CN115447587A - Data processing method, device, non-volatile storage medium and computer equipment - Google Patents

Abstract

The invention discloses a data processing method, device, nonvolatile storage medium and computer equipment. Wherein, the method includes: acquiring state information of the vehicle, wherein the state information includes first state information of the vehicle at a first moment and second state information of the vehicle at a second moment, and any one of the state information includes: Velocity, longitudinal acceleration, slope, driving force and wheel angular acceleration, the direction of longitudinal acceleration is from the rear of the vehicle to the front; according to the first state information and the second state information, the unscented Kalman filter method is used to calculate the vehicle at the The target slope at the second moment, where the unscented Kalman filter method uses the kinematics formula and dynamics formula of the vehicle to estimate the target slope. The invention solves the technical problem of low precision of the slope estimation method in the prior art.

Description

Data processing method, device, non-volatile storage medium and computer equipment

technical field

The present invention relates to the field of vehicle assisted driving, in particular to a data processing method, device, non-volatile storage medium and computer equipment.

Background technique

The slope signal is one of the important signals for the development of automobile function software. Whether it is a traditional fuel vehicle or a new energy vehicle, the development of software algorithms such as fuel saving and energy saving is inseparable from this signal; for vehicles such as off-road vehicles or SUVs, the slope signal affects the entire State switching of terrain software or driving mode software; for self-driving vehicles, slope signals affect vehicle driving safety algorithm modules, control algorithm modules, etc. In short, the slope signal is one of the very important basic signals. The accuracy of the signal directly affects the results of the upper-level functional software, and ultimately affects the performance of the vehicle. The estimation accuracy of the slope in the prior art is low, and the estimation range is small.

For the above problems, no effective solution has been proposed yet.

Contents of the invention

Embodiments of the present invention provide a data processing method, device, non-volatile storage medium and computer equipment, so as to at least solve the technical problem of low accuracy of the slope estimation method in the prior art.

According to an aspect of an embodiment of the present invention, there is provided a data processing method, including: acquiring state information of the vehicle, wherein the state information includes the first state information of the vehicle at the first moment and the first state information of the vehicle at the second moment Two state information, any one of the state information includes: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration, the direction of longitudinal acceleration is from the rear to the front of the vehicle; according to the first state information and the second state information , using the unscented Kalman filter method to calculate the target slope of the vehicle at the second moment, wherein the unscented Kalman filter method uses the vehicle's kinematics formula and dynamics formula to estimate the target slope.

Optionally, according to the first state information and the second state information, the unscented Kalman filter method is used to calculate the target slope of the vehicle at the second moment, including: according to the first state information and the kinematic formula, calculating the slope of the vehicle at the second moment The third state information at the second moment, wherein the state information includes the third state information; according to the third state information and the dynamic formula, calculate the fourth state information of the vehicle at the second moment, wherein the state information includes the fourth state information; calculate the Kalman gain at the second moment according to the third state information and the fourth state information; calculate the target slope according to the second state information, the third state information, the fourth state information and the Kalman gain.

Optionally, according to the first state information and the kinematic formula, calculating the third state information of the vehicle at the second moment includes: performing unscented transformation on the first state information to generate multiple first sampling points and multiple first sampling points A weighted value of a sampling point; calculated according to the kinematic formula, multiple first sampling points and the weighted values of multiple first sampling points, to obtain multiple second sampling points and multiple second sampling points of the vehicle at the second moment The weighted value of the sampling point; the third state information is obtained by calculating according to the multiple second sampling points and the weighted values of the multiple second sampling points.

Optionally, according to the third state information and the dynamic formula, calculating the fourth state information of the vehicle at the second moment includes: performing unscented transformation on the third state information to generate multiple third sampling points and multiple first The weighted value of three sampling points; according to the dynamic formula, the weighted value of multiple third sampling points and multiple third sampling points, calculate the weighted value of multiple fourth sampling points and multiple fourth sampling points; according to multiple The fourth sampling point and weighted values of the plurality of fourth sampling points are calculated to obtain fourth state information.

Optionally, calculating the Kalman gain at the second moment according to the third state information and the fourth state information includes: calculating a plurality of sampling points and a plurality of fourth sampling points according to a plurality of third sampling points and a plurality of fourth sampling points The cross-covariance matrix between the fourth sampling points; according to the cross-covariance matrix, the Kalman gain is calculated.

Optionally, acquiring the second state information of the vehicle includes: at the second moment, receiving the physical quantity measured by the sensor, wherein the physical quantity measured by the sensor includes the longitudinal acceleration, driving force, speed and wheel angular acceleration of the vehicle; according to The physical quantity measured by the sensor and the dynamic formula are calculated to obtain the second slope included in the second state information.

