Gradient determination method and vehicle

Abstract

The invention provides a gradient determination method and a vehicle, and the method comprises the steps: calculating a prediction vector of a current moment through employing an extended Kalman filtering algorithm according to a first calibration vector corresponding to a previous moment of the current moment; the first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated gradient, and a first calibrated gradient change rate. According to the prediction vector at the current moment and the observation vector at the current moment, calculating a calibration gradient at the current moment by adopting an extended Kalman filtering algorithm; wherein the observation vector comprises kinematically estimated longitudinal vehicle speed and pitch angle speed. In the process of calibrating the prediction vector, the pitch angular velocity is fused, a gradient can be obtained through pitch angular velocity integration, that is, the gradient obtained through the pitch angular velocity and the predicted gradient in the prediction vector are mutually verified, and the precision and robustness of gradient estimation can be effectively improved. And the calculation rate of the pitch angle speed to obtain the gradient is higher, so that the tracking effect of gradient estimation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle data processing, and in particular to a slope determination method and a vehicle. Background Art

[0002] During vehicle driving, the road slope has a significant impact on the vehicle's energy consumption, driving stability and safety. Especially under complex and changeable road conditions, fast and accurate estimation of the road slope can improve the stability of vehicle control, fuel economy and driving comfort. At present, the estimation of road slope is usually based on dynamics and kinematics methods, but both methods have certain limitations, resulting in inaccurate road slope estimation. Summary of the invention

[0003] In view of this, the purpose of the present application is to propose a slope determination method and a vehicle to solve the problem of inaccurate road slope estimation in the prior art.

[0004] Based on the above purpose, the first aspect of the present application provides a slope determination method, comprising: Calculating a prediction vector at the current moment using an extended Kalman filter algorithm according to a first calibration vector corresponding to a moment before the current moment; the first calibration vector includes a first calibration longitudinal vehicle speed, a first calibration slope, and a first calibration slope change rate; The calibration slope at the current moment is calculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment; wherein the observation vector includes a kinematically estimated longitudinal vehicle speed and a pitch angular velocity.

[0005] In this embodiment, the slope obtained by the pitch angular velocity is cross-checked with the predicted slope in the prediction vector, which can effectively improve the accuracy and robustness of the slope estimation. Compared with the indirect calculation of the slope based on the longitudinal acceleration collected by the sensor, the pitch angular velocity represents the rate of change of the slope. The calculation rate of the slope obtained by the pitch angular velocity is faster, which can improve the tracking effect of the slope estimation.

[0006] Optionally, the step of calculating the prediction vector at the current moment using an extended Kalman filter algorithm according to the first calibration vector determined at a previous moment before the current moment includes: Construct the state vector of the current moment and the state vector of the previous moment; Constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation; Determine a state transition matrix according to the state transition equation; According to the state transition matrix and the first calibration vector, an extended Kalman filter algorithm is used to perform a priori calculation to determine the prediction vector at the current moment.

[0007] This embodiment provides a method for calculating the prediction vector. The calculation of the prediction vector belongs to the a priori calculation step in the extended Kalman filter algorithm. The influence of the yaw rate on the longitudinal vehicle speed dynamic equation is introduced in the process. The appropriate dynamic equation can be used in the specific driving trajectory of the vehicle to make the method of this embodiment more applicable. By accurately determining the state transition matrix, the accuracy of the prediction vector is ensured, providing a data basis for the a posteriori calculation.

[0008] Optionally, the longitudinal vehicle speed dynamic equation includes a first dynamic equation and a second dynamic equation; and constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation includes: Determine the yaw rate at the current moment; In response to determining that the absolute value of the yaw rate is less than a preset yaw rate threshold, constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment, and the first dynamic equation; wherein the first dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, and the slope; In response to determining that the absolute value of the yaw rate is greater than or equal to a preset yaw rate threshold, a state transition equation is constructed based on the state vector at the current moment, the state vector at the previous moment and the second dynamic equation; wherein the second dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, the kinematically estimated lateral vehicle speed, the yaw rate and the slope.

[0009] By determining the corresponding dynamic equation according to the yaw angular velocity, the influence of the lateral vehicle speed on the longitudinal vehicle speed in the current situation is fully considered, so that the method in this embodiment can be used by the vehicle under different motion trajectories, which is more practical.

[0010] Optionally, the state vector includes longitudinal vehicle speed, slope, and slope change rate; The state conversion equation is constructed based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation, including: Establishing a first conversion relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment according to the longitudinal vehicle speed at the previous moment, the solution period of the extended Kalman filter algorithm, and the longitudinal vehicle speed dynamic equation; Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment according to the slope at the previous moment, the solution period and the slope change rate at the previous moment; Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment according to the slope change rate at the previous moment, the solution cycle and the pre-built slope model; A state conversion equation is constructed based on the first conversion relationship, the second conversion relationship and the third conversion relationship.

[0011] This embodiment provides a method for constructing a state transition equation, which is equivalent to providing a method for converting the state vector at the current moment to the state vector at the previous moment. By constructing a reasonable slope model, it is convenient to construct subsequent state transition equations, and then to calculate the prediction vector at the current moment.

[0012] Optionally, according to the prediction vector at the current moment and the observation vector at the current moment, an extended Kalman filter algorithm is used to calculate the calibration slope at the current moment, including: The extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment; According to the prediction vector, the Kalman gain at the current moment and the observation vector at the current moment, an extended Kalman filter algorithm is used to perform calibration calculation to determine a second calibration vector corresponding to the current moment, wherein the second calibration vector includes the calibration slope at the current moment.

