A method and system for correcting the error of asphalt pavement wear caused by vehicle deviation

Abstract

The present invention discloses a method and system for correcting the error of asphalt pavement wear due to the deviation of a detection vehicle, including obtaining the elevation data point cloud of the asphalt pavement through the detection vehicle, drawing the average cross-section structural depth MPD thermal map of the road section, calculating the wear of the detection vehicle track without deviation and the wear rate of the detection vehicle after different track deviations, respectively calculating the absolute error and relative error of the road surface wear rate before and after different track deviations of the detection vehicle, thereby correcting the deviation error of the detection vehicle track; the difference between the deviation wear rate of the detection vehicle track without deviation and the maximum wear rate value of the road section is used to obtain the system error of the detection vehicle, thereby correcting the system error of the detection vehicle wear detection. The present invention can better simulate the vehicle track deviation in the actual road wear detection process, so as to accurately correct the vehicle system error and the vehicle track deviation error, reduce the impact of the detection vehicle track deviation, improve the accuracy of road detection, determine the maintenance time, and extend the road life.

Description

Technical Field

[0001] The present invention relates to the technical field of road detection, and in particular to a method and system for analyzing and correcting the error of a detection vehicle track deviation on the wear degree of an asphalt pavement. Background Art

[0002] Road wear mainly refers to the process in which the surface of the road aggregate is worn away and deformed significantly, which will lead to a decrease in the anti-skid performance of the road surface, and the vehicle will deviate and lose control during braking and turning, endangering driving safety. The "Highway Technical Condition Assessment Standard" (JTG5210-2018) proposes a new indicator - the pavement wear index PWI, which can accurately and reliably measure the wear rate X WR The test results provide an important basis for road management departments at all levels to evaluate road conditions and predict maintenance opportunities.

[0003] At present, wear automation detection equipment is generally developed based on multi-point laser technology. It is widely used because it can quickly, non-destructively and continuously obtain road elevation point cloud data. However, due to factors such as road line shape and the environment along the road, or driver fatigue and distraction, it is difficult for the detection vehicle to always drive along the center line of the lane, and lateral deviation is inevitable. The wear rate X calculated based on this WR The test results will inevitably produce errors; road wear is mainly affected by the repeated effects of vehicle loads, but in reality, the vehicle cannot guarantee that the driving position is at the same position on the road cross section. It is affected by factors such as road linearity, road surface conditions, driver control, and vehicle vibration offset, which leads to the fact that the actual wheel track of the vehicle does not completely match the theoretical wheel track. It can be seen that the most serious vehicle wear is not a straight line, but a section of wear area. In theory, the maximum value of the lateral wear of each road in the wear area can truly provide the most reasonable road wear detection value for the selection of road maintenance timing. Therefore, even if the inspection vehicle can ensure that the road elevation point cloud is obtained completely along the unoffset path, it cannot accurately provide the most reasonable road wear rate X. WR .

[0004] The wear detection method currently specified in the specification is a three-line pavement structure depth detection. In the existing detection vehicle track deviation analysis and correction method for asphalt pavement wear degree: first, the three-line pavement structure depth detection method can only detect representative sections, and cannot detect and calculate the entire lane or section; second, in calculating the pavement wear rate X WR The error of the three-line pavement structure depth detection method is not quantitatively characterized, and it is difficult to correct the error; finally, in actual detection, the MPD value measured when the detection vehicle is not offset cannot fully represent the deepest pavement structure depth of wear. There is an error between the wear rate of the detection vehicle track without offset and the maximum wear rate of the road section, that is, the detection vehicle itself has a systematic error. Therefore, the existing error analysis and correction method of asphalt pavement wear degree caused by the deviation of the detection vehicle track needs to be improved. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a method and system for analyzing and correcting the error of asphalt pavement wear degree due to detection vehicle trajectory deviation. By improving the method for analyzing and correcting the error of asphalt pavement wear degree due to detection vehicle trajectory deviation, the vehicle trajectory deviation in the actual road wear detection process can be better simulated to accurately correct the vehicle system error and vehicle trajectory deviation error.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for analyzing and correcting the error of asphalt pavement wear caused by vehicle track deviation, comprising the following steps:

