Road slope estimation method, system, vehicle and device

Abstract

The application discloses a road slope estimation method, system, vehicle and equipment. The road slope estimation method comprises the following steps: obtaining vehicle motion information and vehicle information, wherein the vehicle motion information comprises vehicle longitudinal speed and acceleration, and the vehicle information comprises vehicle total mass; a vehicle longitudinal dynamics model is established according to the vehicle motion information and the vehicle information; an analytical equation of vehicle longitudinal speed, acceleration and road slope is constructed according to the vehicle longitudinal dynamics model, wherein the road slope is a value to be solved; and the analytical equation is solved by using an unscented Kalman filtering estimation algorithm, so as to estimate the road slope. The application effectively solves the problems that current slope estimation algorithms are greatly affected by noise, have large linear errors and poor adaptability, can provide reliable slope information for a vehicle control system, reduces production cost, and improves the accuracy and robustness of vehicle slope estimation.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a road gradient estimation method, system, vehicle, and equipment. Background Technology

[0002] Road slope estimation is a crucial component of autonomous driving technology. In the field of autonomous driving research, road longitudinal slope estimation algorithms represent a key technical challenge. Road longitudinal slope, the angle of inclination of the road along the direction of travel, significantly impacts vehicle dynamics and energy efficiency. Accurate longitudinal slope information is essential for vehicle functions such as power control, braking systems, stability control, and energy management systems.

[0003] In related technologies, longitudinal slope estimation methods rely on onboard sensors in the vehicle, such as accelerometers and gyroscopes, as well as external reference systems, such as the Global Positioning System (GPS). Data from these sensors is processed using data fusion algorithms to estimate road slope. However, these methods are limited by sensor accuracy and environmental factors, such as satellite signal obstruction and multipath effects, which may lead to inaccurate estimation results. Furthermore, the large number of sensors required also increases the cost of vehicle manufacturing.

[0004] Furthermore, vision-based slope estimation is a method that uses vehicle-mounted cameras to capture road images and then estimates the road slope through image processing and computer vision techniques. The core of this method lies in extracting the geometric features of the road from the image and combining the camera's installation parameters and visual algorithms to infer the road's inclination. However, while vision-based slope estimation has the advantages of being intuitive and non-contact, it also has some drawbacks and limitations: Environmental sensitivity: Vision systems are very sensitive to lighting conditions; strong light, backlight, or low light environments can all affect image quality, leading to difficulties and inaccuracies in road feature extraction. In addition, adverse weather conditions such as rain, snow, and fog can severely affect the camera's visual clarity. Difficulty in visual feature extraction: Lane lines, cracks, stains, etc., on the road can be confused with true road surface features, increasing the difficulty for image processing algorithms to identify and extract road features. Computational complexity: Image processing and analysis typically require significant computational resources, especially when processing high-resolution images in real time. This can lead to system response delays, affecting driving decisions. Summary of the Invention

[0005] Therefore, it is necessary to provide a road slope estimation method, system, vehicle, and equipment to address the aforementioned technical problems. This effectively solves the issues of current slope estimation algorithms being greatly affected by noise, having large linear errors, and poor adaptability. Furthermore, the algorithm is relatively simple to implement and easy to integrate into the vehicle's electronic control unit, providing reliable slope information for the vehicle control system and reducing production costs. It also improves the accuracy and robustness of vehicle slope estimation, providing real-time and reliable slope information for the vehicle's dynamic control system.

[0006] Firstly, a method for estimating road slope is provided, including: Obtain vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A longitudinal dynamics model of the vehicle is established based on the vehicle motion information and vehicle information. Based on the vehicle longitudinal dynamics model, analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope are constructed, where the road slope is a value to be determined. The road slope is estimated by solving the analytical equation using the unscented Kalman filter estimation algorithm.

[0007] In some examples, before establishing the vehicle longitudinal dynamics model based on the vehicle motion information and vehicle information, the following steps are also included: The longitudinal velocity and acceleration of the vehicle are subjected to low-pass filtering to obtain the processed longitudinal velocity and acceleration of the vehicle.

[0008] In some examples, the analytical equations for the vehicle's longitudinal velocity, acceleration, and road gradient based on the vehicle's longitudinal dynamics model include: A system state-space model is established based on the vehicle longitudinal dynamics model, wherein the system state variables are the vehicle longitudinal velocity, displacement, and road slope, and the system state-space model is the analytical equation.

[0009] In some examples, establishing the system state-space model based on the vehicle longitudinal dynamics model includes: Establish the system's state-space equations based on the vehicle's longitudinal dynamics model; Discretize the state-space equations to obtain the processed state-space equations; Select the vehicle's longitudinal velocity and displacement as measurement variables, and establish the system measurement equations; Based on the processed state-space equations and the system measurement equations, a system state-space model is established.

