Transverse slope estimation method, device, vehicle and storage medium

Abstract

Embodiments of the present application disclose a lateral slope estimation method and device, a vehicle and a storage medium. The method comprises: obtaining a current observation variable; and obtaining a current lateral slope based on the current observation variable and a pre-constructed Kalman filter. In this way, the current observation variable can be obtained, and the current lateral slope can be obtained based on the current observation variable and the pre-constructed Kalman filter. Since the measured lateral acceleration in the observation variable is measured by an acceleration sensor arranged in the lateral motion direction of the vehicle, the measured lateral acceleration is associated with the lateral component of the gravitational acceleration, and the state variable of the Kalman filter also includes the lateral component of the gravitational acceleration, and the lateral component of the gravitational acceleration is related to the lateral slope, so that the accuracy of the obtained lateral slope can be improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and more specifically, to a method and device for estimating a lateral slope, a vehicle, and a storage medium. Background Art

[0002] The road slope changes frequently throughout a vehicle's driving process, and the magnitude of the road's transverse slope significantly impacts the vehicle's handling stability. Therefore, accurately estimating the road's transverse slope during vehicle operation is crucial for vehicle safety and control. A high-precision six-axis IMU (Inertial Measurement Unit) sensor can be used to convert positioning quaternions into Euler angles to obtain the roll angle, thereby deriving the road's transverse slope. However, the accuracy of this method still needs to be improved. Summary of the Invention

[0003] In view of the above problems, the present application proposes a lateral slope estimation method, device, vehicle and storage medium to improve the above problems.

[0004] In a first aspect, the present application provides a method for estimating a lateral slope, the method comprising: obtaining a current observation variable, the observation variable comprising a measured lateral acceleration, the measured lateral acceleration being measured by an acceleration sensor arranged in the lateral motion direction of the vehicle; obtaining a current lateral slope based on the current observation variable and a pre-constructed Kalman filter, the state variable of the Kalman filter comprising the lateral component of the gravitational acceleration.

[0005] In the second aspect, the present application provides a lateral slope estimation device, which includes: an observation variable acquisition unit for acquiring current observation variables, wherein the observation variables include measured lateral acceleration, and the measured lateral acceleration is measured by an acceleration sensor set in the lateral movement direction of the vehicle; a lateral slope estimation unit for obtaining the current lateral slope based on the current observation variables and a pre-constructed Kalman filter, wherein the state variables of the Kalman filter include the lateral component of the gravitational acceleration.

[0006] In a third aspect, the present application provides a vehicle comprising one or more processors and a memory; one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the above-mentioned method.

[0007] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores program code, wherein the above method is executed when the program code is run.

[0008] The present application provides a transverse slope estimation method, device, vehicle, and storage medium. After obtaining the current observed variables, the current transverse slope is obtained based on the current observed variables and a pre-constructed Kalman filter. This method allows obtaining the current observed variables and obtaining the current transverse slope based on the current observed variables and a pre-constructed Kalman filter. Since the measured transverse acceleration in the observed variables is measured by an acceleration sensor positioned in the vehicle's transverse motion direction, the measured transverse acceleration is also associated with the transverse component of gravity acceleration. Furthermore, the state variables of the Kalman filter also include the transverse component of gravity acceleration, which is related to the transverse slope. This improves the accuracy of the obtained transverse slope. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a method for estimating a transverse slope proposed in an embodiment of the present application;

[0011] Figure 2 A schematic diagram showing a vehicle movement direction proposed in this application;

[0012] Figure 3 A schematic diagram showing a transverse slope proposed in this application is shown;

[0013] Figure 4 A flow chart of a method for estimating a transverse slope proposed in another embodiment of the present application is shown;

[0014] Figure 5 The following is a structural block diagram of a device for estimating a transverse slope proposed in an embodiment of the present application;

[0015] Figure 6 Shown is a structural block diagram of a vehicle proposed in this application. DETAILED DESCRIPTION

[0016] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] In an embodiment of the present application, the inventors propose a transverse slope estimation method, apparatus, vehicle, and storage medium. After obtaining the current observed variables, the current transverse slope is obtained based on the current observed variables and a pre-constructed Kalman filter. Through the above-described method, the current observed variables can be obtained, and the current transverse slope can be obtained based on the current observed variables and a pre-constructed Kalman filter. Since the measured lateral acceleration in the observed variables is measured by an acceleration sensor disposed in the lateral motion direction of the vehicle, the measured lateral acceleration is also associated with the transverse component of gravity acceleration, and the state variable of the Kalman filter also includes the transverse component of gravity acceleration, which is related to the transverse slope. Therefore, the accuracy of the obtained transverse slope can be improved.