[0013] The method of this embodiment provides a method for determining the calibration slope during the calibration phase. When determining the calibration slope, the pitch angular velocity in the observation vector is incorporated, and a slope can be obtained by integrating the pitch angular velocity, that is, the slope obtained by the pitch angular velocity is cross-checked with the predicted slope in the prediction vector, which can effectively improve the accuracy and robustness of the slope estimation. Compared with the indirect calculation of the slope based on the longitudinal acceleration collected by the sensor, the pitch angular velocity represents the rate of change of the slope. The calculation rate of the slope obtained by the pitch angular velocity is faster, which can improve the tracking effect of the slope estimation.

[0014] Optionally, the using of an extended Kalman filter algorithm to calculate the Kalman gain at the current moment includes: Based on the Jacobian matrix, error covariance matrix and process covariance matrix of the previous moment, the error covariance matrix of the current moment is calculated; The Kalman gain at the current moment is calculated based on the error covariance matrix, the measurement matrix and the measurement covariance matrix at the current moment.

[0015] The method of this embodiment provides a method for calculating the Kalman gain, thereby providing a data basis for subsequent calculation of the calibration slope.

[0016] Optionally, the method further includes: In response to the difference between the first calibration slope and the calibration slope at the current moment exceeding a preset slope threshold, the second calibration vector is replaced according to the first calibration vector and the observation vector at the previous moment, the value corresponding to the longitudinal vehicle speed in the process covariance matrix is ​​increased, and the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix is ​​increased.

[0017] Through the method of this embodiment, when it is determined that the second calibration slope is inaccurate, all values ​​in the second calibration vector are replaced, thereby reducing the weight of the longitudinal vehicle speed in the slope estimation process and reducing the impact on the slope estimation, so that the slope estimation result at the next moment can converge quickly and reduce the error of the slope estimation.

[0018] Optionally, before calculating the calibration slope at the current moment by using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment, the method further includes: In response to the current vehicle operating condition being the preset operating condition, a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment is increased.

[0019] In this embodiment, the increase in the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix is ​​equivalent to reducing the confidence of the longitudinal vehicle speed, reducing the weight of the longitudinal vehicle speed in the slope estimation process, thereby reducing the impact of measurement distortion on the slope estimation and reducing the fluctuation of the slope estimation result. Optionally, the method further includes: In response to a difference between a calibration slope change rate in the second calibration vector and the first calibration slope change rate exceeding a preset change rate threshold, increasing a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment; According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is recalculated using an extended Kalman filter algorithm.

[0020] The method of this embodiment provides a method for correcting the slope estimation result when the slope change rate at the previous and next moments is greatly different, thereby reducing the weight of the longitudinal vehicle speed in the slope estimation and improving the accuracy of the slope estimation.

[0021] Based on the same inventive concept, the second aspect of the present application also provides a vehicle, including a memory for storing executable program code; a processor for calling and running the executable program code from the memory, so that the vehicle executes the method described in the first aspect.

[0022] As can be seen from the above, the slope determination method and vehicle provided by the present application, wherein the method includes using the extended Kalman filter algorithm to calculate the prediction vector at the current moment according to the first calibration vector corresponding to the previous moment of the current moment; the first calibration vector includes the first calibration longitudinal vehicle speed, the first calibration slope and the first calibration slope change rate. The first calibration vector is the state vector estimated according to the extended Kalman filter algorithm at the previous moment. The prediction vector is a vector predicted according to the first calibration vector. The prediction vector includes the predicted slope. Then, the prediction of the slope at the current moment is realized by the first calibration vector at the previous moment. Since the first calibration vector includes not only the first calibration slope but also the first calibration slope change rate, the slope change can also be monitored by the first calibration slope change rate, which can further improve the accuracy of the slope prediction at the current moment. According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is calculated by the extended Kalman filter algorithm; wherein, the observation vector includes the kinematically estimated longitudinal vehicle speed and pitch angular velocity. In the process of calibrating the prediction vector, the pitch angular velocity is incorporated, and a slope can be obtained by integrating the pitch angular velocity. That is, the slope obtained by the pitch angular velocity is cross-checked with the predicted slope in the prediction vector, which can effectively improve the accuracy and robustness of the slope estimation. Compared with the related art, which indirectly calculates the slope based on the longitudinal acceleration collected by the sensor, the pitch angular velocity represents the rate of change of the slope. The calculation rate of the slope obtained by the pitch angular velocity is faster, which can improve the tracking effect of the slope estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 A schematic diagram of a flow chart of a slope determination method according to an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of a slope determination device according to an embodiment of the present application; Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION 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.

[0025] 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.

[0026] In the related art, the road slope is usually estimated quickly based on dynamics or kinematics methods, but both methods have certain limitations. The dynamics-based method requires the acquisition of the vehicle longitudinal dynamics model, but the rolling resistance and wind resistance required in the vehicle longitudinal dynamics model are often difficult to obtain and belong to random disturbances. On a flat road section, the rolling resistance is equal to the product of the road surface support force on the vehicle and the rolling coefficient, but on a slope section, the slope factor needs to be considered for the vehicle support force, and the slope is difficult to obtain without the help of external equipment, which makes it difficult to obtain the rolling resistance. Wind resistance needs to consider the relative speed between the vehicle and the air, and it is currently difficult to obtain accurate wind resistance. In addition, the dynamics-based method requires sufficient excitation, and it is difficult to achieve accurate slope estimation under conditions where the vehicle speed is low or stationary. Therefore, the accuracy of slope estimation based on the dynamic method is low.

[0027] The kinematics-based method relies more on signals collected by sensors, among which the reliance on acceleration signals is large, which can easily lead to cumulative errors, resulting in large fluctuations in slope estimation results, poor adjustability, poor tracking of estimation results, and the slope estimation results being dependent on the longitudinal vehicle speed accuracy.

