracy, the speed-related parameters detected by the sensor at the current moment will be combined, and the initial calibration parameter quantity at the next moment will be recalibrated to obtain 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, 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

[0009] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a method for determining a vehicle lateral speed according to an embodiment of the present application; Figure 2 A schematic diagram of a two-degree-of-freedom model of an embodiment of the present application; Figure 3 A logical diagram of a method for determining a lateral speed of a vehicle according to an embodiment of the present application; Figure 4 A schematic diagram of the structure of a device for determining the lateral speed of a vehicle according to an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0012] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] In the related art, the lateral speed of a vehicle is generally estimated by means of Kalman filtering. This method of estimating based on the speed change trend will be inaccurate if the vehicle's lateral speed changes greatly in complex road conditions (for example, various complex road conditions including large-angle U-turns, accelerated lane changes, and straight driving).

[0014] If the vehicle is in the intelligent driving state, due to inaccurate lateral speed estimation, the vehicle's driving state cannot be accurately determined, making the next control strategy determined based on the vehicle's driving state inaccurate, which will increase driving risks and make intelligent driving unsafe.

[0015] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0016] The method for determining the lateral speed of a vehicle proposed in the embodiment of the present application is as follows: Figure 1 As shown, including: Step 101, construct a two-degree-of-freedom model of the vehicle.

[0017] In specific implementation, the two-degree-of-freedom model of the vehicle is a two-dimensional model with the center of mass 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 (such as Figure 2 The two-degree-of-freedom model of the vehicle is the basis for subsequent dynamic processing of the vehicle.

[0018] Step 102, 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, wherein the predicted parameter quantity includes the predicted speed-related parameters.

[0019] In specific implementation, the speed feature quantity at the current moment is some speed-related feature quantity after prediction, initial calibration and recalibration at the previous moment. In this way, based on the two-degree-of-freedom model, the Jacobian matrix combined with the Kalman filter can be used to predict the motion law corresponding to the motion algorithm to determine the prediction parameter quantity at the next moment, which can characterize the specific value of the lateral speed at the next moment.

[0020] Step 103, 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.

[0021] In specific implementation, since the prediction parameter quantity at the next moment may not be accurate, it needs to be calibrated. The specific calibration method can be to perform initial calibration on the prediction parameter quantity at the next moment by means of Kalman filtering, so that the initial calibration parameter quantity at the next moment is more accurate. The initial calibration parameter quantity at the next moment can characterize the specific value of the lateral speed at the next moment.

[0022] like Figure 3 As shown, the process of the above steps 102 and 103 is the execution process of the dynamic extended Kalman filter.

[0023] Step 104, obtain the speed-related parameters detected by the sensor at the current moment, recalibrate based on the speed-related parameters and the initial calibration parameter quantity at the next moment, and obtain the recalibrated parameter quantity at the next moment, wherein the recalibrated parameter quantity includes the recalibrated speed-related parameters.

[0024] In a specific implementation, the velocity-related parameters detected by the sensor at the current moment include: a detected lateral acceleration, a detected yaw angular velocity, a detected longitudinal acceleration, and a detected longitudinal velocity.

[0025] Since the initial calibration parameters at the next moment are only the result of prediction and initial calibration based on the law of vehicle movement, there may be deviations from the actual driving conditions of the vehicle, resulting in inaccurate initial calibration parameters at the next moment. Therefore, in order to further improve the accuracy, the initial calibration parameters at the next moment will be recalibrated according to the speed-related parameters actually detected by the sensor at the current moment, so that the recalibrated parameters at the next moment can be combined with the actual driving conditions of the vehicle and have higher accuracy.

[0026] Step 105: Determine the calibrated lateral speed of the vehicle at the next moment according to the recalibrated parameter at the next moment.

[0027] In specific implementation, the calibrated lateral speed at the next moment can be retrieved or calculated according to the calibration parameter at the next moment. In this way, the vehicle can be controlled according to the calibrated lateral speed at the next moment, such as controlling the steering angle of the steering wheel and controlling the degree of acceleration or deceleration of the vehicle.