[0008] Step 1: Obtain the elevation data point cloud of the asphalt pavement through the inspection vehicle, and then reconstruct the three-dimensional wear model of the asphalt pavement;

[0009] Step 2: Based on the three-dimensional asphalt pavement wear model obtained in step 1, draw a thermal map of the average cross-sectional structural depth MPD of the road section;

[0010] Step 3: Based on the MPD heat map of the average cross-section structure depth of the road section obtained in step 2, extract the MPD values ​​of the three-line road surface and calculate the wear rate of the un-deviated track of the inspection vehicle

[0011] Step 4: Based on the average cross-section structural depth MPD heat map of the road section obtained in step 2, the displacement process of the inspection vehicle is modeled according to the three-line detection method, and the wear rate of the inspection vehicle after different trajectory displacement is simulated.

[0012] Step 5: According to the test vehicle track obtained in step 3, the wear rate is not offset and the wear rate after the detection vehicle track deviation obtained in step 4 Calculate the absolute error of the road wear rate before and after the detection vehicle has different trajectory deviations and relative error

[0013] Step 6: Based on the absolute error of the road wear rate before and after the different offsets of the test vehicle obtained in step 5 and relative error Thereby correcting the deviation error of the detection vehicle trajectory;

[0014] Step 7: According to the test vehicle track obtained in step 3, the wear rate is not offset The maximum wear rate of the road section The difference between them gives the system error of the detection vehicle. Thereby correcting the error of the wear detection system of the inspection vehicle.

[0015] Preferably, the specific process of reconstructing the asphalt pavement wear three-dimensional model in step 1 is:

[0016] The pavement elevation data point cloud of the entire section and lane of the asphalt road section is obtained by a multi-point laser detection vehicle. The elevation data point cloud of the original data is filtered using a type-II Chebyshev filter based on the MATLAB compilation platform to obtain a three-dimensional model of asphalt pavement wear.

[0017] Preferably, the specific process of drawing the road section average cross-sectional structural depth MPD heat map in step 2 is:

[0018] Intercept a section of the elevation data point cloud after filtering in the asphalt pavement wear 3D model, decompose the section into multiple small blocks, and calculate the average cross-sectional structural depth of each small block of the road surface, recorded as MPD mn , the average cross-sectional structural depths of multiple small pavement sections are summarized and integrated, and the pavement MPD heat map is drawn based on the Python compilation platform.

[0019] Preferably, in step 3, the vehicle track is not offset from the wear rate. The specific calculation process is:

[0020] Through the average section structural depth MPD heat map of the road section, the road surface MPD values ​​of the left line, middle line and right line of the three-line road section are extracted according to the three-line road surface structural depth detection method, and the road surface wear rate X is calculated. WR , the road wear rate X WR Defined as the wear rate of the vehicle track without deviation

[0021] Preferably, the road wear rate X WR The calculation method is as follows:

[0022]

[0023] Where: MPD C MPD is the depth value of the road centerline section; L MPD is the depth value of the left section of the road; R Constructs depth values ​​for the right-of-road section.

[0024] Preferably, in step 4, the displacement process of the detection vehicle is modeled according to the three-line detection method, and the wear rate of the detection vehicle after displacement of different tracks is simulated. The specific process is:

[0025] The displacement process is modeled by the displacement parameters of the three-line structure depth detection vehicle in the average section structure depth MPD thermal map of the road section. When the detection vehicle is offset horizontally by a distance d, the laser transmitter is also offset by the same distance d along the cross-sectional direction to obtain a new three-line road section structure.

[0026] The depth is denoted as MPD′ mn , calculate the road wear rate after the detection vehicle trajectory deviates d

[0027] Preferably, the absolute error in step 5 is and relative error The calculation formula is as follows:

[0028]

[0029]

[0030] Where: To detect the wear rate when the vehicle track is not offset; To detect the wear rate after the vehicle track is offset; absolute error The calculation result is the algebraic difference, relative error The calculation result is an absolute value.

