Slope determination method and vehicle

Abstract

The present application provides a slope determination method and vehicle, wherein the method includes using an extended Kalman filter algorithm to calculate a prediction vector at the current moment based on a 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 change rate. Based on 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; wherein the observation vector includes a kinematically estimated longitudinal vehicle speed and a 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. The calculation rate of the slope obtained by the pitch angular velocity is faster, which can improve the tracking effect of the slope estimation.

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 operation, road slope significantly impacts vehicle energy consumption, driving stability, and safety. Especially under complex and changing road conditions, rapid and accurate estimation of road slope can enhance vehicle control stability, improve fuel economy, and enhance driving comfort. Currently, road slope estimation is typically achieved using dynamics and kinematics methods, but both approaches have limitations, resulting in inaccurate road slope estimation. Summary of the Invention

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

[0004] Based on the above objectives, the first aspect of the present application provides a slope determination method, comprising:

[0005] Calculating a prediction vector for the current moment using an extended Kalman filter algorithm based on a first calibration vector corresponding to a moment before the current moment, wherein the first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated slope, and a first calibrated slope change rate;

[0006] The calibration slope at the current moment is calculated 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 kinematically estimated longitudinal vehicle speed and pitch angular velocity.

[0007] In this embodiment, the slope derived from the pitch angular velocity is cross-checked with the predicted slope in the prediction vector, effectively improving the accuracy and robustness of slope estimation. Compared to indirect slope calculation based on longitudinal acceleration collected by the sensor, the pitch angular velocity represents the rate of change of slope. Determining slope from the pitch angular velocity is faster, thereby improving the tracking performance of slope estimation.

[0008] Optionally, the step of calculating the prediction vector at the current moment using an extended Kalman filter algorithm based on the first calibration vector determined at a moment before the current moment includes:

[0009] Construct the state vector of the current moment and the state vector of the previous moment;

[0010] 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;

[0011] Determine a state transition matrix according to the state transition equation;

[0012] An extended Kalman filter algorithm is used to perform a priori calculation based on the state transition matrix and the first calibration vector to determine the prediction vector at the current moment.

[0013] This embodiment provides a method for calculating a prediction vector. This calculation is a priori calculation step within the extended Kalman filter algorithm. This process considers the influence of yaw rate on the dynamic equations for longitudinal vehicle velocity. This allows for the use of appropriate dynamic equations for specific vehicle trajectories, making the method more adaptable. Accurately determining the state transition matrix ensures the accuracy of the prediction vector, providing a data foundation for a posteriori calculations.

[0014] 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 current state vector, the previous state vector, and a predetermined longitudinal vehicle speed dynamic equation includes:

[0015] Determine the yaw rate at the current moment;

[0016] 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 current state vector, the previous state vector, and the first dynamic equation; wherein the first dynamic equation represents a relationship between a longitudinal vehicle speed change rate, a longitudinal acceleration, and a slope;

[0017] 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.

[0018] By determining the corresponding dynamic equation based on 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 in different vehicle motion trajectories, which is more practical.

[0019] Optionally, the state vector includes longitudinal vehicle speed, slope, and slope change rate;

[0020] The state transition 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:

[0021] Establishing a first conversion relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment based on the longitudinal vehicle speed at the previous moment, a solution period of the extended Kalman filter algorithm, and a longitudinal vehicle speed dynamic equation;

[0022] Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment based on the slope at the previous moment, the solution period, and the slope change rate at the previous moment;

[0023] Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment based on the slope change rate at the previous moment, the solution period, and the pre-built slope model;

[0024] A state transition equation is constructed based on the first conversion relationship, the second conversion relationship, and the third conversion relationship.

[0025] 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 facilitates the construction of subsequent state transition equations, and further facilitates the subsequent calculation of the prediction vector at the current moment.

[0026] Optionally, the calibration slope at the current moment is calculated using an extended Kalman filter algorithm based on the prediction vector at the current moment and the observation vector at the current moment, including:

[0027] The extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment;

[0028] 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, where the second calibration vector includes the calibration slope at the current moment.

[0029] 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 to 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.

