An instant analysis method for pavement rutting index based on automated detection

Abstract

The present invention belongs to the field of road surface automatic detection and Internet data transmission, and specifically relates to an instant analysis method for rutting indexes of road surfaces based on automatic detection, which includes the following steps: 1) A laser road detection vehicle detects the road surface to obtain road surface index data; 2) According to the obtained road surface index data, it is determined whether there is rutting on the road surface; if there is rutting on this section of the road surface, the corresponding road mileage stake number of the road surface with rutting is determined, and the road surface index data including the mileage stake number is saved to the on-vehicle server; 3) The on-vehicle server sends the road surface index data to the application server through the communication module; 4) The application server conducts analysis to obtain road surface analysis data and sends it to the application server through the database. The present invention is based on automatic detection technology and utilizes a 5G communication module, which mainly solves the problem of poor timeliness in processing detection data, and closely combines automatic detection, data processing and calculation, and the Internet.

Description

Technical Field

[0001] The invention belongs to the field of pavement automatic detection and Internet data transmission, and particularly relates to an instant analysis method for pavement rut indexes based on automatic detection. Background Art

[0002] With the development of society, earth-shaking changes have taken place in road traffic. The construction of expressways has a direct impact on the development of each region. It strengthens the connection between regions, improves the development speed of regional economy, and makes the communication between the inside and the outside closer. Due to the large traffic volume and high vehicle speed on expressways, the construction quality of expressways has attracted much attention. Especially with the development of technology in the field of highway traffic, driven by high-tech, people are more actively seeking advanced pavement detection technologies. The effect of pavement automatic detection in participating in highway construction by using technology is relatively remarkable.

[0003] Automatic detection mainly includes pavement damage, road evenness, pavement deflection, pavement skid resistance, pavement rut, front image, etc. With the rapid development of highway construction, most ordinary highways such as national and provincial trunk lines in China have entered the stage of major repair and medium repair. Pavement automatic detection technology is used to detect the construction quality of pavement and its performance during the operation period, and can provide relatively accurate basic information of the highway. It helps designers to propose the best prevention and repair measures and repair plans, and formulate targeted design plans for specific road projects. Automatic detection has strongly promoted the scientific and modern engineering design.

[0004] Although the current automatic detection technology has made great progress, the data detected still needs to be re-analyzed and processed after detection, which is quite labor-consuming and time-consuming, and the timeliness is also relatively poor. And in the process of rut detection, traditional rut detection methods include: straightedge measurement method, surface altimeter measurement method, laser profiler, ultrasonic rangefinder. However, there are many interference factors in the traditional methods, resulting in low measurement accuracy. And in the current traditional methods, using a laser rangefinder instead of a laser profiler will also increase the usage amount of the laser rangefinder, which will cause huge costs in the detection of large and extra-long pavements. At the same time, most of the traditional detection methods use manual detection methods. This method is to place a detection crossbar across the pavement to be detected, and then use a straightedge to measure the distance from the crossbar to the bottom of the rut. This distance is the depth of the rut. This measurement method is greatly affected by human factors, the measurement is not accurate enough, the work efficiency is relatively low, and the workload on the implementer is large. Therefore, it is particularly important to design a new type of pavement rut index detection method for automatic detection and an instant analysis method. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for real-time automatic analysis of road surface indicators based on automated detection, which can reduce costs, improve efficiency and have stable performance, so as to overcome the defects of the above-mentioned traditional road surface rutting index detection method.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a method for real-time analysis of road rutting indicators based on automated detection, comprising the following steps:

[0007] 1) The laser road inspection vehicle inspects the road surface and obtains road surface index data;

[0008] 2) Based on the acquired pavement index data, determine whether there are ruts on the pavement using a template convolution method. If ruts are present on the pavement, determine the corresponding road mileage and pile number, and save the pavement index data including the mileage and pile number to the onboard server. Otherwise, directly save the pavement index data to the onboard server.

[0009] 3) The onboard server sends the road index data to the application server through the communication module;

[0010] 4) The application server processes and analyzes the pavement index data to obtain pavement analysis data, saves the pavement analysis data to the database, and sends the pavement analysis data to the application server. The application server sends the pavement analysis data to each terminal through the Internet platform for real-time display.