[0031] Preferably, the specific process of performing error correction on the detection vehicle track deviation in step 6 is as follows:

[0032] Summary of relative error of pavement wear rate The calculated results are used as the reliability of the detection results at different trajectory deviation levels of the detection vehicle; the absolute error of the road wear rate at different deviation levels The calculation result is used as the correction value of the detection vehicle trajectory deviation for the road section, and the error of the detection vehicle trajectory deviation is corrected.

[0033] Preferably, in step 7, the detection vehicle system error is corrected The specific process is:

[0034] Analyze the detection results of the vehicle trajectory not deviating in each cross section of the road section The maximum wear rate of the road section The difference between them is taken as the system error of the detection vehicle. Summarize the systematic errors of each cross-section inspection vehicle The error correction value of the wear detection system of the detection vehicle is obtained through fitting analysis; among them, the maximum wear rate value of the road section It is calculated by the minimum MPD value and the maximum MPD value in each cross section of the road section; the maximum wear rate value of the road section The calculation process is as follows:

[0035]

[0036] Where: MPD max is the maximum MPD value of the cross section; MPD min is the minimum MPD value of the cross section.

[0037] A system for analyzing and correcting the error of asphalt pavement wear caused by vehicle track deviation, comprising:

[0038] Inspection vehicle, model construction module, drawing module, calculation module and error correction module;

[0039] The inspection vehicle is used to obtain elevation data point cloud of asphalt pavement;

[0040] The model construction module is used to reconstruct the three-dimensional model of asphalt pavement wear and establish a model of the detection vehicle's deviation process;

[0041] The drawing module is used to draw a road section average section structural depth MPD thermal map;

[0042] The calculation module is used to calculate the wear rate of the detection vehicle track without deviation Detect the wear rate of the vehicle after the track is deviated The absolute error of the road wear rate before and after the different trajectory deviations of the detection vehicle is calculated. and relative error

[0043] The error correction module is used to correct the detection vehicle track deviation error and the detection vehicle wear detection system error.

[0044] Compared with the prior art, the present invention has the following beneficial technical effects:

[0045] The present invention provides a method for analyzing and correcting the error of the detection vehicle track deviation on the wear degree of the asphalt pavement. The detection vehicle obtains the elevation data point cloud of the asphalt pavement to reconstruct the wear model of the entire lane and the entire section of the asphalt pavement. The present invention calculates the pavement wear rate X WR The absolute error and relative error of the detection error are quantitatively characterized, and then the detection error of the road wear caused by the trajectory deviation is corrected. The present invention corrects the trajectory deviation error of the detection vehicle to reduce the impact of the detection vehicle trajectory deviation, improve the road detection accuracy, determine the maintenance time, and extend the road life; in actual detection, the present invention calculates the detection result of the detection vehicle trajectory not deviating in each cross section of the road section. The maximum wear rate of the road section The difference between them is taken as the system error of the detection vehicle. Comprehensive analysis quantitatively characterizes and corrects the inspection vehicle system error; the present invention improves and perfects the existing inspection vehicle track deviation error analysis and correction method for asphalt pavement wear degree, and can better simulate the vehicle track deviation and its detection system error in the actual road wear detection process; in addition, the present invention provides an inspection vehicle track deviation error analysis and correction system for asphalt pavement wear degree, which effectively improves the accuracy and precision of the inspection vehicle error correction, provides data support for full-section wear detection, and provides technical guidance for inspection departments at all levels to evaluate wear levels and improve inspection equipment.

[0046] Furthermore, the relative error of the road wear rate of the present invention is The calculation results can be used as a basis for evaluating the reliability of the detection results of different track deviation levels of the detection vehicle, and can better improve the road wear rate X WR In order to improve the accuracy of the detection, the deviation error of the detection vehicle's trajectory is corrected to reduce the impact of the deviation of the detection vehicle's trajectory.