[0018] See also Figure 1 , an embodiment of the present application provides a method for estimating a transverse slope, the method comprising:

[0019] S110: Acquire current observed variables, where the observed variables include measured lateral acceleration, and the measured lateral acceleration is measured by an acceleration sensor arranged in the lateral motion direction of the vehicle.

[0020] The measured lateral acceleration may refer to the acceleration measured in the lateral direction of the vehicle. The lateral direction of movement may refer to the direction in which the vehicle is moving left or right, such as Figure 2 As shown, the lateral movement direction may refer to the Y-axis direction, and the longitudinal movement direction may refer to the X-axis direction.

[0021] In the embodiment of the present application, the observed variables may also include the vehicle's measured lateral speed, measured longitudinal speed, and measured yaw rate. The measured lateral speed may refer to the speed measured in the vehicle's lateral direction of motion, the measured longitudinal speed may refer to the speed measured in the vehicle's lateral direction of motion, and the measured yaw rate may refer to the vehicle's measured yaw rate. The observed variables may be expressed as:

[0022]

[0023] Among them, a ym It can be expressed as the measured lateral acceleration, v y Can be expressed as measuring lateral vehicle speed, v x It can be expressed as the longitudinal speed of the vehicle, and ω can be expressed as the yaw rate of the vehicle.

[0024] As a way, the current measured lateral vehicle speed, measured longitudinal vehicle speed, and measured yaw angular velocity can be obtained through the IMU built into the vehicle's ESP (electronic stability program), and the measured lateral acceleration can be obtained through an acceleration sensor set in the lateral movement direction of the vehicle.

[0025] S120: Obtaining a current lateral slope based on the current observed variables and a pre-built Kalman filter, wherein the state variables of the Kalman filter include a lateral component of gravity acceleration.

[0026] Among them, the Kalman filter can refer to a filter constructed by the Kalman filtering method. The Kalman filtering method can essentially be a data fusion algorithm. The Kalman filtering method can fuse data with the same measurement purpose, from different sensors, and (possibly) with different units, to obtain a more accurate target measurement value. There can be many types of Kalman filters, such as: standard Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), etc. The Kalman filter used in the embodiment of the present application can be an extended Kalman filter. The lateral component of the gravitational acceleration can refer to the component of the gravitational acceleration in the lateral motion direction of the vehicle obtained according to the relevant formula of force analysis. The state variables of the Kalman filter can also include the lateral acceleration, lateral vehicle speed, longitudinal vehicle speed, and yaw angular velocity of the vehicle obtained according to the relevant formula of kinematics. The state variables can be expressed as:

[0027]

[0028] Among them, a ym It can be expressed as lateral acceleration, v y Can represent the lateral speed, v x can represent the longitudinal speed, ω can represent the yaw angular velocity, g y It can represent the lateral component of gravitational acceleration.

[0029] The Kalman filter may include a state equation and an observation equation. The state equation after linearizing the two equations based on the extended Kalman filter principle can be expressed as:

[0030] x k =A k-1 x k-1 +w k-1

[0031] Among them, A k-1 It can represent the state Jacobian matrix. k-1 It can be expressed as obeying the mean of 0 and the covariance matrix Q k-1 The process white noise of Gaussian distribution is k-1 It is usually a preset value, so it can be abbreviated as Q.

[0032] The observation equation after linearizing the two equations based on the extended Kalman filter principle can be expressed as:

[0033] y k-1 =C k-1 x k-1 +z k-1

[0034] Among them, C k-1 It can represent the observation Jacobian matrix. k-1 It can be expressed as obeying the mean of 0 and the covariance matrix R k-1 The Gaussian distribution of the process white noise. Since the measurement covariance matrix R k-1 It is usually a preset value, so it can be abbreviated as R.

