Road surface two-parameter state identification method and equipment

Abstract

The invention belongs to the technical field of pavement recognition, and particularly discloses a pavement two-parameter state recognition method and equipment. Comprising the steps of constructing an automobile acceleration sensor detection model based on an automobile dynamics model; acquiring an automobile acceleration sensor detection derivative model; squaring the model to obtain an automobile acceleration sensor detection square model and an automobile acceleration sensor detection derivative square model; carrying out summation processing by utilizing the square model to obtain an automobile acceleration sensor detection comprehensive model, and calculating an adhesion coefficient of a road surface; constructing an automobile acceleration sensor detection model conversion formula according to the adhesion coefficient, and constructing an automobile acceleration sensor detection sine model according to the automobile acceleration sensor detection model conversion formula; and detecting a sine model by using an automobile acceleration sensor to carry out arcsine transformation to obtain the ramp of the road surface. According to the invention, the adhesion coefficient and the ramp of the road surface where the vehicle is located can be accurately calculated by using the detection value and the derivative of the vehicle acceleration sensor.

Description

Technical Field

[0001] This invention belongs to the field of road surface recognition, and more specifically, relates to a method and device for road surface dual-parameter state recognition. Background Technology

[0002] With the rapid development of automotive intelligence, electrification, and autonomous driving technologies, real-time and accurate perception of road conditions has become a prerequisite for achieving high-level motion control (such as adaptive cruise control, automatic emergency braking, and active yaw moment control). Current technologies primarily focus on the independent estimation of single parameters (such as the road adhesion coefficient μ or road slope θ), failing to simultaneously obtain coupled information on the adhesion coefficient and slope within the same algorithm framework. This results in control strategies being based only on partial or delayed road priors, making it difficult to guarantee vehicle stability and safety under complex operating conditions.

[0003] On the other hand, existing methods generally rely on prior parameters such as vehicle curb weight and axle load distribution to infer road conditions. However, in actual use, passenger vehicles exhibit significant uncertainty in overall vehicle weight due to variations in the number of occupants and the weight of luggage or cargo; simultaneously, dynamic operating conditions such as vehicle acceleration / deceleration, steering, and hill driving cause real-time axle load transfer. The combination of these factors leads to systematic biases in road estimation models based on fixed mass and static axle load assumptions. The estimation accuracy deteriorates with load changes and dynamic operating conditions, failing to provide stable, reliable, and high-precision road condition input for the underlying actuators of intelligent vehicles. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and device for identifying two-parameter road surface conditions. By constructing a vehicle acceleration sensor detection model and its derivative model, and cleverly utilizing squared transformation and sine transformation, it can accurately separate and calculate the road surface adhesion coefficient μ and road surface slope θ in real time during driving with only one acceleration sensor. This significantly simplifies the hardware structure, reduces system costs, and improves the real-time performance and accuracy of road surface parameter identification. It provides highly reliable and low-cost key road surface information support for vehicle stability control, hill start assist, and autonomous driving decision-making, solving the defects of the inability to perform two-parameter estimation of road surface conditions and the low estimation accuracy.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for identifying the dual-parameter state of a road surface is proposed, comprising the following steps: Step 1: Construct a vehicle acceleration sensor detection model based on the vehicle dynamics model; Step 2: Differentiate the vehicle acceleration sensor detection model to obtain the vehicle acceleration sensor detection derivative model; Step 3: Square the vehicle acceleration sensor detection model and the vehicle acceleration sensor detection derivative model to obtain the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model. Step 4: Summing the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model to obtain the vehicle acceleration sensor detection comprehensive model, and calculating the road surface adhesion coefficient based on the vehicle acceleration sensor detection comprehensive model; Step 5: Construct the transformation formula of the vehicle acceleration sensor detection model based on the adhesion coefficient, and construct the vehicle acceleration sensor detection sinusoidal model accordingly. Step 6: Use the vehicle acceleration sensor to detect the sinusoidal model and perform an arcsine transformation to obtain the road slope.

