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

Abstract

The present invention discloses a data processing method, device, non-volatile storage medium and computer equipment. The method comprises: obtaining vehicle status information, wherein the status information comprises first status information of the vehicle at a first moment and second status information of the vehicle at a second moment, any one of the status information comprises: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration, and the direction of longitudinal acceleration is from the rear of the vehicle to the front of the vehicle; according to the first status information and the second status information, the target slope of the vehicle at the second moment is calculated by using an unscented Kalman filter method, wherein the unscented Kalman filter method uses the kinematic formula and dynamic formula of the vehicle to estimate the target slope. The present invention solves the technical problem of low accuracy of slope estimation methods in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of vehicle assisted driving, and in particular to a data processing method, device, non-volatile storage medium and computer equipment. Background Art

[0002] The slope signal is one of the important signals for the development of automotive functional software. Whether it is a traditional fuel vehicle or a new energy vehicle, the development of fuel-saving and energy-saving software algorithms cannot be separated from this signal. For vehicles such as off-road vehicles or SUVs, the slope signal affects the state switching of all-terrain software or driving mode software. For autonomous driving vehicles, the slope signal affects the vehicle driving safety algorithm module, control algorithm module, etc. In short, the slope signal is one of the most important basic signals. The accuracy of this signal directly affects the results of the upper-level functional software and ultimately affects the performance of the entire vehicle. The existing technology has low accuracy and a small estimation range for the slope estimation.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a data processing method, apparatus, non-volatile storage medium and computer equipment to at least solve the technical problem of low accuracy of slope estimation methods in the prior art.

[0005] According to one aspect of an embodiment of the present invention, a data processing method is provided, comprising: obtaining 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, any one of the state information comprises: speed, longitudinal acceleration, slope, driving force and wheel angular acceleration, and the direction of the longitudinal acceleration is from the rear end of the vehicle to the front end of the vehicle; based on the first state information and the second state information, an unscented Kalman filtering method is used to calculate a target slope of the vehicle at the second moment, wherein the unscented Kalman filtering method uses the kinematic formula and the dynamic formula of the vehicle to estimate the target slope.

[0006] Optionally, based on the first state information and the second state information, an unscented Kalman filtering method is used to calculate the target slope of the vehicle at the second moment, including: calculating the third state information of the vehicle at the second moment based on the first state information and the kinematic formula, wherein the state information includes the third state information; calculating the fourth state information of the vehicle at the second moment based on the third state information and the dynamic formula, wherein the state information includes the fourth state information; calculating the Kalman gain at the second moment based on the third state information and the fourth state information; and calculating the target slope based on the second state information, the third state information, the fourth state information and the Kalman gain.

[0007] Optionally, according to the first state information and the kinematic formula, the third state information of the vehicle at the second moment is calculated, including: performing a traceless transformation on the first state information to generate multiple first sampling points and weighted values ​​of the multiple first sampling points; calculating according to the kinematic formula, the multiple first sampling points and the weighted values ​​of the multiple first sampling points to obtain multiple second sampling points and weighted values ​​of the multiple second sampling points of the vehicle at the second moment; and calculating the third state information according to the multiple second sampling points and the weighted values ​​of the multiple second sampling points.

[0008] Optionally, according to the third state information and the dynamic formula, the fourth state information of the vehicle at the second moment is calculated, including: performing a traceless transformation on the third state information to generate multiple third sampling points and weighted values ​​of the multiple third sampling points; according to the dynamic formula, the multiple third sampling points and the weighted values ​​of the multiple third sampling points, calculating multiple fourth sampling points and the weighted values ​​of the multiple fourth sampling points; according to the multiple fourth sampling points and the weighted values ​​of the multiple fourth sampling points, calculating the fourth state information.

[0009] Optionally, calculating the Kalman gain at the second moment according to the third state information and the fourth state information includes: calculating a cross covariance matrix between the multiple third sampling points and the multiple fourth sampling points according to the multiple third sampling points and the multiple fourth sampling points; and calculating the Kalman gain according to the cross covariance matrix.

[0010] Optionally, obtaining the second state information of the vehicle includes: at a second moment, receiving physical quantities measured by sensors, 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 slope included in the second state information based on the physical quantities measured by the sensors and dynamic formulas.