[0030] Optionally, the calculating the Kalman gain at the current moment by using an extended Kalman filter algorithm includes:

[0031] 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;

[0032] 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.

[0033] The method of this embodiment provides a calculation method for the Kalman gain, thereby providing a data basis for subsequent calculation of the calibration slope.

[0034] Optionally, the method further includes:

[0035] 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.

[0036] 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, reducing the error of the slope estimation.

[0037] Optionally, before calculating 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, the method further includes:

[0038] 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.

[0039] 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.

[0040] Optionally, the method further includes:

[0041] 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 predetermined change rate threshold, increasing a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment;

[0042] The calibration slope at the current moment is recalculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment.

[0043] 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 significantly different, thereby reducing the weight of the longitudinal vehicle speed in the slope estimation and improving the accuracy of the slope estimation.

[0044] Based on the same inventive concept, the second aspect of the present application also provides a vehicle, comprising a memory for storing executable program code; and 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.

[0045] As can be seen from the above description, the present application provides a slope determination method and vehicle, wherein the method includes calculating a prediction vector for the current moment using an extended Kalman filter algorithm based on a first calibration vector corresponding to the moment before 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 first calibration vector is a state vector estimated by the extended Kalman filter algorithm at the previous moment. The prediction vector is a vector predicted based on the first calibration vector. The prediction vector includes the predicted slope. The slope at the current moment is thus predicted using the first calibration vector at the previous moment. Because the first calibration vector includes not only the first calibrated slope but also the first calibrated slope change rate, the first calibrated slope change rate can also be used to monitor slope changes, further improving the accuracy of the slope prediction at the current moment. The extended Kalman filter algorithm is used to calculate the calibrated slope at the current moment based on the prediction vector at the current moment and the observation vector at the current moment; wherein the observation vector includes the kinematically estimated longitudinal vehicle speed and pitch angular velocity. The pitch angular velocity is incorporated into the prediction vector calibration process. By integrating the pitch angular velocity, a slope is derived. This cross-checks the slope derived from the pitch angular velocity with the predicted slope in the prediction vector, effectively improving the accuracy and robustness of slope estimation. Compared to related technologies that indirectly calculate slope based on longitudinal acceleration collected by sensors, the pitch angular velocity represents the rate of change of slope. This allows for faster slope calculation, thereby improving the tracking performance of slope estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 Schematic diagram of the flow of the slope determination method according to an embodiment of the present application;

[0048] Figure 2 This is a schematic structural diagram of a slope determination device according to an embodiment of the present application;

[0049] 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

[0050] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0051] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before 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 may 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.

[0052] Related technologies usually use dynamics or kinematics methods to quickly estimate the road slope, 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 are random disturbances. On flat roads, the rolling resistance is equal to the product of the road's support force on the vehicle and the rolling coefficient, but on sloped roads, the vehicle's support force also needs to consider the slope factor, and the slope is difficult to obtain without the aid 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.

[0053] The kinematics-based method relies more on signals collected by sensors, especially acceleration signals, which can easily lead to cumulative errors. This can cause large fluctuations in slope estimation results, poor adjustability, poor tracking of estimation results, and the slope estimation results being dependent on the accuracy of the longitudinal vehicle speed.

[0054] In light of this, this application proposes a slope determination method that uses an extended Kalman filter to fuse the longitudinal slope calculated from the pitch angular velocity integration with the longitudinal slope indirectly measured from the longitudinal vehicle speed and longitudinal acceleration, effectively improving the tracking performance of the slope estimate. Cross-verification of the longitudinal slope value calculated from the pitch angular velocity with the longitudinal slope indirectly measured from the estimated longitudinal vehicle speed and sensor-collected longitudinal acceleration effectively improves the accuracy and robustness of slope estimation.

[0055] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0056] This application proposes a slope determination method, referring to Figure 1 , including the following steps:

[0057] 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 moment before 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.

[0058] Specifically, the extended Kalman filter algorithm is a state estimation method suitable for nonlinear systems. By introducing a linear approximation of the nonlinear system, the extended Kalman filter algorithm can be applied to nonlinear systems, thereby achieving accurate identification of the slope. The first calibration vector is the estimated vector obtained at the previous moment according to the extended Kalman filter algorithm. The first calibration vector includes the first calibrated longitudinal vehicle speed, the first calibrated slope, and the first calibrated slope change rate. The prediction vector at the current moment can be predicted based on 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 of 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.