[0006] As a further preferred embodiment, the vehicle acceleration sensor detection model is such that the vehicle acceleration sensor detection value is equal to the sum of the value obtained by multiplying the cosine of the slope by the road surface adhesion coefficient and the value obtained by multiplying the sine of the slope by the gravity acceleration. Preferably, the calculation formula for the vehicle acceleration sensor detection model is as follows: in: The value detected by the car's acceleration sensor; A ramp for the road surface; The coefficient of adhesion of the road surface, The cosine value of the road surface slope; Let be the sine value of the road surface slope. This is the acceleration due to gravity.

[0007] As a further preferred embodiment, the derivative model of the vehicle acceleration sensor detection is that the derivative value of the vehicle acceleration sensor detection value is equal to the sum of the value obtained by multiplying the gravitational acceleration by the inverse of the sine of the slope and then multiplying it by the road adhesion coefficient, and the value obtained by multiplying the gravitational acceleration by the cosine of the slope. Preferably, the calculation formula for the derivative model of the vehicle acceleration sensor detection is as follows: in: This is the derivative of the value detected by the car's acceleration sensor. A ramp for the road surface; The coefficient of adhesion of the road surface, The cosine value of the road surface slope; Let be the sine value of the road surface slope. It is the acceleration due to gravity; Furthermore, ,in: This is the value detected by the car's acceleration sensor at the previous moment. The time interval between the previous value detected by the vehicle acceleration sensor and the sampling time of the vehicle acceleration sensor detection value.

[0008] As a further preferred embodiment, in step four, the formula for calculating the adhesion coefficient is: in, The value detected by the car's acceleration sensor; This is the derivative of the value detected by the car's acceleration sensor. It is the acceleration due to gravity. This is the adhesion coefficient of the road surface.

[0009] As a further preferred option, in step five, both sides of the calculation formula for the vehicle acceleration sensor detection model are divided by the gravitational acceleration value to obtain the converted formula for the vehicle acceleration sensor detection model.

[0010] As a further preferred embodiment, in step five, the calculation formula for the sinusoidal model detected by the vehicle acceleration sensor is as follows: Furthermore, it can be seen that: in: , Furthermore, it can be seen that the sinusoidal model detected by the car acceleration sensor can be transformed into: in: The reference angle for road surface adhesion coefficient, , The value detected by the car's acceleration sensor; For road surface ramps, It is the acceleration due to gravity. The coefficient of adhesion of the road surface, The cosine value of the road surface slope; This is the sine value of the road surface slope.

[0011] As a further preferred embodiment, in step six, the slope of the road surface is equal to the square root of the sum of the square of the road surface adhesion coefficient and 1, multiplied by the reciprocal of the obtained value of the obtained value of gravitational acceleration, multiplied by the obtained value of the arcsine transformation of the vehicle acceleration sensor detection value, and then subtracted from the reference angle of the road surface adhesion coefficient. The road surface adhesion coefficient and road surface slope were successfully obtained, and the calculation formula is as follows: In the formula, For road surface ramps, It is the acceleration due to gravity. The coefficient of adhesion of the road surface, The reference angle for road surface adhesion coefficient, For vehicle acceleration sensor detection, This is the derivative of the value detected by the car's acceleration sensor.

[0012] According to another aspect of the present invention, a road surface dual-parameter state recognition system is also provided, which is used to perform the method involved in any of the above embodiments, including: The first main control module is used to build a vehicle acceleration sensor detection model based on the vehicle dynamics model; The second main control module is used to differentiate the vehicle acceleration sensor detection model and obtain the vehicle acceleration sensor detection derivative model. The third main control module is used to square the vehicle acceleration sensor detection model and the vehicle acceleration sensor detection derivative model to obtain the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model. The fourth main control module is used to obtain a comprehensive model of vehicle acceleration sensor detection by summing the square model of vehicle acceleration sensor detection and the square model of vehicle acceleration sensor detection derivative, and to calculate the adhesion coefficient of the road surface based on the comprehensive model of vehicle acceleration sensor detection. The fifth main control module is used to construct a transformation formula for the vehicle acceleration sensor detection model based on the adhesion coefficient, and to construct a sinusoidal model for vehicle acceleration sensor detection accordingly. The sixth main control module is used to obtain the road slope by using the vehicle acceleration sensor to detect the sinusoidal model and perform arcsine transformation.