[0028] In view of this, the present application proposes a slope determination method, which uses an extended Kalman filter to fuse the longitudinal slope obtained by integrating the pitch angular velocity with the longitudinal slope indirectly measured according to the longitudinal vehicle speed and longitudinal acceleration, which can effectively improve the tracking effect of the slope estimation value. The slope value of the longitudinal slope calculated according to the pitch angular velocity is cross-checked with the longitudinal slope indirectly measured according to the estimated longitudinal vehicle speed and the longitudinal acceleration collected by the sensor, which can effectively improve the accuracy and robustness of the slope estimation.

[0029] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0030] This application proposes a slope determination method, referring to Figure 1 , including the following steps: Step 102: Calculate the prediction vector at the current moment using an extended Kalman filter algorithm based on a first calibration vector corresponding to the previous moment before the current moment; the first calibration vector includes a first calibration longitudinal vehicle speed, a first calibration slope and a first calibration slope change rate.

[0031] Specifically, the extended Kalman filter algorithm is a state estimation method suitable for nonlinear systems. By introducing the linear approximation of the nonlinear system, the extended Kalman filter algorithm can be applied to the nonlinear system, thereby realizing accurate identification of the slope. The first calibration vector is the estimation vector obtained according to the extended Kalman filter algorithm at the previous moment. The first calibration vector includes the first calibration longitudinal vehicle speed, the first calibration slope and the first calibration slope change rate. The prediction vector at the current moment can be predicted by the first calibration vector at the previous moment. The prediction vector includes the predicted slope, the predicted longitudinal vehicle speed and the predicted slope change rate at the current moment. The process of obtaining the prediction vector belongs to the a priori calculation (prediction calculation) part in the extended Kalman filter algorithm, and a posteriori calculation (calibration calculation) is required later to calibrate the prediction vector obtained by the a priori calculation.

[0032] Since it is an estimation of the slope, when selecting the state vector required by the extended Kalman filter algorithm, the state vector includes the slope, the longitudinal vehicle speed which is kinematically related to the slope, and the slope change rate which can characterize the slope change. The prediction vector is the state vector obtained in the prediction phase, and the calibration vector is the state vector obtained in the calibration phase. The variables contained in the prediction vector and the calibration vector are the same as the state vector, including the longitudinal vehicle speed, the slope, and the slope change rate. The prediction vector at the current moment can be inferred from the calibration vector at the previous moment through the extended Kalman filter algorithm.

[0033] At the initial moment, the state vector can be randomly determined as the first calibration vector at the initial moment, and the prediction vector of the next moment of the initial moment is determined by the extended Kalman filter algorithm according to the first calibration vector, and then the calibration vector of the next moment is determined by the extended Kalman filter algorithm according to the prediction vector of the next moment and the observation vector of the next moment. The first calibration vector at the randomly determined initial moment includes: longitudinal vehicle speed 30m / s, slope 0, and slope change rate 0.

[0034] Step 104, using an extended Kalman filter algorithm to calculate the calibration slope at the current moment according to the prediction vector at the current moment and the observation vector at the current moment; wherein the observation vector includes the dynamically estimated longitudinal vehicle speed and pitch angular velocity.

[0035] Specifically, the prediction vector obtained is only the result of prediction based on the first calibration vector at the previous moment, and it is also necessary to calibrate it in combination with the information at the current moment to obtain a more accurate calibration slope that conforms to the current moment. The calibration method is to incorporate the observation vector into the extended Kalman filter algorithm, and determine the second calibration vector at the current moment through a posteriori calculation, and the second calibration vector includes the calibration slope at the current moment. For the convenience of distinction, the calibration slope at the current moment will be referred to as the second calibration slope in the following application.

[0036] The observation vector includes the dynamically estimated longitudinal vehicle speed obtained through dynamic estimation and the pitch angular velocity directly collected by the inertial measurement unit. Compared with the slope calculated by the longitudinal acceleration of the vehicle in the related art, the calculation rate of the slope obtained by integrating the pitch angular velocity in this embodiment is faster, and the tracking effect of the calibration slope obtained by fusing the prediction vector and the observation vector is better. In addition, the predicted slope in the prediction vector and the slope obtained by integrating the pitch angular velocity in the observation vector can be verified with each other, effectively improving the estimation accuracy and robustness of the calibration slope in the calibration vector.

[0037] Based on the above steps 102 to 104, the slope determination method provided in this embodiment includes using the extended Kalman filter algorithm to calculate the prediction vector at the current moment according to the first calibration vector corresponding to the previous moment of the current moment; the first calibration vector includes the first calibration longitudinal vehicle speed, the first calibration slope and the first calibration slope change rate. The first calibration vector is a state vector estimated according to the extended Kalman filter algorithm at the previous moment. The prediction vector is a vector predicted according to the first calibration vector. The prediction vector includes the predicted slope. Then, the prediction of the slope at the current moment is realized by the first calibration vector at the previous moment. Since the first calibration vector includes not only the first calibration slope but also the first calibration slope change rate, the slope change can also be monitored by the first calibration slope change rate, which can further improve the accuracy of the slope prediction at the current moment. According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is calculated by the extended Kalman filter algorithm; wherein, the observation vector includes the kinematically estimated longitudinal vehicle speed and pitch angular velocity. In the process of calibrating the prediction vector, the pitch angular velocity is incorporated, and a slope can be obtained by integrating the pitch angular velocity. That is, the slope obtained by the pitch angular velocity is cross-checked with the predicted slope in the prediction vector, which can effectively improve the accuracy and robustness of the slope estimation. Compared with the related art, which indirectly calculates the slope based on the longitudinal acceleration collected by the sensor, the pitch angular velocity represents the rate of change of the slope. The calculation rate of the slope obtained by the pitch angular velocity is faster, which can improve the tracking effect of the slope estimation.