[0010] In some examples, the method of using an unscented Kalman filter estimation algorithm to solve the analytical equation and estimate the road slope includes: Define the nonlinear state equation and observation equation; Within each sampling period, the sampling point Sigma and its corresponding weights are calculated using the UT transformation. Substitute the sample point set into the nonlinear state equation to calculate the one-step prediction of the sample point set; Using the UT transform, new sampling points Sigma are generated and substituted into the observation equation to obtain the predicted value; Obtain the predicted values ​​of the system mean and the state error covariance matrix; Calculate the Kalman gain coefficient; The system state variables and state error covariance matrix are updated iteratively to estimate the road slope.

[0011] In some examples, obtaining the predicted quantities of the system mean and state error covariance matrix includes: The predicted values ​​of the system mean and the state error covariance matrix are obtained by weighted summation.

[0012] Secondly, a road slope estimation system is provided, including: The acquisition module is used to obtain vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A module is established to build a longitudinal dynamics model of the vehicle based on the vehicle motion information and vehicle information. A construction module is used to construct analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope based on the vehicle's longitudinal dynamics model, wherein the road slope is a value to be determined. The solution module is used to solve the analytical equation using the unscented Kalman filter estimation algorithm to estimate the road slope.

[0013] Thirdly, a vehicle is provided, comprising: the road slope estimation system described in the second aspect above.

[0014] Fourthly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the road slope estimation method described in the first aspect and any possible implementation thereof.

[0015] Fifthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the road slope estimation method described in the first aspect and any possible implementation thereof.

[0016] In a sixth aspect, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the road slope estimation method described in the first aspect and any possible implementation thereof.

[0017] The embodiments of this application utilize the vehicle's longitudinal dynamics model and vehicle motion information to estimate the vehicle's current slope state. This effectively solves the problems of current slope estimation algorithms being greatly affected by noise, having large linear errors, and poor adaptability. Simultaneously, the algorithm is relatively simple to implement and easily integrated into the vehicle's electronic control unit, providing reliable slope information for the vehicle control system and reducing production costs. It improves the accuracy and robustness of vehicle slope estimation, providing real-time and reliable slope information for the vehicle's dynamic control system. This not only optimizes the vehicle's power output and braking performance, improving fuel economy and energy recovery efficiency, but also enhances the safety and stability of autonomous driving and advanced driver assistance systems. Furthermore, the real-time processing capability and predictive function of the unscented Kalman filter enable the vehicle to make more accurate driving decisions under various road conditions, thereby improving driving safety and comfort. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A flowchart illustrating the road slope estimation method provided in this application embodiment; Figure 2 A schematic diagram of the vehicle longitudinal dynamics model in the road slope estimation method provided in the embodiments of this application; Figure 3 A schematic diagram illustrating the computational framework of the road slope estimation method provided in this application embodiment; Figure 4 This is a structural block diagram of the road slope estimation system provided in the embodiments of this application; Figure 5 This is a structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0020] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] The following describes in detail, with reference to the accompanying drawings, a road slope estimation method, system, vehicle, and device according to embodiments of this application.

[0022] Figure 1 This is a flowchart of a road slope estimation method according to an embodiment of this application. Figure 1 As shown, the road slope estimation method according to an embodiment of this application includes the following steps: S101: Obtain vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass.

[0023] In practical applications, the information obtained includes not only the vehicle's longitudinal speed, acceleration, and total mass, but also data such as air density, drag coefficient, and frontal area driving force.

[0024] S102: Establish a longitudinal dynamics model of the vehicle based on the vehicle motion information and vehicle information.

[0025] Generally, before establishing a longitudinal dynamics model of the vehicle based on the vehicle motion information and vehicle information, the process typically includes low-pass filtering of the vehicle's longitudinal velocity and acceleration to obtain processed longitudinal velocity and acceleration. That is, when the longitudinal velocity and acceleration signals are collected by sensors, due to the influence of sensor accuracy and system noise, the collected longitudinal acceleration signals and vehicle longitudinal velocity signals usually have some errors. Therefore, before establishing a longitudinal dynamics model of the vehicle based on the vehicle motion information and vehicle information, low-pass filtering and other methods can be used to process the signals to suppress high-frequency noise, thereby improving data quality.

