Method and device for detecting the friction coefficient of a road surface

Abstract

The application discloses a method and device for detecting the friction coefficient of a road surface, and establishes a road surface friction coefficient detection model based on the relationship between the water film thickness and the friction coefficient BPN value of the road surface, and calculates the current friction coefficient BPN value in real time through the road surface friction coefficient detection model. Since the method does not need to contact the road surface, the method is more simple and convenient, and practice proves that the method has the advantages of high calculation precision, strong real-time performance and low cost.

Description

Technical Field

[0001] This invention relates to the field of infrared spectroscopy application technology, and in particular to a method and apparatus for detecting the friction coefficient of road surfaces. Background Technology

[0002] In the field of road traffic safety, road surface conditions have a crucial impact on driving safety. Slippery roads are a major cause of vehicle skidding and increased braking distance, seriously affecting driver safety and driving efficiency. Among these factors, the road surface friction coefficient is one of the key indicators for assessing slippery road conditions.

[0003] Currently, although there are many contact-based methods for testing the coefficient of friction of road surfaces, existing methods require contact with the road surface, which is inconvenient to operate and costly. Summary of the Invention

[0004] This invention provides a method and apparatus for detecting the friction coefficient of road surfaces, thereby solving the problem that existing technologies cannot easily, conveniently, and accurately detect the friction coefficient of road surfaces.

[0005] In a first aspect, the present invention provides a method for detecting the friction coefficient of a road surface, the method comprising:

[0006] Based on the water film thickness, a correspondence is established between the road surface friction coefficient BPN value and reflectance spectral data. A road surface friction coefficient detection model is then established using this correlation. Where a and b are the fitting parameters, I wet These are the reflectivity data corresponding to different water film thicknesses, I dry These are the reflectance data for the dry area at the characteristic wavelength, where μ is the friction coefficient (BPN) value of the road surface.

[0007] By detecting the current road surface and combining it with the road surface friction coefficient detection model, the friction coefficient BPN value of the current road surface is finally obtained.

[0008] Optionally, establishing the correspondence between the road surface friction coefficient BPN value and reflectance spectral data based on the water film thickness, and establishing a road surface friction coefficient detection model based on the road surface friction coefficient BPN value and reflectance spectral data, includes: determining the typical characteristic wavelengths corresponding to the road surface based on the reflectance spectral characteristics of the road surface, wherein the typical characteristic wavelengths include a first typical characteristic wavelength λ1, a second typical characteristic wavelength λ2, and a third typical characteristic wavelength λ3; determining the wavelength thresholds for each typical characteristic wavelength, i.e., the first typical characteristic wavelength corresponds to a first wavelength threshold θ1, the second typical characteristic wavelength corresponds to a second wavelength threshold θ2, and the third typical characteristic wavelength corresponds to a third wavelength threshold θ3, and selecting the spectral data at a preset water film height H as the water accumulation judgment thresholds, i.e., ε1, ε2, ε3, where ε1 is the water accumulation judgment threshold corresponding to the first typical characteristic wavelength λ1, ε2 is the water accumulation judgment threshold corresponding to the second typical characteristic wavelength λ2, and θ3 is the water accumulation judgment threshold corresponding to the third typical characteristic wavelength λ3; and establishing a model of reflectance spectral data and water film thickness at each typical characteristic wavelength using the Lambert-Beer law. Regression analysis of the measured water film thickness and friction coefficient data yielded the formula μ = -6.67ln(W) for the friction coefficient BPN value and water film thickness. D +61; Based on the formula for the friction coefficient BPN value and water film thickness, as well as the reflectance spectral data and the water film thickness model, a road surface friction coefficient detection model is obtained:

[0009] Optionally, after obtaining the road surface friction coefficient detection model, the method further includes: by substituting the fitting parameters a and b obtained at each typical characteristic wavelength, the road surface friction coefficient detection model at three typical characteristic wavelengths is finally obtained, and the average value of the friction coefficient BPN value output by the road surface friction coefficient detection model at the three typical characteristic wavelengths is used as the final output friction coefficient BPN value.