Optionally, receiving the wheel angular acceleration measured by the sensor includes: receiving the wheel angular velocity measured by the sensor and the sampling period of the wheel angular velocity measured by the sensor; and calculating the angular acceleration according to the wheel angular velocity and the sampling period.

According to another aspect of the embodiments of the present invention, there is also provided a data processing device, including: an acquisition module, configured to acquire state information of the vehicle, wherein the state information includes the first state information of the vehicle at the first moment and the vehicle In the second state information at the second moment, any one of the state information includes: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration, the direction of longitudinal acceleration is from the rear to the front of the vehicle; the calculation module, It is used to calculate the target slope of the vehicle at the second moment by using the unscented Kalman filter method according to the first state information and the second state information, wherein the unscented Kalman filter method uses the kinematics formula and the dynamics formula of the vehicle Estimate the target slope.

According to yet another aspect of the embodiments of the present invention, a non-volatile storage medium is also provided, and the non-volatile storage medium includes a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned Any one of the data processing methods.

According to still another aspect of the embodiments of the present invention, there is also provided a computer device, the computer device includes a processor, and the processor is used to run a program, wherein, when the program is running, any one of the above data processing methods is executed.

In the embodiment of the present invention, by acquiring the state information of the vehicle, the state information includes the first state information of the vehicle at the first moment and the second state information of the vehicle at the second moment, any one of the state information Including: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration. The direction of longitudinal acceleration is from the rear of the vehicle to the front of the vehicle; according to the first state information and the second state information, the unscented Kalman filter method is used to calculate the vehicle The target slope at the second moment, where the unscented Kalman filter method uses the kinematics formula and dynamics formula of the vehicle to estimate the target slope, and achieves the purpose of merging vehicle state information data to calculate the slope, thereby realizing The technical effect of expanding the slope calculation range and improving the slope calculation accuracy further solves the technical problem of low precision of the slope estimation method in the prior art.

Description of drawings

The accompanying drawings described here are used to provide a further understanding of the present invention and constitute a part of the application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations to the present invention. In the attached picture:

Fig. 1 shows a kind of hardware structure block diagram for realizing the computer terminal of data processing method;

Fig. 2 is a schematic flowchart of a data processing method provided according to an embodiment of the present invention;

Fig. 3 is a structural block diagram of a data processing device provided according to an embodiment of the present invention.

detailed description

In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

According to an embodiment of the present invention, an embodiment of a data processing method is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and, although A logical order is shown in the flowcharts, but in some cases the steps shown or described may be performed in an order different from that shown or described herein.

The method embodiment provided in Embodiment 1 of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Fig. 1 shows a block diagram of the hardware structure of a computer terminal for realizing the data processing method. As shown in Figure 1, the computer terminal 10 may include one or more (shown by 102a, 102b, ..., 102n in the figure) processors (processors may include but not limited to microprocessors MCU or programmable logic devices Processing device such as FPGA), memory 104 for storing data. In addition, it can also include: a display, an input/output interface (I/O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply, and/or or camera. Those of ordinary skill in the art can understand that the structure shown in FIG. 1 is only a schematic diagram, and it does not limit the structure of the above-mentioned electronic device. For example, computer terminal 10 may also include more or fewer components than shown in FIG. 1 , or have a different configuration than that shown in FIG. 1 .

It should be noted that the one or more processors and/or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuit may be implemented in whole or in part as software, hardware, firmware or other arbitrary combinations. In addition, the data processing circuit can be a single independent processing module, or be fully or partially integrated into any of the other components in the computer terminal 10 . As mentioned in the embodiment of the present application, the data processing circuit is used as a processor control (for example, the selection of the terminal path of the variable resistor connected to the interface).

The memory 104 can be used to store software programs and modules of application software, such as the program instruction/data storage device corresponding to the data processing method in the embodiment of the present invention, and the processor runs the software programs and modules stored in the memory 104 to execute various A functional application and data processing, that is, a data processing method for realizing the above-mentioned application program. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory that is remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10 .

Fig. 2 is a schematic flow chart of a data processing method provided according to an embodiment of the present invention. As shown in Fig. 2, the method includes the following steps:

Step S202, acquire the state information of the vehicle, wherein the state information includes the first state information of the vehicle at the first moment and the second state information of the vehicle at the second moment, any one of the state information includes: speed, longitudinal Acceleration, slope, driving force and wheel angular acceleration, the direction of longitudinal acceleration is from the rear of the vehicle to the front of the vehicle.

During the running of the vehicle, the sensors installed on the vehicle can measure the state parameters when the vehicle is running, and process the measured state parameters to obtain the state information when the vehicle is running. The state information can include speed, longitudinal acceleration , the slope of the vehicle, the driving force of the vehicle and the angular acceleration of the wheels. It should be noted that the slope of the vehicle is not a state parameter that can be directly measured, and needs to be estimated by other state parameters through inferential calculation. Compared with state parameters, state information is more convenient to participate in unscented Kalman filter calculation, and state information can be represented by vector during calculation.