[0013] According to another aspect of the invention, an electronic device is also provided, comprising: At least one processor, at least one memory, and a communication interface; wherein, The processor, memory, and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute a road surface dual-parameter state recognition method according to any of the above embodiments or combinations of embodiments.

[0014] According to another aspect of the present invention, a non-transitory computer-readable storage medium is provided, characterized in that the non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute a road surface dual-parameter state recognition method according to any of the above embodiments or a combination of multiple embodiments.

[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention mainly possess the following technical advantages: 1. This invention utilizes the detection values ​​and derivatives of a vehicle's acceleration sensor to accurately calculate the adhesion coefficient and slope of the road surface where the vehicle is located. It overcomes the shortcomings of inability to perform dual-parameter estimation of road conditions and low estimation accuracy. Specifically, by constructing a vehicle acceleration sensor detection model and its derivative model, and cleverly utilizing squared transformation and sine transformation, this invention requires only one acceleration sensor to accurately separate and calculate the road adhesion coefficient μ and road slope θ in real time during driving. This significantly simplifies the hardware structure, reduces system costs, and improves the real-time performance and accuracy of road parameter identification. It provides highly reliable and low-cost critical road information support for vehicle stability control, hill start assist, and autonomous driving decision-making.

[0016] 2. This invention can simultaneously estimate the road surface adhesion coefficient μ and slope θ online using only existing vehicle-mounted acceleration sensors, without the need for additional wheel speed, gyroscope or camera, resulting in a very simple hardware structure and the lowest cost.

[0017] 3. This invention eliminates the coupling error between θ and μ by using the sum of squares identity, and then uses the arcsine transform to solve θ. The entire process involves only algebraic operations and the arcsine function, which has a square suppression effect on sensor zero bias and noise, and has high identification accuracy and strong algorithm robustness. Attached Figure Description

[0018] Figure 1 This is a flowchart of a road surface dual-parameter state recognition method according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] like Figure 1 As shown, this invention proposes a two-parameter road surface condition identification method to address the shortcomings of inability to perform two-parameter estimation and low estimation accuracy in understanding road surface conditions. The method includes the following steps: Step 1: Create a vehicle acceleration sensor detection model based on the vehicle dynamics model. The vehicle acceleration sensor detection model calculates the value by multiplying the gravitational acceleration by the cosine of the slope, then by the road adhesion coefficient, and finally by multiplying the gravitational acceleration by the sine of the slope. The calculation formula for the vehicle acceleration sensor detection model is: in: —The values ​​detected by the vehicle acceleration sensor are obtained directly from the vehicle acceleration sensor installed on the vehicle; —The slope of the road surface needs to be calculated and obtained; The coefficient of adhesion of the road surface needs to be calculated. —The cosine value of the road surface slope; —The sine value of the road surface slope. —Acceleration due to gravity Step 2: Calculate the derivative of the formula for the vehicle acceleration sensor detection model to obtain the vehicle acceleration sensor detection derivative model.

[0021] The derivative model for vehicle acceleration sensor detection is as follows: the derivative value of the vehicle acceleration sensor detection value is equal to the sum of the product of gravitational acceleration multiplied by the inverse of the sine of the slope, multiplied by the road adhesion coefficient, and the product of gravitational acceleration multiplied by the cosine of the slope. The calculation formula for the derivative model of vehicle acceleration sensor detection is: in: —The derivative value of the vehicle acceleration sensor readings.