[0035] Among them, w k-1 and z k-1 Independent of each other.

[0036] In the embodiment of the present application, the transverse slope may refer to the slope of the road in the direction of the vehicle's transverse movement. For example, Figure 3 As shown, the transverse slope can be

[0037] As a method, the current lateral component of gravity acceleration can be obtained based on the current observed variable and the Kalman filter; and the current lateral slope can be obtained based on the current lateral component of gravity acceleration.

[0038] Optionally, based on the current observed variables and the Kalman filter, the process of obtaining the current lateral component of the gravitational acceleration can be:

[0039] Step S1: Obtain the predetermined x0 and the initial value P0 of the error covariance matrix.

[0040] Among them, since most intelligent driving cars start on roads without lateral slopes, the initial estimated value of the state can be taken as:

[0041]

[0042] P0 can be given a smaller value, for example:

[0043]

[0044] Step S2: Time update.

[0045] Predict the state of the next cycle

[0046]

[0047] Predict the estimated error covariance matrix P for the next period k|k_1 :

[0048] P k|k-1 =AP k-1|k-1 A T +Q

[0049] In the above formula, the process covariance matrix Q can be used to measure the magnitude of the state change. A larger Q indicates that the cross slope is more dependent on the current observed variable. Furthermore, a larger Q indicates a faster response to changes in the measured value when updating the estimated value.

[0050] Step S3: Measurement update.

[0051] Extended Kalman filter gain matrix K k|k :

[0052] K k|k =P k|k-1 C T [CP k|k-1 C T +R] -1

[0053] In the above formula, the larger the measurement covariance matrix R is, the greater the K k|k The smaller it is, the smaller the value of y is in the next step of the estimation formula. k|k The lower the confidence in the measurement, the smaller the R is. In addition, the larger the R is, the slower the state response will be when the estimated value is updated.

[0054] According to the measured value y k|k Update Estimates

[0055]

[0056] Among them, the estimated value g in y is the current lateral component of the gravitational acceleration.

[0057] Update the error covariance matrix P of the Kalman filter estimate k|k , to facilitate the next estimate:

[0058] P k|k =[IK k|k C]P k|k-1

[0059] Optionally, based on the current lateral component of the gravitational acceleration, the calculation formula for obtaining the current lateral slope can be:

[0060]

[0061] wherein g y represents the current lateral component of the gravity acceleration, and g represents the gravity acceleration. Because -1≤g y / g≤1, so lateral slope The sign of the lateral slope is positive (right-hand rule) for the angle of rotation of the Z-axis positive direction to the Y-axis positive direction, Figure 3 the lateral slope is negative, and the lateral slope is positive when it is inclined in the other direction.

[0062] It should be noted that when setting the process covariance matrix Q and the measurement covariance matrix R, comprehensive consideration can be given according to the actual situation, for example, the accuracy of the observed variable measured by the sensor can be determined, R can be increased when the accuracy is low, and Q can be increased when the accuracy is high.

[0063] The lateral slope estimation method provided in the embodiment can obtain the current observed variable, and obtain the current lateral slope based on the current observed variable and the Kalman filter constructed in advance. Since the measured lateral acceleration in the observed variable is measured by the acceleration sensor arranged in the lateral motion direction of the vehicle, the measured lateral acceleration is also associated with the lateral component of the gravity acceleration, and the state variable of the Kalman filter also includes the lateral component of the gravity acceleration, and the lateral component of the gravity acceleration is related to the lateral slope, so the accuracy of the obtained lateral slope can be improved. Moreover, only one low-cost acceleration sensor needs to be additionally added, and real-time and accurate estimation of the lateral slope can be realized. Compared with the method of directly calculating the lateral slope based on the collected data by additionally arranging a high-cost six-axis IMU (high-precision sensor) to collect data, the cost can be reduced, and compared with the method of directly calculating the lateral slope based on the data collected by a common sensor (low-precision sensor), the problem of large jitter and delay in directly calculating the lateral slope by the sensor can be avoided.