Estimating the slope value of the vehicle's current location can be a dynamic cycle process. For two adjacent estimation processes, the state information estimation result of the last time will play a significant role in the next estimation process. Therefore, in this estimation process, the vehicle state information at two moments can be obtained, which are the first state information of the vehicle at the first moment and the second state information of the vehicle at the second moment. The first moment is At the moment when the previous estimation process starts, the first state information of the vehicle is the estimation result obtained in the previous estimation process; the second moment is the moment when this estimation process starts, and the second state information of the vehicle is obtained from the measurement at the second moment The status information of the status parameters obtained after data processing.

Step S204, according to the first state information and the second state information, the unscented Kalman filter method is used to calculate the target slope of the vehicle at the second moment, wherein the unscented Kalman filter method uses the kinematics formula and dynamics of the vehicle The formula estimates the target slope.

In this step, after the first state information and the second state information are acquired, the unscented Kalman filter method may be used to process the first state information and the second state information according to appropriate kinematics formulas and dynamics formulas, Estimate the target slope of the vehicle at the second moment. It should be noted that during the data processing process of the first state information and the second state information using the unscented Kalman filter method, the representation method of the state information can be expressed by using a vector, and the state information includes Therefore, in the actual calculation, a vector can be obtained through the operation between vectors, and there is a number representing the target slope in the obtained vector, that is, the vector representation of the first state information and the vector representation of the second state information obtain a vector representation containing The vector representation of the status information of the target slope, the number at the position indicating the slope in the obtained status information is the value of the target slope.

Through the above steps, the purpose of merging the state information data of the vehicle at different times to calculate the slope can be achieved, thereby achieving the technical effect of expanding the calculation range of the slope and improving the calculation accuracy of the slope, and then solving the problem of the slope estimation method in the prior art. Technical problems with low accuracy.

As an optional embodiment, according to the first state information and the second state information, the target gradient of the vehicle at the second moment is calculated by using the unscented Kalman filter method, which can be realized by the following steps: according to the first state information and the kinematics formula to calculate the third state information of the vehicle at the second moment, wherein the state information includes the third state information; according to the third state information and the dynamics formula, calculate the fourth state information of the vehicle at the second moment , wherein the state information includes the fourth state information; according to the third state information and the fourth state information, calculate the Kalman gain at the second moment; according to the second state information, the third state information, the fourth state information and the Kalman gain Gain, calculated to get the target slope.

The unscented Kalman filter method is an estimation method that estimates the state vector at this moment based on the state vector at the previous moment, and updates or corrects the state vector at this moment according to the observed value to obtain the final estimated value. That is to say, the unscented Kalman filter method needs an initial state vector and an observation value when estimating the state vector at this moment.

It should be noted that the kinematics formula is a formula generated based on the kinematics law during the running of the vehicle, and the dynamics formula is a formula generated based on the dynamics law during the running of the vehicle. Since the slope in the state information cannot be directly measured by sensors, other physical parameters that can be measured by sensors can be calculated according to dynamic formulas or kinematic formulas to obtain the slope of the vehicle at a certain moment. However, this The direct estimation is not accurate, so the Kalman filter can be used to estimate the slope of the vehicle at the current moment more accurately.

Optionally, when the unscented Kalman filter method is used to estimate the first state information and the second state information to obtain the target slope, the representation methods of the first state information and the second state information can be vector representations, and all state information The vectors of each include five parameters, and the five parameters represent the angular acceleration of the wheel, the longitudinal acceleration, the slope of the vehicle, the driving force of the vehicle, and the vehicle speed in turn. When vector operations are performed between multiple state information, the state information does not change. vector format. The first state information is the state vector at the previous moment required in the unscented Kalman filter method. Through the first state information and the kinematics formula followed by the vehicle during driving, the third state of the vehicle at the second moment can be estimated information, and according to the third state information and the dynamic formula followed by the vehicle during driving, the fourth state information and Kalman gain at the second moment can be obtained, and the Kalman gain is the Kalman gain matrix; according to the Kalman gain matrix and As the second state information of the observed value in the Kalman filter method, the third state information at the second moment can be corrected, and the final state information including the target slope can be calculated, that is, the vehicle passes through Karl at the second moment Mann filter method to modify the state information.

As an optional embodiment, the calculation of the third state information of the vehicle at the second moment according to the first state information and the kinematic formula can be realized by the following steps: performing unscented transformation on the first state information to generate multiple The weighted value of a first sampling point and a plurality of first sampling points; calculate according to kinematics formula, a plurality of first sampling points and the weighted value of a plurality of first sampling points, obtain a plurality of vehicle under the second moment The second sampling point and the weighted values of the multiple second sampling points; the third state information is obtained by calculating according to the multiple second sampling points and the weighted values of the multiple second sampling points.