[0038] The following is an explanation of the method for calculating the prediction vector through a specific embodiment.

[0039] In some embodiments, the step of calculating the prediction vector at the current moment using an extended Kalman filter algorithm based on a first calibration vector determined at a previous moment before the current moment includes: Construct the state vector of the current moment and the state vector of the previous moment; Constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation; Determine a state transition matrix according to the state transition equation; According to the state transition matrix and the first calibration vector, an extended Kalman filter algorithm is used to perform a priori calculation to determine the prediction vector at the current moment.

[0040] Specifically, the state vector selected in this embodiment is specifically recorded as ,in, Indicates the longitudinal speed, Indicates the slope, represents the slope change rate, the current moment is recorded as k+1, and the previous moment is recorded as k. Then the state vector at the current moment is recorded as , the state vector at the previous moment is recorded as .

[0041] The longitudinal vehicle speed dynamic equation includes a first dynamic equation and a second dynamic equation. Based on the state vector at the current moment, the state vector at the previous moment and the predetermined longitudinal vehicle speed dynamic equation, a state conversion equation is constructed, which specifically includes: Determine the yaw rate at the current moment; In response to determining that the absolute value of the yaw rate is less than a preset yaw rate threshold, constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment, and the first dynamic equation; wherein the first dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, and the slope; In response to determining that the absolute value of the yaw rate is greater than or equal to a preset yaw rate threshold, a state transition equation is constructed based on the state vector at the current moment, the state vector at the previous moment and the second dynamic equation; wherein the second dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, the dynamically estimated lateral vehicle speed, the yaw rate and the slope.

[0042] Longitudinal acceleration measured by the inertial measurement unit (IMU) Contains ramp information as follows: (1) in, represents the longitudinal acceleration, represents the longitudinal vehicle speed change rate, represents the dynamic estimated lateral vehicle speed obtained through dynamic estimation, represents the yaw angular velocity, represents the acceleration due to gravity, represents the slope. By converting formula (1), we can get the dynamic equation of the longitudinal speed of the vehicle when climbing a slope: (2) In this embodiment, the lateral speed of the vehicle is also considered when estimating the slope. The proportion of the lateral speed can be determined by the yaw rate. If the lateral speed accounts for a large proportion, the influence of the lateral speed on the longitudinal acceleration needs to be considered in the longitudinal speed dynamic equation. If the lateral speed accounts for a small proportion, the lateral speed can be discarded to ensure the stability of the slope estimation. Therefore, first determine the yaw rate at the current time k+1 If the absolute value of the yaw rate Less than the preset yaw rate threshold , which means that the lateral swing amplitude of the vehicle is small, the lateral speed accounts for a small proportion, and the lateral speed can be ignored. The first dynamic equation is used to construct the state conversion equation. The first dynamic equation is specifically: (3) If the absolute value of the yaw rate Greater than or equal to the preset yaw rate threshold , which means that the vehicle has a large lateral swing amplitude and the lateral speed accounts for a large proportion, which will affect the longitudinal speed of the vehicle. The lateral speed at this time cannot be ignored. The second dynamic equation is used to construct the state conversion equation. The second dynamic equation is specifically, (4) By determining the corresponding dynamic equation according to the yaw rate, the influence of the lateral vehicle speed on the longitudinal vehicle speed in the current situation is fully considered, so that the method in this embodiment can be used in different motion trajectories of the vehicle, which is more practical. In the case of a large yaw rate or a large lateral acceleration, such as when moving in a circle or in an 8-shaped motion, the second dynamic equation is applicable. When driving in a straight line, the first dynamic equation is applicable.

[0043] Due to the longitudinal acceleration It is a physical quantity that can be directly measured. The above selection of dynamic equations can also be achieved by determining the input quantity of the extended Kalman filter algorithm. It can be set as follows: (5) According to the actual collected yaw angular velocity Determine the input quantity to be used Which expression to adapt to the current condition of the vehicle.

[0044] Since the current state vector can be calculated from the state vector of the previous moment, the current state vector is equal to the sum of the state vector of the previous moment and the state change between the two moments. Therefore, the current state vector can be determined based on the state vector of the previous moment, the solution cycle of the extended Kalman filter algorithm, and the state change rate.

[0045] Furthermore, the state vector includes longitudinal vehicle speed, slope, and slope change rate; The state conversion equation is constructed based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation, including: Establishing a first conversion relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment according to the longitudinal vehicle speed at the previous moment, the solution period of the extended Kalman filter algorithm, and the longitudinal vehicle speed dynamic equation; Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment according to the slope at the previous moment, the solution period and the slope change rate at the previous moment; Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment according to the slope change rate at the previous moment, the solution cycle and the pre-built slope model; A state conversion equation is constructed based on the first conversion relationship, the second conversion relationship and the third conversion relationship.

[0046] Specifically, the first conversion relationship is: , represents the slope at the current moment, T represents the solution period, It represents the rate of change of the longitudinal vehicle speed at the previous moment, which can be determined by the longitudinal vehicle speed dynamic equation. The second conversion relationship is: , Indicates the slope at the current moment, Indicates the slope change rate at the previous moment. The third conversion relationship is: , Indicates the slope change rate at the current moment, represents the slope change rate at the previous moment, Represents the differential of the slope change rate at the previous moment. The pre-built slope model is as follows: (6) According to the slope model, the third conversion relationship can be converted to .