[0047] Furthermore, the pavement wear rate correction value obtained by calculation at different trajectory offset distances in the present invention can be used as a theoretical correction value for wear error caused by trajectory offset when the inspection vehicle inspects the pavement wear index in the future, and can be used as a data basis and technical template for inspection departments at all levels to improve the accuracy of road equipment inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the error analysis and correction method for the wear degree of asphalt pavement caused by the deviation of the detection vehicle track of the present invention;

[0049] Figure 2 It is a flow chart of the error analysis and correction method of the asphalt pavement wear degree caused by the deviation of the detection vehicle trajectory in the embodiment;

[0050] Figure 3 It is a schematic diagram of the asphalt pavement structural depth detection method;

[0051] Figure 4 3D reconstruction of asphalt pavement wear;

[0052] Figure 5 This is a schematic diagram of the MPD indicator calculation method;

[0053] Figure 6 This is an example of MPD calculation;

[0054] Figure 7 It is the MPD heat map of the entire lane road surface;

[0055] Figure 8 MPD obtained by the three-line method when the vehicle is not offset mn Summarize;

[0056] Fig. 9To detect the road wear rate when the vehicle is not offset Summarize;

[0057] Fig.10 The wear rate of the section 2 before and after the vehicle offset is X WR contrast;

[0058] Fig.11 The road wear rate X before and after the vehicle is offset WR Longitudinal changes;

[0059] Fig.12 is the road wear rate before and after different offset distances X WR Comparison; (a) is the detection vehicle track deviating 100mm to the left, (b) is the detection vehicle track deviating 100mm to the right, (c) is the detection vehicle track deviating 200mm to the left, (d) is the detection vehicle track deviating 200mm to the right, (e) is the detection vehicle track deviating 300mm to the left, and (f) is the detection vehicle track deviating 300mm to the right;

[0060] Fig.13 The road wear rate X before and after the vehicle is offset WR Summarize;

[0061] Fig.14 To test the vehicle's trajectory without deviation and compare it with the theoretical maximum wear rate. DETAILED DESCRIPTION

[0062] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0063] A method for analyzing and correcting the error of asphalt pavement wear caused by vehicle trajectory deviation, such as Figure 1 The figure shows a flow chart of the error analysis and correction method of the wear degree of asphalt pavement due to the deviation of the detection vehicle track;

[0064] The following steps are involved:

[0065] Step 1: Obtain the elevation data point cloud of the asphalt pavement through the inspection vehicle, so as to reconstruct the asphalt pavement wear three-dimensional model; the specific process of reconstructing the asphalt pavement wear three-dimensional model in step 1 is:

[0066] The pavement elevation data point cloud of the entire section and lane of the asphalt road section is obtained by a multi-point laser detection vehicle. The elevation data point cloud of the original data is filtered using a type-II Chebyshev filter based on the MATLAB compilation platform to obtain a reconstructed three-dimensional model of asphalt pavement wear.

[0067] Step 2: Based on the reconstructed asphalt pavement wear three-dimensional model obtained in step 1, a heat map of the average cross-sectional structural depth MPD of the road section is drawn; the specific process of drawing the heat map of the average cross-sectional structural depth MPD of the road section in step 2 is as follows:

[0068] Intercept a section of the filtered elevation data point cloud in the reconstructed asphalt pavement wear 3D model, decompose the section into multiple small blocks, and calculate the average cross-sectional structural depth of each small block of the road surface, recorded as MPD mn , the average cross-sectional structural depths of multiple small pavement sections are summarized and integrated, and the pavement MPD heat map is drawn based on the Python compilation platform.

[0069] Step 3: Based on the MPD heat map of the average cross-section structure depth of the road section obtained in step 2, extract the MPD values ​​of the three-line road surface and calculate the wear rate of the un-deviated track of the inspection vehicle In step 3, the vehicle track is detected without deviation wear rate The specific calculation process is:

[0070] Through the average section structural depth MPD heat map of the road section, the road surface MPD values ​​of the left line, middle line and right line of the three-line road section are extracted according to the three-line road surface structural depth detection method, and the road surface wear rate X is calculated. WR , the road wear rate X WR Defined as the wear rate of the vehicle track without deviation

[0071] The road wear rate X WR The calculation method is as follows:

[0072]

[0073] Where: MPD C MPD is the depth value of the road centerline section; L MPD is the depth value of the left section of the road; R Constructs depth values ​​for the right-of-road section.