Optionally, performing unscented transformation on the first state information is an algorithm of unscented Kalman filtering, the first state information can be a sampling point, and according to the error of the physical quantity in the first state information, it can be calculated according to the unscented transformation The method obtains a plurality of first sampling points centered on the first state information, and obtains a plurality of weighted values corresponding to each of the plurality of first sampling points. Bring multiple first sampling points into the kinematics formula for calculation to obtain multiple second sampling points. The weighted values corresponding to the multiple second sampling points are the same as the weighted values corresponding to the multiple first sampling points, and the corresponding relationship is also does not change, the multiple second sampling points represent multiple possible values at the second moment. According to the one-to-one correspondence between the plurality of second sampling points and the weighted values of the plurality of second sampling points, an average value of the plurality of second sampling points, that is, the third state information may be obtained.

Optionally, in this embodiment, the number of the first sampling points is selected as 11, and the kinematic formula used is as follows:

为车辆行驶的纵向位移的二阶导数；g为重力加速度，取值为9.81m/s²。其中，对于位移的二阶导数

可以根据如下公式，根据从动轮的车轮角速度进行计算：In the formula: a _{G, x} is the longitudinal acceleration at the center of mass of the vehicle, where the longitudinal direction is the direction of the x-axis in the vehicle coordinate system. When the vehicle is on a level ground and in a static state, the coordinate origin of the vehicle coordinate system coincides with the center of mass of the vehicle , the x-axis is parallel to the ground and points forward, the z-axis points upward through the center of mass, and the y-axis points to the left of the driver;

is the second derivative of the longitudinal displacement of the vehicle; g is the acceleration due to gravity, which is 9.81m/s ² . where, for the second derivative of the displacement

It can be calculated according to the wheel angular velocity of the driven wheel according to the following formula:

In the formula: r _stat is the rolling radius of the vehicle at rest, in m; ω is the rolling angular velocity of the wheel, in rad/s.

As an optional embodiment, receiving the wheel angular acceleration measured by the sensor can be achieved through the following steps: receiving the wheel angular velocity of the vehicle measured by the sensor and the sampling period of the sensor measuring the wheel angular velocity; according to the wheel angular velocity and the sampling period, calculate angular acceleration.

Optionally, in the dynamic formula, the following formula can be used for the derivative calculation of the angular velocity of the wheel in a discrete state:

In the formula: ω _k is the angular velocity of the wheel at time k, ω _k-1 is the speed of the wheel at time k-1; Δt is the sampling period of the wheel signal, and the unit is s.

When calculating the slope of the vehicle, a small angle estimation method is used in the related art, that is, when the slope angle χ _road is less than 10°, the following formula is used for approximation:

χ _road = sin(χ _road ) = tan(χ _road );

cos(χ _road )=1.

In this embodiment, the above approximation scheme is not adopted, and the gradient estimation is performed directly using the chord function, which expands the nonlinear range of the gradient signal.

As an optional embodiment, according to the third state information and the dynamic formula, the fourth state information of the vehicle at the second moment can be calculated through the following steps: perform unscented transformation on the third state information to generate multiple The weighted value of a third sampling point and a plurality of third sampling points; according to the weighted value of a kinetic formula, a plurality of third sampling points and a plurality of third sampling points, calculate a plurality of fourth sampling points and a plurality of fourth sampling points The weighted value of the sampling point; the fourth state information is obtained by calculating according to the multiple fourth sampling points and the weighted values of the multiple fourth sampling points.

Optionally, unscented transformation is also performed on the third state information, using the third state information as a central sampling point to obtain a plurality of third sampling points and weighted values corresponding to each of the plurality of third sampling points. Bring multiple third sampling points into the dynamic formula for calculation, and obtain multiple fourth sampling points. The weighted values corresponding to multiple fourth sampling points are the same as the weighted values corresponding to multiple third sampling points, and the corresponding relationship is also do not change. According to the one-to-one correspondence between the multiple fourth sampling points and the weighted values of the multiple fourth sampling points, the average value of the multiple fourth sampling points, that is, the fourth state information, can be calculated. Preferably, the present invention selects 11 first sampling points.

As an optional embodiment, obtaining the second state information of the vehicle may be achieved through the following steps: at the second moment, receiving the physical quantity measured by the sensor, wherein the physical quantity measured by the sensor includes the longitudinal acceleration of the vehicle, the driving force, speed and wheel angular acceleration; according to the physical quantity measured by the sensor and the dynamic formula, the second slope included in the second state information is calculated.

Optionally, the acquisition of the second state information of the vehicle may be directly calculated through the following dynamic formula according to multiple physical quantities measured by the vehicle sensors at the second moment.