[0010] Optionally, the step of determining the typical characteristic wavelength corresponding to the road surface based on the roadside spectral data and the friction coefficient BPN value, wherein the typical characteristic wavelength includes a first typical characteristic wavelength λ1, a second typical characteristic wavelength λ2, and a third typical characteristic wavelength λ3, includes:

[0011] Spectral data of multiple dry road surfaces and different water film thicknesses were obtained, and the friction coefficient BPN value was obtained. Based on the obtained spectral data and friction coefficient BPN value, the typical characteristic wavelengths corresponding to the road surface were determined. The typical characteristic wavelengths include the first typical characteristic wavelength λ1, the second typical characteristic wavelength λ2, and the third typical characteristic wavelength λ3.

[0012] Optionally, determining the wavelength thresholds for typical characteristic wavelengths, i.e., the first typical characteristic wavelength corresponding to the first wavelength threshold θ1, the second typical characteristic wavelength corresponding to the second wavelength threshold θ2, and the third typical characteristic wavelength corresponding to the third wavelength threshold θ3, includes:

[0013] In the acquired full-spectrum reflectance information of the dry road surface, the fluctuation range of each typical characteristic wavelength is examined, and the minimum value in the corresponding range is established as the wavelength threshold of the corresponding typical characteristic wavelength. That is, the first typical characteristic wavelength corresponds to the first wavelength threshold θ1, the second typical characteristic wavelength corresponds to the second wavelength threshold θ2, and the third typical characteristic wavelength corresponds to the third wavelength threshold θ3.

[0014] Optionally, the step of detecting the current road surface and combining it with the road surface friction coefficient detection model to finally obtain the friction coefficient BPN value of the current road surface includes:

[0015] If the spectral data of the current road surface is detected and it is confirmed that the current road surface is dry, the friction coefficient BPN value of the current roadside is determined to be the first preset value; otherwise, proceed to the next step.

[0016] Determine whether the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation judgment threshold. If the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation judgment threshold, then the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model. Otherwise, the current road surface is determined to be a waterlogged road surface, and the friction coefficient BPN value of the current roadside is determined to be the second preset value.

[0017] Optionally, the spectral data of the current road surface is detected. If the detected spectral data confirms that the current road surface is dry, the friction coefficient BPN value of the current roadside is determined to be a first preset value, including:

[0018] The spectral data at each typical characteristic wavelength is compared with the corresponding wavelength threshold. If the spectral data at each typical characteristic wavelength is greater than the corresponding wavelength threshold, the current road surface is determined to be a dry road surface and the road friction coefficient BPN value is output as the first preset value.

[0019] Optionally, the step of determining whether the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation determination threshold, and if the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation determination threshold, then the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model; otherwise, the current road surface is determined to be a waterlogged road surface, and the friction coefficient BPN value of the current roadside is determined to be a second preset value, including:

[0020] The spectral data at each typical characteristic wavelength is compared with the corresponding water accumulation judgment threshold. When the spectral data at each typical characteristic wavelength is less than the corresponding water accumulation judgment threshold, the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model.

[0021] If the spectral data at each typical characteristic wavelength is less than the corresponding water accumulation judgment threshold, the current road surface is determined to be a water accumulation road surface, and the road surface friction coefficient BPN value is output as the second preset value.

[0022] Optionally, obtaining the friction coefficient BPN value of the current road surface through the road surface friction coefficient detection model includes: inputting the spectral data of the current road surface into the road surface friction coefficient detection model to obtain friction coefficient data obtained from three typical characteristic wavelengths, and averaging the results obtained from the three characteristic wavelengths to obtain the final road surface friction coefficient BPN value.

[0023] Secondly, the present invention provides a detection device for implementing any of the methods described above, the device comprising:

[0024] A light source is used to emit stable and continuous infrared light to provide supplemental illumination to the target object at specific wavelengths.

[0025] The detector is used to detect and receive the light signal reflected by a preset light source and send the detected light signal to the spectrometer;

[0026] A spectrometer is used to obtain spectral data of the current road surface based on the light signals detected by the detector.

[0027] The processor is used to establish a road friction coefficient detection model based on the correspondence between the road surface friction coefficient and reflectance spectral data. Where a and b are the fitting parameters, I we t represents the reflectivity data corresponding to different water film thicknesses, and I represents the reflectivity data. dry The reflectance data at the dry point of the characteristic wavelength, μ is the friction coefficient BPN value of the road surface, and the friction coefficient BPN value of the current road surface is finally obtained by combining the spectral data detected by the spectrometer with the road surface friction coefficient detection model.