[0011] Optionally, receiving the wheel angular acceleration measured by a sensor includes: receiving the wheel angular velocity of the vehicle measured by the sensor and a sampling period of the sensor measuring the wheel angular velocity; and calculating the angular acceleration according to the wheel angular velocity and the sampling period.

[0012] According to another aspect of an embodiment of the present invention, a data processing device is also provided, including: an acquisition module, used to acquire state information of a 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: 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; a calculation module, used to calculate the target slope of the vehicle at the second moment according to the first state information and the second state information by using an unscented Kalman filter method, wherein the unscented Kalman filter method uses the kinematic formula and dynamic formula of the vehicle to estimate the target slope.

[0013] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, the non-volatile storage medium including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above data processing methods.

[0014] According to yet another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above data processing methods is executed.

[0015] In an embodiment of the present invention, by acquiring the state information of the vehicle, wherein the state information includes the first state information of the vehicle at a first moment and the second state information of the vehicle at a 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, an unscented Kalman filtering method is used to calculate the target slope of the vehicle at the second moment, wherein the unscented Kalman filtering method uses the kinematic formula and the dynamic formula of the vehicle to estimate the target slope, thereby achieving the purpose of fusing the vehicle state information data to calculate the slope, thereby achieving the technical effect of expanding the slope calculation range and improving the slope calculation accuracy, and further solving the technical problem of low accuracy of the slope estimation method in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a computer terminal for implementing a data processing method is shown;

[0018] Figure 2 is a flow chart of a data processing method provided according to an embodiment of the present invention;

[0019] Figure 3 is a structural block diagram of a data processing device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a method embodiment of data processing is provided. It should be noted that the steps shown in the flowchart 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 flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a data processing method. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1Different configurations are shown.

[0024] 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 circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0025] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the data processing method of the above-mentioned application program. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0027] Figure 2 is a flow chart of a data processing method according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0028] Step S202, obtaining status information of the vehicle, wherein the status information includes first status information of the vehicle at a first moment and second status information of the vehicle at a second moment, any one of the status 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.

[0029] During the driving process of the vehicle, the sensors installed on the vehicle can measure the state parameters of the vehicle during driving, and obtain the state information of the vehicle during driving after data processing of the measured state parameters, wherein the state information may include speed, longitudinal acceleration, the slope of the vehicle, the vehicle driving force and the wheel angular acceleration. It should be noted that the slope of the vehicle is not a state parameter that can be directly measured, and it needs to be estimated by other state parameters through inference calculation. Compared with state parameters, state information is more convenient to participate in the unscented Kalman filter calculation, and the state information can be represented by a vector during calculation.

[0030] Estimating the slope value of the current position of the vehicle can be a dynamic cycle process. For two adjacent estimation processes, the estimation result of the previous state information will play a great role in the next estimation process. Therefore, in this estimation process, the vehicle state information at two moments can be obtained, namely, 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 the moment when the previous estimation process starts, and the first state information of the vehicle is the estimation result obtained by the previous estimation process. The second moment is the moment when the current estimation process starts, and the second state information of the vehicle is the state information obtained after data processing of the state parameters measured at the second moment.

[0031] Step S204, according to the first state information and the second 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 kinematic formula and the dynamic formula of the vehicle to estimate the target slope.

[0032] In this step, after obtaining the first state information and the second state information, the unscented Kalman filter method can be used to process the first state information and the second state information according to appropriate kinematic formulas and dynamic formulas to estimate the target slope of the vehicle at the second moment. It should be noted that, since the state information can be represented by a vector in the process of using the unscented Kalman filter method to process the first state information and the second state information, and the state information includes the slope of the vehicle, a vector can be obtained by operation between vectors during actual calculation, and the obtained vector contains a number representing the target slope, that is, the vector representation of the first state information and the vector representation of the second state information obtain a vector representation of the state information containing the target slope, and the number at the position of the slope in the obtained state information is the value of the target slope.

[0033] Through the above steps, the purpose of calculating the slope by fusing the vehicle's status information data at different times can be achieved, thereby achieving the technical effect of expanding the slope calculation range and improving the slope calculation accuracy, and further solving the technical problem of low accuracy of the slope estimation method in the prior art.

