Transverse gradient estimation method and device, vehicle and storage medium

Abstract

The embodiment of the invention discloses a transverse gradient estimation method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring a current observation variable; and obtaining the current transverse gradient based on the current observation variable and a pre-constructed Kalman filter. By means of the mode, the current observation variable can be obtained, the current transverse slope can be obtained based on the current observation variable and the pre-constructed Kalman filter, and the measured transverse acceleration in the observation variable is measured by the acceleration sensor arranged in the transverse movement direction of the vehicle, so that the transverse slope can be obtained. The measured transverse acceleration is also associated with the transverse component of the gravitational acceleration, the state variable of the Kalman filter also comprises the transverse component of the gravitational acceleration, and the transverse component of the gravitational acceleration is related to the transverse gradient, so that the accuracy of the obtained transverse gradient can be improved.

Description

Technical Field

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

[0002] The road slope of a vehicle will often change during the entire driving process, and the magnitude of the lateral slope of the road has a great influence on the handling stability of the entire vehicle. Therefore, accurately estimating the lateral slope of the road during vehicle operation is very important for vehicle safety and control. In related methods, a high-precision six-axis IMU (Inertial Measurement Unit) sensor can be used to convert the positioning quaternion to the Euler angle to obtain the roll angle to obtain the lateral slope of the road, but the accuracy of the related method 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 current observed variables, the observed variables comprising measured lateral acceleration, the measured lateral acceleration being measured by an acceleration sensor arranged in the lateral movement direction of the vehicle; obtaining a current lateral slope based on the current observed variables and a pre-constructed Kalman filter, the state variables of the Kalman filter comprising the lateral component of gravity acceleration.

[0005] In the second aspect, the present application provides a lateral slope estimation device, which includes: an observation variable acquisition unit, used to obtain current observation variables, 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, used to obtain the current lateral slope based on the current observation variables and a pre-constructed Kalman filter, the state variables of the Kalman filter include the lateral component of gravity 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, in which program code is stored, wherein the above method is executed when the program code is run.

[0008] 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 is 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 set in the lateral motion direction of the vehicle, the measured lateral acceleration is also associated with the lateral component of gravity acceleration, and the state variable of the Kalman filter also includes the lateral component of gravity acceleration, which is related to the lateral slope. Therefore, the accuracy of the obtained lateral slope can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. 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 the present application;

[0012] Figure 3 A schematic diagram of a transverse slope proposed in the present 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 A structural block diagram of a lateral slope estimation device proposed in an embodiment of the present application is shown;

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

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

[0017] In an embodiment of the present application, the inventor proposes 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. Through the above method, the current observed variables can be obtained, and the current lateral 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 set 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.

[0018] See also Figure 1 , an embodiment of the present application provides a method for estimating a lateral 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 motion direction of the vehicle. The lateral motion direction 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 measured lateral speed, the measured longitudinal speed, and the measured yaw rate of the vehicle. The measured lateral speed may refer to the speed measured in the lateral direction of the vehicle, the measured longitudinal speed may refer to the speed measured in the lateral direction of the vehicle, and the measured yaw rate may refer to the yaw rate measured by the vehicle. The observed variables may be expressed as:

[0022]

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

[0024] As a method, the current measured lateral vehicle speed, longitudinal vehicle speed, and 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: Based on the current observed variables and a pre-constructed Kalman filter, a current lateral slope is obtained, wherein the state variables of the Kalman filter include a lateral component of gravity acceleration.

[0026] Among them, the Kalman filter may refer to a filter constructed by the Kalman filtering method. The Kalman filtering method may be essentially a data fusion algorithm. The Kalman filtering method may fuse data with the same measurement purpose, from different sensors, and (possibly) with different units to obtain a more accurate target measurement value. There may 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 may be an extended Kalman filter. The lateral component of gravity acceleration may refer to the component of gravity 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 may 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 may be expressed as:

[0027]

[0028] Among them, a ym It can be expressed as the lateral acceleration, v y It can represent the lateral speed, v x can represent the longitudinal speed, ω can represent the yaw rate, 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 covariance matrix Q k-1 This 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 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 This 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 lateral slope may refer to the slope of the road in the lateral direction of the vehicle. 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 variables 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 gravity acceleration can be:

[0039] Step S1: Get a predetermined x 0 and the initial value of the error covariance matrix P 0 .

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

[0041]

[0042] P 0 A smaller value can be given, 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 change in state. The larger the Q, the more dependent the obtained cross slope is on the current observed variable. In addition, the larger the Q, the faster the response to the change in the measured value when the estimated value is updated.

[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 larger K k|k The smaller it is, the smaller the measured value 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 estimation:

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

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

[0060]

[0061] Among them, g y Indicates the current lateral component of gravity acceleration, g represents gravity acceleration. Because -1≤g y / g≤1, so Cross slope The sign of is positive when the angle rotated from the positive direction of the Z axis to the positive direction of the Y axis (right-hand rule). Figure 3 The cross slope of the situation shown is is a negative value, and a positive value if the cross slope is in the other direction.