[0028] The beneficial effects of this invention are as follows:

[0029] This invention establishes a road surface friction coefficient detection model based on the relationship between water film thickness and the road surface friction coefficient (BPN) value. This model is then used to calculate the current BPN value in real time. Since the method described in this invention does not require contact with the road surface, it is simpler and faster. Furthermore, practical experience has shown that the method described in this invention has advantages such as high calculation accuracy, strong real-time performance, and low cost. The above description is merely an overview of the technical solution of this invention. To better understand the technical means of this invention and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this invention more apparent, specific embodiments of this invention are described below. Attached Figure Description

[0030] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0031] Figure 1 This is a schematic flowchart of the method for detecting the road surface friction coefficient based on water film thickness provided in an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of the road surface friction coefficient detection device provided in an embodiment of the present invention;

[0033] Figure 3 These are the spectral absorption curves of different water film thicknesses provided in the embodiments of the present invention;

[0034] Figure 4 This is a graph showing the relationship between spectral data at a characteristic wavelength of 1200nm and the corresponding water film thickness, provided in an embodiment of the present invention.

[0035] Figure 5 These are curves showing the relationship between different water film thicknesses and friction coefficients provided in embodiments of the present invention.

[0036] Figure 6 This is a graph showing the relationship between spectral reflectance and friction coefficient provided in an embodiment of the present invention;

[0037] Figure 7 This is a new model diagram of spectral reflectance and friction coefficient provided in an embodiment of the present invention. Detailed Implementation

[0038] The present 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 of the invention and do not limit the scope of the invention.

[0039] Currently, the sensors used in autonomous vehicles lack the ability to detect the coefficient of friction of road surfaces. The inability to perceive road conditions during driving can affect vehicle braking strategies, alter braking distances, and lead to traffic accidents such as collisions and skidding. Therefore, accurately assessing and detecting the coefficient of friction of road surfaces can provide strong evidence for vehicle decision-making, and conducting research on road conditions can help improve travel safety and the driving experience.

[0040] To address the above problems, embodiments of the present invention provide a method for detecting the friction coefficient of road surfaces, see below. Figure 1 The method includes:

[0041] S101. Establish the correspondence between the road surface friction coefficient BPN value and reflectance spectral data based on the water film thickness, and establish a road surface friction coefficient detection model based on the road surface friction coefficient BPN value and reflectance spectral data:

[0042] Where a and b are the fitting parameters, I wet These are the reflectivity data corresponding to different water film thicknesses, I dry These are the reflectance data for the dry area at the characteristic wavelength, where μ is the friction coefficient (BPN) value of the road surface.

[0043] Specifically, the method described in this embodiment of the invention includes: determining the typical characteristic wavelength corresponding to the road surface based on the reflection spectral characteristics of the road surface, wherein the typical characteristic wavelength includes a first typical characteristic wavelength λ1, a second typical characteristic wavelength λ2, and a third typical characteristic wavelength λ3; it should be noted that in this embodiment of the invention, spectral data of multiple dry road surfaces and different water film thicknesses are obtained, and the friction coefficient BPN value is obtained, and the typical characteristic wavelength corresponding to the road surface is determined based on the obtained spectral data and the friction coefficient BPN value, wherein the typical characteristic wavelength includes the first typical characteristic wavelength λ1, the second typical characteristic wavelength λ2, and the third typical characteristic wavelength λ3.

[0044] The wavelength thresholds for each typical characteristic wavelength are determined, namely, the first typical characteristic wavelength corresponds to the first wavelength threshold θ1, the second typical characteristic wavelength corresponds to the second wavelength threshold θ2, and the third typical characteristic wavelength corresponds to the third wavelength threshold θ3. The spectral data at a preset water film height H is selected as the water accumulation judgment threshold, namely ε1, ε2, and ε3, where ε1 is the water accumulation judgment threshold corresponding to the first typical characteristic wavelength λ1, ε2 is the water accumulation judgment threshold corresponding to the second typical characteristic wavelength λ2, and θ3 is the water accumulation judgment threshold corresponding to the third typical characteristic wavelength λ3. That is, in the embodiment of the present invention, the fluctuation range of each typical characteristic wavelength is examined in the multiple full-spectrum reflectance spectral information of the acquired dry road surface, and the minimum value in the corresponding range is established as the wavelength threshold of the corresponding typical characteristic wavelength, namely, the first typical characteristic wavelength corresponds to the first wavelength threshold θ1, the second typical characteristic wavelength corresponds to the second wavelength threshold θ2, and the third typical characteristic wavelength corresponds to the third wavelength threshold θ3.