[0011] The step 2) comprises the following steps:

[0012] (1) Define the detection template;

[0013] (2) Move the vertical centerline of the inspection template along the forward direction of the inspection vehicle so that the vertical centerline of the template coincides with the centerline of the road inspection cross section;

[0014] (3) Extract the relative elevation H between the measured road surface and the reference surface detected by the laser road detection vehicle in the road surface index data; set a threshold D0 and calculate the relative elevation H of all the relative elevations H obtained by the detection. i Make the following judgment;

[0015]

[0016] When H i When D0, all relative heights are set to 0. i >D0 is processed; n is the F(Xi) function;

[0017] When H i >D0, take F(X i )=H j , j is Hi ≤A point in D0 that is closest to point i, that is: H i >D0, take the relative elevation of point j as the relative elevation H of this point j ;

[0018]

[0019] The coefficient z of the template corresponds to the relative height H of the template i Perform product operation and add up all the product values obtained. The cumulative relative elevation H of the template s ,Right now:

[0020]

[0021] Among them, the coefficient z on the template is 1;

[0022] (4) According to the cumulative relative elevation H of the template s , get the average value of relative elevation H p ,Right now:

[0023] H p =H s ÷n

[0024] Among them, n is the corresponding relative height H i The number of

[0025] (5) The relative elevation of the template and the average value of the relative elevation obtained in step (4) are H p Make a difference and get the difference ΔH between the relative elevation of each point and the mean i ,Right now:

[0026] ΔH i =H i -H p

[0027] (6) Check the difference ΔH calculated in step (5) i , set ΔH i The fluctuation range is determined to determine whether there is a ΔH that exceeds the fluctuation range. i , if there is one or more ΔH i If the fluctuation range is exceeded, the ΔH exceeding the fluctuation range is extracted. i Otherwise, it is determined that there is no rutting on the road section;

[0028] (7) According to the ΔH that exceeds the fluctuation range i , determine the corresponding road mileage pile number, within the range of which there are ruts.

[0029] The template is defined as follows: Define the length and width of the template: the detection width of the laser detection vehicle is the width of one lane; set the starting position to any position, and the detection of one cross section is 5 to 20 cm;

[0030] When defining the template width in the laser road inspection vehicle scanning data, the scanning width when the data is collected is taken, and the length is taken as the length of the 5 detection cross sections during the forward movement.

[0031] The step 4) comprises the following steps:

[0032] a) Cleaning the pavement index data received from the application server according to the template format library and removing duplicate data from the pavement index data;

[0033] b) Analyze the cleaned-up pavement index data, obtain the section mileage according to the mileage pile number, and then obtain the impact value of the section mileage according to the section mileage;

[0034] c) Compare the impact values in the section mileage with the road condition database and evaluate them according to the road condition database to obtain an overall road condition evaluation;

[0035] d) The overall road condition evaluation is sent to the database for storage and waits for retrieval by the application server.

[0036] In step b), the section mileage is obtained according to the mileage pile number, specifically:

[0037] According to the two nearby points ΔH i The distance between the two points is calculated based on the coordinate difference, that is, the instantaneous speed of the inspection vehicle is obtained, and the cross-sectional mileage is obtained by accumulating the instantaneous speed of the inspection vehicle.

[0038] In step b), the influence value of the section mileage is obtained according to the section mileage, specifically:

[0039] Determine the section mileage type, which are: circular curve type and transition curve type;

[0040] Obtain the influence value Δl or Δl in the section mileage by replacing the circular curve type and transition curve type with a straight line segment s .

[0041] The circular curve type is: a circular arc curve connecting two adjacent straight line segments when the road plane changes direction or the vertical slope changes;

[0042] The transition curve type is: a transition curve with a curvature radius gradually changing from infinity to a circular curve radius; the transition curve type is set between a straight line and a circular curve type or a circular curve with the same turning direction whose radius differs from the circular curve by more than a threshold.

[0043] The circular curve type is replaced by a straight line segment, that is:

[0044]

[0045] Δl=Rα-T

[0046] Among them, Δl is the impact value caused by the straight line replacing the circular curve, R is the radius of the circular curve, T is the chord length, and α is the curve angle, that is, the central angle of the circular curve.