[0074] Step 4: Based on the average cross-section structural depth MPD heat map of the road section obtained in step 2, the offset process model is built according to the offset parameters of the three-line structural depth detection vehicle, and the wear rate after the detection vehicle trajectory is simulated In step 4, the offset process modeling is performed according to the offset parameters of the three-line structure depth detection vehicle, and the wear rate after the detection vehicle track offset is simulated. The specific process is:

[0075] The displacement process is modeled by the displacement parameters of the three-line structural depth detection vehicle in the average section structural depth MPD thermal map of the road section. When the three-line detection vehicle is offset horizontally by a distance d, the laser emitter is also offset by the same distance d along the cross-sectional direction, and the new three-line road section structural depth is recorded as MPD′. mn , calculate the road wear rate after the detection vehicle trajectory deviates d

[0076] Step 5: According to the test vehicle track obtained in step 3, the wear rate is not offset and the wear rate after the detection vehicle track deviation obtained in step 4 Calculate the absolute error of the road wear rate before and after different offsets of the test vehicle and relative error The absolute error in step 5 and relative error The calculation formula is as follows:

[0077]

[0078]

[0079] Where: To detect the wear rate when the vehicle track is not offset; To detect the wear rate after the vehicle track is offset; absolute error The calculation result is the algebraic difference, relative error The calculation result is an absolute value.

[0080] Step 6: Based on the absolute error of the road wear rate before and after the different offsets of the test vehicle obtained in step 5 and relative error Thereby correcting the deviation error of the detection vehicle track; the specific process of correcting the deviation error of the detection vehicle track in step 6 is as follows: summarizing the relative error of the road wear rate The calculated results are used as the reliability of the detection results at different trajectory deviation levels of the detection vehicle; the absolute error of the road wear rate at different deviation levels The calculation result is used as the correction value of the detection vehicle trajectory deviation for the road section, and the error of the detection vehicle trajectory deviation is corrected.

[0081] Step 7: According to the test vehicle track obtained in step 3, the wear rate is not offset The maximum wear rate of the road section The difference between them gives the system error of the detection vehicle. Thus, the wear detection system error of the detection vehicle is corrected. In step 7, the system error of the detection vehicle is corrected. The specific process is: Analyze the detection results of the vehicle trajectory in each cross section of the road section without deviation The maximum wear rate of the road section The difference between them is taken as the system error of the detection vehicle. Summarize the systematic errors of each cross-section inspection vehicle The error correction value of the wear detection system of the detection vehicle is obtained through fitting analysis; among them, the maximum wear rate value of the road section It is calculated by the minimum MPD and the maximum MPD in each cross section of the road section.

[0082] The maximum wear rate value of the road section The calculation process is as follows:

[0083]

[0084] Where: MPD max is the maximum MPD value of the cross section; MPD min is the minimum MPD value of the cross section.

[0085] Error correction of the detection vehicle system: The system error is mainly composed of instrument error and method error. The wear of the road surface during actual vehicle driving is different from the theoretical simulation of the detection vehicle without deviation. The true maximum wear rate of each section in the road section should be obtained by the maximum and minimum values ​​of the MPD of the entire lane, not necessarily the difference between the wheel track and the center line. The maximum wear rate of the road section The difference between them is taken as the system error of the detection vehicle. The system error is mainly composed of instrument error and method error. Among them, the maximum wear rate value is calculated based on the minimum and maximum MPD values ​​in each cross section of the road section based on formula (4).

[0086] A system for analyzing and correcting the error of asphalt pavement wear caused by vehicle track deviation, comprising:

[0087] Inspection vehicle, model construction module, drawing module, calculation module and error correction module;

[0088] The inspection vehicle is used to obtain elevation data point cloud of asphalt pavement;

[0089] The model construction module is used to reconstruct the three-dimensional model of asphalt pavement wear and establish a model of the detection vehicle's deviation process;

[0090] The drawing module is used to draw a road section average section structural depth MPD thermal map;

[0091] The calculation module is used to calculate the wear rate of the detection vehicle track without deviation Detect the wear rate of the vehicle after the track is deviated The absolute error of the road wear rate before and after the different trajectory deviations of the detection vehicle is calculated. and relative error

[0092] The error correction module is used to correct the detection vehicle track deviation error and the detection vehicle wear detection system error.