[0045] It should be noted that the preset water film height H selected for determining the spectral data in this embodiment of the invention is obtained based on experience. Those skilled in the art can set it according to actual needs, and this invention does not impose specific limitations on it.

[0046] At various typical characteristic wavelengths, a model of reflectance spectral data and water film thickness was established using the Lambert-Beer law.

[0047] Regression analysis of the measured water film thickness and friction coefficient data yielded the formula μ = -6.67ln(W) for the friction coefficient BPN value and water film thickness. D +61;

[0048] Based on the formula for the friction coefficient BPN value and water film thickness, as well as the reflectance spectral data and the model of water film thickness, a road surface friction coefficient detection model is obtained:

[0049] Then, by substituting the fitting parameters a and b obtained at each typical characteristic wavelength, the road surface friction coefficient detection model at the three typical characteristic wavelengths is finally obtained. The average value of the friction coefficient BPN value output by the road surface friction coefficient detection model at the three typical characteristic wavelengths is used as the final output friction coefficient BPN value.

[0050] S102. By detecting the current road surface and combining it with the road surface friction coefficient detection model, the friction coefficient BPN value of the current road surface is finally obtained.

[0051] In specific implementation, step S102 of this embodiment of the invention specifically includes: detecting and obtaining the spectral data of the current road surface; if the detected spectral data of the current road surface confirms that the current road surface is a dry road surface, then the friction coefficient BPN value of the current roadside is determined to be a first preset value; otherwise, proceed to the next step.

[0052] Determine whether the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation judgment threshold. If the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation judgment threshold, then the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model. Otherwise, the current road surface is determined to be a waterlogged road surface, and the friction coefficient BPN value of the current roadside is determined to be the second preset value.

[0053] Specifically, in this embodiment of the invention, the spectral data at each typical characteristic wavelength is compared with the corresponding wavelength threshold. If the spectral data at each typical characteristic wavelength is greater than the corresponding wavelength threshold, the current road surface is determined to be a dry road surface and the road friction coefficient BPN value is output as a first preset value.

[0054] Then, the spectral data at each typical characteristic wavelength is compared with the corresponding water accumulation judgment threshold. When the spectral data at each typical characteristic wavelength is less than the corresponding water accumulation judgment threshold, the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model.

[0055] When the spectral data at each typical characteristic wavelength is less than the corresponding water accumulation judgment threshold, the current road surface is determined to be a waterlogged road surface, and the road surface friction coefficient BPN value is output as the second preset value.

[0056] In other words, in this embodiment of the invention, the spectral data of the current road surface is input into the road surface friction coefficient detection model to obtain friction coefficient data obtained from three typical characteristic wavelengths. The final road surface friction coefficient BPN value is obtained by averaging the results obtained from the three characteristic wavelengths.

[0057] The following will combine Figures 1-7 The method described in this embodiment of the invention will be explained and illustrated in detail through a specific example. This embodiment of the invention provides a method for detecting the friction coefficient using a model. See [link to relevant documentation]. Figure 1 and Figure 2 The method described in this embodiment of the invention specifically includes the following steps: See Figure 3 and Figure 4 Spectral data and friction coefficient BPN values ​​of multiple dry road surfaces and different water film thicknesses were obtained, and characteristic wavelengths λ1, λ2, and λ3 were selected.

[0058] It should be noted that the above-mentioned characteristic wavelengths are typical characteristic wavelengths corresponding to the road surface determined based on the reflection spectrum characteristics of the road surface. Those skilled in the art can set them according to actual needs. This invention only uses three typical characteristic wavelengths as examples to illustrate the method described in the embodiments of this invention. In specific implementation, in order to obtain a more accurate friction coefficient BPN value, those skilled in the art can use more typical characteristic wavelengths for calculation according to actual needs.

