WO2024187869A1 - Kalman filter-based method and apparatus for acquiring longitudinal drag of vehicle, and vehicle - Google Patents

Abstract

A Kalman filter-based method and apparatus for acquiring a longitudinal drag of a vehicle, a medium, and a vehicle. The method comprises: in a prediction stage, constructing a state representation of a longitudinal drag of an entire vehicle, constructing a process model according to a dynamic model related to the state representation, and acquiring a predicted value of the state representation of the longitudinal drag of the entire vehicle at a current moment on the basis of the process model (S101); in an update stage, selectively fusing the predicted value and an observed value of the state representation of the longitudinal drag of the entire vehicle at the current moment according to a preset condition, updating the predicted value of the state representation of the longitudinal drag of the entire vehicle, and obtaining the longitudinal drag of the entire vehicle at the current moment (S102). The problem of how to accurately estimate the longitudinal drag of the entire vehicle for an autonomous driving vehicle is solved, and significant reference information of longitudinal dynamics is provided for an assisted driving system, to assist a decision-making and control process for autonomous driving.

Description

Vehicle longitudinal resistance acquisition method, device and vehicle based on Kalman filtering Technical Field

This application claims the priority of Chinese patent application 202310254848.6 filed on March 16, 2023, with the invention name “Vehicle longitudinal resistance acquisition method, device and vehicle based on Kalman filtering”. The entire contents of the above Chinese patent application are incorporated into this application by reference.

Technical Field

The present invention relates to the field of autonomous driving technology, and specifically provides a method, device, medium and vehicle for obtaining vehicle longitudinal resistance based on Kalman filtering.

Background Art

Advanced driver assistance functions are gaining more and more attention, and their use scenarios are also increasing with the advancement of sensors and information technology, and their functional experience is also constantly improving. With the expansion of assisted driving coverage scenarios, more and more longitudinal disturbances will be encountered in low-speed longitudinal control, such as speed bumps, potholes on the road, V-grooves of battery swap stations, stepped parking spaces, etc. Accurately estimating the longitudinal resistance of the vehicle caused by these longitudinal disturbances will directly affect the control effect of the assisted driving system.

Accordingly, the art needs a new solution for obtaining the longitudinal resistance of an autonomous driving vehicle to solve the above problems.

Summary of the invention

In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least partially solve the problem of how to accurately estimate the longitudinal resistance of an autonomous driving vehicle.

In a first aspect, the present invention provides a vehicle longitudinal resistance acquisition method based on Kalman filtering, the method comprising:

Prediction stage:

Construct the state representation of the longitudinal resistance of the vehicle;

constructing a process model based on a kinetic model associated with the state representation;

Acquire a predicted value of the state representation of the longitudinal resistance of the entire vehicle at the current moment based on the process model;

Update phase:

Based on preset conditions, the predicted value is selectively fused with the observed value representing the state of the vehicle longitudinal resistance at the current moment, and the predicted value representing the state of the vehicle longitudinal resistance is updated to obtain the vehicle longitudinal resistance at the current moment.

In a technical solution of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, the state representation includes the longitudinal resistance of the whole vehicle and the longitudinal velocity of the center of mass;

The dynamic model is a vehicle longitudinal dynamic model.

In a technical solution of the vehicle longitudinal resistance acquisition method based on Kalman filtering, the state representation also includes the output shaft speed of the main reducer and the tire longitudinal force;

The dynamic model also includes an electric axis longitudinal dynamic model.

In a technical solution of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, the control variable in the process model is the output shaft torque of the vehicle's main reducer.

In a technical solution of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, the observed values include the output shaft speed of the main reducer and the longitudinal speed of the center of mass.

In a technical solution of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, the method further includes:

respectively obtaining the change gradients of the output shaft torque, the output shaft speed and the longitudinal velocity of the center of mass;

Comparing the change gradients of the output shaft torque, the output shaft speed and the center-of-mass longitudinal velocity with corresponding gradient thresholds respectively;

When the change gradients of the output shaft torque, the output shaft speed and the center of mass longitudinal velocity are all greater than the corresponding gradient thresholds, the observation value representing the state of the vehicle longitudinal resistance at the current moment is low-pass filtered at a lower filter cutoff frequency to reduce the update speed, otherwise it is low-pass filtered at a higher filter cutoff frequency to increase the update speed.

