CN116039647A

CN116039647A - Road gradient estimation method, device, equipment and storage medium

Info

Publication number: CN116039647A
Application number: CN202211528817.7A
Authority: CN
Inventors: 李洋; 陈华武; 李阳; 毕雅梦; 管杰
Original assignee: Dongfeng Trucks Co ltd
Current assignee: Dongfeng Trucks Co ltd
Priority date: 2022-11-30
Filing date: 2022-11-30
Publication date: 2023-05-02

Abstract

The invention discloses a road gradient estimation method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring the current accelerator opening, steering wheel angle and vehicle speed of the vehicle according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle; calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel corner and the vehicle speed; and acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface. The method and the device can enable longitudinal running control of the automobile to be more accurate, are favorable for the requirement that the intelligent automobile needs a high-precision stop position, are simple to use, small in calculated amount and good in instantaneity, and can update the gradient in a self-adaptive mode under the condition that the gradient of the road surface changes.

Description

Road gradient estimation method, device, equipment and storage medium

Technical Field

The present invention relates to the field of intelligent driving technologies, and in particular, to a method, an apparatus, a device, and a storage medium for estimating a road gradient.

Background

For a control system of an intelligent driving vehicle, longitudinal control is a very critical control system, and related indexes such as path tracking accuracy, comfort level, oil consumption and the like of the vehicle are directly influenced. The actual road gradient is a control variable which is critical to the longitudinal driving control system, and in actual driving, especially for commercial trucks, whether the road gradient parameter can be obtained quickly and accurately is a factor which can play a decisive role in planning speed following. The intelligent vehicle can utilize the advanced sensor configured by the intelligent vehicle to identify the gradient on line on the road surface in front of the vehicle in the running process of the vehicle so as to meet the requirement of the intelligent driving vehicle on the speed control precision. In the prior art, an acceleration sensor is mostly adopted for identification, but the acceleration sensor is very easy to influence in a complex environment where a vehicle runs, and the output signal quality of the acceleration sensor is difficult to ensure, so that the accuracy of road gradient identification is poor.

Therefore, how to improve the accuracy of road gradient identification is a technical problem to be solved.

Disclosure of Invention

The invention mainly aims to provide a road gradient estimation method, a device, equipment and a storage medium, which can enable longitudinal running control of an automobile to be more accurate, meet the requirement of an intelligent automobile on a high-precision stop position, are simple to use, small in calculated amount and good in instantaneity, and can be used for adaptively updating gradient under the condition of road gradient change.

In a first aspect, the present application provides a road surface gradient estimation method, the method comprising the steps of:

acquiring the current accelerator opening, steering wheel angle and vehicle speed of the vehicle according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle;

calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel corner and the vehicle speed;

and acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface.

With reference to the first aspect, as an optional implementation manner, calculating the current gradient value through the current vehicle longitudinal acceleration and the vehicle accelerator opening, the vehicle steering wheel angle and the vehicle speed includes the following steps:

determining a first acceleration using a gyroscope based on the vehicle longitudinal acceleration;

linear interpolation is carried out on the opening degree of the accelerator of the vehicle, the steering wheel angle of the vehicle and the speed of the vehicle, and a second acceleration is determined;

and calculating a current gradient value through a difference value between the first acceleration and the second acceleration.

With reference to the first aspect, as an optional implementation manner, if a difference between the current gradient value and the real-time updated gradient value is greater than a set gradient threshold value, the real-time updated gradient value is replaced with the current gradient value, and an actual gradient of the road surface is determined through current vehicle positioning information and three-dimensional linear interpolation.

With reference to the first aspect, as an optional implementation manner, if a difference between the current gradient value and the real-time updated gradient value is smaller than a set gradient threshold value, current positioning information of the vehicle and the current gradient value are kept unchanged.

With reference to the first aspect, as an optional implementation manner, the obtaining, according to the established vehicle labeling model, a current vehicle accelerator opening, a steering wheel angle, and a vehicle speed to determine a current vehicle longitudinal acceleration includes the steps of:

according to the analysis of the longitudinal running movement process of the vehicle, a vehicle marking model is established;

and according to the established vehicle labeling model, acquiring the current vehicle accelerator opening, steering wheel rotation angle and vehicle speed, and calculating the current vehicle longitudinal acceleration by utilizing a=f (v, tho, delta), wherein a is the vehicle longitudinal acceleration, tho is the vehicle accelerator opening, delta is the vehicle steering wheel rotation angle, and v is the current vehicle speed.