[0059] The minimum value among data from multiple dry road surface characteristic wavelengths is selected and set as the threshold for different characteristic wavelengths, denoted as θ1, θ2, and θ3. Since the friction coefficient BPN value of the road surface water film fluctuates around a fixed value when it exceeds a certain height, the spectral data at the water film height H is selected as the water accumulation determination threshold, i.e., ε1, ε2, and ε3. (See [reference needed]). Figure 5 ;

[0060] At three characteristic wavelengths, a model for reflectance spectral data and water film thickness is established using Beer-Lambert's law. The formulas for the spectrum and water film thickness are as follows: Where a and b are the parameters that need to be fitted and calculated, W D It is the thickness of the water film, I we t represents the reflectivity data for different thicknesses, and I represents the reflectivity data for different thicknesses. dry This data pertains to the characteristic wavelength drying region. Based on the measured data, a and b are calculated using methods such as least squares fitting to obtain the parameters in the formula. (See [link to relevant documentation]). Figure 6 ;

[0061] By regressing the measured water film thickness and friction coefficient data, the formula for the friction coefficient BPN value in relation to the water film thickness is obtained as μ = -6.67ln(W). D )+60, where μ is the friction coefficient BPN value of the road surface;

[0062] W exists in both formulas D This parameter serves as the model; by combining these parameters, we obtain the spectral and friction coefficient models, namely:

[0063] Substituting the fitted parameters a and b at different characteristic wavelengths, we obtain the final model of the spectrum and friction coefficient at the three wavelengths.

[0064] By using threshold judgments on spectral data of dry, wet, and waterlogged conditions, a more accurate measurement of the road surface friction coefficient can be achieved in practice.

[0065] First, set up Figure 2 The road surface spectral data acquisition system collects spectral reflectance data and detects the spectral data of a certain road surface.

[0066] The spectral data at characteristic wavelengths λ1, λ2, and λ3 are compared with dry and wet reflectance thresholds θ1, θ2, and θ3, respectively. If the values ​​are greater than the thresholds, the road surface is identified as dry, and the road friction coefficient BPN value is output as 80. If the values ​​are less than the thresholds, the process proceeds to the next step; that is, the dry road surface is calibrated by collecting spectral data of the road surface with different water film thicknesses. Figure 3 .

[0067] The spectral data of λ1, λ2, and λ3 at the characteristic wavelengths are compared with the water reflectance thresholds ε1, ε2, and ε3, respectively. If they are less than the threshold, the road surface is determined to be waterlogged and the road friction coefficient BPN value is output as 51. If they are greater than the threshold, the process proceeds to the next step.

[0068] By inputting the spectral data into the road friction coefficient detection model, friction coefficient data obtained from the inversion at three characteristic wavelengths are obtained. The final road friction coefficient BPN value is obtained by averaging the three results.

[0069] right Figure 4 and Figure 5 By integrating the relational models in the data, the final result is obtained. Figure 6 The spectral data and friction coefficient BPN value model are shown. Data from three characteristic wavelengths are selected to construct a curve relating the spectrum and water film thickness, as shown below. Figure 4 This is an example with a characteristic wavelength of 1200 nm. Data on different water film thicknesses and their corresponding friction coefficients were collected to construct a regression model, as follows: Figure 5 After detecting any road surface, based on Figure 1 process and Figure 6 The relational model outputs the friction coefficient BPN value of the road surface.

[0070] Compared to traditional contact methods, this invention employs non-contact infrared spectroscopy technology, avoiding direct contact with the road surface and thus eliminating potential damage to the road, while also making operation more convenient. Furthermore, the non-contact method better protects the road structure in practical applications, reduces operational difficulty, and brings greater safety and convenience to road surface assessment.

[0071] Secondly, this invention establishes a correlation model between spectral data and road surface friction coefficient, enabling the accurate acquisition of the road surface friction coefficient from spectral data. This fills a research gap in the technical field and provides a precise and reliable non-contact method for evaluating the road surface friction coefficient for practical applications.

[0072] Furthermore, practical experience has shown that, compared to the accuracy issues of traditional contact methods in detecting road surface friction coefficients, infrared spectroscopy can acquire data with higher precision, thus providing more reliable road surface friction coefficient assessment results. This not only provides drivers with more accurate driving information but also offers more reliable decision support in fields such as autonomous driving.