In the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, a technical In the technical solution, based on the preset conditions, the predicted value is selectively merged with the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment, and the predicted value of the state representation of the longitudinal resistance of the whole vehicle is updated to obtain the longitudinal resistance of the whole vehicle at the current moment, including:

Determining whether the mechanical brake of the vehicle is engaged;

If yes, use the updated prediction value at the previous moment as the updated prediction value at the current moment to obtain the longitudinal resistance of the vehicle at the current moment; and/or

If not, the predicted value of the state representation of the longitudinal resistance of the whole vehicle is updated using the fused result to obtain the longitudinal resistance of the whole vehicle at the current moment.

In a technical solution of the vehicle longitudinal resistance acquisition method based on Kalman filtering, the judgment condition for mechanical brake intervention is:

The vehicle's brake pedal opening is greater than a preset opening; or,

The braking pressure of the vehicle is greater than a preset pressure value; or,

The vehicle's chassis braking system automatically intervenes.

In a technical solution of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, the method further includes:

Obtaining a braking distance after the mechanical brake intervenes;

When the braking distance is greater than the preset distance, the updated prediction value at the current moment is cleared.

In a technical solution of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering, the observation value representing the state of the whole vehicle longitudinal resistance at the current moment is obtained by low-pass filtering the input observation value representing the state of the whole vehicle longitudinal resistance so that the observation values are phase-aligned.

In a second aspect, a control device is provided, which includes at least one processor and at least one storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the vehicle longitudinal resistance acquisition method based on Kalman filtering described in any one of the technical solutions of the above-mentioned vehicle longitudinal resistance acquisition method based on Kalman filtering.

In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored in the computer-readable storage medium, wherein the program codes are suitable for being loaded and run by a processor to execute the technical solution of the vehicle longitudinal resistance acquisition method based on Kalman filtering. The vehicle longitudinal resistance acquisition method based on Kalman filtering described in any technical solution.

In a fourth aspect, a vehicle is provided, comprising the control device of the control device technical solution.

The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

In the technical solution for implementing the present invention, in the prediction stage, the present invention constructs a state representation of the longitudinal resistance of the whole vehicle, and constructs a process model according to a dynamic model related to the state representation, and obtains the predicted value of the state representation of the longitudinal resistance of the whole vehicle at the current moment based on the process model; in the update stage, according to preset conditions, the predicted value and the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment are selectively fused to update the predicted value of the state representation of the longitudinal resistance of the whole vehicle, thereby obtaining the longitudinal resistance of the whole vehicle at the current moment. Through the above configuration, the present invention can accurately estimate the longitudinal resistance of the whole vehicle at the current moment based on Kalman filtering, thereby providing important reference information of longitudinal dynamics for the auxiliary driving system, and assisting the decision-making and control process of automatic driving.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. Among them:

FIG1 is a schematic flow chart of main steps of a method for acquiring vehicle longitudinal resistance based on Kalman filtering according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of the main components of a vehicle longitudinal resistance acquisition method based on Kalman filtering according to an implementation of an embodiment of the present invention.

DETAILED DESCRIPTION

Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

In the description of the present invention, "module" or "processor" may include hardware, software or a combination of both. A module may include hardware circuits, various suitable sensors, communication Ports, memories, may also include software parts, such as program codes, or may be a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and/or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and/or B" means all possible combinations of A and B, such as just A, just B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and/or B", and may include just A, just B, or A and B. The singular terms "one" and "the" may also include plural forms.

Referring to FIG. 1 , FIG. 1 is a schematic flow chart of the main steps of a vehicle longitudinal resistance acquisition method based on Kalman filtering according to an embodiment of the present invention. As shown in FIG. 1 , the vehicle longitudinal resistance acquisition method based on Kalman filtering in the embodiment of the present invention mainly includes the following steps S101 to S102.

Step S101: Prediction stage:

Step S1011: construct a state representation of the vehicle's entire longitudinal resistance.

Step S1012: construct a process model based on a dynamic model related to the state representation.

Step S1013: Obtain a predicted value representing the state of the entire vehicle longitudinal resistance at the current moment based on the process model.

In this embodiment, in the prediction stage of Kalman filtering, a state representation of the longitudinal resistance of the vehicle can be constructed, a process model of Kalman filtering can be constructed based on a dynamic model related to the state representation, and a predicted value of the state representation of the longitudinal resistance of the vehicle at the current moment can be obtained based on the process model. The state representation is a state parameter that needs to be estimated in the Kalman filtering process. The process model is a model that estimates the predicted value at the current moment based on the predicted value updated at the previous moment.

In one implementation, the state representation may include the longitudinal resistance of the entire vehicle and the longitudinal velocity of the center of mass; and the dynamic model may be a longitudinal dynamic model of the vehicle.

In one implementation, the state representation may further include the output shaft speed of the final reducer and the tire longitudinal force; and the dynamic model may further include an electric shaft longitudinal dynamic model.