[0062] It should be noted that when setting the process covariance matrix Q and the measurement covariance matrix R, a comprehensive consideration can be made based on the actual situation. For example, it can be determined based on the accuracy of the observed variables measured by the sensor. When the accuracy is low, R can be increased, and when the accuracy is high, Q can be increased.

[0063] The present embodiment provides a method for estimating a transverse slope, which obtains the current transverse slope based on the current observed variables and a pre-constructed Kalman filter after obtaining the current observed variables. The above method allows the current observed variables to be obtained, and the current transverse slope to be obtained based on the current observed variables and a pre-constructed Kalman filter, wherein, since the measured transverse acceleration in the observed variables is measured by an acceleration sensor set in the transverse motion direction of the vehicle, the measured transverse acceleration is also associated with the transverse component of the gravity acceleration, and the state variable of the Kalman filter also includes the transverse component of the gravity acceleration, and the transverse component of the gravity acceleration is related to the transverse slope, so the accuracy of the obtained transverse slope can be improved. Moreover, only a low-cost acceleration sensor needs to be added to achieve real-time and accurate estimation of the lateral slope. Compared with the need to set up an additional high-cost six-axis IMU (high-precision sensor) to collect data, the method of directly calculating the lateral slope based on the collected data can reduce costs. Compared with the method of directly calculating the lateral slope based on data collected by ordinary sensors (low-precision sensors), it can avoid the problems of jitter and large delay in directly calculating the lateral slope through sensors.

[0064] See also Figure 4 , an embodiment of the present application provides a method for estimating a lateral slope, the method comprising:

[0065] S210: Constructing a vehicle kinematics model, wherein the vehicle kinematics model represents the relationship between the lateral speed of the vehicle and the lateral component of 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 gravity 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 be expressed as the differential of the lateral vehicle velocity, v x 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 lateral slope of the road where the real-time vehicle is located, with greater adaptability.

[0077] Since the lateral vehicle speed will not change suddenly, according to 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, the lateral vehicle speed equation can be obtained as follows:

[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: Constructing a nonlinear Kalman filter based on the vehicle kinematics 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 Can include w 1 (k-1)~w 5 (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 observed variables. The observation equation of the nonlinear discrete-time system can be:

[0089]

[0090] Among them, z in step S120 k-1 Can include z 1 (k-1)~z 4 (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, the first-order Taylor expansion of the state equation of the nonlinear discrete-time system can be understood as each formula in the state equation of the nonlinear discrete-time system respectively finding the first-order partial derivative of the state variable, and 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 the observation variable for each formula in the observation equation of the nonlinear discrete-time system, and 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: Based on the current observed variables and a pre-constructed Kalman filter, a current lateral slope is obtained, wherein the state variables of the Kalman filter include a lateral component of gravity acceleration.

[0103] The present embodiment provides a method for estimating a lateral slope. Through the above method, the current observed variables can be obtained, and the current lateral slope can be obtained based on the current observed variables and the pre-constructed Kalman filter. Since the measured lateral acceleration in the observed variables is measured by an acceleration sensor set in the lateral motion direction of the vehicle, the measured lateral acceleration is also related to 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. In addition, in the present embodiment, the state equation of the nonlinear discrete time system and the observation equation of the nonlinear discrete time system are subjected to a first-order Taylor expansion to obtain a state Jacobian matrix and an observation Jacobian matrix; based on the state Jacobian matrix, a linearized state equation is constructed; based on the observation Jacobian matrix, a linearized observation equation is constructed, which can make linearization based on the extended Kalman filter principle, thereby reducing the difficulty of calculation. In addition, the method of analyzing based on vehicle kinematics proposed in the present application does not require obtaining tire lateral forces, and also eliminates the need for complex nonlinear tire modeling.

[0104] See also Figure 5 The present application provides a lateral slope estimation device 400, the device 400 comprising:

[0105] An observed variable acquisition unit 410 is used to acquire current observed variables, wherein 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;

[0106] The transverse slope estimation unit 420 is used to obtain the current transverse slope based on the current observed variables and a pre-built Kalman filter, wherein the state variables of the Kalman filter include the transverse component of gravity acceleration.

[0107] As a method, the lateral slope estimation unit 420 is specifically used to obtain the current lateral component of gravity acceleration based on the current observed variable and the Kalman filter; and obtain the current lateral slope based on the current lateral component of gravity 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 speed 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 gravity acceleration; construct a lateral acceleration equation based on the lateral vehicle speed, yaw angular velocity and longitudinal vehicle speed of the vehicle; construct a lateral vehicle speed equation based on the lateral vehicle speed of adjacent sampling periods of the vehicle; and obtain the vehicle kinematic model based on the measured lateral acceleration equation, the lateral acceleration equation and the lateral vehicle 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 vehicle speed, longitudinal vehicle 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, so as to obtain a state Jacobian matrix and an 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 the present application is described.