[0047] The state transition equation constructed based on the first conversion relationship, the second conversion relationship and the third conversion relationship is as follows: (7) This embodiment provides a method for constructing a state transition equation, which is equivalent to providing a method for converting the state vector at the current moment to the state vector at the previous moment. By constructing a reasonable slope model, it is convenient to construct subsequent state transition equations, and then to calculate the prediction vector at the current moment.

[0048] Furthermore, according to the discretization of the forward Euler method, the motion equation in the extended Kalman filter algorithm is determined as follows: (8) in, represents the process noise at the previous moment, , represents a Gaussian distribution, represents the process covariance matrix of the previous moment, T represents the solution cycle of the extended Kalman filter estimation, Table k input quantity at time.

[0049] Furthermore, the state transition matrix is ​​determined according to the state transition equation as follows: (9) The prior calculation formula in the extended Kalman filter algorithm is: (10) in, represents the state vector of the prediction stage at time k+1, represents the state vector of the calibration phase at time k, which is also the first calibration vector. Calculate the prediction vector of the current time k+1 .

[0050] This embodiment provides a method for calculating the prediction vector. The calculation of the prediction vector belongs to the a priori calculation step in the extended Kalman filter algorithm. The influence of the yaw rate on the longitudinal vehicle speed dynamic equation is introduced in the process. The appropriate dynamic equation can be determined according to the specific driving trajectory of the vehicle, so that the method of this embodiment is more applicable. By accurately determining the state transition matrix, the accuracy of the prediction vector is ensured, providing a data basis for the a posteriori calculation.

[0051] The following is a specific example to illustrate the calculation method of the calibration slope in the a posteriori process.

[0052] In some embodiments, the calibration slope at the current moment is calculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment, including: The extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment; According to the prediction vector, the Kalman gain at the current moment and the observation vector at the current moment, an extended Kalman filter algorithm is used to perform calibration calculation to determine a second calibration vector corresponding to the current moment, wherein the second calibration vector includes the calibration slope at the current moment.

[0053] Specifically, the observation vector includes the dynamic estimation of the longitudinal vehicle speed and pitch angular velocity The measurement equation is constructed according to the extended Kalman filter algorithm, as follows: + (11) in, represents the observation vector at time k+1, represents the dynamically estimated longitudinal vehicle speed at time k+1, represents the pitch angular velocity at time k+1. represents the measurement noise at time k+1, , Represents the measurement covariance matrix at time k+1. Represents the measurement matrix, and the specific value is (12) Since the motion equation has certain nonlinear components, the extended Kalman filter algorithm can be used to fuse the motion equation and the measurement equation. The recursive process of the extended Kalman filter algorithm is as follows:

[0054] in, represents the state vector at the prediction stage k+1 (i.e. the prediction vector at the current moment), Represents the state vector of the calibration phase at time k (that is, the first calibration vector at the previous moment). represents the error covariance matrix at the k+1 moment in the prediction phase, represents the Jacobian matrix at time k, represents the error covariance matrix at time k in the calibration phase, represents the process covariance matrix at time k. represents the Kalman gain at time k+1, represents the measurement matrix, Represents the measurement covariance matrix at time k+1. represents the state vector at time k in the calibration phase (i.e., the second calibration vector at the current time), Represents the observation vector at time k+1. represents the error covariance matrix at time k+1 in the calibration phase, represents the unit diagonal matrix.

[0055] Furthermore, the extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment, including: Based on the Jacobian matrix, error covariance matrix and process covariance matrix of the previous moment, the error covariance matrix of the current moment is calculated; The Kalman gain at the current moment is calculated based on the error covariance matrix, the measurement matrix and the measurement covariance matrix at the current moment.

[0056] Specifically, when calculating the Kalman gain, we can use equations (14)-(15) to get , where the Jacobian matrix F is as follows: (18) in, represents the acceleration of gravity, T represents the solution period of the extended Kalman filter estimation, represents the second variable in the state vector at time k in the calibration phase, that is, the slope in the state vector at time k in the calibration phase. The method of this embodiment provides a method for calculating the Kalman gain, thereby providing a data basis for subsequent calculation of the calibration slope.

[0057] After the Kalman gain is calculated, equation (14) is used to calculate , That is, the second calibration vector, ,in, Represents the calibration slope at the current moment (that is, moment k+1). The calibration slope can be used to predict the prediction vector at the next moment (moment k+2). The method of this embodiment provides a method for determining the calibration slope in the calibration stage. When determining the calibration slope, the pitch angular velocity in the observation vector is incorporated, and a slope can be obtained by integrating the pitch angular velocity, that is, the slope obtained by the pitch angular velocity is cross-checked with the predicted slope in the prediction vector, which can effectively improve the accuracy and robustness of the slope estimation. Compared with the related art, in which the slope is indirectly calculated based on the longitudinal acceleration collected by the sensor, the pitch angular velocity represents the rate of change of the slope, and the calculation rate of the slope obtained by the pitch angular velocity is faster, thereby improving the tracking effect of the slope estimation.

[0058] In order to avoid slope estimation anomalies caused by the dynamic estimation longitudinal vehicle speed switching introduced by the observation vector, after the second calibration slope is calculated, it is necessary to detect the difference between the first calibration slope and the second calibration slope. If the difference is too large, it means that the accuracy of the second calibration slope is low, and the second calibration slope needs to be adjusted to ensure that the slope estimation at the next moment can converge quickly. The following is an explanation through a specific embodiment.

[0059] In some embodiments, the method further comprises: In response to the difference between the first calibration slope and the calibration slope at the current moment exceeding a preset slope threshold, the second calibration vector is replaced according to the first calibration vector and the observation vector at the previous moment, the value corresponding to the longitudinal vehicle speed in the process covariance matrix is ​​increased, and the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix is ​​increased.