[0026] Combination Figure 2 As shown, the vehicle's motion is mainly divided into longitudinal, lateral, and yaw directions. Furthermore, the vehicle's gradient is primarily affected by longitudinal forces. Therefore, based on Newton's second law, a longitudinal dynamic model of the vehicle's mass and gradient is established, as follows: Figure 1 As shown. The equations of motion for the longitudinal forces of the vehicle are expressed as follows:

[0027] in F x represent, F f The rolling resistance coefficient, These represent air density, drag coefficient, and frontal area driving force, respectively. For road slope, This refers to the total mass of the vehicle.

[0028] S103: Construct analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope based on the vehicle's longitudinal dynamics model, wherein the road slope is a value to be determined.

[0029] In one embodiment of this application, constructing analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope based on the vehicle's longitudinal dynamics model includes: establishing a system state-space model based on the vehicle's longitudinal dynamics model, wherein the system's state variables are the vehicle's longitudinal velocity, displacement, and road slope, and the system state-space model is the analytical equations.

[0030] In the above example, establishing a system state-space model based on the vehicle longitudinal dynamics model includes: establishing the system state-space equations based on the vehicle longitudinal dynamics model; discretizing the state-space equations to obtain processed state-space equations; selecting vehicle longitudinal velocity and displacement as measurement variables to establish system measurement equations; and establishing the system state-space model based on the processed state-space equations and the system measurement equations.

[0031] Specifically, the state-space equations of the estimated system are established using the aforementioned longitudinal dynamics model. The system's state variables are selected as longitudinal vehicle speed, displacement, and slope angle, combined with the displacement relationship. get:

[0032] To use the unscented transform Kalman filtering method, the above model is discretized to obtain:

[0033] in, The sampling period is W k This represents system noise.

[0034] Since the ramp angle is difficult to measure directly, it needs to be estimated based on the measured state variables. Vehicle speed and displacement are selected as measurement variables, and the system measurement equations are established:

[0035] in, V k For measuring noise.

[0036] Therefore, the system state-space model is established as follows:

[0037] S104: The road slope is estimated by solving the analytical equation using the unscented Kalman filter estimation algorithm.

[0038] In a specific example, the road slope is estimated by solving the analytical equation using an unscented Kalman filter estimation algorithm, including: defining a nonlinear state equation and an observation equation; calculating the sampling point Sigma and its corresponding weights through a UT transform in each sampling period; substituting the sampling point set into the nonlinear state equation to calculate the one-step prediction of the sampling point set; using the UT transform to generate new sampling point Sigma, which is then substituted into the observation equation to obtain the predicted value; obtaining the predicted values ​​of the system mean and the state error covariance matrix; calculating the Kalman gain coefficient; and performing iterative update operations on the system state variables and the state error covariance matrix to estimate the road slope.

[0039] The process of obtaining the predicted values ​​of the system mean and the state error covariance matrix includes obtaining the predicted values ​​of the system mean and the state error covariance matrix through a weighted summation method.

[0040] Specifically, the classical Kalman filter is mainly suitable for state estimation of linear systems. When calculating nonlinear systems, the computational cost is large, and the linearization of the function will affect the accuracy of the result. In contrast, the unscented Kalman filter uses the UT transform, which does not require linearization of the nonlinear function and has higher computational accuracy.

[0041] For the UT transform, suppose a nonlinear system y = f(x) has a state variable x that is an n-dimensional random variable with a known mean. The variance P is obtained by transforming it using UT to obtain 2n+1 Sigma points and weights. Then, the statistical characteristics of y are calculated, and the calculation formula is shown below: Calculate 2n+1 Sigma sampling points:

[0042] in, The first root of the matrix represents the square root of the matrix. i List; Calculate the weights corresponding to the Sigma sampling points:

[0043] Among them, subscript m and c These represent the mean and covariance, respectively, with the superscript indicating the sequence number of the sampling point. This is the scaling factor. As a scale factor, These are non-negative weight coefficients.

[0044] By combining the above-mentioned UT transform with Kalman filtering, the calculation steps of the unscented Kalman filter are as follows: First, design and define the nonlinear state equation and observation equation:

[0045] Within each sampling period, the Sigma point and its corresponding weights are calculated using the UT transformation, as follows:

[0046] Substitute the obtained Sigma set into the state-space equations of the nonlinear system to calculate the one-step prediction of the sample point set:

[0047] in, Let k be the prior estimate of the system state variables at time k. The prior estimate of the covariance matrix at time k; Using the UT transform again, new Sigma points are generated and substituted into the observation equation to obtain the predicted values:

[0048] The predicted values ​​of the system mean and state error covariance matrix are obtained by weighted summation, as shown in the following formula:

[0049] Calculate the Kalman gain coefficient:

[0050] Finally, the system state variables and state error covariance matrix are updated to facilitate the next iteration. After the final iteration is completed, the road slope is obtained.