In one embodiment, the control variable in the process model can be the main reducer Output shaft torque: The output shaft torque can be measured by a torque sensor.

In one embodiment, the final reducer may include a front final reducer and a rear final reducer. The output shaft speed may include a front final reducer output shaft speed and a rear final reducer output shaft speed. The output shaft torque may include a front final reducer output shaft torque and a rear final reducer output shaft torque. The tire longitudinal force may include a front tire longitudinal force and a rear axle tire longitudinal force.

In one embodiment, the dynamic model composed of the vehicle longitudinal dynamic model and the electric shaft longitudinal dynamic model can be obtained according to the following formulas (1) to (6):

Among them, T _fa is the torque of the output shaft of the front main reducer, which is positive when driven and the unit is Nm; T _ra is the torque of the output shaft of the rear main reducer, which is positive when driven and the unit is Nm; ω _fa is the speed of the output shaft of the front main reducer, which is radps; ω _ra is the speed of the output shaft of the rear main reducer, which is radps; V _x is the longitudinal velocity of the center of mass, which is mps; F _x-fa is the longitudinal force of the front axle tire, which is positive when facing forward and the unit is N; F _x-ra is the longitudinal force of the rear axle tire, which is positive when facing forward and the unit is N; F _x-Rxt is the longitudinal resistance of the whole vehicle, which is positive when facing backward and the unit is N; R _whl is the tire radius, which is m; J _f is the moment of inertia of the front axle, which is kg·m ² ; J _r is the moment of inertia of the rear axle, which is kg·m ² ; m is the mass of the whole vehicle, which is kg.

Step S102: Update phase.

Step S1021: Based on preset conditions, the predicted value is selectively merged with the observed value representing the state of the vehicle longitudinal resistance at the current moment, and the predicted value representing the state of the vehicle longitudinal resistance is updated to obtain the vehicle longitudinal resistance at the current moment.

In this embodiment, the predicted value and the observed value represented by the current state can be selectively fused according to preset conditions, so as to update the predicted value represented by the state to obtain the longitudinal resistance of the whole vehicle at the current moment. The observed value refers to the value obtained by measuring or calculating according to the actual state of the vehicle.

In one embodiment, the observed values may include the output shaft speed of the main reducer and The longitudinal velocity of the center of mass. The output shaft speed can be measured by a speed sensor, and the longitudinal velocity of the center of mass can be obtained by differential GPS (Global Positioning System) signals.

In one embodiment, the front axle tire longitudinal force, rear axle tire longitudinal force and vehicle longitudinal resistance can be calculated through a dynamic model based on the output shaft speed of the main reduction gear and the longitudinal velocity of the center of mass, and the calculated front axle tire longitudinal force, rear axle tire longitudinal force and vehicle longitudinal resistance are also used as observation values representing the state at the current moment.

In one implementation, since different observation values may have time delays, signal noise, and other issues when measured by sensors, the noise can be filtered out and the phases of the observation values can be aligned by performing a low-pass filter on the observation values represented by the input longitudinal resistance state of the vehicle to facilitate subsequent fusion based on the observation values.

In one implementation, the Kalman filter equation can be obtained by the following formulas (7) to (11):

Prediction stage:

Update phase:

in, is the predicted value of the state representation at time k; A _d is the state transfer matrix; X′ _k-1 is the predicted value of the updated state representation at time k-1; B _d is the input control matrix; U _k is the control variable at time k; is the covariance matrix at time k; P′ _k-1 is the updated covariance matrix at time k-1; A _d ^T is the transpose of the state transfer matrix; Q is the process excitation noise covariance; K _k is the Kalman gain at time k; H is the state observation matrix; ^HT is the transpose of the state observation matrix; R is the observation noise covariance; X′ _k is the predicted value of the updated state representation at time k; Z _k is the observed value of the state representation at time k; P′ _k is the updated covariance matrix at time k.

Based on the above steps S101 and S102, in the prediction stage, the embodiment of the present invention constructs a state representation of the longitudinal resistance of the vehicle, and constructs a process model according to a dynamic model related to the state representation, and obtains the state representation of the longitudinal resistance of the vehicle at the current moment based on the process model. In the updating stage, according to the preset conditions, the predicted value and the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment are selectively merged to update the predicted value of the state representation of the longitudinal resistance of the whole vehicle, thereby obtaining the longitudinal resistance of the whole vehicle at the current moment. Through the above configuration, the embodiment of the present invention can accurately estimate the longitudinal resistance of the whole vehicle at the current moment based on the Kalman filter, thereby providing important reference information of longitudinal dynamics for the auxiliary driving system, and assisting the decision-making and control process of automatic driving.