[0073] This invention relates to the inversion of the road surface friction coefficient (BPN) value based on the spectral characteristics under different water film thicknesses. The method utilizes an optical sensor to measure the road surface reflectance spectrum. By using the water film thickness as a bridge, a model is obtained showing the relationship between the spectrum and the BPN value at characteristic wavelengths, allowing direct calculation of the BPN value. Compared to traditional contact-based detection methods, this method eliminates the need for contact sensors to touch the road surface, reducing damage and interference. Through the selection of characteristic wavelengths and the establishment of a multiple linear regression model, high-precision detection of the road surface friction coefficient can be achieved. This method has a wide range of applications and can be used for friction coefficient detection on roads, bridges, airport runways, etc., offering advantages such as high calculation accuracy, strong real-time performance, and low cost. This improves the safety and stability of road traffic.

[0074] Accordingly, embodiments of the present invention also provide a detection device for implementing any of the methods described in the above embodiments, see [link to previous document]. Figure 7 The device includes:

[0075] A light source is used to emit stable and continuous infrared light to provide supplemental illumination to the target object at specific wavelengths.

[0076] The detector is used to detect and receive the light signal reflected by a preset light source and send the detected light signal to the spectrometer;

[0077] A spectrometer is used to obtain spectral data of the current road surface based on the light signals detected by the detector.

[0078] The processor is used to establish a road friction coefficient detection model based on the correspondence between the road surface friction coefficient and reflectance spectral data. Where a and b are the fitting parameters, I wet These are the reflectivity data corresponding to different water film thicknesses, I dry The reflectance data at the dry point of the characteristic wavelength, μ is the friction coefficient BPN value of the road surface, and the friction coefficient BPN value of the current road surface is finally obtained by combining the spectral data detected by the spectrometer with the road surface friction coefficient detection model.

[0079] It should be noted that this device can be installed on various devices such as cars and bicycles that require measurement of the coefficient of friction (BPN) value.

[0080] For a better understanding of the relevant content of the embodiments of the present invention, please refer to the first embodiment of the present invention, which will not be discussed in detail here.

[0081] Although preferred embodiments of the invention have been disclosed for illustrative purposes, those skilled in the art will recognize that various modifications, additions, and substitutions are possible, and therefore the scope of the invention should not be limited to the embodiments described above.

Claims

1. A method for detecting the friction coefficient of a road surface, characterized in that, include: Based on the water film thickness, a correspondence is established between the road surface friction coefficient BPN value and reflectance spectral data. A road surface friction coefficient detection model is then established using this correlation. Where a and b are the fitting parameters, I wet These are the reflectivity data corresponding to different water film thicknesses, I dry These are the reflectance data for the dry area at the characteristic wavelength, where μ is the friction coefficient (BPN) value of the road surface. By detecting the current road surface and combining it with the road surface friction coefficient detection model, the friction coefficient BPN value of the current road surface is finally obtained.

2. The method according to claim 1, characterized in that, The process of establishing the correspondence between the road surface friction coefficient BPN value and reflectance spectral data based on the water film thickness, and establishing a road surface friction coefficient detection model based on the road surface friction coefficient BPN value and reflectance spectral data, includes: The typical characteristic wavelengths corresponding to the road surface are determined based on the reflectance spectrum characteristics of the road surface, wherein the typical characteristic wavelengths include a first typical characteristic wavelength λ1, a second typical characteristic wavelength λ2, and a third typical characteristic wavelength λ3. Determine the wavelength thresholds corresponding to each typical characteristic wavelength, namely, the first typical characteristic wavelength λ1 corresponds to the first wavelength threshold θ1, the second typical characteristic wavelength λ2 corresponds to the second wavelength threshold θ2, and the third typical characteristic wavelength λ3 corresponds to the third wavelength threshold θ3. Select the spectral data at the preset water film height H as the water accumulation judgment thresholds, namely ε1, ε2, and ε3, where ε1 is the water accumulation judgment threshold corresponding to the first typical characteristic wavelength λ1, ε2 is the water accumulation judgment threshold corresponding to the second typical characteristic wavelength λ2, and θ3 is the water accumulation judgment threshold corresponding to the third typical wavelength λ3. At various typical characteristic wavelengths, a model of reflectance spectral data and water film thickness was established using the Lambert-Beer law. Regression was performed on the measured water film thickness and friction coefficient BPN values ​​to obtain the formula for the relationship between the friction coefficient BPN value and the water film thickness: μ = -6.67ln(W D +61; Based on the formula for the friction coefficient BPN value and water film thickness, as well as the reflectance spectral data and the model of water film thickness, a road surface friction coefficient detection model is obtained:

3. The method according to claim 2, characterized in that, After obtaining the road surface friction coefficient detection model, the method further includes: By substituting the fitting parameters a and b obtained at each typical characteristic wavelength, a road surface friction coefficient detection model at three typical characteristic wavelengths is finally obtained. The average value of the friction coefficient BPN value output by the road surface friction coefficient detection model at the three typical characteristic wavelengths is used as the final output friction coefficient BPN value.