[0059] Since the goal is to estimate the slope, the state vector required by the extended Kalman filter algorithm includes the slope, the longitudinal vehicle speed, which is kinematically closely related to the slope, and the slope rate, which characterizes the slope's changes. The prediction vector is the state vector obtained during the prediction phase, and the calibration vector is the state vector obtained during the calibration phase. Both the prediction and calibration vectors contain the same variables as the state vector: longitudinal vehicle speed, slope, and slope rate. The extended Kalman filter algorithm uses the calibration vector from the previous moment to extrapolate the current prediction vector.

[0060] At the initial moment, a state vector can be randomly determined as the first calibration vector for the initial moment. Based on the first calibration vector, an extended Kalman filter algorithm is used to determine a prediction vector for the next moment after the initial moment. Based on the prediction vector and the observation vector for the next moment, an extended Kalman filter algorithm is then used to determine a calibration vector for the next moment. The randomly determined first calibration vector for the initial moment includes a longitudinal speed of 30 m / s, a slope of 0, and a slope change rate of 0.

[0061] Step 104: 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.

[0062] Specifically, the predicted vector is only the result of a prediction based on the first calibration vector at the previous moment. It also needs to be calibrated with information from the current moment to obtain a more accurate calibration slope that matches the current moment. The calibration method is to integrate the observation vector into the extended Kalman filter algorithm and determine the second calibration vector at the current moment through a posteriori calculation. The second calibration vector includes the calibration slope at the current moment. For ease of distinction, the calibration slope at the current moment will be referred to as the second calibration slope in this application.

[0063] The observation vector includes the dynamically estimated longitudinal vehicle speed and the pitch angular velocity directly captured by the inertial measurement unit. Compared to the slope calculated from the vehicle's longitudinal acceleration in related technologies, the slope calculated by integrating the pitch angular velocity in this embodiment is faster. The fusion of the predicted and observed vectors yields a more accurate tracking of the calibrated slope. Furthermore, the predicted slope in the predicted vector and the slope derived from integrating the pitch angular velocity in the observed vector can be cross-checked, effectively improving the accuracy and robustness of the estimated calibrated slope in the calibration vector.

[0064] Based on steps 102 to 104 above, the slope determination method provided in this embodiment includes calculating a prediction vector for the current moment using an extended Kalman filter algorithm based on a first calibration vector corresponding to the previous moment. The first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated slope, and a first calibrated slope change rate. The first calibration vector is a state vector estimated by the extended Kalman filter algorithm at the previous moment. The prediction vector is a vector predicted based on the first calibration vector. The prediction vector includes the predicted slope. The slope at the current moment is thus predicted using the first calibration vector at the previous moment. Because the first calibration vector includes not only the first calibrated slope but also the first calibrated slope change rate, the first calibrated slope change rate can also be used to monitor slope changes, further improving the accuracy of the slope prediction at the current moment. The calibrated slope at the current moment is calculated using the extended Kalman filter algorithm based on the current prediction vector and the current observation vector. The observation vector includes the kinematically estimated longitudinal vehicle speed and pitch angular velocity. The pitch angular velocity is incorporated into the prediction vector calibration process. By integrating the pitch angular velocity, a slope is derived. This cross-checks the slope derived from the pitch angular velocity with the predicted slope in the prediction vector, effectively improving the accuracy and robustness of slope estimation. Compared to related technologies that indirectly calculate slope based on longitudinal acceleration collected by sensors, the pitch angular velocity represents the rate of change of slope. This allows for faster slope calculation, thereby improving the tracking performance of slope estimation.

[0065] The following describes a method for calculating a prediction vector through a specific embodiment.

[0066] In some embodiments, the step of calculating the prediction vector at the current moment using an extended Kalman filter algorithm based on the first calibration vector determined at the previous moment includes:

[0067] Construct the state vector of the current moment and the state vector of the previous moment;

[0068] 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;

[0069] Determine a state transition matrix according to the state transition equation;

[0070] An extended Kalman filter algorithm is used to perform a priori calculation based on the state transition matrix and the first calibration vector to determine the prediction vector at the current moment.