[0022] Furthermore, ,in: —The previous value detected by the vehicle acceleration sensor is obtained directly from the vehicle acceleration sensor installed on the vehicle. —The time interval between the previous value detected by the vehicle acceleration sensor and the sampling time of the vehicle acceleration sensor detection value.

[0023] Step 3: Squaring the vehicle acceleration sensor detection model and the vehicle acceleration sensor detection derivative model yields the vehicle acceleration sensor squared model and the vehicle acceleration sensor detection derivative squared model. The calculation formulas for the vehicle acceleration sensor squared model and the vehicle acceleration sensor detection derivative squared model are as follows: Furthermore, the square model of vehicle acceleration sensor detection and the square model of vehicle acceleration sensor detection derivative can be transformed into: Step 4: Summate the square model of vehicle acceleration sensor detection and the square model of vehicle acceleration sensor detection derivative to obtain the comprehensive model of vehicle acceleration sensor detection. The calculation formula for the comprehensive model of vehicle acceleration sensor detection is: The comprehensive model for vehicle acceleration sensor detection is transformed into: the sum of the squares of the derivatives of the vehicle acceleration sensor readings and the values ​​obtained from the road surface adhesion is equal to the sum of the squares of the gravitational acceleration multiplied by the gravitational acceleration obtained from the road surface adhesion. In other words, the calculation formula for the comprehensive model of vehicle acceleration sensor detection can be simplified as follows: Step 5: The road surface adhesion coefficient can be calculated using the calculation formula based on the comprehensive model detected by the vehicle acceleration sensor. The road surface adhesion coefficient is equal to the square root of the sum of the squares of the derivatives of the vehicle acceleration sensor readings and the sum of the squares of the gravitational acceleration values ​​minus 1. The formula for calculating the road surface adhesion coefficient is: Step 6: Divide both sides of the calculation formula for the vehicle acceleration sensor detection model by the value of gravitational acceleration to obtain the converted formula for the vehicle acceleration sensor detection model. The calculation formula for the converted formula for the vehicle acceleration sensor detection model is: Step 7: Based on the transformation formula of the vehicle acceleration sensor detection model, calculate the sinusoidal model of vehicle acceleration sensor detection using the sine sum formula. The calculation formula for the sinusoidal model of vehicle acceleration sensor detection is: Furthermore, it can be seen that: in: , Furthermore, it can be seen that the sinusoidal model detected by the car acceleration sensor can be transformed into: in: —Reference angle for road surface adhesion coefficient .

[0024] Step 8: Use the vehicle acceleration sensor to detect the sinusoidal model and perform an arcsine transformation to obtain the road surface slope. The road surface slope is equal to the square root of the sum of the square of the road adhesion coefficient and 1, multiplied by the reciprocal of the obtained value of gravitational acceleration, multiplied by the arcsine transformation value obtained from the vehicle acceleration sensor detection value, and then subtracted from the road adhesion coefficient reference angle.

[0025] In summary, the road surface adhesion coefficient and road surface slope have been successfully obtained, and the calculation formula is as follows: .

[0026] In summary, this invention, by constructing a vehicle acceleration sensor detection model and its derivative model, cleverly utilizes squared transformation and sine transformation. It requires only one acceleration sensor to accurately and in real-time separate and calculate the road adhesion coefficient μ and road slope θ during driving, significantly simplifying the hardware structure and reducing system costs. Simultaneously, it improves the real-time performance and accuracy of road parameter identification, providing highly reliable and low-cost critical road information support for vehicle stability control, hill start assist, and autonomous driving decision-making. It overcomes the shortcomings of the inability to perform two-parameter estimation of road conditions and the low accuracy of such estimation.