[0064] Please refer to Figure 4 The lateral slope estimation method provided in the embodiment includes the following steps.

[0065] S210: Construct a vehicle kinematic model, which represents the relationship between the lateral vehicle speed and the lateral component of the gravity acceleration.

[0066] As a method, a measured lateral acceleration equation can be constructed based on the vehicle's measured lateral acceleration, the true value of the lateral acceleration, and the lateral component of the gravitational acceleration; a lateral acceleration equation can be constructed based on the vehicle's lateral speed, yaw angular velocity, and longitudinal speed; a lateral speed equation can be constructed based on the vehicle's lateral speed in adjacent sampling periods; and a vehicle kinematic model can be obtained based on the measured lateral acceleration equation, the lateral acceleration equation, and the lateral speed equation.

[0067] Among them, the equation for measuring lateral acceleration can be:

[0068]

[0069] Among them, a ym Can represent the measured lateral acceleration; a y It can represent the true value of lateral acceleration; g can represent the acceleration due to gravity; It can represent the cross slope of the road.

[0070] in,

[0071]

[0072] Among them, g y It can represent the lateral component of gravitational acceleration; g can represent gravitational acceleration; It can represent the cross slope of the road.

[0073] The instantaneous motion of the vehicle in the lateral direction can be decomposed into the vehicle's lateral uniformly accelerated linear motion and the vehicle's longitudinal uniform circular motion. Therefore, the lateral acceleration equation can be:

[0074]

[0075] Among them, a y It can represent the true value of lateral acceleration, It can express the differential of the lateral velocity, v x It can represent the longitudinal vehicle speed, and ω can represent the yaw angular velocity.

[0076] In the embodiment of the present application, the differential of the lateral vehicle speed is taken into account. That is, the rate of change of the vehicle's speed in lateral motion can be more consistent with actual application conditions, and can more accurately estimate the real-time lateral slope of the vehicle's road position, with greater adaptability.

[0077] Since the lateral vehicle speed does not change suddenly, the lateral vehicle speed equation can be obtained based on the lateral vehicle speed at the kth sampling period after the sampling period of Δt and the lateral vehicle speed at the kth sampling period:

[0078]

[0079] Optionally, the vehicle kinematic model obtained based on the measured lateral acceleration equation, the lateral acceleration equation, and the lateral vehicle speed equation may be:

[0080] v y (k)=v y (k-1)+(a ym (k-1)+g y (k-1)-v x (k-1)ω(k-1))Δt

[0081] S220: Construct the nonlinear separation of the Kalman filter based on the vehicle kinematic model.

[0082] The equation of state of a discrete-time system.

[0083] As a way, the state equation of the nonlinear discrete-time system can be constructed based on the vehicle kinematic model and state variables.

[0084] Among them, the state equation of the nonlinear discrete-time system can be:

[0085]

[0086] Among them, w in step S120 k-1 It can include w1(k-1)~w5(k-1), w k-1 ~N(0,Q k-1 ).

[0087] S230: Constructing an observation equation of the nonlinear discrete-time system of the Kalman filter.

[0088] As a way, the observation equation of the nonlinear discrete-time system can be constructed based on the observation variables. The observation equation of the nonlinear discrete-time system can be:

[0089]

[0090] Among them, z in step S120 k-1 It can include z1(k-1)~z4(k-1), z k-1 ~N(0,R k-1 ).

[0091] S240: Constructing the Kalman filter based on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system.

[0092] As a method, the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system can be respectively subjected to first-order Taylor expansion to obtain the state Jacobian matrix and the observation Jacobian matrix; based on the state Jacobian matrix, the linearized state equation is constructed; based on the observation Jacobian matrix, the linearized observation equation is constructed.