[0034] As an optional embodiment, the target slope of the vehicle at the second moment is calculated by using an unscented Kalman filtering method based on the first state information and the second state information. This can be achieved by the following steps: calculating the third state information of the vehicle at the second moment based on the first state information and the kinematic formula, wherein the state information includes the third state information; calculating the fourth state information of the vehicle at the second moment based on the third state information and the dynamic formula, wherein the state information includes the fourth state information; calculating the Kalman gain at the second moment based on the third state information and the fourth state information; and calculating the target slope based on the second state information, the third state information, the fourth state information and the Kalman gain.

[0035] The unscented Kalman filter method is a method for estimating 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 based on the observed value to obtain the final estimated value. In other words, the unscented Kalman filter method requires an initial state vector and an observed value when estimating the state vector at this moment.

[0036] It should be noted that the kinematic formula is a formula generated based on the kinematic law of the vehicle during driving, and the dynamic formula is a formula generated based on the dynamic law of the vehicle during driving. Since the slope in the state information cannot be directly measured by the sensor, other physical parameters that can be measured by the sensor can be inferred according to the dynamic formula or kinematic formula to obtain the slope of the vehicle at a certain moment. However, such a direct estimation is not accurate, so the Kalman filter method can be used to make a more accurate estimate of the slope of the vehicle at the current moment.

[0037] 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 both be vector representations, and the vectors of all state information include five parameters, which represent the wheel angular acceleration, the longitudinal acceleration, the slope of the vehicle, the vehicle driving force and the vehicle speed in turn. When vector operations are performed between multiple state information, the vector format of the state information is not changed. The first state information is the state vector of the previous moment required in the unscented Kalman filter method. The third state information of the vehicle at the second moment can be estimated by the first state information and the kinematic formula followed by the vehicle during driving. Then, according to the third state information and the dynamic formula followed by the vehicle during driving, the fourth state information and the 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 the second state information as the observation 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 is calculated, that is, the state information of the vehicle at the second moment after being corrected by the Kalman filter method.

[0038] As an optional embodiment, calculating the third state information of the vehicle at the second moment according to the first state information and the kinematic formula can be achieved by the following steps: performing a traceless transformation on the first state information to generate multiple first sampling points and weighted values ​​of the multiple first sampling points; calculating according to the kinematic formula, the multiple first sampling points and the weighted values ​​of the multiple first sampling points to obtain multiple second sampling points and weighted values ​​of the multiple second sampling points of the vehicle at the second moment; and calculating the third state information according to the multiple second sampling points and the weighted values ​​of the multiple second sampling points.

[0039] Optionally, performing an untraceable transformation on the first state information is an algorithm of untraceable Kalman filtering. The first state information may be a sampling point. According to the error of the physical quantity in the first state information, multiple first sampling points centered on the first state information can be obtained according to the untraceable transformation method, and multiple weighted values ​​corresponding to the multiple first sampling points are obtained. The multiple first sampling points are respectively substituted into the kinematic 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 does not change. At this time, the multiple second sampling points represent multiple possible values ​​at the second moment. According to the multiple second sampling points and the weighted values ​​corresponding to the multiple second sampling points, the average of the multiple second sampling points can be calculated, that is, the third state information.

[0040] Optionally, in this embodiment, the number of first sampling points is selected as 11, and the kinematic formula used is as follows:

[0041]

[0042] Where: 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 horizontal ground and is stationary, the 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 passes through the center of mass and points upward, and the y-axis points to the left of the driver; is the second-order derivative of the longitudinal displacement of the vehicle; g is the acceleration due to gravity, which is 9.81 m / s 2 Among them, for the second-order derivative of displacement It can be calculated according to the wheel angular velocity of the driven wheel according to the following formula:

[0043]

[0044] Where: r stat is the rolling radius of the vehicle at rest, in m; ω is the wheel rolling angular velocity, in rad / s.

[0045] 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; calculating the angular acceleration according to the wheel angular velocity and the sampling period.

[0046] Optionally, in the dynamics formula, the following formula may be used to calculate the derivative of the angular velocity of the wheel in a discrete state:

[0047]

[0048] Where: 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, in seconds.