[0028] like Figure 3 As shown, the process of the above steps 104 and 105 is the execution process of the kinematic Kalman filter.

[0029] Through the above scheme, based on the constructed two-degree-of-freedom model, the motion algorithm is used according to the motion law to predict the speed-related prediction parameter quantity at the next moment, and in order to improve the accuracy, the prediction parameter quantity at the next moment will be initially calibrated. However, the accuracy of the initial calibration parameter quantity at the next moment is still not high enough. In order to improve the accuracy, the speed-related parameters of the current moment detected by the sensor will be combined, and the initial calibration parameter quantity at the next moment will be recalibrated to obtain a more accurate recalibration parameter quantity at the next moment. In this way, the accuracy of the calibrated lateral speed at the next moment determined according to the recalibration parameter quantity 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.

[0030] In some embodiments, step 102 includes: Step 1021, 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.

[0031] In 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 tire footprint during longitudinal slip under steady-state conditions and the longitudinal and lateral deformation of the footprint during longitudinal slip and sideways are analyzed. The modeling mechanism of the steady-state longitudinal slip brush model and the longitudinal slip and sideways brush model when there is slip in the footprint area are analyzed. The relationship between the longitudinal shear stress and the braking force and the driving force, the vector relationship between the total shear force and the longitudinal and lateral components during longitudinal slip and sideways are discussed, as well as the internal relationship between the longitudinal slip and sideways brush model and the longitudinal slip brush model and the sideways brush model, and these relationships are integrated to form a brush tire model.

[0032] Step 1022: 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.

[0033] In a specific implementation, the torque characteristic quantity is a vehicle driving parameter related to the lateral speed, including: a derivative of the sideslip angle of the center of mass and a derivative of the yaw angular velocity, etc.

[0034] The distribution and value of various driving parameters of the vehicle are determined by the coordinate distribution corresponding to the two-degree-of-freedom model, so that calculation processing can be performed according to the torque balance to obtain the calculation formula of each torque characteristic quantity at the current moment.

[0035] Step 1023, determine the speed feature quantity at the current moment, and combine it with the torque feature quantity at the current moment, and use a motion algorithm to predict the prediction parameter quantity related to the speed at the next moment.

[0036] Through the above scheme, on the basis of the two-degree-of-freedom model, the brush tire model is used to more accurately determine the wheel lateral force at the current moment according to the tire slip angle at the current moment, and then the torque characteristic quantity at the current moment is determined according to the wheel lateral force at the current moment in combination with the torque balance, so that the torque characteristic quantity at the current moment is more in line with the principle of torque balance, and thus can meet the characteristics of torque balance under various road conditions; and then the torque characteristic quantity at the current moment is combined with the speed characteristic quantity at the current moment, and a motion algorithm is used for prediction, so that the predicted parameter quantity at the next moment is more in line with the motion law of the vehicle under various road conditions.

[0037] In some embodiments, step 1021 includes: Step 10211, determine the front wheel sideslip angle at the current moment and the rear wheel sideslip angle at the current moment.

[0038] In a specific 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.

[0039] Since the lateral speed is related to the sideslip angles of the front wheels and the rear wheels, determining the sideslip angle of the front wheels and the sideslip angle of the rear wheels at the current moment is helpful in determining the lateral force and the lateral speed.

[0040] The front wheel sideslip angle at the current moment and the rear wheel slip angle at the current moment The formula is: ,in, 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 angular velocity at the current moment k, is the longitudinal velocity at the current time k, is the front wheel turning angle at the current time k.

[0041] Step 10212, 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.

[0042] In specific implementation, since the brush tire model stores the functional relationship of the lateral force corresponding to various slip angles, the lateral force of the front axle at the current moment can be obtained by inputting the front wheel slip angle at the current moment into the brush tire model. The front wheels include: the left front wheel and the right front wheel.

[0043] Step 10213, 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, 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.

[0044] In specific implementation, since the brush tire model stores the functional relationship of the lateral force corresponding to various slip angles, the rear axle lateral force at the current moment can be obtained by inputting the rear wheel slip angle at the current moment into the brush tire model. The rear wheels include: left rear wheel and right rear wheel.