[0060] Specifically, if it is determined that the difference between the first calibration slope and the second calibration slope exceeds the preset slope threshold, illustratively, the preset slope threshold may be 1%, indicating that the second calibration slope is significantly different from the first calibration slope, then the second calibration slope may be inaccurate, and at this time, the value in the second calibration vector needs to be adjusted to avoid affecting the slope estimation at the next moment. Since the second calibration vector is inaccurate at this time, it is necessary to replace all values ​​in the second calibration vector to avoid affecting the slope estimation at the next moment. The longitudinal vehicle speed and slope are both replaced with the values ​​in the first calibration vector. Since the pitch angular velocity can characterize the slope change rate, and the pitch angular velocity can be directly measured with high accuracy, the second calibration slope change rate is replaced with the pitch angular velocity directly measured at the previous moment.

[0061] Before adjustment, the second calibration vector , the second calibration vector after adjustment That is, the second calibrated longitudinal vehicle speed in the second calibration vector Replaced by the first calibrated longitudinal speed , the second calibration slope Replaced with the first calibration slope , the second calibration slope change rate Replaced with the pitch angular velocity measured at the last moment .

[0062] In addition to replacing the second calibration vector, the process covariance matrix and the measurement covariance matrix also need to be adjusted. represents the process covariance matrix, which represents the confidence of the slope calculated based on acceleration and longitudinal speed, The larger the value, the less reliable the calculation result, so you can adjust The value of reduces the weight of the longitudinal speed in the estimation process. The three values ​​on the middle diagonal line correspond to the confidence of the calculated longitudinal speed, the confidence of the slope, and the confidence of the slope change rate. The larger the confidence value, the worse the confidence. , the confidence level of longitudinal speed, slope and slope change rate is 0.01. As the next moment (k+2 moment) in the estimation , which is equivalent to increasing The confidence corresponding to the longitudinal vehicle speed in indicates that the confidence of the currently calculated longitudinal vehicle speed is poor, so that the weight of the longitudinal vehicle speed in the ramp estimation process is reduced by increasing the confidence value of the longitudinal vehicle speed.

[0063] Represents the measurement covariance matrix, which characterizes the confidence of the observed data. A larger value indicates a lower confidence in the observation vector. The two values ​​on the middle diagonal correspond to the confidence of the measured longitudinal speed (dynamically estimated longitudinal speed) and the confidence of the pitch angular velocity (directly measured by the inertial measurement unit). , the confidence value corresponding to the longitudinal speed is 5, and the confidence value of the pitch angular velocity is 0.1. As the next moment estimate The confidence value corresponding to the longitudinal speed is 100, and the confidence value of the pitch angular velocity is 0.001. This is equivalent to adding The confidence value corresponding to the longitudinal speed in the value is lowered, indicating that the confidence of the measured longitudinal speed is poor, thereby reducing the weight of the measured longitudinal speed. At the same time, the confidence value corresponding to the pitch angular velocity is also reduced, indicating that the confidence of the measured pitch angular velocity is high, thereby increasing the weight of the measured pitch angular velocity.

[0064] By the method of this embodiment, when it is determined that the second calibration slope is inaccurate, all values ​​in the second calibration vector are replaced, and by adjusting and The weight of the longitudinal vehicle speed in the slope estimation process is reduced, and the influence of the longitudinal vehicle speed on the slope estimation is reduced, so that the slope estimation result at the next moment can converge quickly and reduce the error of the slope estimation.

[0065] The slope determination method proposed in this application is to perform mutual correction between the slope obtained by integrating the pitch angular velocity of the vehicle body and the slope calculated by the longitudinal acceleration sensor model while ignoring the pitch of the vehicle body relative to the chassis. However, in some special working conditions, the accuracy of the slope estimation will be affected. In order to avoid the above problems, this application proposes a maintenance strategy, which is specifically described in the following embodiments.

[0066] In some embodiments, before calculating the calibration slope at the current moment using the extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment, the method further includes: In response to the current vehicle operating condition being the preset operating condition, a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment is increased.

[0067] Specifically, before performing the calibration phase calculation of the extended Kalman filter algorithm, the current working condition of the vehicle must first be determined. If the current working condition is a preset working condition, it will affect the accuracy of the slope estimation, and the calculation of the calibration phase needs to be adjusted accordingly to avoid this influence. The preset working condition is a large lateral working condition or a full-wheel slip working condition. A large lateral working condition refers to a working condition in which the yaw rate or lateral acceleration of the vehicle is large. The threshold value under the large lateral working condition can be set according to the actual driving process of the vehicle. If it is determined that the yaw rate is greater than a certain threshold or the lateral acceleration is greater than a certain threshold, the current working condition is judged to be a large lateral working condition. At this time, since the lateral acceleration of the vehicle is relatively large, it will affect the calculation of the longitudinal vehicle speed, resulting in inaccurate longitudinal vehicle speed, which in turn affects the slope estimation result.

[0068] If all four wheels of the vehicle are detected to be slipping, the current working condition is determined to be a full-wheel slip condition. In this case, the pitch angular velocity measurement value may be missing. The pitch angular velocity measurement value can be set to 0, which is equivalent to introducing a pseudo measurement to ensure the existence of an observation vector. At the same time, full-wheel slip will also affect the calculation of the longitudinal vehicle speed, resulting in inaccurate longitudinal vehicle speed, which in turn affects the slope estimation result.