[0049] When calculating the slope of the vehicle, the related art adopts a small angle estimation method, that is, when the slope angle χ road When the angle is less than 10°, the following formula is used for approximation:

[0050] χ road = sin(χ road ) = tan(χ road );

[0051] cos(χ road )=1.

[0052] In this embodiment, the above approximation scheme is not adopted, and the chord function is directly used to estimate the slope, thereby expanding the nonlinear range of the slope signal.

[0053] As an optional embodiment, calculating the fourth state information of the vehicle at the second moment according to the third state information and the dynamic formula can be achieved through the following steps: performing a traceless transformation on the third state information to generate multiple third sampling points and weighted values ​​of the multiple third sampling points; calculating multiple fourth sampling points and weighted values ​​of the multiple fourth sampling points according to the dynamic formula, the multiple third sampling points and the weighted values ​​of the multiple third sampling points; and calculating the fourth state information according to the multiple fourth sampling points and the weighted values ​​of the multiple fourth sampling points.

[0054] Optionally, the third state information is also subjected to a traceless transformation, and a plurality of third sampling points and weighted values ​​corresponding to the plurality of third sampling points are obtained by taking the third state information as the central sampling point. The plurality of third sampling points are respectively substituted into the dynamic formula for calculation to obtain a plurality of fourth sampling points, and the weighted values ​​corresponding to the plurality of fourth sampling points are the same as the weighted values ​​corresponding to the plurality of third sampling points, and the corresponding relationship does not change. Based on the plurality of fourth sampling points and the weighted values ​​corresponding to the plurality of fourth sampling points, the average of the plurality of fourth sampling points, i.e., the fourth state information, can be calculated. Preferably, the present invention selects 11 first sampling points.

[0055] As an optional embodiment, obtaining the second state information of the vehicle can be achieved through the following steps: at a second moment, receiving physical quantities measured by sensors, wherein the physical quantities measured by the sensors include the longitudinal acceleration, driving force, speed and wheel angular acceleration of the vehicle; and calculating the second slope included in the second state information based on the physical quantities measured by the sensors and dynamic formulas.

[0056] Optionally, the second state information of the vehicle can be directly calculated using the following dynamic formula based on multiple physical quantities measured by the vehicle sensor at the second moment.

[0057] For a car driving on a sloped road, the force balance equation is established, that is, the dynamics formula is as follows:

[0058]

[0059] Where: m is the vehicle mass, in kg; F x The driving force of the ground on the tire, in N; c air is the air resistance coefficient, which can be obtained through wind resistance test; A l is the frontal area, which is an inherent parameter of the vehicle and is expressed in m 2 ρ a is the air density, in kg / m 3 ;v G，xis the vehicle center of mass speed, in m / s. Among them, for the driving force, when the vehicle is in a low slip rate, it can be obtained by converting the rotational torque provided by the vehicle itself through the engine according to the following formula. In electric vehicles, it can also be obtained by the rotational torque provided by the motor according to the following formula:

[0060]

[0061] Where: T tq is the engine torque, unit N*m; i g is the transmission ratio of the transmission; i0 is the transmission ratio of the main reducer; η T is the mechanical efficiency of the transmission system; for the driving force, it can also be obtained by the driving force provided by the ground to the wheel, which is equal to the vertical force multiplied by the ground adhesion coefficient. However, considering that the ground adhesion coefficient has different magnitudes under different working conditions, it is not recommended to use this method to obtain the driving force.

[0062] As an optional embodiment, calculating the Kalman gain at the second moment according to the third state information and the fourth state information can be achieved by the following steps: calculating the cross covariance matrix between the multiple third sampling points and the multiple fourth sampling points according to the multiple third sampling points and the multiple fourth sampling points; and calculating the Kalman gain according to the cross covariance matrix.

[0063] Optionally, according to the multiple third sampling points and the multiple fourth sampling points, an unscented Kalman filtering method can be used. Since the multiple fourth sampling points are respectively obtained from the multiple third sampling points, there is a one-to-one correspondence between the multiple fourth 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, a cross-covariance matrix between the multiple third sampling points and the multiple fourth sampling points can be calculated, and the cross-covariance matrix is ​​multiplied by the inverse matrix of the cross-covariance matrix to obtain the Kalman gain matrix.