[0051]

[0052] It should be noted that due to the influence of sensor accuracy and system noise on vehicle signals, the acquired longitudinal acceleration signal and bus speed signal will have certain errors. In actual vehicle use, a low-pass filtering method can be used to process abrupt changes in measurement values. The specific algorithm is as follows:

[0053] in These are the filter coefficients. v i Input values ​​for the system. v k-1 This is the output value from the previous cycle.

[0054] like Figure 3As shown, the road slope estimation method of this application collects vehicle longitudinal speed and acceleration signals through sensors, and uses low-pass filtering to suppress high-frequency noise on the collected signals; then, a longitudinal dynamic model of the whole vehicle is built, and analytical equations of vehicle speed, acceleration and slope are constructed through longitudinal dynamic equations; finally, the road longitudinal slope angle is estimated in real time using the unscented Kalman filter estimation algorithm.

[0055] The road slope estimation method according to embodiments of this application utilizes the vehicle's longitudinal dynamics model and vehicle motion information to estimate the vehicle's slope state. This effectively solves the problems of current slope estimation algorithms being greatly affected by noise, having large linear errors, and poor adaptability. Simultaneously, the algorithm is relatively simple to implement and easily integrated into the vehicle's electronic control unit, providing reliable slope information for the vehicle control system and reducing production costs. It improves the accuracy and robustness of vehicle slope estimation, providing real-time and reliable slope information for the vehicle's dynamic control system. This not only optimizes the vehicle's power output and braking performance, improving fuel economy and energy recovery efficiency, but also enhances the safety and stability of autonomous driving and advanced driver assistance systems. Furthermore, the real-time processing capability and predictive function of the unscented Kalman filter enable the vehicle to make more accurate driving decisions under various road conditions, thereby improving driving safety and comfort.

[0056] Figure 4 This is a structural block diagram of a road slope estimation system according to an embodiment of this application. Figure 4 As shown, a road slope estimation system according to an embodiment of this application includes: an acquisition module 410, an establishment module 420, a construction module 430, and a solution module 440, wherein: The acquisition module 410 is used to acquire vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass. Module 420 is used to establish a longitudinal dynamics model of the vehicle based on the vehicle motion information and vehicle information. The construction module 430 is used to construct analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope based on the vehicle's longitudinal dynamics model, wherein the road slope is a value to be determined. The solver module 440 is used to solve the analytical equation using the unscented Kalman filter estimation algorithm to estimate the road slope.

[0057] The road slope estimation system according to embodiments of this application estimates the slope state of a vehicle using its longitudinal dynamics model and motion information. This effectively solves the problems of current slope estimation algorithms being greatly affected by noise, having large linear errors, and poor adaptability. Simultaneously, the algorithm is relatively simple to implement and easy to integrate into the vehicle's electronic control unit, providing reliable slope information for the vehicle control system and reducing production costs. It improves the accuracy and robustness of vehicle slope estimation, providing real-time and reliable slope information for the vehicle dynamic control system. This not only optimizes the vehicle's power output and braking performance, improving fuel economy and energy recovery efficiency, but also enhances the safety and stability of autonomous driving and advanced driver assistance systems. Furthermore, the real-time processing capability and predictive function of the unscented Kalman filter enable the vehicle to make more accurate driving decisions under various road conditions, thereby improving driving safety and comfort.

[0058] Specific limitations regarding the road slope estimation system can be found in the limitations of the road slope estimation method described above, and will not be repeated here. Each module of the aforementioned road slope estimation system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0059] Furthermore, a vehicle is provided, comprising: a road slope estimation system according to any of the above embodiments. The system estimates the slope state of the vehicle using its longitudinal dynamics model and motion information. This effectively solves the problems of current slope estimation algorithms being greatly affected by noise, having large linear errors, and poor adaptability. Simultaneously, the algorithm is relatively simple to implement and easy to integrate into the vehicle's electronic control unit, providing reliable slope information for the vehicle control system and reducing production costs. It improves the accuracy and robustness of vehicle slope estimation, providing real-time and reliable slope information for the vehicle's dynamic control system. This not only optimizes the vehicle's power output and braking performance, improving fuel economy and energy recovery efficiency, but also enhances the safety and stability of autonomous driving and advanced driver assistance systems. Furthermore, the real-time processing capability and predictive function of the unscented Kalman filter enable the vehicle to make more accurate driving decisions under various road conditions, thereby improving driving safety and comfort.

[0060] Furthermore, other components and functions of the vehicle according to the embodiments of this application are known to those skilled in the art and will not be described in detail here.