With reference to the first aspect, as an optional implementation manner, the vehicle positioning information is obtained in real time through a point cloud of the RTK and/or the lidar.

In a second aspect, the present application provides a road surface gradient estimation device, the device comprising:

the acquisition module is used for acquiring the current accelerator opening of the vehicle, the steering wheel angle and the vehicle speed according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle;

the calculating module is used for calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel angle and the vehicle speed;

and the determining module is used for acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time so as to determine the actual slope of the road surface.

With reference to the second aspect, as an optional implementation manner, the determining module is further configured to: and if the difference value between the current gradient value and the real-time updated gradient value is larger than a set gradient threshold value, replacing the current gradient value with the real-time updated gradient value, and determining the actual gradient of the road surface through the current vehicle positioning information and three-dimensional linear interpolation.

In a third aspect, the present application further provides an electronic device, including: a processor; a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of the first aspects.

In a fourth aspect, the present application also provides a computer readable storage medium storing computer program instructions which, when executed by a computer, cause the computer to perform the method of any one of the first aspects.

The application provides a road surface gradient estimation method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring the current accelerator opening, steering wheel angle and vehicle speed of the vehicle according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle; calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel corner and the vehicle speed; and acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface. The method and the device can enable longitudinal running control of the automobile to be more accurate, are favorable for the requirement that the intelligent automobile needs a high-precision stop position, are simple to use, small in calculated amount and good in instantaneity, and can update the gradient in a self-adaptive mode under the condition that the gradient of the road surface changes.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a flow chart of a road surface gradient estimation method provided in an embodiment of the present application;

fig. 2 is a schematic diagram of a road gradient estimation device according to an embodiment of the present application;

fig. 3 is a schematic diagram of an electronic device provided in an embodiment of the present application;

fig. 4 is a schematic diagram of a computer readable program medium according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.

The embodiment of the application provides a road gradient estimation method, device, equipment and storage medium, which can enable longitudinal running control of an automobile to be more accurate, are beneficial to the requirement that an intelligent vehicle needs a high-precision stop position, are simple to use, small in calculated amount and good in instantaneity, and can adaptively update gradient under the condition of road gradient change.

In order to achieve the technical effects, the general idea of the application is as follows:

a road surface gradient estimation method, the method comprising the steps of:

s101: and acquiring the current accelerator opening of the vehicle, steering wheel rotation angle and vehicle speed according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle.

S102: and calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel angle and the vehicle speed.

S103: and acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface.

Embodiments of the present application are described in further detail below with reference to the accompanying drawings.

Referring to fig. 1, fig. 1 is a flowchart of a road gradient estimation method provided by the present invention, and as shown in fig. 1, the method includes the steps of:

and step S101, acquiring the current accelerator opening of the vehicle, the steering wheel angle and the vehicle speed according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle.

Specifically, the vehicle longitudinal running kinematics passing through the vehicle is analyzed to establish a vehicle labeling model, and the vehicle longitudinal acceleration can be expressed by the following formula:

wherein F is _aero Is the running wind resistance, m is the automobile preparation quality,

Is the acceleration of the automobile, F _xf Is the driving force of the rear wheel of the automobile, F _xr Is the driving force of the front wheel of the automobile, R _xf Is the friction resistance of the rear wheel of the automobile, R _xr The friction resistance of the front wheel of the automobile, g is the gravity acceleration, and theta is the road surface gradient.

The vehicle driving force is entirely provided by the engine, and the engine power is determined by the accelerator opening degree. To obtain vehicle motion characteristics, ignoring other dynamics effects, the formula may be simplified to a=f (tho); wherein a is the longitudinal acceleration of the vehicle, tho is the opening degree of the accelerator of the vehicle; meanwhile, the steering wheel angle of the vehicle is considered, the longitudinal acceleration of the vehicle is greatly influenced, meanwhile, the wind resistance of the vehicle is related to the speed of the vehicle, and the longitudinal acceleration, the accelerator opening degree, the steering wheel angle and the longitudinal speed are strongly related by the analysis of the process; thus, the above-described vehicle longitudinal acceleration may be represented by the formula: a=f (v, tho, δ), where δ is the vehicle steering wheel angle; v is the current speed of the vehicle, and tho is the accelerator opening of the vehicle.