[0064] As a specific example, a G，x , F x The three quantities are known quantities and can be obtained by substituting relevant data collected by vehicle sensors or stored in the vehicle system into the above-given formula.

[0065] For the above kinematics and dynamics formulas, the state vector X is set as follows:

[0066]

[0067] For three known quantities, assuming that their errors follow a normal distribution, the error formula is as follows:

[0068] v ω，k ~N(0,Q k);

[0069] V a，k ~N(0,R k );

[0070] v F，k ~N(0,T k );

[0071] In the above error formula, v ω，k for a G，x , F x The error, v a，k for a G，x The error, v F，k F x The error, Q k , R k , T k They are v ω，k , v a，k , v F，k The variance of the normal distribution.

[0072] In a discrete system, the relationship between the state vector at time k+1 and the state vector at time k is as follows:

[0073]

[0074] In the initial state, Q k , R k , T k The value is 0.01.

[0075] The basic calculation process of the unscented Kalman filter is:

[0076] First, obtain a vector representation of the first state information, and determine the number and weights of sampling points. In this optional embodiment, the number of sampling points is 11;

[0077] Secondly, according to the errors of various physical quantities in the first state information, the Coleski decomposition of the covariance matrix of the first state information is calculated;

[0078] Secondly, a plurality of first sampling points are calculated according to the Coleski decomposition of the covariance matrix calculated in the previous step, the first state information, and the number and weights of the sampling points determined in the first step;

[0079] 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, the values ​​of the multiple first sampling points at the second time are calculated, and the values ​​of the multiple first sampling points at the second time are respectively brought into the kinematic formula for calculation, and the multiple first sampling points are updated. a G，x , χroad The value of , obtains multiple second sampling points;

[0080] Secondly, according to the plurality of second sampling points and the initially determined weights, the mean and covariance matrix of the plurality of second sampling points calculated by solving the kinematic formula are obtained, wherein the mean of the plurality of second sampling points is the third state vector;

[0081] Secondly, perform Kholesky decomposition based on the covariance matrix obtained in the previous step;

[0082] Secondly, according to the number of samples determined in the first step and the Coleski decomposition of the covariance matrix calculated in the previous step, the third state vector is sampled to obtain a plurality of third sampling points;

[0083] Secondly, multiple third sampling points are substituted into the dynamics formula for calculation, and a in the multiple third sampling points is updated. G，x , χ road , F x , v G，x , obtaining a plurality of fourth sampling points, and calculating the mean and covariance matrix of the plurality of fourth sampling points, wherein the mean of the plurality of fourth sampling points is the fourth state vector;

[0084] Secondly, according to the third state vector and the fourth state vector, the cross covariance matrix and the Kalman gain of the results obtained by the kinematic formula and the dynamic formula are calculated respectively;

[0085] Secondly, the measurement value of the vehicle sensor at the second moment is obtained, and the second state vector is directly calculated according to the dynamics formula as the observation value;

[0086] Finally, the second state vector is subtracted from the value of the fourth state vector, multiplied by the Kalman gain, and then added to the third state vector to obtain the final updated state vector. The slope in the final updated state vector is the target slope.

[0087] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described 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 according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that the data processing method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0089] According to an embodiment of the present invention, a device for implementing the above data processing method is also provided. Figure 3 is a structural block diagram of a data processing device provided according to an embodiment of the present invention, such as Figure 3 As shown, the data processing device includes: an acquisition module 32 and a calculation module 34. The data processing device is described below.

[0090] It should be noted 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 implemented by the two modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0091] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one network device among multiple network devices of a computer network. The computer device includes a memory and a processor.

[0092] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the data processing method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned data processing method. The memory may include a high-speed random access memory, and may also include a 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 remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0093] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the vehicle's status information, wherein the status information includes first status information of the vehicle at a first moment and second status information of the vehicle at a second moment, and any one of the status 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; based on the first status information and the second status information, use 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 kinematic formula and dynamic formula of the vehicle to estimate the target slope.