[0047] The transition curve type is replaced by a straight line segment, that is:

[0048]

[0049]

[0050]

[0051]

[0052] Where, Δl s is the impact value of using a straight line instead of a circular curve, l s is the length of the transition curve, l0 is the theoretical length of the transition curve, R is the radius of the circular curve connected to the transition curve, p is the inward displacement, q is the tangent growth, and β0 is the central angle corresponding to the theoretical length of the transition curve, that is, the transition curve angle.

[0053] The present invention has the following beneficial effects and advantages:

[0054] 1. The present invention connects the automated detection tool and the data processing server to provide the detection results of the road surface in real time.

[0055] 2. This invention is based on automated detection technology and utilizes a 5G communication module, which crucially solves the problem of poor timeliness in detection data processing and closely combines automated detection, data processing and calculation, and the Internet.

[0056] 3. After the detection data is calculated and archived in real time, the present invention can view the road surface details anytime and anywhere through the Internet platform, and there is no need to wait for a long time for data processing.

[0057] 4. Compared with traditional detection methods, the road rutting detection method of the present invention greatly reduces the influence of human factors, inaccurate measurement and low work efficiency, and reduces the workload of implementers.

[0058] 5. Compared with traditional automated detection methods, the road rutting detection method of the present invention reduces the computational complexity of automated detection and transmits basic data to an application server for analysis and evaluation.

[0059] 6. The present invention applies the template convolution method to rutting detection. The cross-sectional mileage of a certain section of rutting can be roughly analyzed by simply judging the fluctuation range. Compared with the existing automated detection method, it is more efficient and does not require analysis of each section of the rutting distance, thus reducing the amount of analysis required for automated detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A framework diagram of the method for real-time analysis of road rutting indicators by automated detection according to the present invention; DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0062] like Figure 1 The figure shows the framework of the method for real-time analysis of pavement rutting indicators for automated detection according to the present invention. The key to the present invention lies in the immediacy of data. Results and images are generated immediately after detection. After the inspector drives the inspection vehicle past, even people thousands of miles away can immediately see the automated processed pavement inspection results, so that corresponding plans can be formulated in a timely manner.

[0063] 1) The principle of road rutting detection by a laser road inspection vehicle is to measure the relative elevation H between the road surface and a reference surface to determine whether there are rutting on the road surface. The purpose of processing the laser scanning data is to find the deformation points of the road surface in order to realize road rutting detection.

[0064] The inspector drives the inspection vehicle to conduct automated inspections. The basic inspection data issued by the automated inspection is first stored on the mobile server on the vehicle. The laser road inspection vehicle inspects the road surface and obtains road surface index data.

[0065] 2) Based on the acquired pavement index data, determine whether there are ruts on the pavement using a template convolution method. If ruts are present on the pavement, determine the corresponding road mileage and pile number, and save the pavement index data including the mileage and pile number to the onboard server. Otherwise, directly save the pavement index data to the onboard server.

[0066] Template convolution is the process of performing convolution using a template. The basic idea is to assign a pixel value as a function of its own grayscale value and the grayscale values of its neighboring pixels. Therefore, the first step is to define a suitable width and length for the template. The detection width of a laser road inspection vehicle is 3.75m (the width of a lane), and the cross-sectional detection interval is 5 to 20cm (arbitrarily set). Therefore, when defining the template width in the laser road inspection vehicle's scanned data, the scan width is taken as the data acquisition time, and the length is taken as the length of the five detection sections in the forward direction. After defining the template, the main steps for implementing template convolution are as follows:

[0067] 2-1) Move the vertical centerline of the inspection template along the forward direction of the inspection vehicle so that the vertical centerline of the template coincides with the centerline of the road inspection cross section;

[0068] 2-2) Extract the relative elevation H between the measured road surface and the reference surface detected by the laser road detection vehicle in the road surface index data; set a threshold D0 and calculate the relative elevation H of all the relative elevations H obtained by the detection. i Make the following judgment;

[0069]

[0070] When H i When D0, all relative heights are set to 0. i >D0 is processed; n is the F(Xi) function;

[0071] When H i >D0, take F(X i )=H j , j is H i ≤A point in D0 that is closest to point i, that is: H i >D0, take the relative elevation of point j as the relative elevation H of this point j ;

[0072]

[0073] The coefficient z of the template corresponds to the relative height H of the template i Perform product operation and add up all the product values obtained. The cumulative relative elevation H of the template s ,Right now:

[0074]

[0075] Among them, the coefficient z on the template is 1;