For driving on a sloped road, the force balance equation of the car is established, that is, the dynamic formula is as follows:

In the formula: m is the mass of the vehicle, the unit is kg; F _x is the driving force acting on the tires on the ground, the unit is N; c _air is the air resistance coefficient, which can be obtained through the wind resistance test; A _l is the windward area, which is the inherent parameter of the vehicle, The unit is m ² ; ρ _a is the air density, the unit is kg/m ³ ; v _{G, x} is the velocity of the center of mass of the vehicle, the unit is m/s. Among them, for the driving force, when the vehicle is in the case of low slip ratio, it can be obtained by converting the rotational torque provided by the vehicle itself through the engine according to the following formula. On an electric vehicle, the rotational torque provided by the motor can also be obtained according to the following formula The formula is obtained:

In the formula: T _tq is the engine torque, unit N*m; i _g is the gear ratio of the transmission; i ₀ is the gear ratio of the main reducer; η _T is the mechanical efficiency of the drive train; the driving force can also be provided to the wheels through the ground The driving force is equal to the vertical force multiplied by the ground adhesion coefficient, but considering that the ground adhesion coefficient varies in different working conditions, it is not recommended to use this method to obtain the driving force.

As an optional embodiment, according to the third state information and the fourth state information, calculating the Kalman gain at the second moment can be realized through the following steps: according to multiple third sampling points and multiple fourth sampling points , to calculate the cross-covariance matrix between the multiple third sampling points and the multiple fourth sampling points; according to the cross-covariance matrix, calculate the Kalman gain.

Optionally, according to the plurality of third sampling points and the plurality of fourth sampling points, the unscented Kalman filtering method can be used, because the plurality of fourth sampling points are respectively obtained from the plurality of third sampling points, so the plurality of fourth sampling points There is a one-to-one correspondence between the sampling points and the multiple third sampling points. According to the one-to-one correspondence between the multiple fourth sampling points and the multiple third sampling points, the multiple third sampling points and the multiple The cross-covariance matrix between the fourth sampling points is obtained by multiplying the cross-covariance matrix by the inverse matrix of the cross-covariance matrix to obtain the Kalman gain matrix.

a_G，x，F_x三个量是已知量，均可以通过车辆传感器采集或车辆系统中存储的相关数据带入上述给出的公式得到。As a specific example,

The three quantities of a _{G, x} , and F _x are known quantities, which can be obtained by bringing the relevant data collected by the vehicle sensor or stored in the vehicle system into the formula given above.

For the above kinematics formula and dynamics formula, set the state vector X as follows:

For the three known quantities, assuming that their errors obey the normal distribution, the error formula is as follows:

v _{ω, k} ~ N(0, Q _k );

V _{a, k} ~ N (0, R _k );

v _{F, k} ~ N (0, T _k );

a_G，x，F_x的误差，v_a，k为a_G，x的误差，v_F，k为F_x的误差，Q_k，R_k，T_k分别为v_ω，k，v_a，k，v_F，k服从的正态分布的方差。In the above error formula, v _{ω, k} is

a _{G, x} , the error of F _x , v _{a, k} is the error of a _{G, x} , v _{F, k} is the error of F _x , Q _k , R _k , T _k are respectively v _{ω, k} , v _{a, k} , v _{F, the variance of the normal distribution that k} obeys.

In a discrete system, the relationship between the state vector at time k+1 and the state vector at time k is as follows:

In the initial state, the values of Q _k , R _k , and T _k are 0.01.

The basic calculation process of the unscented Kalman filter calculation process is:

First, obtain the vector representation of the first state information, and determine the number and weight of sampling points. In this optional embodiment, the number of sampling points is 11;

Secondly, according to the error of each physical quantity in the first state information, calculate the Kolesky decomposition of the covariance matrix of the first state information;

Secondly, according to the Kolesky decomposition of the covariance matrix calculated in the previous step, the first state information and the number and weight of the sampling points determined in the first step, calculate a plurality of first sampling points;

a_G，x，χ_road的值，得到多个第二采样点；Secondly, according to the relationship between the state vector at time k+1 and the state vector at time k, and the vector representation of the first state information, calculate the values of the multiple first sampling points at the second time, and divide the multiple first sampling points into The value of the sampling point at the second moment is brought into the kinematics formula for calculation, and the values in multiple first sampling points are updated

a _{G, x} , the value of χ _road , obtain a plurality of second sampling points;

Secondly, according to the plurality of second sampling points and the initially determined weights, solve the mean value and covariance matrix of the plurality of second sampling points calculated by the kinematic formula, wherein the mean value of the plurality of second sampling points is the third state vector;

Secondly, perform Koleski decomposition according to the covariance matrix obtained in the previous step;