In an implementation of an embodiment of the present invention, in addition to the above steps S101 and S102, the present invention may further include the following steps S103 to S105:

Step S103: respectively obtaining the change gradients of the output shaft torque, the output shaft speed and the longitudinal velocity of the center of mass.

Step S104: Compare the change gradients of the output shaft torque, the output shaft speed and the center of mass longitudinal velocity with the corresponding gradient thresholds respectively.

Step S105: When the change gradients of the output shaft torque, the output shaft speed and the longitudinal velocity of the center of mass are all greater than the corresponding gradient thresholds, the observation value representing the state of the longitudinal resistance of the whole vehicle at the current moment is low-pass filtered at a lower filter cutoff frequency to reduce the update speed, otherwise it is low-pass filtered at a higher filter cutoff frequency to increase the update speed.

In this embodiment, the observed value represented by the state can be filtered, that is, the change gradients of the output shaft torque, the output shaft speed and the longitudinal velocity of the center of mass are calculated, and the calculated change gradients are compared with their corresponding gradient thresholds. If the change gradients of the output shaft torque, the output shaft speed and the longitudinal velocity of the center of mass are all greater than their corresponding gradient thresholds, the observed value represented by the state of the longitudinal resistance of the whole vehicle at the current moment can be low-pass filtered at a lower filter cutoff frequency to reduce the update speed. Otherwise, it can be low-pass filtered at a higher filter cutoff frequency to increase the update speed, that is, allow the longitudinal resistance of the whole vehicle output by the Kalman filter to be updated quickly.

In one implementation of the embodiment of the present invention, step S1021 may further include the following steps S10211 to S10213:

Step S10211: Determine whether the vehicle's mechanical brake is engaged; if so, jump to step S10212; if not, jump to step S10213.

Step S10212: Use the predicted value updated at the previous moment as the predicted value updated at the current moment to obtain the longitudinal resistance of the vehicle at the current moment.

Step S10213: Use the fused result to update the predicted value of the state representation of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment.

In this embodiment, since there will be unknown friction resistance torque on the electric shaft after the mechanical brake intervenes, the update of the Kalman filter should be suspended at this time, and the prediction value updated at the last moment is used as the prediction value updated at the current moment.

In one embodiment, the judgment conditions for mechanical brake intervention may include:

The vehicle's brake pedal opening is greater than the preset opening; or,

The vehicle's brake pressure is greater than a preset pressure value; or,

The vehicle's chassis braking system automatically intervenes.

Those skilled in the art can set the preset opening and the preset pressure value according to the needs of actual application.

In one implementation, when mechanical braking intervenes, the braking distance may be acquired, and when the braking distance is greater than a preset distance, the updated prediction value at the current moment may be cleared.

In this embodiment, when the braking distance is greater than the preset distance, the updated prediction value at the current moment can be cleared. Those skilled in the art can set the preset distance according to the needs of actual applications.

In one implementation, the braking distance may be obtained by integrating the vehicle speed and the sampling time.

In one implementation, please refer to FIG. 2, which is a schematic diagram of the main components of a vehicle longitudinal resistance acquisition method based on Kalman filtering according to an implementation of an embodiment of the present invention. As shown in FIG. 2, the observation value can be input for filtering pre-processing, and the suspension and update of the Kalman filter data fusion can be controlled based on whether the mechanical brake is involved, and the update speed can be controlled by the change gradient of the observation value to achieve disturbance observation post-processing, thereby obtaining the longitudinal resistance of the whole vehicle at the current moment. Among them, the suspension of data fusion refers to using the predicted value updated at the previous moment as the predicted value updated at the current moment or clearing the predicted value updated at the current moment; updating refers to using the fused result to update the predicted value represented by the state of the longitudinal resistance of the whole vehicle.

It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each of the above method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

Furthermore, the present invention also provides a control device. In an embodiment of the control device according to the present invention, the control device includes a processor and a storage device, the storage device can be configured to store a program for executing the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment, and the processor can be configured to execute the program in the storage device, the program including but not limited to executing the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, and for the specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.

In the embodiment of the present invention, the control device may be a control device device formed by various electronic devices. In some possible implementations, the control device may include multiple storage devices and multiple processors. The program for executing the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment may be divided into multiple subprograms, and each subprogram may be loaded and run by a processor to execute different steps of the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment. Specifically, each subprogram may be stored in different storage devices, and each processor may be configured to execute the programs in one or more storage devices to jointly implement the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment, that is, each processor executes different steps of the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment to jointly implement the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment.