[0045] The formula in the brush tire model is: in, is the equivalent cornering stiffness of the wheel axle, μ is the road adhesion coefficient, is the vertical ground force on the wheel, is the front wheel slip angle at the current time k Or the rear wheel slip angle at the current time k , For the general The front axle lateral force at the current time k obtained after inputting the brush tire model Or The rear axle lateral force at the current time k obtained after inputting the brush tire model .

[0046] Through the above scheme, the brush tire model is directly used to determine the front axle lateral force corresponding to the front wheel sideslip angle at the current moment, and the rear axle lateral force corresponding to the rear wheel sideslip angle at the current moment is determined. The determination using the brush tire model will be more accurate.

[0047] In some embodiments, step 1022 includes: Step 10221, 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.

[0048] In specific implementation, the component of the front axle longitudinal force in the lateral direction at the current moment k is , the formula is: ,in, is the front axle longitudinal force at the current time k, is the front wheel turning angle at the current time k.

[0049] The compensation component of the front axle longitudinal force for the yaw moment at the current time k , the formula is: in; is the distance from the center of mass to the front axle, is the longitudinal force of the left wheel on the front axle at the current time k, is the longitudinal force of the right wheel on the front axle at the current time k, is the front axle track, is the longitudinal force of the left wheel on the rear axle at the current time k, is the longitudinal force of the right wheel on the rear axle at the current time k, Rear axle track.

[0050] Step 10222: Determine a moment balance algorithm for the lateral direction of the two-degree-of-freedom model and a moment balance algorithm for the yaw direction of the two-degree-of-freedom model.

[0051] In specific implementation, 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: ,in, is the lateral force of the front axle at the current moment k, is the rear axle lateral force 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 velocity at the current time 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 vehicle around the center of mass in the vertical direction at the current moment k, is the derivative of the yaw rate at the current time k.

[0052] 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, and 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.

[0053] Among them, the center of mass is the coordinate center point of the two-degree-of-freedom model, the center of mass sideslip angle 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.

[0054] The derivative of the sideslip angle of the center of mass at the current time k is: ; The derivative of the yaw rate at the current time k is: .

[0055] Through the above scheme, 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.

[0056] In some embodiments, a first state quantity X1 including a sideslip angle of the center of mass, a yaw rate, and a longitudinal rate is preset. , where β is the sideslip angle of the center of mass, is the yaw angular velocity, is the longitudinal speed.

[0057] Step 1023 includes: Step 10231, determine the calibration parameter quantity at the current moment including the calibration first state quantity at the current moment, determine the calibration longitudinal speed at the current moment according to the calibration first state quantity at the current moment, and calculate 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.

[0058] In specific implementation, the calibration first state quantity at the current time k is: , is the sideslip angle of the center of mass at the current moment k after calibration, is the yaw angular velocity at the current time k after calibration, is the longitudinal velocity at the current time k after calibration.

[0059] The longitudinal velocity at the current time k after calibration is determined Substituting into the formula of the moment balance algorithm in the lateral direction, the derivative of the longitudinal velocity at the current moment is obtained: , the formula is: .

[0060] The derivative of the longitudinal velocity at the current time k for: ,in, is the longitudinal acceleration at the current moment, is the yaw angular velocity at the current moment k.

[0061] Step 10232, 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 rate at the current moment .

[0062] In specific implementation, the derivative of the yaw rate at the current moment k is The formula is: .

[0063] Step 10233: 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 sideslip 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. , wherein the predicted parameter quantity at the next moment includes: the predicted first state quantity at the next moment.

[0064] When implemented, the formula of the motion algorithm is: ; Where t is the sampling time, is the lateral force of the front axle at the current moment k, is the rear axle lateral force 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 turning angle at the current time k, is the front axle longitudinal force at the current time k, is the compensation component of the yaw moment at the current moment k, is the lateral component of the front axle longitudinal force at the current moment k, The process noise belongs to the interval (0, )in is the process covariance matrix.