[0093] The following detailed descriptions are all descriptions of embodiments, and are intended to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0094] like Figure 2 As shown, the present invention is a method for analyzing and correcting the error of the vehicle track deviation on the wear degree of the asphalt pavement, comprising the following steps:

[0095] 1. Reconstruct the three-dimensional model of asphalt pavement wear.

[0096] The inspection vehicle drives along the wheel track of the lane and obtains the elevation data of a certain section of asphalt road surface through the on-board 3D laser inspection system. The schematic diagram of the inspection process is shown in the figure below. Figure 3 As shown. Based on the MATLAB compilation platform, the three-dimensional model of the road section was reconstructed. Table 1 is an example of the original detection data, which is the elevation data of each detection point of the laser section, with a unit of 0.1 mm. The data in the table comes from a multi-point laser device.

[0097] Table 1 Example of raw data of road surface detection

[0098]

[0099] When reconstructing the three-dimensional model of asphalt pavement: the preprocessing algorithms of the original data are all compiled based on the MATLAB platform, and the cyclic 8-neighborhood filling method is used to repair invalid data points. The type II Chebyshev filter is used for data processing and three-dimensional surface visualization, and MATLAB is used to post-process the original data of the wear area. A high-pass filter is used to remove the noise of the laser scanning sensor system with a wavelength less than 20 mm and the macro texture of the asphalt pavement. A low-pass filter is used to eliminate the road surface roughness with a wavelength greater than 500 mm. After smoothing the lateral profile, the linear interpolation of the continuously post-processed lateral profile is performed to reconstruct the three-dimensional model of pavement wear, such as Figure 4 shown.

[0100] 2. Draw the average section structural depth MPD of the entire lane and the entire section of the road surface mn Heat map.

[0101] 2.1 Calculation of average pavement section depth MPD

[0102] The World Road Association's Technical Committee on Road Surface Characteristics PIARC stipulates that when calculating the average cross-sectional structural depth MPD, the road surface profile within the measurement range should be divided into two sections on average, and MPD is the average value of the highest peaks in the two sections (h 1 +h 2 ) / 2 and the midline value h, see Figure 5 , and its calculation is shown in formula (5).

[0103]

[0104] The longitudinal 100mm discrete elevation point data of the road are extracted from the real road surface laser point cloud, and connected point by point, and then corrected and fitted to obtain its morphological profile. The highest peak value h of the two cycles is obtained based on the peak analysis by the above method. 1 、h 2 , and the median elevation h of the whole cycle, the average section structural depth MPD of each 100*100mm small block of the road surface is calculated according to formula (4): mn ,See Figure 6 ;

[0105]

[0106] 2.2 Draw the MPD heat map of the entire lane and the entire section of the road

[0107] Calculate the MPD value of each 100*100mm small block of pavement, a total of 35*100, summarize and integrate them, and then draw the MPD heat map of the average cross-section structural depth of the full lane and full-section pavement based on the Python compilation platform, such as Figure 7 shown.

[0108] 3. Calculate the road wear rate when the detection vehicle track is not deviated

[0109] According to the three-line pavement structure depth detection method, the pavement MPD values ​​of the left, middle and right lines of the section are extracted respectively, and the pavement wear rate X is calculated. WR , which is defined as the wear rate of the undeflected track of the detection vehicle Road wear rate X WR The calculation method is shown in formula (1):

[0110]

[0111] Where: MPD C : Road centerline section structural depth value; MPD L : The structural depth value of the left line section of the road; MPD R : The structural depth value of the right line section of the road.