The above-mentioned multiple processors may be processors deployed on the same device. For example, the above-mentioned control device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors may also be processors deployed on different devices. For example, the above-mentioned control device may be a server cluster, and the above-mentioned multiple processors may be processors on different servers in the server cluster.

Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the vehicle longitudinal resistance acquisition method based on Kalman filtering of the above method embodiment, and the program can be loaded and run by a processor to implement the above vehicle longitudinal resistance acquisition method based on Kalman filtering. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

Furthermore, the present invention also provides a vehicle. In a vehicle embodiment according to the present invention, the vehicle may include the control device in the control device embodiment.

Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims (13)

1. A vehicle longitudinal resistance acquisition method based on Kalman filtering, characterized in that the method comprises:

   Prediction stage:

   Constructing a state representation of the vehicle's full vehicle longitudinal resistance;

   constructing a process model based on a kinetic model associated with the state representation;

   Acquire a predicted value of the state representation of the longitudinal resistance of the entire vehicle at the current moment based on the process model;

   Update phase:

   Based on preset conditions, the predicted value is selectively fused with the observed value representing the state of the vehicle longitudinal resistance at the current moment, and the predicted value representing the state of the vehicle longitudinal resistance is updated to obtain the vehicle longitudinal resistance at the current moment.
2. The method according to claim 1, characterized in that

   The state representation includes the longitudinal resistance of the vehicle and the longitudinal velocity of the center of mass;

   The dynamic model is a vehicle longitudinal dynamic model.
3. The method according to claim 2, characterized in that

   The state representation also includes the output shaft speed of the final drive and the tire longitudinal force;

   The dynamic model also includes an electric axis longitudinal dynamic model.
4. The method according to claim 3, characterized in that

   The controlled variable in the process model is the output shaft torque of the vehicle's final reducer.
5. The method according to claim 1, characterized in that

   The observed values include the output shaft speed of the final reducer and the longitudinal velocity of the center of mass.
6. The method according to claim 5, characterized in that the method further comprises:

   respectively obtaining the change gradients of the output shaft torque, the output shaft speed and the longitudinal velocity of the center of mass;

   Comparing the change gradients of the output shaft torque, the output shaft speed and the center-of-mass longitudinal velocity with corresponding gradient thresholds respectively;

   When the change gradients of the output shaft torque, the output shaft speed and the center of mass longitudinal velocity are all greater than the corresponding gradient thresholds, the observation value representing the state of the vehicle longitudinal resistance at the current moment is low-pass filtered at a lower filter cutoff frequency to reduce the update speed, otherwise it is low-pass filtered at a higher filter cutoff frequency to increase the update speed.
7. The method according to any one of claims 1 to 6 is characterized in that, based on a preset condition, selectively fusing the predicted value with the observed value of the state representation of the longitudinal resistance of the whole vehicle at the current moment, and updating the predicted value of the state representation of the longitudinal resistance of the whole vehicle to obtain the longitudinal resistance of the whole vehicle at the current moment, comprises:

   Determining whether the mechanical brake of the vehicle is engaged;

   If yes, use the updated prediction value at the previous moment as the updated prediction value at the current moment to obtain the longitudinal resistance of the vehicle at the current moment; and/or

   If not, the predicted value of the state representation of the longitudinal resistance of the whole vehicle is updated using the fused result to obtain the longitudinal resistance of the whole vehicle at the current moment.
8. The method according to claim 7 is characterized in that the judgment condition for mechanical brake intervention is:

   The vehicle's brake pedal opening is greater than a preset opening; or,

   The braking pressure of the vehicle is greater than a preset pressure value; or,

   The vehicle's chassis braking system automatically intervenes.
9. The method according to claim 8, characterized in that the method further comprises:

   Obtaining a braking distance after the mechanical brake intervenes;

   When the braking distance is greater than the preset distance, the updated prediction value at the current moment is cleared.
10. The method according to claim 1, characterized in that

    The observation value of the state representation of the vehicle longitudinal resistance at the current moment is obtained by The observation values represented by the input longitudinal resistance state of the whole vehicle are obtained after low-pass filtering is performed to align the phases of the observation values.
11. A control device comprises at least one processor and at least one storage device, wherein the storage device is suitable for storing multiple program codes, and is characterized in that the program codes are suitable for being loaded and run by the processor to execute the method described in any one of claims 1 to 10.
12. A computer-readable storage medium having a plurality of program codes stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the method according to any one of claims 1 to 10.
13. A vehicle, characterized in that the vehicle comprises the control device according to claim 11.