Secondly, according to the number of samples determined in the first step and the Kolesky decomposition of the covariance matrix calculated in the previous step, the third state vector is sampled to obtain a plurality of third sampling points;

Secondly, a plurality of third sampling points are respectively substituted into the kinetic formula calculation, and the values of a _{G, x} , χ _road , F _x , v _{G, x} in the plurality of third sampling points are updated to obtain a plurality of fourth sampling points , and calculate the mean value and covariance matrix of a plurality of fourth sampling points, wherein, the mean value of a plurality of fourth sampling points is the fourth state vector;

Secondly, according to the third state vector and the fourth state vector, calculate the cross-covariance matrix and Kalman gain of the results obtained by the kinematic formula and the dynamic formula respectively;

Secondly, the measured value of the vehicle sensor at the second moment is obtained, and the second state vector is directly calculated according to the dynamic formula as the observed value;

Finally, subtract the value of the fourth state vector from the second state vector, multiply by the Kalman gain, and add the third state vector to obtain the final updated state vector, and the slope in the final updated state vector is the target slope.

It should be noted that for the foregoing method embodiments, for the sake of simple description, they are expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence. Because of the present invention, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

Through the description of the above embodiments, those skilled in the art can clearly understand that the data processing method according to the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases The former is a better implementation. Based on such an understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products are stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to enable a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

According to an embodiment of the present invention, a device for implementing the above data processing method is also provided. FIG. 3 is a structural block diagram of a data processing device provided according to an embodiment of the present invention. As shown in FIG. 3 , the data processing device includes: The acquisition module 32 and the calculation module 34, the data processing device will be described below.

It should be noted here that the acquisition module 32 and the calculation module 34 correspond to steps S202 to S204 in the embodiment, and the examples and application scenarios realized by the two modules are the same as those of the corresponding steps, but they are not limited to those in the above embodiment. public content. It should be noted that, as a part of the device, the above modules can run in the computer terminal 10 provided in the embodiment.

An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the above computer device may be located in at least one network device among multiple network devices in a computer network. The computer device includes memory and a processor.

Among them, the memory can be used to store software programs and modules, such as the program instructions/modules corresponding to the data processing method and device in the embodiment of the present invention, and the processor executes various functional applications by running the software programs and modules stored in the memory. And data processing, that is, realizing the above-mentioned data processing method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: acquire the state information of the vehicle, wherein the state information includes the first state information of the vehicle at the first moment and the state information of the vehicle at the second moment. The second state information of the state information, any one of the state information includes: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration, the direction of the longitudinal acceleration is from the rear of the vehicle to the front of the vehicle; according to the first state information and the second For state information, an unscented Kalman filter method is used to calculate the target slope of the vehicle at the second moment, wherein the unscented Kalman filter method uses the vehicle's kinematics formula and dynamics formula to estimate the target slope.

Optionally, the above-mentioned processor can also execute the program code of the following steps: according to the first state information and the second state information, calculate the target slope of the vehicle at the second moment by using the unscented Kalman filter method, including: according to the first A state information and kinematics formula, calculating the third state information of the vehicle at the second moment, wherein the state information includes the third state information; according to the third state information and the dynamics formula, calculating the third state information of the vehicle at the second moment Four state information, wherein the state information includes the fourth state information; according to the third state information and the fourth state information, calculate the Kalman gain at the second moment; according to the second state information, the third state information, the fourth state information and Kalman gain to calculate the target slope.

Optionally, the above-mentioned processor may also execute the program code of the following steps: calculating the third state information of the vehicle at the second moment according to the first state information and the kinematic formula, including: performing unscented transformation on the first state information , generate multiple first sampling points and weighted values of multiple first sampling points; calculate according to the kinematics formula, multiple first sampling points and multiple first sampling points’ weighted values, and obtain the vehicle at the second moment The plurality of second sampling points and the weighted values of the plurality of second sampling points; according to the plurality of second sampling points and the weighted values of the plurality of second sampling points, the third state information is obtained through calculation.

Optionally, the above-mentioned processor can also execute the program code of the following steps: calculating the fourth state information of the vehicle at the second moment according to the third state information and the dynamic formula, including: performing unscented transformation on the third state information , generate multiple third sampling points and weighted values of multiple third sampling points; calculate multiple fourth sampling points and multiple weighted values according to the dynamic formula, multiple third sampling points and multiple third sampling points A weighted value of a fourth sampling point; the fourth state information is obtained by calculating according to the multiple fourth sampling points and the weighted values of the multiple fourth sampling points.

Optionally, the above-mentioned processor may also execute the program code of the following steps: calculating the Kalman gain at the second moment according to the third state information and the fourth state information, including: according to multiple third sampling points and multiple first sampling points Four sampling points, calculating the cross-covariance matrix between multiple sampling points and multiple fourth sampling points; according to the cross-covariance matrix, calculate the Kalman gain.