[0071] 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 .

[0072] 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 transition equation is constructed, specifically including:

[0073] Determine the yaw rate at the current moment;

[0074] 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 current state vector, the previous state vector, and the first dynamic equation; wherein the first dynamic equation represents a relationship between a longitudinal vehicle speed change rate, a longitudinal acceleration, and a slope;

[0075] 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.

[0076] Longitudinal acceleration measured by the inertial measurement unit (IMU) Contains ramp information as follows:

[0077] (1)

[0078] 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 Equation (1), we can obtain the dynamic equation of the longitudinal speed of the vehicle when climbing a slope:

[0079] (2)

[0080] 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 effect 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 vehicle's lateral swing amplitude is small and the lateral speed accounts for a small proportion, so the lateral speed can be ignored. The first dynamic equation is used to construct the state transition equation. The first dynamic equation is specifically:

[0081] (3)

[0082] 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 transition equation. The second dynamic equation is specifically,

[0083] (4)

[0084] By determining the corresponding dynamic equation based on the yaw rate, the influence of the lateral vehicle speed on the longitudinal vehicle speed in the current situation is fully considered. This allows the method of this embodiment to be applied to vehicles under different motion trajectories, making it more practical. In situations with high yaw rate or high lateral acceleration, such as circular motion or figure-of-eight motion, the second dynamic equation is applicable. When driving in a straight line, the first dynamic equation is applicable.

[0085] 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:

[0086] (5)

[0087] According to the actual collected yaw angular velocity Determine the input quantity to use Which expression to adapt to the current condition of the vehicle.

[0088] Since the current state vector can be inferred from the previous state vector, the current state vector is equal to the sum of the previous state vector and the state change between the two moments. Therefore, the current state vector can be determined based on the previous state vector, the extended Kalman filter solution period, and the state change rate.

[0089] Furthermore, the state vector includes longitudinal vehicle speed, slope, and slope change rate;

[0090] The state transition 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:

[0091] Establishing a first conversion relationship between the longitudinal vehicle speed at the current moment and the longitudinal vehicle speed at the previous moment based on the longitudinal vehicle speed at the previous moment, a solution period of the extended Kalman filter algorithm, and a longitudinal vehicle speed dynamic equation;

[0092] Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment based on the slope at the previous moment, the solution period, and the slope change rate at the previous moment;

[0093] Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment based on the slope change rate at the previous moment, the solution period, and the pre-built slope model;

[0094] A state transition equation is constructed based on the first conversion relationship, the second conversion relationship, and the third conversion relationship.

[0095] Specifically, the first conversion relationship is: , Indicates the slope at the current moment, T indicates the solution period, The longitudinal speed change rate at the previous moment can be determined by the longitudinal 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:

[0096] (6)

[0097] According to the slope model, the third conversion relationship can be converted to .

[0098] The state transition equation constructed based on the first conversion relationship, the second conversion relationship, and the third conversion relationship is as follows:

[0099] (7)

[0100] 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 facilitates the construction of subsequent state transition equations, and further facilitates the subsequent calculation of the prediction vector at the current moment.

[0101] Furthermore, according to the discretization of the forward Euler method, the motion equation in the extended Kalman filter algorithm is determined as follows:

[0102] (8)

[0103] 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 time input.

[0104] Furthermore, the state transition matrix is ​​determined according to the state transition equation as follows:

[0105] (9)

[0106] The prior calculation formula in the extended Kalman filter algorithm is:

[0107] (10)

[0108] 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 .

[0109] This embodiment provides a method for calculating a prediction vector. This calculation is a priori calculation step within the extended Kalman filter algorithm. This process incorporates the influence of yaw rate on the dynamic equation for longitudinal vehicle velocity. This allows for the determination of an appropriate dynamic equation based on the vehicle's specific trajectory, making this method more adaptable. Accurately determining the state transition matrix ensures the accuracy of the prediction vector, providing a data foundation for a posteriori calculations.