[0076] 2-3) According to the template's cumulative relative elevation H s , get the average value of relative elevation H p ,Right now:

[0077] H p =H s ÷n

[0078] Among them, n is the corresponding relative height H i The number of

[0079] 2-4) The relative elevation of the template is compared with the average value H of the relative elevation obtained in step (4) p Make a difference and get the difference ΔH between the relative elevation of each point and the mean i ,Right now:

[0080] ΔH i =H i -H p

[0081] 2 - 5) Check the difference ΔH calculated in step 2 - 4 i , set the fluctuation range of ΔH i , and determine whether there is a ΔH exceeding the fluctuation range i . If there is one or more ΔH i exceeding this fluctuation range, extract the ΔH exceeding this fluctuation range i ; otherwise, it is determined that there is no rut on this section of the road surface;

[0082] 2 - 6) According to the extracted ΔH exceeding this fluctuation range i , determine its corresponding road mileage stake number, and there is a rut within the range of this mileage stake number.

[0083] 3) The on - vehicle server sends the road surface index data to the application server through the communication module; in this embodiment, the communication module uses a 5G communication module;

[0084] 4) The application server processes and analyzes the road surface index data;

[0085] 4 - 1) Clean the format of the road surface index data received by the application server according to the template format library, especially the removal of duplicate data (the data format is set in the data format library in advance according to different automatic detection devices);

[0086] 4 - 2) When the detection length reaches the calculated and satisfied section unit after format cleaning (for example: every 100 meters or 1 kilometer is a calculation unit), analyze the sub - item indexes such as rut, evenness, vehicle jump, and texture depth. Since the evenness, vehicle jump, texture depth, etc. are all detected by existing inspection methods, in this embodiment, further analysis is carried out on the improved rut detection method, including:

[0087] Obtain the cross - section mileage according to the mileage stake number, calculate the distance between two points based on the coordinate difference of ΔH between two adjacent points i , that is, obtain the instantaneous speed of the detection vehicle, and accumulate according to the instantaneous speed of the detection vehicle to obtain the cross - section mileage.

[0088] Then obtain the influence value in the cross - section mileage according to the cross - section mileage;

[0089] Judge the cross - section mileage type, which are respectively: circular curve type and transition curve type;

[0090] Replace the circular curve type and transition curve type with straight line segments to obtain the influence value Δl or Δl s ;

[0091] The circular curve type is: an arc-shaped curve set to connect two adjacent straight line segments when the horizontal alignment of the road changes direction or the vertical alignment changes slope;

[0092] The transition curve type is: a transition curve with a radius of curvature gradually changing from infinity to the radius of the circular curve; the transition curve type is set between the straight line and the circular curve type or between circular curves with the same turning direction and a radius difference exceeding the threshold.

[0093] By replacing the circular curve type with a straight line segment, that is:

[0094]

[0095] Δl = Rα - T

[0096] Where, Δl is the influence value generated by replacing the circular curve with a straight line, R is the radius of the circular curve, T is the chord length, and α is the curve deflection angle, that is, the central angle of the circular curve.

[0097] By replacing the transition curve type with a straight line segment, that is:

[0098]

[0099]

[0100]

[0101]

[0102] Where, Δl s is the influence value generated by replacing the circular curve with a straight line, l s is the length of the transition curve, l0 is the theoretical length of the transition curve, R is the radius of the circular curve connected to the transition curve, p is the inward shift distance, q is the tangent increase, and β0 is the central angle corresponding to the theoretical length of the transition curve, that is, the transition curve angle.

[0103] 4-3) Compare the influence values in the cross-section mileage according to the road condition database and evaluate according to the road condition database to obtain the overall evaluation of the road conditions; in this embodiment, the flatness analysis and rut analysis are used for discussion:

[0104] 4-3-1) Rut analysis:

[0105] (1) Set the corresponding formats for data cleaning of each different detection device. For example, for the automated detection tool, the corresponding format for Device A is Format A, and the corresponding format for Device B is Tool B.

[0106] (2) The inspector conducts automated inspections using Equipment A, and the detected data is stored locally in the inspection equipment. The basic inspection data is automatically and real-time sent to the data processing server through the 5G communication module.

[0107] (3) After identifying that the data is sent back by Equipment A, it is cleaned and converted according to Format A in the format library.