[0115] See also Figure 6Based on the above-mentioned lateral slope estimation method and device, the embodiment of the present application also provides another vehicle 100 that can execute the above-mentioned lateral slope estimation method. The vehicle 100 includes a processor 102, a memory 104, and a data acquisition module 106. Among them, the memory 104 stores a program that can execute the content of the above-mentioned embodiment, and the processor 102 can execute the program stored in the memory 104.

[0116] Among them, the processor 102 may include one or more processing cores. The processor 102 uses various interfaces and lines to connect various parts in the entire vehicle 100, and executes various functions and processes data of the vehicle 100 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 hardware form of a network processor (Neural network Processing Unit, NPU), a digital signal processing (Digital Signal Processing, DSP), a field programmable gate array (Field-Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). The processor 102 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU), a network processor (Neural network Processing Unit, NPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing display content; the NPU is responsible for processing multimedia data such as video and images; and the modem is used to process wireless communications. It is understandable that the above-mentioned modem may not be integrated into the processor 102, but may be implemented separately through a communication chip.

[0117] The memory 104 may include a random access memory (RAM), and may also include a read-only memory (RAM) and a 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, wherein 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 following various method embodiments, 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 record data), etc.

[0118] The data acquisition module 106 may include but is not limited to: IMU (Inertial Measurement Unit), multi-eye camera, level, light sensor, motion sensor, pressure sensor, infrared thermal sensor, distance sensor, acceleration sensor, and other sensors. Among them, the IMU can obtain the first IMU information of the vehicle 100. The multi-eye camera can collect environmental information around the vehicle 100, such as the vehicle's seats, windows, etc.

[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, such as 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] Among them, the acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, and can be used for applications to identify the posture of the vehicle 100 (such as magnetometer posture calibration), vibration recognition related functions (such as pedometer, knocking), etc. In addition, the vehicle 100 can also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, etc., which will not be repeated here.

[0121] An embodiment of the present application provides a computer-readable storage medium in which a program code is stored, and the program code can be called by a processor to execute the method described in the above method embodiment.

[0122] The computer readable storage medium may 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. Optionally, the computer readable storage medium includes a non-transitory computer-readable storage medium. The computer readable storage medium has storage space for program codes that execute any of the method steps in the above method. These program codes can be read from or written to one or more computer program products. The program code 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 is 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 set in the lateral motion direction of the vehicle, the measured lateral acceleration is also related to the lateral component of gravity acceleration, and the state variable of the Kalman filter also includes 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 it. 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: Acquire current observed variables, where the observed variables include measured lateral acceleration, where the measured lateral acceleration is measured by an acceleration sensor disposed in the lateral motion direction of the vehicle; Based on the current observed variables and a pre-built Kalman filter, a current lateral slope is obtained, wherein the state variables of the Kalman filter include a lateral component of gravity acceleration.

2. The method according to claim 1, characterized in that Before obtaining the current observed variable, the following is also included: Constructing a vehicle kinematics model, wherein the vehicle kinematics model represents the relationship between the lateral speed of the vehicle and the lateral component of gravity acceleration; 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.

3. The method according to claim 2, characterized in that The constructing of the vehicle kinematic model comprises: 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; The vehicle kinematic model is obtained based on the measured lateral acceleration equation, the lateral acceleration equation, and the lateral vehicle speed equation.

4. The method according to claim 2, 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 vehicle speed, longitudinal vehicle speed, and yaw angular velocity.

5. The method according to claim 2, characterized in that: The observation equation of the nonlinear discrete time system for constructing the Kalman filter includes: The observation equation of the nonlinear discrete-time system is constructed based on the observation variables, and the observation variables also include the measured lateral vehicle speed, the measured longitudinal vehicle speed, and the measured yaw angular velocity of the vehicle.

6. The method according to claim 2, characterized in that 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, thereby obtaining a state Jacobian matrix and an observation Jacobian matrix; Based on the state Jacobian matrix, construct the linearized state equation; Based on the observation Jacobian matrix, the linearized observation equation is constructed.

7. The method according to any one of claims 1 to 6, characterized in that: The method of obtaining the current lateral slope based on the current observed variable and the Kalman filter comprises: Based on the current observed variables 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.

8. A lateral slope estimation device, characterized in that: The device comprises: An observed variable acquisition unit, used to acquire current observed variables, wherein 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; The lateral slope estimation unit is used to obtain the current lateral slope based on the current observed variables and a pre-built Kalman filter, wherein the state variables of the Kalman filter include the lateral component of the gravitational acceleration.

9. 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 7.

10. 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 7 is executed.

Images