[0093] Optionally, performing a first-order Taylor expansion on the state equation of the nonlinear discrete-time system can be understood as taking the first-order partial derivative of each formula in the state equation of the nonlinear discrete-time system with respect to the state variable. The state Jacobian matrix can be:

[0094]

[0095] Based on the state Jacobian matrix, the linearized state equation can be:

[0096] x k =A k-1 x k-1 +w k-1

[0097] Optionally, performing a first-order Taylor expansion on the observation equation of the nonlinear discrete-time system can be understood as taking the first-order partial derivative of each formula in the observation equation of the nonlinear discrete-time system with respect to the observation variable. The observation Jacobian matrix can be:

[0098]

[0099] Based on the observation Jacobian matrix, the linearized observation equation can be:

[0100] y k-1 =C k-1 x k-1 +z k-1

[0101] S250: Acquire current observed variables, where the observed variables include measured lateral acceleration, and the measured lateral acceleration is measured by an acceleration sensor arranged in the lateral motion direction of the vehicle.

[0102] S260: Obtaining a current lateral slope based on the current observed variables and a pre-built Kalman filter, wherein the state variables of the Kalman filter include a lateral component of gravitational acceleration.

[0103] The transverse slope estimation method provided in the embodiment can obtain the current observation variable, and based on the current observation variable and the Kalman filter constructed in advance, the current transverse slope is obtained. Since the measured transverse acceleration in the observation variable is measured by the acceleration sensor arranged in the transverse motion direction of the vehicle, the measured transverse acceleration is also related to the transverse component of the gravitational acceleration, and the state variable of the Kalman filter also includes the transverse component of the gravitational acceleration, and the transverse component of the gravitational acceleration is related to the transverse slope, so that the accuracy of the obtained transverse slope can be improved. In the embodiment, the state Jacobian matrix and the observation Jacobian matrix are obtained by performing first-order Taylor expansion on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system; the linearized state equation is constructed based on the state Jacobian matrix; and the linearized observation equation is constructed based on the observation Jacobian matrix, so that linearization is performed based on the extended Kalman filtering principle, thereby reducing the calculation difficulty. In addition, the vehicle kinematics analysis method provided in the application does not need to obtain the tire lateral force, and the complex nonlinear tire model modeling is also saved.

[0104] Please refer to Figure 5 The transverse slope estimation device 400 provided in the application includes:

[0105] The observation variable acquisition unit 410 is configured to obtain a current observation variable, and the observation variable includes a measured transverse acceleration measured by an acceleration sensor arranged in the transverse motion direction of the vehicle.

[0106] The transverse slope estimation unit 420 is configured to obtain a current transverse slope based on the current observation variable and a Kalman filter constructed in advance, and the state variable of the Kalman filter includes a transverse component of gravitational acceleration.

[0107] As a kind of way, the transverse slope estimation unit 420 is specifically configured to obtain the current transverse component of gravitational acceleration based on the current observation variable and the Kalman filter, and obtain the current transverse slope based on the current transverse component of gravitational acceleration.

[0108] The device 400 further includes:

[0109] The Kalman filter construction unit 430 is used to construct a vehicle kinematic model, which characterizes the relationship between the lateral velocity of the vehicle and the lateral component of the gravitational acceleration; construct the state equation of the nonlinear discrete-time system of the Kalman filter based on the vehicle kinematic model; construct the observation equation of the nonlinear discrete-time system of the Kalman filter; and construct the Kalman filter based on the state equation of the nonlinear discrete-time system and the nonlinear observation equation of the discrete-time system.

[0110] As a method, the Kalman filter construction unit 430 is specifically used to construct a measured lateral acceleration equation based on the measured lateral acceleration of the vehicle, the true value of the lateral acceleration and the lateral component of the gravitational acceleration; construct a lateral acceleration equation based on the lateral speed, yaw angular velocity and longitudinal speed of the vehicle; construct a lateral speed equation based on the lateral speed of the vehicle in adjacent sampling periods; and obtain the vehicle kinematic model based on the measured lateral acceleration equation, the lateral acceleration equation and the lateral speed equation.

[0111] As a method, the Kalman filter construction unit 430 is specifically used to construct the state equation of the nonlinear discrete-time system based on the vehicle kinematic model and the state variables, and the state variables also include the vehicle's lateral acceleration, lateral speed, longitudinal speed, and yaw angular velocity.

[0112] As a method, the Kalman filter construction unit 430 is specifically used to construct the observation equation of the nonlinear discrete-time system based on the observation variables, and the observation variables also include the vehicle's measured lateral speed, measured longitudinal speed, and measured yaw angular velocity.