[0065]

[0066] Through the above scheme, the motion algorithm can accurately predict and then obtain the predicted first state quantity at the next moment, thereby ensuring the accuracy of the prediction and improving the efficiency.

[0067] In some embodiments, the parameters of the first measurement quantity are preset. The first measurement quantity is ,in, The lateral acceleration of the vehicle, is the yaw angular velocity, is the longitudinal speed.

[0068] Step 103 includes: Step 1031, determining the first Jacobian matrix corresponding to the motion algorithm at the current moment and the first error covariance matrix at the current moment.

[0069] The first Jacobian matrix of the current time k The calculation formula is:

[0070]

[0071] in, is the first state quantity of calibration at the current time k, is the sideslip angle of the center of mass at the current moment k after calibration, is the yaw angular velocity at the current time k after calibration, is the longitudinal velocity at the current time k after calibration, is the moment of inertia of the vehicle in the vertical direction about its center of mass.

[0072] The first error covariance matrix at the current time k is the first error covariance matrix after calibration at the current moment k.

[0073] Step 1032: according to the first Jacobian matrix and the first error covariance matrix at the current moment , predict the first error covariance matrix at the next moment.

[0074] In specific implementation, it is also necessary to call the process covariance matrix , so that the first error covariance matrix of the next moment k+1 can be predicted , the formula is: .

[0075] 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.

[0076] In specific implementation, according to the formula: ,in, is the first state quantity at the current moment k.

[0077] The formula for the second Jacobian matrix at the next moment is: ,in is the predicted first state quantity at the next time k+1.

[0078]

[0079]

[0080] Among them, the predicted first state quantity at the next moment k+1 is The formula is: in, .

[0081] Among them, according to the predicted first state quantity at the next moment k+1 Get the predicted center of mass sideslip angle at the next moment k+1 , the predicted yaw rate at the next moment k+1 , the predicted longitudinal velocity at the next moment k+1 .

[0082] Calculate the first Kalman gain matrix at the next time k+1 The formula is: ,in, is the measurement covariance matrix.

[0083] Step 1034, determining the first measurement quantity at the next moment.

[0084] In specific implementation, the measurement quantity at the next moment k+1 , the formula is:

[0085] in, is the measurement error, belong , is the measurement covariance matrix.

[0086] Step 1035: Obtain the predicted first state quantity at the next moment , the first measurement quantity at the next moment , the second Jacobian matrix and the first Kalman gain matrix at the next moment Combined with the predicted first state quantity at the next moment Perform the initial calibration to obtain the first state quantity of the initial calibration at the next moment , wherein the initial calibration parameter quantity at the next moment includes the initial calibration first state quantity at the next moment.

[0087] In specific implementation, the first state quantity of the initial calibration at the next time k+1 is The formula is: . This way you can Determine the initial calibration longitudinal velocity at the next time k+1 , and the center of mass sideslip angle at the next moment k+1 .

[0088] Then determine the error covariance matrix of the calibration at the next time k+1 , so as to continue to perform the initial calibration at the next time k+2. The specific formula is: ,in, is a 3×3 identity matrix.

[0089] Through the above scheme, a more accurate initial calibration first state quantity can be obtained.

[0090] In some embodiments, the method further comprises: Step 103a, in response to determining that the vehicle is in a straight-line driving state or the vehicle speed is lower than a low-speed threshold (eg, 2 kph or 1 kph), the center of mass sideslip angle at the current moment is cleared to zero, and the calibrated lateral speed at the current moment is cleared to zero.

[0091] In specific implementation, the method of detecting the vehicle's straight-line driving is to average a period of historical steering wheel angle signals to obtain a mean steering wheel angle. If the mean steering wheel angle is less than a predetermined threshold (the predetermined threshold can be calibrated), the vehicle is considered to be in a straight-line driving state. The vehicle speed can be directly determined based on the speed displayed on the instrument panel.