[0108] Assume that the road design adopts the general minimum radius, the length of the transition curve adopts the minimum length value, and the running speeds of the inspection vehicle are 60 km / h, 80 km / h, 100 km / h, and 120 km / h respectively. Then, the calculation results of the influence values generated by replacing the curve with a straight line segment within one sampling interval are shown in Table 1 below.

[0109] Table 1 Calculation Results of Influence Values

[0110]

[0111] It can be seen from the table data that the influence values of replacing the curve segment with a straight line segment are all at the centimeter level, and the larger the curve radius, the smaller the influence value; the longer the transition curve, the smaller the influence (in actual design, it usually adopts values greater than the general minimum radius and the minimum transition curve length, so the influence will be even smaller).

[0112] Experts and leaders can view the road surface conditions in real time after the inspection vehicle has passed, and immediately make corresponding plans and strategies.

[0113] 4-3-2) Flatness Analysis:

[0114] (1) Set the corresponding formats for data cleaning of various different inspection equipment. For example, for the automated inspection tool, the corresponding format for Equipment A is Format A, and the corresponding format for Equipment B is Tool B.

[0115] (2) Set the calculation formulas and related weight parameters for each index. For example, for the pavement bounce index (PBI), the formula is set as follows:

[0116]

[0117] Among them, PBI is the pavement bounce of the i-th degree, a i is the unit deduction for the pavement bounce of the i-th degree, and takes values according to the regulations in the following table. i is the type of pavement bounce, and i0 is the total number of pavement bounce types, taking 3;

[0118] Table 2 Pavement Bounce Deduction Standards

[0119]

[0120] (3) The inspector conducts automated detection using Equipment A, and the detected data is stored locally in the detection equipment. The basic detected data is automatically and real-time sent to the data processing server through the 5G communication module.

[0121] (4) It is recognized that the data is sent back by Equipment A and is cleaned and converted according to the corresponding Format A in the format library. After conversion, index calculations are performed. As shown in Table 2, for example, the calculation unit is 1 KM, within 1 KM, there is 1 severe vehicle bounce, and the number of moderate and mild vehicle bounces is 0:

[0122] The calculated vehicle bounce PBI = 100 - (0 * 0 + 0 * 25 + 1 * 50) = 50 (unit: points).

[0123] Corresponding calculations are also performed for other indicators, and then the road surface conditions are obtained. Experts and leaders can real-time view the road surface conditions after the inspection vehicle passes by and immediately make corresponding plans and schemes.

[0124] 4-4) The overall evaluation of the road conditions is sent to the database for storage, waiting for the application server to retrieve. After obtaining the road surface analysis data, the road surface analysis data is saved to the database and then sent to the application server. The application server sends the road surface analysis data to each terminal for real-time display through the Internet platform. Through the Internet platform, the archived data is displayed in real-time, and the display can be performed on multiple terminals (office desktops, mobile phones, display large screens in the exhibition hall, etc.). According to the real-time detection results, it can be intuitively viewed to facilitate the formulation of corresponding plans.

[0125] The above is only the implementation mode of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, expansions, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. An instant analysis method for pavement rutting index based on automated detection, characterized in that, The following steps are involved: 1) The laser road inspection vehicle inspects the road surface and obtains road surface index data; 2) Based on the acquired road surface index data, determine whether there are ruts on the road surface through the template convolution method; If there are ruts on the road surface, determine the road mileage and pile number corresponding to the rutted road surface, and save the road surface index data including the mileage and pile number to the onboard server; otherwise, directly save the road surface index data to the onboard server; The step 2) comprises the following steps: (1) Define the detection template; (2) Move the vertical centerline of the inspection template along the forward direction of the inspection vehicle so that the vertical centerline of the template coincides with the centerline of the road inspection cross section; (3) Extract the relative elevation H between the measured road surface and the reference plane adopted in the laser road detection vehicle from the road surface index data; set a threshold D0 for all the detected relative elevations H i Make the following judgments; When H i > D0, all relative elevations are taken as 0, and for H i > D0, processing is carried out; n is the function F(Xi); When H i > D0, take F(X i ) = H j , where j is a point within H i ≤ D0 and is the point closest to point i, that is: when H i > D0, take the relative elevation of point j as the relative elevation H j ; Multiply the coefficient z of the template by the corresponding relative elevation H of the template i and add up all the obtained product values to obtain the cumulative relative elevation H of the template s , that is: Among them, the coefficient z on the template is 1; (4) According to the cumulative relative elevation H of the template s , get the average value of relative elevation H p ,Right now: H p =H s ÷n where n is the number corresponding to the relative elevation H i ; (5) Subtract the relative elevation of the template from the average value H of the relative elevations obtained in step (4) p to obtain the difference ΔH between the relative elevation of each point and the mean value i , i.e.: ΔH i = H i - H p (6) Check the difference ΔH calculated in step (5). i , set the fluctuation range of ΔH i , and determine whether there is a ΔH exceeding the fluctuation range. i , if there is one or more ΔH i exceeding this fluctuation range, then extract the ΔH exceeding this fluctuation range. i ; otherwise, it is determined that there is no rut on this section of the road surface. (7) Determine the corresponding road mileage stake number according to the extracted ΔH exceeding the fluctuation range, and there are ruts within the range of this mileage stake number; i ​ 3) The onboard server sends the road index data to the application server through the communication module; 4) The application server processes and analyzes the pavement index data to obtain pavement analysis data, saves the pavement analysis data to the database, and sends the pavement analysis data to the application server. The application server sends the pavement analysis data to each terminal through the Internet platform for real-time display.