[0094] Optionally, the processor may also execute the program code of the following steps: based on the first state information and the second state information, using the unscented Kalman filtering method to calculate the target slope of the vehicle at the second moment, including: based on the first state information and the kinematic formula, calculating the third state information of the vehicle at the second moment, wherein the state information includes the third state information; based on the third state information and the dynamic formula, calculating the fourth state information of the vehicle at the second moment, wherein the state information includes the fourth state information; based on the third state information and the fourth state information, calculating the Kalman gain at the second moment; based on the second state information, the third state information, the fourth state information and the Kalman gain, calculating the target slope.

[0095] Optionally, the processor may also execute 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 a traceless transformation on the first state information to generate multiple first sampling points and weighted values ​​of the multiple first sampling points; performing calculation according to the kinematic formula, the multiple first sampling points and the weighted values ​​of the multiple first sampling points to obtain multiple second sampling points and weighted values ​​of the multiple second sampling points of the vehicle at the second moment; and calculating the third state information according to the multiple second sampling points and the weighted values ​​of the multiple second sampling points.

[0096] Optionally, the processor may also execute 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 a traceless transformation on the third state information to generate multiple third sampling points and weighted values ​​of the multiple third sampling points; calculating multiple fourth sampling points and weighted values ​​of the multiple fourth sampling points according to the dynamic formula, the multiple third sampling points and the weighted values ​​of the multiple third sampling points; and calculating the fourth state information according to the multiple fourth sampling points and the weighted values ​​of the multiple fourth sampling points.

[0097] Optionally, the 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: calculating the cross covariance matrix between the multiple third sampling points and the multiple fourth sampling points according to the multiple third sampling points and the multiple fourth sampling points; and calculating the Kalman gain according to the cross covariance matrix.

[0098] Optionally, the processor may also execute the program code of the following steps: obtaining the second state information of the vehicle, including: receiving the physical quantities measured by the sensor at the second moment, wherein the physical quantities measured by the sensor include the longitudinal acceleration, driving force, speed and wheel angular acceleration of the vehicle; and calculating the second slope included in the second state information according to the physical quantities measured by the sensor and the dynamic formula.

[0099] Optionally, the processor may 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; and calculating the angular acceleration according to the wheel angular velocity and the sampling period.

[0100] According to an embodiment of the present invention, a data processing scheme 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, 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 target slope of the vehicle at the second moment is calculated by using the unscented Kalman filter method, wherein the unscented Kalman filter method uses the kinematic formula and the dynamic formula of the vehicle to estimate the target slope, so as to achieve the purpose of fusing the vehicle state information data to calculate the slope, thereby achieving the technical effect of expanding the slope calculation range and improving the slope calculation accuracy, thereby solving the technical problem of low accuracy of the slope estimation method in the prior art.

[0101] A person 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, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0102] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the data processing method provided by the above embodiment.

[0103] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0104] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining status information of the vehicle, wherein the status information includes first status information of the vehicle at a first moment and second status information of the vehicle at a second moment, any one of the status 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; based on the first status information and the second status information, using an unscented Kalman filter method to calculate a target slope of the vehicle at the second moment, wherein the unscented Kalman filter method uses the kinematic formula and dynamic formula of the vehicle to estimate the target slope.

[0105] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: calculating the target slope of the vehicle at the second moment using an unscented Kalman filtering method based on the first state information and the second state information, including: calculating the third state information of the vehicle at the second moment based on the first state information and the kinematic formula, wherein the state information includes the third state information; calculating the fourth state information of the vehicle at the second moment based on the third state information and the dynamic formula, wherein the state information includes the fourth state information; calculating the Kalman gain at the second moment based on the third state information and the fourth state information; and calculating the target slope based on the second state information, the third state information, the fourth state information and the Kalman gain.

[0106] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing 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 a traceless transformation on the first state information to generate multiple first sampling points and weighted values ​​of the multiple first sampling points; calculating according to the kinematic formula, the multiple first sampling points and the weighted values ​​of the multiple first sampling points to obtain multiple second sampling points and weighted values ​​of the multiple second sampling points of the vehicle at the second moment; and calculating the third state information according to the multiple second sampling points and the weighted values ​​of the multiple second sampling points.