It can be understood that the established vehicle labeling model can obtain the current longitudinal acceleration of the vehicle from the three variables of the accelerator opening degree, the vehicle speed and the steering wheel angle of the current vehicle.

In one embodiment, the driver drives the target vehicle to run on a straight road surface, the driver keeps running, and the driver only presses the accelerator pedal and does not press the brake pedal in operation; the step is to repeat the steps with different pedal opening degrees, different vehicle speeds and different steering wheel angles as many times as possible, and the first three parameters should cover all driving scenes as much as possible. The following chart data were obtained:

Tho	v	δ	a
				Tho1	V1	Det1	A1
Tho2	V2	Det2	A2
				…	…	…	…

it will be appreciated that different vehicle longitudinal accelerations are obtained with different pedal opening, different vehicle speeds, different steering wheel angles.

And step S102, calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel angle and the vehicle speed.

Specifically, the longitudinal acceleration of the vehicle is obtained in real time in the running process of the vehicle, the gyroscope is utilized to obtain the first acceleration, namely the current vehicle acceleration, linear interpolation is carried out through the current accelerator opening, speed and steering wheel rotation angle of the vehicle to obtain the second acceleration, namely the marked acceleration, and the first acceleration is subtracted from the second acceleration to obtain the current gradient value, so that the actual longitudinal acceleration of the vehicle is conveniently understood and illustrated; obtaining a current acceleration a1, performing linear interpolation through the current accelerator opening, speed and steering wheel angle of the vehicle to obtain an acceleration a2, and obtaining a current gradient value theta according to a formula gsin (theta) =a1-a 2.

Step 103, according to the current vehicle positioning information, acquiring a slope value updated in real time, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface.

Specifically, accurate gradient information is theoretically stable and accurate for controlling the speed of the longitudinal vehicle, when the road surface changes, the gradient information should also be updated, vehicle positioning information is obtained in real time on the road surface which actually needs to run, and a gradient value updated in real time is obtained.

For example, when the current position of the vehicle is X1, Y1, the current gradient value is obtained by the difference between the first acceleration and the second acceleration, the current gradient value is compared with the current gradient value, if the current gradient value minus the current gradient value is greater than the set gradient value, the current gradient value is replaced by the current gradient value, and if the current gradient value minus the current gradient value is less than the set gradient value, the current gradient value is used as the actual gradient of the road surface, and if the current gradient value minus the current gradient value is less than the set gradient value, the current positioning information of the vehicle and the current gradient value are kept unchanged, i.e. the current gradient value is not required to be replaced by the current gradient value, it can be understood that the calculated current gradient value is used as the actual gradient of the road surface.

Optionally, the vehicle positioning information is obtained in real time through the point cloud of the RTK and/or the laser radar.

In one embodiment, the actual road gradient is determined according to the formula |err-err '| > errThreshold, err is the current road gradient value, err' is the road gradient value updated in real time, and err threshold is the road gradient threshold (custom setting).

It can be appreciated that the method, the device, the equipment and the storage medium for estimating the road gradient provided by the application comprise the following steps: acquiring the current accelerator opening, steering wheel angle and vehicle speed of the vehicle according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle; calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel corner and the vehicle speed; and acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface. The method and the device can enable longitudinal running control of the automobile to be more accurate, are favorable for the requirement that the intelligent automobile needs a high-precision stop position, are simple to use, small in calculated amount and good in instantaneity, and can update the gradient in a self-adaptive mode under the condition that the gradient of the road surface changes.

Referring to fig. 2, fig. 2 is a schematic diagram of a road gradient estimation device according to the present invention, and as shown in fig. 2, the device includes:

the acquisition module 201: the method is used for acquiring the current accelerator opening, steering wheel rotation angle and vehicle speed of the vehicle according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle.

The calculation module 202: the method is used for calculating the current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel angle and the vehicle speed.

Determination module 203: the method is used for acquiring a real-time updated gradient value according to the current vehicle positioning information, and comparing the current gradient value with the real-time updated gradient value to determine the actual gradient of the road surface.