[0112] When the inspection vehicle is not offset, the average cross-sectional structural depth MPD values ​​of some pavements are shown in Table 2. The MPD values ​​of the pavements with three non-tread strips and two wheel track strips are summarized in Table 2. Figure 8 .

[0113] Table 2 Road MPD values ​​obtained when the vehicle is not offset

[0114]

[0115] Taking section 2 as an example, the wear rate of the road surface without deviation of the inspection vehicle is calculated according to formula (1):

[0116]

[0117] Then, the wear rate of the road surface without deviation of the inspection vehicle in 100 cross sections of the road section can be calculated using the same method. like Fig. 9 shown.

[0118] 4. Road wear rate obtained after simulating the deviation of the test vehicle Changes

[0119] The offset process is modeled according to the offset parameters of the three-line structural depth detection vehicle. The symbol indicates the offset direction, "+" indicates rightward offset, and "-" indicates leftward offset. The number indicates the offset distance. Since the vehicle tire width is about 200mm, it is believed that the maximum distance of the lateral offset is generally 300mm, and offset distances less than 300mm are recognized. The track offset distance is selected from any real number between 0-300, in mm. The specific modeling process is: when the simulated three-line method detection vehicle is offset to the left by a horizontal distance d, its three laser devices are also offset to the left by the same distance d along the cross-sectional direction to obtain the new three-line road section structural depth MPD′ mn ; The opposite is true.

[0120] Detect the road wear rate after the vehicle deflects Calculation method and unbiased wear rate The method is the same as step 3 to obtain the road wear rate after offset. Taking section 2 as an example, the wear rate before and after the offset is X WR For example Fig.10 shown.

[0121] To more clearly compare the road wear rate X before and after the vehicle offset between different cross sections WR Changes, output once every 1m the road wear rate before and after the offset X WR ,like Fig.11 shown.

[0122] 5. Calculation of road wear rate error before and after the detection vehicle deflects

[0123] Calculate the absolute error of the road wear rate before and after different offsets and relative error The calculation method is shown in formula (2)(3):

[0124]

[0125]

[0126] Where: Absolute error ΔX WR The calculation result is the algebraic difference, the relative error δX WR The calculation result is an absolute value.

[0127] The road wear rate obtained by different offset distances of the test vehicle The pavement wear rate obtained without offset is Summarize to Fig.12 Compare and then calculate the absolute error of the road wear rate before and after different offset distances according to formula (2) and (3) and relative error

[0128] According to formula (2), the absolute error of the road wear rate before and after the detection vehicle at different offset distances is: See Table 3 for the values:

[0129] Table 3 Absolute error of road wear rate before and after offset

[0130]

[0131] Then, the relative error of the road wear rate before and after the detection vehicle has different trajectory offset distances is obtained from formula (3): See Table 4:

[0132] Table 4 Relative error of pavement wear rate before and after offset

[0133]

[0134] Table 4 Relative error of pavement wear rate The calculation results can be used as a basis for evaluating the reliability of the detection results of different track deviation levels of the detection vehicle. Analysis shows that as the detection vehicle track deviation increases, no matter in which direction the deviation is, the relative error of the road wear rate becomes larger, that is, the detection reliability decreases. Therefore, it is necessary to correct the detection vehicle track deviation error to reduce the impact of the detection vehicle track deviation, improve road detection accuracy, determine the maintenance time, and extend the road life.

[0135] 6. Correct the deviation error of the detection vehicle trajectory

[0136] Then the pavement wear rate X of the 100 sections in the road section before and after the detection vehicle deflects is calculated. WR Summarized as a box plot, such as Fig.13 As shown, it can be seen that relatively speaking, the road wear rate is slightly greater when the inspection vehicle is not offset than after the offset. The greater the offset distance of the inspection vehicle track, the smaller the overall road wear rate is.

[0137] At the same time, the box plot can be used to compare the road wear rate before and after the vehicle track deviation in 100 sections. The difference in the average wear rate at different offset distances is used as the correction value for the detection vehicle trajectory offset error, see Table 5.