[0092] Through the above scheme, when the vehicle is traveling in a straight line, the logic of clearing the center of mass sideslip angle and the calibrated lateral speed can be adopted to ensure that the vehicle can quickly enter the straight-line driving state and perform driving control according to the straight-line driving state in time; and when the vehicle is traveling at a low speed, the logic of clearing the center of mass sideslip angle and the calibrated lateral speed can be adopted to avoid the various parameters of low-speed driving affecting the subsequent driving control.

[0093] In some embodiments, a second state quantity X2 including a longitudinal speed and a lateral speed is preset. ,in, is the longitudinal velocity, is the lateral speed.

[0094] Step 104 includes: Step 1041, retrieve the current recalibration parameter X2 k , determining the calibrated longitudinal speed at the current moment and the calibrated lateral speed at the current moment according to the recalibrated parameter amount at the current moment.

[0095] In specific implementation, due to The calibrated longitudinal velocity at the current moment can be determined directly and the calibrated lateral velocity at the current moment .

[0096] Step 1042, obtaining the speed-related parameters detected by the sensor at the current moment, combining the speed-related parameters 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.

[0097] In specific implementation, the speed associated parameter u at the current time k is in, 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.

[0098] Predict the second state quantity at the next moment The formula is: , where t is the sampling time, The process noise belongs to the interval (0, )in is the process covariance matrix.

[0099] Step 1043, determine the calibration parameters according to the initial calibration parameter quantity at the next moment, and use the calibration parameters to recalibrate the predicted second state quantity at the next moment 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.

[0100] In specific implementation, in order to ensure the accuracy of the second state quantity at the next moment, the calibration parameters are determined according to the initial calibration parameter quantity at the next moment obtained in the above steps to perform recalibration.

[0101] Through the above solution, it can be ensured that the second state quantity at the next moment obtained after recalibration has higher accuracy.

[0102] In some embodiments, a second measurement quantity Y2 including a longitudinal measurement velocity and a lateral measurement velocity is preset. , is the longitudinal measurement velocity, Measure the speed in the lateral direction.

[0103] Step 1043 includes: Step 10431, determine the third Jacobian matrix at the current moment , and the second error covariance matrix at the current moment .

[0104] In specific implementation, the third Jacobian matrix The formula is: ,in, is a 2×2 identity matrix, t is the sampling time, is the yaw angular velocity detected at the current moment k.

[0105] Step 10432, according to the third Jacobian matrix and the second error covariance matrix at the current moment , predict the second error covariance matrix for the next moment .

[0106] In specific implementation, the second error covariance matrix of the next moment is predicted The formula is: ,in is the process covariance matrix.

[0107] Step 10433, determine the fourth Jacobian matrix H, according to 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 .

[0108] In specific implementation, the second Kalman gain matrix at the next moment The calculation formula is: ,in, is the measurement covariance matrix.

[0109] Step 10434, based on the initial calibration parameter quantity at the next moment, determine the initial calibrated lateral speed and the initial calibrated longitudinal speed at the next moment.

[0110] In specific implementation, the first calibration parameter quantity at the next time k+1 includes: the first state quantity of the first calibration at the next time k+1 The longitudinal velocity of the initial calibration at the next time k+1 is included and the center of mass sideslip angle at the next moment k+1 , calculate the initial calibrated lateral velocity at the next moment = .

[0111] This way you can get .

[0112] Step 10435, using the measurement algorithm to obtain the second measurement value at the next moment according to the first calibrated lateral velocity and the first calibrated longitudinal velocity at the next moment .

[0113] in, is the measurement error, belong , is the measurement covariance matrix.

[0114] Step 10436: The second measurement value at the next moment , the fourth Jacobian matrix H and the second Kalman gain matrix at the next moment Combined with the second state quantity of the next moment prediction Perform recalibration to obtain the second state quantity at the next calibration moment .

[0115] In some embodiments, the second state quantity predicted at the next moment The formula is: Where t is the sampling time, is the longitudinal acceleration detected at the current moment k, is the yaw angular velocity detected at the current moment k, is the calibrated longitudinal velocity at the current time k after recalibration, is the calibrated lateral velocity at the current time k after recalibration.