[0107] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing 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 a traceless transformation on the third state information to generate multiple third sampling points and weighted values ​​of the multiple third sampling points; calculating multiple fourth sampling points and weighted values ​​of the multiple fourth sampling points according to the dynamic formula, the multiple third sampling points and the weighted values ​​of the multiple third sampling points; and calculating the fourth state information according to the multiple fourth sampling points and the weighted values ​​of the multiple fourth sampling points.

[0108] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: calculating the Kalman gain at the second moment based on the third state information and the fourth state information, including: calculating the cross covariance matrix between the multiple sampling points and the multiple fourth sampling points based on the multiple third sampling points and the multiple fourth sampling points; and calculating the Kalman gain based on the cross covariance matrix.

[0109] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining second state information of the vehicle, including: at a second moment, receiving physical quantities measured by sensors, wherein the physical quantities measured by the sensors include the vehicle's longitudinal acceleration, driving force, speed, and wheel angular acceleration; and calculating a second slope included in the second state information based on the physical quantities measured by the sensors and dynamic formulas.

[0110] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing 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; calculating the angular acceleration based on the wheel angular velocity and the sampling period.

[0111] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0112] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, 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. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: 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.

[0117] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A data processing method, characterized in that: include: 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, 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 end of the vehicle to the front end of the vehicle; According to the first state information and the second state information, an unscented Kalman filter method is used to calculate a target slope of the vehicle at the second moment, wherein the unscented Kalman filter method uses a kinematic formula and a dynamic formula of the vehicle to estimate the target slope; Among them, the target slope of the vehicle at the second moment is calculated by using the unscented Kalman filtering method according to the first state information and the second state information, including: 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; calculating the fourth state information of the vehicle at the second moment according to the third state information and the dynamic formula, wherein the state information includes the fourth state information; calculating the Kalman gain at the second moment according to the third state information and the fourth state information; and calculating the target slope according to the second state information, the third state information, the fourth state information and the Kalman gain.

2. The method according to claim 1, characterized in that The calculating, according to the first state information and the kinematic formula, the third state information of the vehicle at the second moment comprises: Performing a traceless 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 the weighted values ​​of the plurality of first sampling points to obtain a plurality of second sampling points of the vehicle at the second moment and the weighted values ​​of the plurality of second sampling points; The third state information is obtained by calculation according to the plurality of second sampling points and weighted values ​​of the plurality of second sampling points.

3. The method according to claim 2, characterized in that The calculating, according to the third state information and the dynamics formula, the fourth state information of the vehicle at the second moment comprises: Performing a traceless 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 the weighted values ​​of the plurality of fourth sampling points according to the kinetic formula, the plurality of third sampling points and the weighted values ​​of the plurality of third sampling points; The fourth state information is obtained by calculation according to the plurality of fourth sampling points and weighted values ​​of the plurality of fourth sampling points.

4. The method according to claim 3, characterized in that The calculating the Kalman gain at the second moment according to the third state information and the fourth state information includes: 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; The Kalman gain is calculated according to the cross covariance matrix.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of the second state information of the vehicle includes: At the second moment, receiving physical quantities measured by a sensor, wherein the physical quantities measured by the sensor include longitudinal acceleration, driving force, speed, and wheel angular acceleration of the vehicle; The second slope included in the second state information is calculated based on the physical quantity measured by the sensor and the dynamic formula.

6. The method according to claim 5, characterized in that The wheel angular acceleration measured by the receiving sensor includes: Receiving the wheel angular velocity of the vehicle measured by the sensor and a sampling period of the sensor for measuring the wheel angular velocity; The angular acceleration is calculated according to the wheel angular velocity and the sampling period.

7. A data processing device, characterized in that: include: an acquisition module, configured to acquire 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, 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 end of the vehicle to the front end of the vehicle; a calculation module, configured to calculate a target slope of the vehicle at the second moment by using an unscented Kalman filter method according to the first state information and the second state information, wherein the unscented Kalman filter method estimates the target slope by using a kinematic formula and a dynamic formula of the vehicle; Wherein, the calculation module is also used to: calculate 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; calculate the fourth state information of the vehicle at the second moment according to the third state information and the dynamic formula, 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.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is run, the processor executes the data processing method according to any one of claims 1 to 6.

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