2. The instant analysis method for pavement rut index based on automated detection according to claim 1, wherein The definition template is as follows: Define the length and width of the template: The detection width of the laser road inspection vehicle is the width of one lane; if you set the starting position to any position, the detection of one cross section is 5 to 20 cm; When defining the template width in the laser road inspection vehicle scanning data, the scanning width when the data is collected is taken, and the length is taken as the length of the 5 detection cross sections during the forward movement.

3. The method for real-time analysis of road rutting indicators based on automated detection according to claim 1, characterized in that: The step 4) comprises the following steps: a) Cleaning the pavement index data received from the application server according to the template format library and removing duplicate data from the pavement index data; b) Analyze the cleaned-up pavement index data, obtain the section mileage according to the mileage pile number, and then obtain the impact value of the section mileage according to the section mileage; c) Compare the impact values in the section mileage with the road condition database and evaluate them according to the road condition database to obtain an overall road condition evaluation; d) The overall road condition evaluation is sent to the database for storage and waits for retrieval by the application server.

4. An instant analysis method for pavement rut index based on automated detection according to claim 3, characterized in that, In step b), the section mileage is obtained according to the mileage pile number, specifically: According to the two nearby points ΔH i The distance between the two points is calculated based on the coordinate difference, that is, the instantaneous speed of the inspection vehicle is obtained, and the cross-sectional mileage is obtained by accumulating the instantaneous speed of the inspection vehicle.

5. The method for real-time analysis of road rutting indicators based on automated detection according to claim 3, characterized in that: In step b), the influence value of the section mileage is obtained according to the section mileage, specifically: Determine the section mileage type, which are: circular curve type and transition curve type; Obtain the influence value Δl or Δl in the section mileage by replacing the circular curve type and transition curve type with a straight line segment s .

6. The instant analysis method for pavement rut index based on automated detection according to claim 5, characterized in that, The circular curve type is: a circular arc curve connecting two adjacent straight line segments when the road plane changes direction or the vertical slope changes; The transition curve type is: a transition curve with a curvature radius gradually changing from infinity to a circular curve radius; the transition curve type is set between a straight line and a circular curve type or a circular curve with the same turning direction whose radius differs from the circular curve by more than a threshold.

7. The instant analysis method for pavement rut index based on automated detection according to claim 5, characterized in that The circular curve type is replaced by a straight line segment, that is: Δl=Rα-T Among them, Δl is the impact value caused by the straight line replacing the circular curve, R is the radius of the circular curve, T is the chord length, and α is the curve angle, that is, the central angle of the circular curve.

8. An instant analysis method for pavement rutting index based on automated detection according to claim 5, characterized in that The transition curve type is replaced by a straight line segment, that is: Where, Δl s is the impact value of using a straight line instead of a circular curve, l s is the length of the transition curve, l0 is the theoretical length of the transition curve, R is the radius of the circular curve connected to the transition curve, p is the inward displacement, q is the tangent growth, and β0 is the central angle corresponding to the theoretical length of the transition curve, that is, the transition curve angle.

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