[0116] The second state quantity of the next moment of calibration The formula is: .

[0117] In this way, according to Directly determine the vehicle's calibrated longitudinal velocity at the next moment .

[0118] Then determine the second error covariance matrix of the calibration at the next time k+1 , the formula is: Based on this The next time point k+2 is subjected to calculation processing.

[0119] Through the above scheme, a more accurate calibrated lateral speed at the next moment can be obtained, which facilitates driving control at the next moment according to the calibrated lateral speed at the next moment.

[0120] If the four wheels are slipping severely, increase Matrix or reduction , to increase the weight of the kinematic estimate.

[0121] If parking or straight-line driving is detected, the Matrix or Augment , in order to reduce the weight of the kinematic estimation and suppress the instability of the lateral velocity at small or zero yaw rate.

[0122] In this application, all the numbers with “∧” represent parameters of the calibration process, and all the numbers with “∨” represent parameters of the prediction process.

[0123] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0124] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a device for determining the lateral speed of a vehicle.

[0126] refer to Figure 4 , the device comprises: A two-degree-of-freedom model building module 201 is configured to build a two-degree-of-freedom model of the vehicle; The prediction module 202 is configured to 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; The initial calibration module 203 is configured to perform 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; The recalibration module 204 is configured to obtain the speed-related parameters detected by the sensor at the current moment, and perform recalibration based on the speed-related parameters and the initial calibration parameter quantity at the next moment to obtain the recalibration parameter quantity at the next moment, wherein the recalibration parameter quantity includes the recalibrated speed-related parameters; The lateral speed determination module 205 is configured to determine the calibrated lateral speed of the vehicle at the next moment according to the recalibrated parameter at the next moment.

[0127] In some embodiments, the prediction module 202 is specifically 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; 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.

[0128] In some embodiments, the prediction module 202 is further configured to: 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.

[0129] In some embodiments, the prediction module 202 is further configured to: 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.

[0130] In some embodiments, a first state quantity including a sideslip angle of the center of mass, a yaw rate, and a longitudinal rate is preset; The prediction module 202 is further configured to: 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.

[0131] In some embodiments, parameters of the first measurement quantity are preset; The initial calibration module 203 is specifically configured as follows: 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.

[0132] In some embodiments, a second state quantity including a longitudinal speed and a lateral speed is preset; The recalibration module 204 is specifically configured as follows: 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.

[0133] In some embodiments, the second measurement quantity including the longitudinal measurement velocity and the lateral measurement velocity is preset; The recalibration module 204 is further configured to: 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.

[0134] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0135] The device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0136] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any of the above embodiments when executing the program.

[0137] Figure 5 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0138] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0139] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0140] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0141] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0142] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0143] 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, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0144] The electronic device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0145] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in any of the above embodiments.

[0146] 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. Information can be computer-readable instructions, data structures, modules of programs, 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0147] The computer instructions stored in the storage medium of the above embodiments are used to enable 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 repeated here.

[0148] Based on the same concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer executes the method described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0149] 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. The beneficial effects of the embodiments with the corresponding device or electronic device are not described in detail here.

[0150] It is understandable that before using the technical solutions of each embodiment of the present application, the type, scope of use, 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.

[0151] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to the electronic device, application, server, storage medium or other software or hardware that performs the operation of the technical solution of the present application according to the prompt message.

[0152] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0153] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of this application. Other methods that meet relevant laws and regulations may also be applied to the implementation method of this application.

[0154] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0155] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram 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 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 scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0156] 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 skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0157] The embodiments of the present application are intended to cover all such substitutions, 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 should be included in the scope of protection 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; The calibrated lateral speed of the vehicle at the next moment is determined according to the recalibrated parameter amount 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 state quantity including a longitudinal speed and a lateral speed; The acquiring of the speed-related parameter detected by the sensor at the current moment, and recalibrating the initial calibration parameter at the next moment based on the speed-related parameter to obtain the recalibrated parameter at the next moment, includes: 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.

8. The method according to claim 7, 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.

9. 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 8 is implemented.

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

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