[0113] As a method, the Kalman filter construction unit 430 is specifically used to perform first-order Taylor expansion on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system, respectively, to obtain the state Jacobian matrix and the observation Jacobian matrix; based on the state Jacobian matrix, construct the linearized state equation; based on the observation Jacobian matrix, construct the linearized observation equation.

[0114] The following will be combined Figure 6 A vehicle provided in this application is described.

[0115] See also Figure 6, based on the above lateral slope estimation method and device, the embodiment of the application further provides another vehicle 100 which can execute the aforementioned lateral slope estimation method. The vehicle 100 comprises a processor 102, a memory 104, and a data acquisition module 106. The memory 104 stores programs which can execute the contents of the aforementioned embodiments, and the processor 102 can execute the programs stored in the memory 104.

[0116] The processor 102 can comprise one or more processing cores. The processor 102 connects various parts in the vehicle 100 through various interfaces and lines, executes various functions of the vehicle 100 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 104, and calling data stored in the memory 104. Optionally, the processor 102 can be implemented in at least one of the following hardware forms: a neural network processing unit (NPU), a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 102 can be integrated with a combination of one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), and a modem. The CPU is mainly used to process operating systems, user interfaces, and application programs; the GPU is used to render and draw display content; the NPU is used to process multimedia data such as videos and images; and the modem is used to process wireless communication. It can be understood that the aforementioned modem can also not be integrated into the processor 102, but can be implemented by a separate communication chip.

[0117] The memory 104 may include random access memory (RAM), read-only memory (ROM), and double data rate synchronous dynamic random access memory (DDR). The memory 104 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 104 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the vehicle 100 during use (such as a phone book, audio and video data, chat history data, etc.).

[0118] The data acquisition module 106 may include, but is not limited to, an IMU (Inertial Measurement Unit), a multi-camera, a level, a light sensor, a motion sensor, a pressure sensor, an infrared thermal sensor, a distance sensor, an acceleration sensor, and other sensors. The IMU may acquire first IMU information from the vehicle 100. The multi-camera may also acquire environmental information surrounding the vehicle 100, such as the vehicle's seats and windows.

[0119] Among them, the pressure sensor may be a sensor that detects pressure generated by pressing on the vehicle 100. That is, the pressure sensor detects pressure generated by contact or pressing between the user and the vehicle 100, for example, pressure generated by contact or pressing between the user's hand and the vehicle 100. Therefore, the pressure sensor can be used to determine whether contact or pressing occurs between the user and the vehicle 100, and the magnitude of the pressure.

[0120] The accelerometer can detect the magnitude of acceleration in all directions (generally three axes) and the magnitude and direction of gravity when stationary. This can be used for applications such as identifying the posture of the vehicle 100 (e.g., magnetometer posture calibration) and vibration recognition functions (e.g., pedometer, tapping). Furthermore, the vehicle 100 may also be equipped with other sensors such as a gyroscope, barometer, hygrometer, and thermometer, which are not detailed here.

[0121] An embodiment of the present application provides a computer-readable storage medium having program code stored therein, wherein the program code can be invoked by a processor to execute the method described in the above method embodiment.

[0122] The computer-readable storage medium can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program codes for executing any of the method steps described above. These program codes can be read from or written to one or more computer program products. The program codes can be compressed, for example, in an appropriate form.