[0069] In the above two working conditions, it is necessary to increase the value related to the longitudinal vehicle speed in the measured covariance matrix to slow down the change rate of the estimated slope and keep the calibration slope obtained at the current moment close to the calibration slope at the previous moment as much as possible. In other words, control the second calibration slope to be close to the first calibration slope. The two values ​​on the diagonal in the middle correspond to the confidence of the measured longitudinal speed (dynamically estimated longitudinal speed) and the confidence of the pitch angular velocity (directly measured by the inertial measurement unit). From the initial value Adjust to After the adjustment, the confidence value corresponding to the measured longitudinal speed is adjusted from 5 to 100, which greatly reduces the confidence of the longitudinal speed. The confidence value of the measured pitch angular velocity is adjusted from 0.1 to 0.05, which improves the confidence of the pitch angular velocity, and thus reduces the weight of the longitudinal speed in the calibration stage.

[0070] When the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix increases, it is equivalent to reducing the confidence of the longitudinal vehicle speed and reducing the weight of the longitudinal vehicle speed in the slope estimation process, thereby reducing the impact of measurement distortion on the slope estimation and reducing the fluctuation of the slope estimation result.

[0071] The calibration vector also includes the calibration slope change rate. In addition to comparing and analyzing the calibration slopes at the current moment and the previous moment, in order to further improve the accuracy of the slope estimation, it is also necessary to compare and analyze the slope change rate at the current moment and the previous moment. If the difference between the calibration slope change rate obtained at the current moment and the calibration slope change rate at the previous moment is large, it also means that the accuracy of the slope estimation at the current moment is low. The following is a specific example to illustrate how to avoid the above problem.

[0072] In some embodiments, the method further comprises: In response to a difference between a calibration slope change rate in the second calibration vector and the first calibration slope change rate exceeding a preset change rate threshold, increasing a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment; According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is recalculated using an extended Kalman filter algorithm.

[0073] Specifically, if the difference between the calibration slope change rate in the second calibration vector and the first calibration slope change rate exceeds a preset change rate threshold, for example, the predicted change rate threshold may be 0.002rad, indicating that the slope change rate at the two moments before and after is quite different, and the slope estimation result is inaccurate. The correction method adopted is to increase the measured covariance matrix ,Will From the initial value Adjust to After the adjustment, the confidence value of the longitudinal speed is adjusted from 5 to 100, reducing the weight of the longitudinal speed in the calibration phase. The confidence value of the pitch angular velocity is adjusted from 0.1 to 0.05, increasing the weight of the pitch angular velocity. Recalculation in the calibration phase is performed, that is, the calculation steps of equations (15)-(17) are re-executed to obtain a new calibration slope, thereby improving the accuracy of slope estimation. The method of this embodiment provides a method for correcting the slope estimation result when the slope change rate at the previous and next moments is greatly different, thereby reducing the weight of the longitudinal vehicle speed in slope estimation and improving the accuracy of slope estimation.

[0074] 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.

[0075] 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.

[0076] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a slope determination device.

[0077] refer to Figure 2 , the slope determination device comprises: A first calculation module 202 is configured to calculate a prediction vector at a current moment using an extended Kalman filter algorithm according to a first calibration vector corresponding to a previous moment of the current moment; the first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated slope, and a first calibrated slope change rate; The second calculation module 204 is configured to calculate the calibration slope at the current moment using an extended Kalman filter algorithm based on the prediction vector at the current moment and the observation vector at the current moment; wherein the observation vector includes the dynamically estimated longitudinal vehicle speed and pitch angular velocity.

[0078] In some embodiments, the first calculation module 202 is configured to construct a state vector at a current moment and a state vector at a previous moment; construct a state transition equation based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation; determine a state transition matrix according to the state transition equation; and use an extended Kalman filter algorithm to perform a priori calculations based on the state transition matrix and the first calibration vector to determine the prediction vector at the current moment.

[0079] In some embodiments, the longitudinal vehicle speed dynamic equation includes a first dynamic equation and a second dynamic equation; a first calculation module 202 is configured to determine the yaw rate at the current moment; In response to determining that the absolute value of the yaw rate is less than a preset yaw rate threshold, constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment, and the first dynamic equation; wherein the first dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, and the slope; In response to determining that the absolute value of the yaw rate is greater than or equal to a preset yaw rate threshold, a state transition equation is constructed based on the state vector at the current moment, the state vector at the previous moment and the second dynamic equation; wherein the second dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, the kinematically estimated lateral vehicle speed, the yaw rate and the slope.

[0080] In some embodiments, the state vector includes the longitudinal vehicle speed, the slope and the slope change rate; the first calculation module 202 is configured to establish a first conversion relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment according to the longitudinal vehicle speed at the previous moment, the solution period of the extended Kalman filter algorithm and the longitudinal vehicle speed dynamic equation; Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment according to the slope at the previous moment, the solution period and the slope change rate at the previous moment; Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment according to the slope change rate at the previous moment, the solution cycle and the pre-built slope model; A state conversion equation is constructed based on the first conversion relationship, the second conversion relationship and the third conversion relationship.

[0081] In some embodiments, the second calculation module 204 is configured to calculate the Kalman gain at the current moment using an extended Kalman filter algorithm; According to the prediction vector, the Kalman gain at the current moment and the observation vector at the current moment, an extended Kalman filter algorithm is used to perform calibration calculation to determine a second calibration vector corresponding to the current moment, wherein the second calibration vector includes the calibration slope at the current moment.

[0082] In some embodiments, the second calculation module 204 is configured to calculate the error covariance matrix at the current moment based on the Jacobian matrix, the error covariance matrix and the process covariance matrix at the previous moment; The Kalman gain at the current moment is calculated based on the error covariance matrix, the measurement matrix and the measurement covariance matrix at the current moment.