[0027] Furthermore, according to another aspect of the present invention, a road surface dual-parameter state recognition system is also provided, which is used to perform the methods involved in any of the above embodiments, including: The first main control module is used to build a vehicle acceleration sensor detection model based on the vehicle dynamics model; The second main control module is used to differentiate the vehicle acceleration sensor detection model and obtain the vehicle acceleration sensor detection derivative model. The third main control module is used to square the vehicle acceleration sensor detection model and the vehicle acceleration sensor detection derivative model to obtain the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model. The fourth main control module is used to obtain a comprehensive model of vehicle acceleration sensor detection by summing the square model of vehicle acceleration sensor detection and the square model of vehicle acceleration sensor detection derivative, and to calculate the adhesion coefficient of the road surface based on the comprehensive model of vehicle acceleration sensor detection. The fifth main control module is used to construct a transformation formula for the vehicle acceleration sensor detection model based on the adhesion coefficient, and to construct a sinusoidal model for vehicle acceleration sensor detection accordingly. The sixth main control module is used to obtain the road slope by using the vehicle acceleration sensor to detect the sinusoidal model and perform arcsine transformation.

[0028] According to another aspect of the invention, an electronic device is also provided, comprising: At least one processor, at least one memory, and a communication interface; wherein, The processor, memory, and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute a road surface dual-parameter state recognition method according to any of the above embodiments or combinations of embodiments.

[0029] According to another aspect of the present invention, a non-transitory computer-readable storage medium is provided, characterized in that the non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute a road surface dual-parameter state recognition method according to any of the above embodiments or a combination of multiple embodiments.

[0030] The methods in the embodiments of the present invention are implemented using electronic devices; therefore, it is necessary to describe the relevant electronic devices. For this purpose, embodiments of the present invention provide an electronic device comprising: at least one processor, a communication interface, at least one memory, and a communication bus, wherein the at least one processor, the communication interface, and the at least one memory communicate with each other via the communication bus. The at least one processor can invoke logical instructions in the at least one memory to execute all or part of the steps of the methods provided in the foregoing method embodiments.

[0031] Furthermore, when the logical instructions in at least one of the aforementioned memories can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various method embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0032] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0033] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0034] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Based on this understanding, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0035] In this patent, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the dual-parameter state of a road surface, characterized in that, Includes the following steps: Step 1: Construct a vehicle acceleration sensor detection model based on the vehicle dynamics model; Step 2: Differentiate the vehicle acceleration sensor detection model to obtain the vehicle acceleration sensor detection derivative model; Step 3: Square the vehicle acceleration sensor detection model and the vehicle acceleration sensor detection derivative model to obtain the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model. Step 4: Summing the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model to obtain the vehicle acceleration sensor detection comprehensive model, and calculating the road surface adhesion coefficient based on the vehicle acceleration sensor detection comprehensive model; Step 5: Construct the transformation formula of the vehicle acceleration sensor detection model based on the adhesion coefficient, and construct the vehicle acceleration sensor detection sinusoidal model accordingly. Step 6: Use the vehicle acceleration sensor to detect the sinusoidal model and perform an arcsine transformation to obtain the road slope.

2. The method for identifying the dual-parameter state of a road surface according to claim 1, characterized in that, The vehicle acceleration sensor detection model is that the detected value of the vehicle acceleration sensor is equal to the sum of the value obtained by multiplying the gravitational acceleration by the cosine of the slope and then by the road surface adhesion coefficient, and the value obtained by multiplying the gravitational acceleration by the sine of the slope. Preferably, the calculation formula for the vehicle acceleration sensor detection model is as follows: a sensor =g*cosθ*μ+g*sinθ Where: α sensor θ is the value detected by the vehicle's acceleration sensor; μ is the road surface slope; cosθ is the cosine value of the road surface slope; sinθ is the sine value of the road surface slope; and g is the acceleration due to gravity.