[0123] In summary, the present application provides a method, device, vehicle, and storage medium for estimating a lateral slope. After obtaining the current observed variables, the current lateral slope is obtained based on the current observed variables and a pre-constructed Kalman filter. The above method allows the current observed variables to be obtained, and the current lateral slope to be obtained based on the current observed variables and a pre-constructed Kalman filter. Since the measured lateral acceleration in the observed variables is measured by an acceleration sensor positioned in the lateral direction of motion of the vehicle, the measured lateral acceleration is also associated with the lateral component of gravity acceleration, and the state variables of the Kalman filter also include the lateral component of gravity acceleration, which is related to the lateral slope. Therefore, the accuracy of the obtained lateral slope can be improved.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for estimating a transverse slope, characterized in that: The method comprises: Obtaining a current observed variable, wherein the observed variable includes a measured lateral acceleration, wherein the measured lateral acceleration is measured by an acceleration sensor disposed in a lateral motion direction of the vehicle; Obtaining a current lateral slope based on the current observed variables and a pre-built Kalman filter, wherein the state variables of the Kalman filter include a lateral component of gravity acceleration; The Kalman filter determination process includes: constructing a measured lateral acceleration equation based on the measured lateral acceleration of the vehicle, the true value of the lateral acceleration, and the lateral component of the gravitational acceleration; Constructing a lateral acceleration equation based on the lateral speed, yaw rate, and longitudinal speed of the vehicle; Constructing a lateral vehicle speed equation based on the lateral vehicle speed of the vehicle in adjacent sampling periods; determining a vehicle kinematics model based on the measured lateral acceleration equation, the lateral acceleration equation, and the lateral vehicle speed equation, so as to characterize the relationship between the lateral vehicle speed and the lateral component of gravity acceleration of the vehicle through the vehicle kinematics model; Constructing a state equation of a nonlinear discrete-time system of the Kalman filter based on the vehicle kinematic model; Constructing an observation equation of a nonlinear discrete-time system of the Kalman filter; The Kalman filter is constructed based on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system.

2. The method according to claim 1, characterized in that The state equation of the nonlinear discrete-time system of the Kalman filter is constructed based on the vehicle kinematic model, including: The state equation of the nonlinear discrete-time system is constructed based on the vehicle kinematic model and the state variables, wherein the state variables further include the vehicle's lateral acceleration, lateral speed, longitudinal speed, and yaw angular velocity.

3. The method according to claim 1, characterized in that The observation equation of the nonlinear discrete-time system for constructing the Kalman filter includes: An observation equation of the nonlinear discrete-time system is constructed based on the observation variables, where the observation variables further include a measured lateral speed, a measured longitudinal speed, and a measured yaw rate of the vehicle.

4. The method according to claim 1, wherein The Kalman filter includes a state equation and an observation equation. The Kalman filter is constructed based on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system, including: Performing first-order Taylor expansion on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system, respectively, to obtain a state Jacobian matrix and an observation Jacobian matrix; Based on the state Jacobian matrix, construct a linearized state equation; Based on the observation Jacobian matrix, a linearized observation equation is constructed.

5. The method according to any one of claims 1 to 4, characterized in that: The method of obtaining the current transverse slope based on the current observed variable and the Kalman filter includes: Based on the current observed variable and the Kalman filter, a current lateral component of gravity acceleration is obtained; The current lateral slope is obtained based on the current lateral component of the gravitational acceleration.

6. A transverse slope estimation device, characterized in that: The device comprises: an observation variable acquisition unit, configured to acquire a current observation variable, wherein the observation variable includes a measured lateral acceleration, the measured lateral acceleration being measured by an acceleration sensor disposed in the lateral motion direction of the vehicle; a transverse slope estimation unit, configured to obtain a current transverse slope based on the current observed variables and a pre-built Kalman filter, wherein the state variables of the Kalman filter include a transverse component of gravity acceleration; The Kalman filter determination process includes: constructing a measured lateral acceleration equation based on the measured lateral acceleration of the vehicle, the true value of the lateral acceleration, and the lateral component of the gravitational acceleration; Constructing a lateral acceleration equation based on the lateral speed, yaw rate, and longitudinal speed of the vehicle; Constructing a lateral vehicle speed equation based on the lateral vehicle speed of the vehicle in adjacent sampling periods; determining a vehicle kinematics model based on the measured lateral acceleration equation, the lateral acceleration equation, and the lateral vehicle speed equation, so as to characterize the relationship between the lateral vehicle speed and the lateral component of gravity acceleration of the vehicle through the vehicle kinematics model; Constructing a state equation of a nonlinear discrete-time system of the Kalman filter based on the vehicle kinematic model; Constructing an observation equation of a nonlinear discrete-time system of the Kalman filter; The Kalman filter is constructed based on the state equation of the nonlinear discrete-time system and the observation equation of the nonlinear discrete-time system.

7. A vehicle, characterized in that: including one or more processors and memory; One or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, wherein when the program code is run, the method according to any one of claims 1 to 5 is executed.

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