Optionally, the above-mentioned processor may also execute the program code of the following steps: obtaining the second state information of the vehicle, including: receiving the physical quantity measured by the sensor at the second moment, wherein the physical quantity measured by the sensor includes the longitudinal direction of the vehicle Acceleration, driving force, speed and wheel angular acceleration; according to the physical quantity measured by the sensor and the dynamics formula, the second slope included in the second state information is calculated.

Optionally, the above-mentioned processor can also execute the program code of the following steps: receiving the wheel angular acceleration measured by the sensor, including: receiving the wheel angular velocity of the vehicle measured by the sensor and the sampling period of the sensor measuring the wheel angular velocity; period, to calculate the angular acceleration.

By adopting the embodiment of the present invention, a data processing solution is provided. By acquiring the state information of the vehicle, wherein the state information includes the first state information of the vehicle at the first moment and the second state information of the vehicle at the second moment, any one of the state information includes: speed, longitudinal acceleration, The direction of the slope, driving force, wheel angular acceleration, and longitudinal acceleration is from the rear to the front; according to the first state information and the second state information, the target of the vehicle at the second moment is calculated by using the unscented Kalman filter method Slope, among them, the unscented Kalman filter method uses the kinematics formula and dynamics formula of the vehicle to estimate the target slope, and achieves the purpose of merging the vehicle state information data to calculate the slope, thereby realizing the expansion of the slope calculation range and improving the slope. The technical effect of the calculation accuracy further solves the technical problem of low accuracy of the slope estimation method in the prior art.

Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, the storage medium It may include: a flash disk, a read-only memory (Read-Only Memory, ROM), a random access device (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.

The embodiment of the present invention also provides a non-volatile storage medium. Optionally, in this embodiment, the above-mentioned non-volatile storage medium may be used to store the program code executed by the data processing method provided by the above-mentioned embodiment.

Optionally, in this embodiment, the above-mentioned non-volatile storage medium may be located in any computer terminal in the group of computer terminals in the computer network, or in any mobile terminal in the group of mobile terminals.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: acquiring state information of the vehicle, wherein the state information includes the first state of the vehicle at the first moment Information and the second state information of the vehicle at the second moment, any one of the state information includes: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration, and the direction of the longitudinal acceleration is from the rear of the vehicle to the front of the vehicle; According to the first state information and the second state information, the unscented Kalman filter method is used to calculate the target slope of the vehicle at the second moment. The slope is estimated.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: According to the first state information and the second state information, the vehicle is calculated by using the unscented Kalman filter method The target slope at the second moment includes: calculating the third state information of the vehicle at the second moment according to the first state information and the kinematic formula, wherein the state information includes the third state information; according to the third state information and The dynamics formula calculates the fourth state information of the vehicle at the second moment, wherein the state information includes the fourth state information; calculates the Kalman gain at the second moment according to the third state information and the fourth state information; The second state information, the third state information, the fourth state information and the Kalman gain are used to calculate the target slope.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: calculate the third state of the vehicle at the second moment according to the first state information and the kinematics formula Information, including: performing unscented transformation on the first state information to generate a plurality of first sampling points and weighted values of the plurality of first sampling points; Calculate the weighted value of the vehicle to obtain the weighted values of multiple second sampling points and multiple second sampling points of the vehicle at the second moment; according to the weighted values of multiple second sampling points and multiple second sampling points, calculate Get the third state information.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: calculate the fourth state of the vehicle at the second moment according to the third state information and the dynamics formula information, including: performing unscented transformation on the third state information to generate a plurality of third sampling points and weighted values of the plurality of third sampling points; according to a dynamic formula, a plurality of third sampling points and a plurality of third sampling points Calculate a plurality of fourth sampling points and weighted values of the plurality of fourth sampling points; calculate and obtain fourth state information according to the plurality of fourth sampling points and the weighted values of the plurality of fourth sampling points.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: calculating the Kalman gain at the second moment according to the third state information and the fourth state information, The method includes: calculating a cross-covariance matrix between the multiple sampling points and the multiple fourth sampling points according to the multiple third sampling points and the multiple fourth sampling points; and calculating a Kalman gain according to the cross-covariance matrix.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: obtaining the second state information of the vehicle, including: at the second moment, receiving the The physical quantity, wherein the physical quantity measured by the sensor includes the vehicle's longitudinal acceleration, driving force, speed and wheel angular acceleration; according to the physical quantity measured by the sensor and the dynamics formula, the second slope included in the second state information is calculated.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: receiving the wheel angular acceleration measured by the sensor, including: receiving the wheel angular velocity and The sensor measures the sampling period of the wheel angular velocity; according to the wheel angular velocity and the sampling period, the angular acceleration is calculated.

The serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.

In the above-mentioned embodiments of the present invention, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed technical content can be realized in other ways. Wherein, the device embodiments described above are only illustrative. For example, the division of the units may be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrate into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of units or modules may be in electrical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes. .

The above is only a preferred embodiment of the present invention, it should be pointed out that, for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.

Claims (10)

1. A data processing method, comprising:

acquiring state information of a vehicle, wherein the state information comprises first state information of the vehicle at a first moment and second state information of the vehicle at a second moment, and any one of the state information comprises: the vehicle-mounted acceleration control system comprises speed, longitudinal acceleration, a slope, driving force and wheel angular acceleration, wherein the direction of the longitudinal acceleration points to a vehicle head from a vehicle tail;

and calculating to obtain the target gradient of the vehicle at the second moment by adopting an unscented kalman filtering method according to the first state information and the second state information, wherein the unscented kalman filtering method adopts a kinematic formula and a dynamic formula of the vehicle to estimate the target gradient.

2. The method according to claim 1, wherein the calculating, according to the first state information and the second state information, a target gradient of the vehicle at the second time by using an unscented kalman filter method includes:

calculating third state information of the vehicle at the second moment according to the first state information and the kinematic formula, wherein the state information comprises the third state information;

according to the third state information and the dynamic formula, fourth state information of the vehicle at the second moment is calculated, wherein the state information comprises the fourth state information;

calculating a Kalman gain at the second moment according to the third state information and the fourth state information;

and calculating to obtain the target gradient according to the second state information, the third state information, the fourth state information and the Kalman gain.

3. The method of claim 2, wherein said calculating third state information of the vehicle at the second time based on the first state information and the kinematic formula comprises:

carrying out unscented transformation on the first state information to generate a plurality of first sampling points and weighted values of the plurality of first sampling points;

calculating according to the kinematic formula, the plurality of first sampling points and weighted values of the plurality of first sampling points to obtain a plurality of second sampling points and weighted values of the plurality of second sampling points of the vehicle at the second moment;

and calculating to obtain the third state information according to the plurality of second sampling points and the weighted values of the plurality of second sampling points.

4. The method of claim 3, wherein said calculating fourth state information of the vehicle at the second time based on the third state information and the dynamics formula comprises:

performing unscented transformation on the third state information to generate a plurality of third sampling points and weighted values of the plurality of third sampling points;

calculating a plurality of fourth sampling points and weighted values of the plurality of fourth sampling points according to the kinetic formula and the weighted values of the plurality of third sampling points;

and calculating to obtain the fourth state information according to the fourth sampling points and the weighted values of the fourth sampling points.

5. The method of claim 4, wherein calculating the Kalman gain at the second time based on the third state information and the fourth state information comprises:

calculating a cross covariance matrix between the plurality of third sampling points and the plurality of fourth sampling points according to the plurality of third sampling points and the plurality of fourth sampling points;

and calculating to obtain the Kalman gain according to the cross covariance matrix.

6. The method of any one of claims 1 to 5, wherein the obtaining second state information of the vehicle comprises:

receiving physical quantities measured by sensors at the second time, wherein the physical quantities measured by the sensors include longitudinal acceleration, driving force, speed and wheel angular acceleration of the vehicle;

and calculating a second gradient included in the second state information according to the physical quantity measured by the sensor and the dynamic formula.

7. The method of claim 6, wherein receiving the wheel angular acceleration measured by the sensor comprises:

receiving the wheel angular velocity of the vehicle measured by the sensor and a sampling period during which the sensor measures the wheel angular velocity;

and calculating the angular acceleration according to the wheel angular velocity and the sampling period.

8. A data processing apparatus, characterized by comprising:

the vehicle state information acquiring device comprises an acquiring module, a judging module and a processing module, wherein the acquiring module is used for acquiring vehicle state information, the state information comprises first state information of a vehicle at a first moment and second state information of the vehicle at a second moment, and any one of the state information comprises: the speed, the longitudinal acceleration, the slope, the driving force and the wheel angular acceleration are measured, and the direction of the longitudinal acceleration is from the tail of the vehicle to the head of the vehicle;

and the calculating module is used for calculating and obtaining the target gradient of the vehicle at the second moment by adopting an unscented kalman filtering method according to the first state information and the second state information, wherein the unscented kalman filtering method adopts a kinematic formula and a dynamic formula of the vehicle to estimate the target gradient.

9. A non-volatile storage medium, comprising a stored program, wherein when the program is executed, a device in which the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 7.

10. A computer device, comprising: a memory and a processor, wherein the processor is capable of,

the memory stores a computer program;

the processor configured to execute a computer program stored in the memory, the computer program when executed causing the processor to perform the data processing method of any one of claims 1 to 7.

Images