[0110] The following describes a method for calculating the calibration slope in the a posteriori process through a specific embodiment.

[0111] In some embodiments, the calibration slope at the current moment is calculated using an extended Kalman filter algorithm based on the prediction vector at the current moment and the observation vector at the current moment, including:

[0112] The extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment;

[0113] 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, where the second calibration vector includes the calibration slope at the current moment.

[0114] 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:

[0115] + (11)

[0116] 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, the specific value is

[0117] (12)

[0118] 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:

[0119]

[0120] in, represents the state vector at time k+1 in the prediction phase (that is, the prediction vector at the current moment), represents the state vector of the calibration phase at time k (i.e., 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 moment), 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.

[0121] Furthermore, the extended Kalman filter algorithm is used to calculate the Kalman gain at the current moment, including:

[0122] 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;

[0123] 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.

[0124] Specifically, when calculating the Kalman gain, we can use equations (14)-(15) to calculate , where the Jacobian matrix F is as follows:

[0125] (18)

[0126] 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.

[0127] After the Kalman gain is calculated, the formula (14) is used to calculate , That is, the second calibration vector, ,in, Represents the calibration slope at the current moment (i.e., 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. The calculation rate of the slope obtained by the pitch angular velocity is faster, thereby improving the tracking effect of the slope estimation.

[0128] To avoid slope estimation anomalies caused by the switching of the dynamically estimated longitudinal vehicle speed introduced by the observation vector, after calculating the second calibration slope, it is necessary to detect the difference between the first and second calibration slopes. If the difference is too large, it indicates that the accuracy of the second calibration slope is low, and the second calibration slope needs to be adjusted to ensure rapid convergence of the slope estimation at the next moment. This is explained below through a specific embodiment.

[0129] In some embodiments, the method further comprises:

[0130] 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.

[0131] Specifically, if it is determined that the difference between the first calibration slope and the second calibration slope exceeds a preset slope threshold, for example, the preset slope threshold may be 1%, indicating that the second calibration slope differs significantly from the first calibration slope. In this case, the second calibration slope may be inaccurate. At this time, the values ​​in the second calibration vector need to be adjusted to avoid affecting the slope estimation at the next moment. Since the second calibration vector is inaccurate at this time, all values ​​in the second calibration vector need to be replaced 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.

[0132] 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 vehicle 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 .

[0133] 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 characterizes 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 is used to reduce 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) estimation , which is equivalent to increasing The confidence level corresponding to the longitudinal vehicle speed in indicates that the confidence level of the currently calculated longitudinal vehicle speed is poor. Therefore, by increasing the confidence level of the longitudinal vehicle speed, the weight of the longitudinal vehicle speed in the slope estimation process is reduced.

[0134] Represents the measurement covariance matrix, which characterizes the confidence of the observed data. A larger value indicates a lower confidence level in the observation vector. The two values ​​on the diagonal line 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 of the longitudinal speed is 100, and the confidence value of the pitch angular velocity is 0.001. The confidence value corresponding to the longitudinal speed in the metric indicates that the confidence level of the measured longitudinal speed is low, and the weight of the measured longitudinal speed is reduced. At the same time, the confidence value corresponding to the pitch angular velocity is also reduced, indicating that the confidence level of the measured pitch angular velocity is high, and the weight of the measured pitch angular velocity is increased.

[0135] 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 the error of the slope estimation is reduced.

[0136] The slope determination method proposed in this application ignores the pitch of the vehicle body relative to the chassis and performs a mutual correction between the slope obtained by integrating the vehicle body pitch angular velocity and the slope calculated by the longitudinal acceleration sensor model. However, in some special operating conditions, the accuracy of the slope estimation can be affected. To avoid this problem, this application proposes a maintenance strategy, which is specifically illustrated in the following example.

[0137] In some embodiments, before calculating 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, the method further includes:

[0138] 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.

[0139] 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 the 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 vehicle's yaw angular velocity or lateral acceleration 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 angular velocity 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.