[0083] In some embodiments, a calibration module is also included, which is configured to replace the second calibration vector according to the first calibration vector and the observation vector at the previous moment in response to the difference between the first calibration slope and the calibration slope at the current moment exceeding a preset slope threshold, increase the value corresponding to the longitudinal vehicle speed in the process covariance matrix, and increase the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix.

[0084] In some embodiments, before using the extended Kalman filter algorithm to calculate the calibration slope at the current moment based on the prediction vector at the current moment and the observation vector at the current moment, the calibration module is configured to increase the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment in response to the current vehicle operating condition being a preset operating condition.

[0085] In some embodiments, the calibration module is configured to increase the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment in response to the difference between the calibration slope change rate in the second calibration vector and the first calibration slope change rate exceeding a preset change rate threshold; According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is recalculated using an extended Kalman filter algorithm.

[0086] 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.

[0087] The device of the above embodiment is used to implement the corresponding slope determination method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0088] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, 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 when the processor executes the program, the slope determination method described in any of the above embodiments is implemented.

[0089] Figure 3 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.).

[0094] 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 ).

[0095] 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.

[0096] The electronic device of the above embodiment is used to implement the corresponding slope determination 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.

[0097] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, 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 slope determination method described in any of the above embodiments.

[0098] 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, magnetic tape magnetic 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.

[0099] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the slope determination method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0100] 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.

[0101] 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 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.

[0102] 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.

[0103] 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.

[0104] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. 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 protection scope of the present application.

Claims

1. A slope determination method, characterized in that: include: Calculate the prediction vector at the current moment using an extended Kalman filter algorithm according to the first calibration vector corresponding to the previous moment of the current moment; The first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated slope, and a first calibrated slope rate; The calibration slope at the current moment is calculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment; wherein the observation vector includes the dynamically estimated longitudinal vehicle speed and pitch angular velocity.

2. The method according to claim 1, characterized in that The method of calculating the prediction vector at the current moment by using an extended Kalman filter algorithm according to the first calibration vector determined at the previous moment of the current moment includes: Construct the state vector of the current moment and the state vector of the previous moment; Constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation; Determine a state transition matrix according to the state transition equation; According to the state transition matrix and the first calibration vector, an extended Kalman filter algorithm is used to perform a priori calculation to determine the prediction vector at the current moment.

3. The method according to claim 2, characterized in that The longitudinal vehicle speed dynamic equation includes a first dynamic equation and a second dynamic equation; the state conversion equation is constructed based on the state vector at the current moment, the state vector at the previous moment and the predetermined longitudinal vehicle speed dynamic equation, including: Determine the yaw rate at the current moment; In response to determining that the absolute value of the yaw rate is less than a preset yaw rate threshold, constructing a state transition equation based on the state vector at the current moment, the state vector at the previous moment, and the first dynamic equation; wherein the first dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, and the slope; In response to determining that the absolute value of the yaw rate is greater than or equal to a preset yaw rate threshold, a state transition equation is constructed based on the state vector at the current moment, the state vector at the previous moment and the second dynamic equation; wherein the second dynamic equation characterizes the relationship between the longitudinal vehicle speed change rate, the longitudinal acceleration, the kinematically estimated lateral vehicle speed, the yaw rate and the slope.

4. The method according to claim 2, characterized in that: The state vector includes longitudinal vehicle speed, slope, and slope change rate; The state conversion equation is constructed based on the state vector at the current moment, the state vector at the previous moment and a predetermined longitudinal vehicle speed dynamic equation, including: Establishing a first conversion relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment according to the longitudinal vehicle speed at the previous moment, the solution period of the extended Kalman filter algorithm, and the longitudinal vehicle speed dynamic equation; Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment according to the slope at the previous moment, the solution period and the slope change rate at the previous moment; Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment according to the slope change rate at the previous moment, the solution cycle and the pre-built slope model; A state conversion equation is constructed based on the first conversion relationship, the second conversion relationship and the third conversion relationship.

5. The method according to claim 1, characterized in that According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is calculated using an extended Kalman filter algorithm, including: The extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment; According to the prediction vector, the Kalman gain at the current moment and the observation vector at the current moment, an extended Kalman filter algorithm is used to perform calibration calculation to determine a second calibration vector corresponding to the current moment, wherein the second calibration vector includes the calibration slope at the current moment.

6. The method according to claim 5, characterized in that The method of using the extended Kalman filter algorithm to calculate the Kalman gain at the current moment includes: Based on the Jacobian matrix, error covariance matrix and process covariance matrix of the previous moment, the error covariance matrix of the current moment is calculated; The Kalman gain at the current moment is calculated based on the error covariance matrix, the measurement matrix and the measurement covariance matrix at the current moment.

7. The method according to claim 6, characterized in that The method further comprises: In response to the difference between the first calibration slope and the calibration slope at the current moment exceeding a preset slope threshold, the second calibration vector is replaced according to the first calibration vector and the observation vector at the previous moment, the value corresponding to the longitudinal vehicle speed in the process covariance matrix is ​​increased, and the value corresponding to the longitudinal vehicle speed in the measurement covariance matrix is ​​increased.

8. The method according to claim 6, characterized in that Before calculating the calibration slope at the current moment by using the extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment, the method further includes: In response to the current vehicle operating condition being the preset operating condition, a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment is increased.

9. The method according to claim 6, characterized in that The method further comprises: In response to a difference between a calibration slope change rate in the second calibration vector and the first calibration slope change rate exceeding a preset change rate threshold, increasing a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment; According to the prediction vector at the current moment and the observation vector at the current moment, the calibration slope at the current moment is recalculated using an extended Kalman filter algorithm.

10. A vehicle, characterized in that: The vehicle comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory so that the vehicle executes the method according to any one of claims 1 to 9.

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