4. The method according to claim 2, characterized in that, The process of determining the typical characteristic wavelength corresponding to the road surface based on the road surface's reflectance spectral characteristics includes: Spectral data of multiple dry road surfaces and different water film thicknesses were obtained, friction coefficient BPN values ​​were acquired, and the typical characteristic wavelengths corresponding to the road surfaces were determined based on the obtained spectral data and friction coefficient BPN values.

5. The method according to claim 2, characterized in that, The determination of the wavelength threshold corresponding to each typical characteristic wavelength includes: In the acquired full-spectrum reflectance information of the dry road surface, the fluctuation range of each typical characteristic wavelength is examined, and the minimum value in the fluctuation range of each typical characteristic wavelength is established as the wavelength threshold of the corresponding typical characteristic wavelength.

6. The method according to claim 3, characterized in that, The process of detecting the current road surface and combining it with the road surface friction coefficient detection model to finally obtain the friction coefficient BPN value of the current road surface includes: If the spectral data of the current road surface is detected and it is confirmed that the current road surface is dry, the friction coefficient BPN value of the current roadside is determined to be the first preset value; otherwise, proceed to the next step. Determine whether the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation judgment threshold. If so, obtain the friction coefficient BPN value of the current road surface through the road surface friction coefficient detection model. Otherwise, determine the friction coefficient BPN value of the current roadside as the second preset value.

7. The method according to claim 6, characterized in that, The detection obtains the spectral data of the current road surface. If the detected spectral data confirms that the current road surface is dry, then the friction coefficient BPN value of the current roadside is determined to be a first preset value, including: The spectral data at each typical characteristic wavelength is compared with the corresponding wavelength threshold. If the spectral data at each typical characteristic wavelength is greater than the corresponding wavelength threshold, the current road surface is determined to be a dry road surface and the road friction coefficient BPN value is output as the first preset value.

8. The method according to claim 6, characterized in that, The step involves determining whether the spectral reflectance of each typical characteristic wavelength of the current road surface is greater than the corresponding water accumulation threshold. If so, the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model; otherwise, the friction coefficient BPN value of the current roadside is determined to be a second preset value, including: The spectral data at each typical characteristic wavelength is compared with the corresponding water accumulation judgment threshold. When the spectral data at each typical characteristic wavelength is less than the corresponding water accumulation judgment threshold, the friction coefficient BPN value of the current road surface is obtained through the road surface friction coefficient detection model. If the spectral data at each typical characteristic wavelength is less than the corresponding water accumulation judgment threshold, the current road surface is determined to be a water accumulation road surface, and the road surface friction coefficient BPN value is output as the second preset value.

9. The method according to claim 8, characterized in that, The process of obtaining the current road surface friction coefficient (BPN) value through the road surface friction coefficient detection model includes: The spectral data of the current road surface is input into the road friction coefficient detection model to obtain friction coefficient data at three typical characteristic wavelengths. The final road friction coefficient BPN value is obtained by averaging the results obtained at the three characteristic wavelengths.

10. A detection device for implementing the method according to any one of claims 1-9, characterized in that, The device includes: A light source is used to emit stable and continuous infrared light to provide supplemental illumination to the target object at specific wavelengths. The detector is used to detect and receive the light signal reflected by a preset light source and send the detected light signal to the spectrometer; A spectrometer is used to obtain spectral data of the current road surface based on the light signals detected by the detector. The processor is used to establish a road friction coefficient detection model based on the correspondence between the road surface friction coefficient and reflectance spectral data. Where a and b are the fitting parameters, I wet These are the reflectivity data corresponding to different water film thicknesses, I dry The reflectance data at the dry point of the characteristic wavelength, μ is the friction coefficient BPN value of the road surface, and the friction coefficient BPN value of the current road surface is finally obtained by combining the spectral data detected by the spectrometer with the road surface friction coefficient detection model.

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