[0140] If all four wheels are detected slipping, the current operating condition is determined to be full-wheel slip. In this case, pitch velocity measurements may be missing. Setting the pitch velocity measurements to 0 effectively introduces a pseudo-measurement to ensure the presence of the observation vector. Full-wheel slip also affects the calculation of longitudinal velocity, leading to inaccurate longitudinal velocity and, in turn, slope estimation.

[0141] In both of these conditions, it's necessary to increase the value related to longitudinal vehicle speed in the measured covariance matrix to slow down the rate of change of the estimated slope and keep the current calibration slope as close to the previous calibration slope as possible. In other words, the second calibration slope should be kept close to the first calibration slope. 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). From the initial value Adjust to After the adjustment, the confidence level for the measured longitudinal speed was increased from 5 to 100, significantly reducing the confidence level for the longitudinal speed. The confidence level for the measured pitch angular velocity was also increased from 0.1 to 0.05, increasing the confidence level for the pitch angular velocity and, in turn, reducing the weight of the longitudinal speed during the calibration phase.

[0142] 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 results.

[0143] The calibration vector also includes the calibration slope rate of change. In addition to comparing the calibration slope at the current moment with the previous moment, to further improve the accuracy of the slope estimation, it is also necessary to compare and analyze the slope rate of change at the current moment with the previous moment. If the calibration slope rate of change obtained at the current moment differs significantly from the calibration slope rate of change obtained at the previous moment, this also indicates that the current slope estimation accuracy is low. The following specific examples illustrate how to avoid this problem.

[0144] In some embodiments, the method further comprises:

[0145] 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 predetermined change rate threshold, increasing a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment;

[0146] The calibration slope at the current moment is recalculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment.

[0147] 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 difference between the two moments is large, 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. Re-calculating the calibration phase, i.e., re-executing the calculation steps of equations (15)-(17), obtains a new calibration slope, thereby improving the accuracy of the slope estimation. The method of this embodiment provides a method for correcting the slope estimation result when the slope change rate differs significantly between the previous and next moments. This reduces the weight of the longitudinal vehicle speed in the slope estimation, thereby improving the accuracy of the slope estimation.

[0148] 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 in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0149] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a slope determination device.

[0151] refer to Figure 2 , the slope determination device comprises:

[0152] A first calculation module 202 is configured to calculate a prediction vector at a current moment using an extended Kalman filter algorithm based on a first calibration vector corresponding to a moment before the current moment, wherein the first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated slope, and a first calibrated slope change rate;

[0153] 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.

[0154] 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 perform a priori calculations using an extended Kalman filter algorithm based on the state transition matrix and the first calibration vector to determine the prediction vector at the current moment.

[0155] 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 a current moment;

[0156] 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 current state vector, the previous state vector, and the first dynamic equation; wherein the first dynamic equation represents a relationship between a longitudinal vehicle speed change rate, a longitudinal acceleration, and a slope;

[0157] 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.

[0158] In some embodiments, the state vector includes longitudinal vehicle speed, slope, and slope change rate; the first calculation module 202 is configured to establish a first conversion relationship between the longitudinal vehicle speed at a current moment and the longitudinal vehicle speed at a previous moment based on the longitudinal vehicle speed at a previous moment, the solution period of the extended Kalman filter algorithm, and the longitudinal vehicle speed dynamic equation;

[0159] Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment based on the slope at the previous moment, the solution period, and the slope change rate at the previous moment;

[0160] Establishing a third conversion relationship between the slope change rate at the current moment and the slope change rate at the previous moment based on the slope change rate at the previous moment, the solution period, and the pre-built slope model;

[0161] A state transition equation is constructed based on the first conversion relationship, the second conversion relationship, and the third conversion relationship.

[0162] 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;

[0163] 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, where the second calibration vector includes the calibration slope at the current moment.

[0164] 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;

[0165] 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.

[0166] In some embodiments, a calibration module is further 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.

[0167] 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.

[0168] In some embodiments, the calibration module is configured to, 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, increase a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment;

[0169] The calibration slope at the current moment is recalculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment.

[0170] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0171] 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.

[0172] 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 runnable on the processor, wherein when the processor executes the program, the slope determination method described in any of the above embodiments is implemented.

[0173] Figure 3 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. 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 communicatively connected to each other within the device via the bus 1050.