3. The method for identifying the dual-parameter state of a road surface according to claim 1, characterized in that, The derivative model for the vehicle acceleration sensor detection is that the derivative value of the vehicle acceleration sensor detection value is equal to the sum of the value obtained by multiplying the gravitational acceleration by the inverse of the sine of the slope and then multiplying it by the road adhesion coefficient, and the value obtained by multiplying the gravitational acceleration by the cosine of the slope. Preferably, the calculation formula for the derivative model of the vehicle acceleration sensor detection is as follows: in: θ is the derivative of the value detected by the vehicle acceleration sensor; μ is the road surface slope; cosθ is the cosine of the road surface slope; sinθ is the sine of the road surface slope; and g is the gravitational acceleration. Furthermore, Where: α sensorlast ΔT represents the previous value detected by the vehicle acceleration sensor, and ΔT represents the time interval between the previous value detected by the vehicle acceleration sensor and the sampling time of the vehicle acceleration sensor value.

4. The method for identifying the dual-parameter state of a road surface according to claim 1, characterized in that, In step four, the formula for calculating the adhesion coefficient is: Where, α sensor The value detected by the car's acceleration sensor; denoted as the derivative of the value detected by the vehicle's acceleration sensor, where g is the gravitational acceleration and μ is the road surface adhesion coefficient.

5. The method for identifying the dual-parameter state of a road surface according to claim 1, characterized in that, In step five, both sides of the calculation formula for the vehicle acceleration sensor detection model are divided by the gravitational acceleration value to obtain the converted formula for the vehicle acceleration sensor detection model.

6. The method for identifying the dual-parameter state of a road surface according to claim 5, characterized in that, In step five, the calculation formula for the sinusoidal model detected by the vehicle acceleration sensor is as follows: Furthermore, it can be seen that: in: Furthermore, it can be seen that the sinusoidal model detected by the car acceleration sensor can be transformed into: Where: θ0 is the reference angle for the road surface adhesion coefficient, θ0=arctanμ, α sensor θ is the value detected by the vehicle's acceleration sensor; g is the road surface slope; μ is the road surface adhesion coefficient; cosθ is the cosine value of the road surface slope; sinθ is the sine value of the road surface slope.

7. The method for identifying the dual-parameter state of a road surface according to claim 1, characterized in that, In step six, the slope of the road surface is equal to the square root of the sum of the square of the road surface adhesion coefficient and 1, multiplied by the reciprocal of the gravitational acceleration, multiplied by the arcsine transform of the vehicle acceleration sensor value, and then subtracted from the road surface adhesion coefficient reference angle. The road surface adhesion coefficient and road surface slope were successfully obtained, and the calculation formula is as follows: In the formula, θ is the road surface slope, g is the gravitational acceleration, μ is the road surface adhesion coefficient, θ0 is the road surface adhesion coefficient reference angle, and α sensor For vehicle acceleration sensor detection, This is the derivative of the value detected by the car's acceleration sensor.

8. A road surface dual-parameter state recognition system, characterized in that, include: The first main control module is used to build a vehicle acceleration sensor detection model based on the vehicle dynamics model; The second main control module is used to differentiate the vehicle acceleration sensor detection model and obtain the vehicle acceleration sensor detection derivative model. The third main control module is used to square the vehicle acceleration sensor detection model and the vehicle acceleration sensor detection derivative model to obtain the vehicle acceleration sensor detection square model and the vehicle acceleration sensor detection derivative square model. The fourth main control module is used to obtain a comprehensive model of vehicle acceleration sensor detection by summing the square model of vehicle acceleration sensor detection and the square model of vehicle acceleration sensor detection derivative, and to calculate the adhesion coefficient of the road surface based on the comprehensive model of vehicle acceleration sensor detection. The fifth main control module is used to construct a transformation formula for the vehicle acceleration sensor detection model based on the adhesion coefficient, and to construct a sinusoidal model for vehicle acceleration sensor detection accordingly. The sixth main control module is used to obtain the road slope by using the vehicle acceleration sensor to detect the sinusoidal model and perform arcsine transformation.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and a communication interface; wherein, The processor, memory, and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the road surface dual-parameter state recognition method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute a road surface dual-parameter state recognition method according to any one of claims 1 to 9.

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