[0061] In one embodiment, a computer device is provided. Figure 5 This is a structural block diagram of the computer device provided in the embodiments of this application, with reference to... Figure 5 The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned road slope estimation method embodiment. For example, it executes: obtaining vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A longitudinal dynamics model of the vehicle is established based on the vehicle motion information and vehicle information. Based on the vehicle longitudinal dynamics model, analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope are constructed, where the road slope is a value to be determined. The road slope is estimated by solving the analytical equation using the unscented Kalman filter estimation algorithm.

[0062] This application also provides a computer-readable storage medium storing a computer program. When the processor executes the computer program, it implements the aforementioned road slope estimation method embodiment. For example, it executes: obtaining vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A longitudinal dynamics model of the vehicle is established based on the vehicle motion information and vehicle information. Based on the vehicle longitudinal dynamics model, analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope are constructed, where the road slope is a value to be determined. The road slope is estimated by solving the analytical equation using the unscented Kalman filter estimation algorithm.

[0063] This application provides a computer program product including instructions that, when executed, cause the method described in this application embodiment to be performed. For example, it can execute... Figure 1 The various steps of the road slope estimation method shown are, for example, performing: obtaining vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A longitudinal dynamics model of the vehicle is established based on the vehicle motion information and vehicle information. Based on the vehicle longitudinal dynamics model, analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope are constructed, where the road slope is a value to be determined. The road slope is estimated by solving the analytical equation using the unscented Kalman filter estimation algorithm.

[0064] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for estimating road slope, characterized in that, include: Obtain vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A longitudinal dynamics model of the vehicle is established based on the vehicle motion information and vehicle information. Based on the vehicle longitudinal dynamics model, analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope are constructed, where the road slope is a value to be determined. The road slope is estimated by solving the analytical equation using the unscented Kalman filter estimation algorithm.

2. The road slope estimation method according to claim 1, characterized in that, Before establishing the vehicle longitudinal dynamics model based on the vehicle motion information and vehicle information, the following steps are also included: The longitudinal velocity and acceleration of the vehicle are subjected to low-pass filtering to obtain the processed longitudinal velocity and acceleration of the vehicle.

3. The road slope estimation method according to claim 1, characterized in that, The analytical equations for the vehicle's longitudinal velocity, acceleration, and road gradient, constructed based on the vehicle's longitudinal dynamics model, include: A system state-space model is established based on the vehicle longitudinal dynamics model, wherein the system state variables are the vehicle longitudinal velocity, displacement, and road slope, and the system state-space model is the analytical equation.

4. The road slope estimation method according to claim 3, characterized in that, The step of establishing a system state-space model based on the vehicle longitudinal dynamics model includes: Establish the system's state-space equations based on the vehicle's longitudinal dynamics model; Discretize the state-space equations to obtain the processed state-space equations; Select the vehicle's longitudinal velocity and displacement as measurement variables, and establish the system measurement equations; Based on the processed state-space equations and the system measurement equations, a system state-space model is established.

5. The road slope estimation method according to claim 1, characterized in that, The step of solving the analytical equation using the unscented Kalman filter estimation algorithm to estimate the road slope includes: Define the nonlinear state equation and observation equation; Within each sampling period, the sampling point Sigma and its corresponding weights are calculated using the UT transformation. Substitute the sample point set into the nonlinear state equation to calculate the one-step prediction of the sample point set; Using the UT transform, new sampling points Sigma are generated and substituted into the observation equation to obtain the predicted values; Obtain the predicted values ​​of the system mean and the state error covariance matrix; Calculate the Kalman gain coefficient; The system state variables and state error covariance matrix are updated iteratively to estimate the road slope.

6. The road slope estimation method according to claim 5, characterized in that, The predicted quantities for obtaining the system mean and state error covariance matrix include: The predicted values ​​of the system mean and the state error covariance matrix are obtained by weighted summation.

7. A road slope estimation system, characterized in that, include: The acquisition module is used to obtain vehicle motion information and vehicle information, wherein the vehicle motion information includes the vehicle's longitudinal velocity and acceleration, and the vehicle information includes the vehicle's total mass; A module is established to build a longitudinal dynamics model of the vehicle based on the vehicle motion information and vehicle information. A construction module is used to construct analytical equations for the vehicle's longitudinal velocity, acceleration, and road slope based on the vehicle's longitudinal dynamics model, wherein the road slope is a value to be determined. The solution module is used to solve the analytical equation using the unscented Kalman filter estimation algorithm to estimate the road slope.

8. A vehicle, characterized in that, include: The road slope estimation system according to claim 7.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the road slope estimation method according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the road slope estimation method according to any one of claims 1-6.

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