[0174] The processor 1010 can be implemented using 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.

[0175] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0176] The input / output interface 1030 is used to connect to input / output modules to enable information input and output. The input / output modules can be configured as components within the device (not shown) or externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, and various sensors. Output devices may include a display, speaker, vibrator, indicator light, and the like.

[0177] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.).

[0178] The bus 1050 comprises a path for transmitting 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 ).

[0179] 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 a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0180] 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.

[0181] 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.

[0182] The computer-readable media of this embodiment includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0183] 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.

[0184] Based on the same concept, corresponding to any of the above-mentioned embodiments, 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.

[0185] Those 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 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.

[0186] In addition, to simplify the description and discussion, and to avoid obscuring the understanding of the embodiments of the present application, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. Furthermore, devices may be shown in block diagram form to avoid obscuring the understanding of the embodiments of the present application, and this also takes into account the fact that the implementation details of these block diagram devices are highly dependent on the platform on which the embodiments of the present application will be implemented (i.e., these details should be fully understood by those skilled in the art). Where specific details (e.g., circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations therefrom. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0187] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the discussed embodiments.

[0188] 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 scope of protection of the present application.

Claims

1. A slope determination method, characterized in that: include: Based on the first calibration vector corresponding to the previous moment of the current moment, the prediction vector of the current moment is calculated using the extended Kalman filter algorithm, including: Construct the state vector of the current moment and the state vector of the previous moment; Based on the state vector at the current moment, the state vector at the previous moment, and a predetermined longitudinal vehicle speed dynamic equation, a state transition equation is constructed, including: Determining the yaw rate at the current moment; wherein the longitudinal vehicle speed dynamic equation includes a first dynamic equation and a second dynamic equation; 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 represents a relationship between a longitudinal vehicle speed change rate, a longitudinal acceleration, and a slope; in the first dynamic equation, the longitudinal vehicle speed change rate is equal to a difference between the longitudinal acceleration and a first related parameter; and the first related parameter is equal to a product of the acceleration due to gravity and the sine value of 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, constructing a state transition equation based on the current state vector, the previous state vector, and the second dynamic equation; wherein the second dynamic equation represents a relationship between a longitudinal vehicle speed change rate, longitudinal acceleration, a kinematically estimated lateral vehicle speed, yaw rate, and slope; in the second dynamic equation, the longitudinal vehicle speed change rate is equal to the sum of the longitudinal acceleration and a second related parameter minus the first related parameter; and the second related parameter is equal to the product of the kinematically estimated lateral vehicle speed and the yaw rate. Determine a state transition matrix according to the state transition equation; Performing a priori calculation using an extended Kalman filter algorithm based on the state transition matrix and the first calibration vector to determine the prediction vector at the current moment; The first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated grade, and a first calibrated grade rate; The calibration slope at the current moment is calculated 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.

2. The method according to claim 1, characterized in that The state vector includes longitudinal vehicle speed, slope, and slope change rate; The state transition 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 based on the longitudinal vehicle speed at the previous moment, a solution period of the extended Kalman filter algorithm, and a longitudinal vehicle speed dynamic equation; Establishing a second conversion relationship between the slope at the current moment and the slope at the previous moment based on 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 based on the slope change rate at the previous moment, the solution period, and the pre-built slope model; A state transition equation is constructed based on the first conversion relationship, the second conversion relationship, and the third conversion relationship.

3. 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 the 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, where the second calibration vector includes the calibration slope at the current moment.

4. The method according to claim 3, characterized in that The method of calculating the Kalman gain at the current moment by using the extended Kalman filter algorithm 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.

5. The method according to claim 4, 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.

6. The method according to claim 4, characterized in that Before calculating 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, 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.

7. The method according to claim 4, 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 predetermined change rate threshold, increasing a value corresponding to the longitudinal vehicle speed in the measurement covariance matrix at the current moment; The calibration slope at the current moment is recalculated using an extended Kalman filter algorithm according to the prediction vector at the current moment and the observation vector at the current moment.

8. A vehicle, characterized in that: The vehicle comprises: a memory for storing executable program code; A processor is 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 7.

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