Further, in a possible implementation manner, the calculation module 202 is further configured to determine, according to the vehicle longitudinal acceleration, a first acceleration using a gyroscope;

Further, in one possible implementation manner, the determining module 203 is further configured to replace the current gradient value with the real-time updated gradient value if the difference between the current gradient value and the real-time updated gradient value is greater than a set gradient threshold value, and determine the actual gradient of the road surface through the current vehicle positioning information and the three-dimensional linear interpolation.

Further, in one possible implementation manner, the determining module 203 is further configured to keep the current positioning information of the vehicle and the current gradient value unchanged if the difference between the current gradient value and the real-time updated gradient value is smaller than a set gradient threshold value.

Further, in one possible implementation manner, the obtaining module 201 is further configured to establish a vehicle labeling model according to the analysis of the longitudinal running motion process of the vehicle;

Further, in one possible implementation, the obtaining module 201 is further configured to obtain the vehicle positioning information in real time through a point cloud of the RTK and/or the lidar.

An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 3, the electronic device 300 is embodied in the form of a general purpose computing device. Components of electronic device 300 may include, but are not limited to: the at least one processing unit 310, the at least one memory unit 320, and a bus 330 connecting the various system components, including the memory unit 320 and the processing unit 310.

Wherein the storage unit stores program code that is executable by the processing unit 310 such that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above-mentioned "example methods" section of the present specification.

The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a Random Access Memory (RAM) 321 and/or a cache memory 322, and may further include a Read Only Memory (ROM) 323.

The storage unit 320 may also include a program/utility 324 having a set (at least one) of program modules 325, such program modules 325 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Bus 330 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 300 may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 300, and/or any device (e.g., router, modem, etc.) that enables the electronic device 300 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 350. Also, electronic device 300 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 360. As shown, the network adapter 360 communicates with other modules of the electronic device 300 over the bus 330. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 300, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

According to an aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.

Referring to fig. 4, a program product 400 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.

In summary, the method, the device, the equipment and the storage medium for estimating the road gradient provided by the application comprise the following steps: acquiring the current accelerator opening, steering wheel angle and vehicle speed of the vehicle according to the established vehicle labeling model so as to determine the current longitudinal acceleration of the vehicle; calculating a current gradient value through the current vehicle longitudinal acceleration, the vehicle accelerator opening, the vehicle steering wheel corner and the vehicle speed; and acquiring a slope value updated in real time according to the current vehicle positioning information, and comparing the current slope value with the slope value updated in real time to determine the actual slope of the road surface. The method and the device can enable longitudinal running control of the automobile to be more accurate, are favorable for the requirement that the intelligent automobile needs a high-precision stop position, are simple to use, small in calculated amount and good in instantaneity, and can update the gradient in a self-adaptive mode under the condition that the gradient of the road surface changes.

The foregoing is merely a specific embodiment of the application to enable one skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims

1. A road surface gradient estimation method, characterized by comprising:

2. The method of claim 1, wherein calculating a current grade value from the current vehicle longitudinal acceleration and the vehicle accelerator opening, vehicle steering wheel angle, vehicle speed, comprises:

3. The method of claim 1, wherein comparing the current grade value with the real-time updated grade value to determine an actual grade of the road surface comprises:

and if the difference value between the current gradient value and the real-time updated gradient value is larger than a set gradient threshold value, replacing the current gradient value with the real-time updated gradient value, and determining the actual gradient of the road surface through the current vehicle positioning information and three-dimensional linear interpolation.

4. A method according to claim 3, further comprising:

and if the difference value between the current gradient value and the gradient value updated in real time is smaller than a set gradient threshold value, keeping the current positioning information of the vehicle and the current gradient value unchanged.

5. The method of claim 1, wherein the obtaining the current vehicle accelerator opening, steering wheel angle, and vehicle speed to determine the current vehicle longitudinal acceleration based on the established vehicle labeling model comprises:

6. The method according to claim 1, characterized in that:

and acquiring vehicle positioning information in real time through the RTK and/or the point cloud of the laser radar.

7. A road surface gradient estimation device, characterized by comprising:

8. The apparatus of claim 7, wherein the means for determining is further for:

9. An electronic device, the electronic device comprising:

a processor;

a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 6.

10. A computer readable storage medium, characterized in that it stores computer program instructions, which when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 6.