[0138] Table 5 Correction values ​​of road wear rate at different track offset distances

[0139]

[0140] The correction values ​​obtained from the above table can be used as theoretical correction values ​​for wear errors caused by trajectory deviation when testing vehicles test road wear indicators in the future. They can also be used as data basis and technical template for testing departments at all levels to improve the accuracy of road equipment testing.

[0141] 7. Detection vehicle system error Correction

[0142] The actual wear of the road surface during vehicle driving is different from the theoretical simulation of the detection vehicle without deviation. The true maximum wear rate of each section in the road section should be obtained by the maximum and minimum MPD values ​​of the entire lane, not necessarily the difference between the wheel track and the center line. The maximum wear rate of the road section The difference between them is taken as the system error of the detection vehicle. Among them, the maximum wear rate value It is calculated based on the minimum and maximum MPD values ​​in each cross section of the road section based on formula (4).

[0143]

[0144] Where: MPD max : Maximum MPD value of cross section; MPD min : Minimum MPD value of cross section.

[0145] According to formula (4), the maximum wear rate of 100 sections can be obtained: The maximum wear rate value of each section, the wear rate value when the test vehicle is not offset, and their respective average values ​​are summarized together. Fig.14 .

[0146] The difference between the average value of the maximum wear rate and the average value of the theoretical wear rate obtained when the test vehicle is not offset is calculated as the system error of the test vehicle. Correction value:

[0147]

[0148] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The following steps are involved: Step 1: Obtain the elevation data point cloud of the asphalt pavement through the inspection vehicle, and then reconstruct the three-dimensional wear model of the asphalt pavement; Step 2: Based on the three-dimensional asphalt pavement wear model obtained in step 1, draw a thermal map of the average cross-sectional structural depth MPD of the road section; Step 3: Based on the MPD heat map of the average cross-section structure depth of the road section obtained in step 2, extract the MPD values ​​of the three-line road surface and calculate the wear rate of the un-deviated track of the inspection vehicle Step 4: Based on the average cross-section structural depth MPD heat map of the road section obtained in step 2, the displacement process of the inspection vehicle is modeled according to the three-line detection method, and the wear rate of the inspection vehicle after different trajectory displacement is simulated. Step 5: According to the test vehicle track obtained in step 3, the wear rate is not offset and the wear rate after the detection vehicle track deviation obtained in step 4 Calculate the absolute error of the road wear rate before and after the detection vehicle has different trajectory deviations and relative error Step 6: Based on the absolute error of the road wear rate before and after the different offsets of the test vehicle obtained in step 5 and relative error Thereby correcting the deviation error of the detection vehicle trajectory; Step 7: According to the test vehicle track obtained in step 3, the wear rate is not offset The maximum wear rate of the road section The difference between them gives the system error of the detection vehicle. Thereby correcting the error of the wear detection system of the inspection vehicle.

2. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The specific process of reconstructing the asphalt pavement wear three-dimensional model in step 1 is: The pavement elevation data point cloud of the entire section and lane of the asphalt road section is obtained by a multi-point laser detection vehicle. The elevation data point cloud of the original data is filtered using a type-II Chebyshev filter based on the MATLAB compilation platform to obtain a three-dimensional model of asphalt pavement wear.

3. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The specific process of drawing the average cross-sectional structural depth MPD heat map of the road section in step 2 is as follows: Intercept a section of the elevation data point cloud after filtering in the asphalt pavement wear 3D model, decompose the section into multiple small blocks, and calculate the average cross-sectional structural depth of each small block of the road surface, recorded as MPD mn , the average cross-section structural depths of multiple small road surfaces are summarized and integrated, and the road surface MPD heat map is drawn based on the Python compilation platform.

4. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: In step 3, the vehicle track is detected without deviation wear rate The specific calculation process is: Through the average section structural depth MPD heat map of the road section, the road surface MPD values ​​of the left line, middle line and right line of the three-line road section are extracted according to the three-line road surface structural depth detection method, and the road surface wear rate X is calculated. WR , the road wear rate X WR Defined as the wear rate of the vehicle track without deviation 5. According to claim 4, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The road wear rate X WR The calculation method is as follows: Where: MPD C MPD is the depth value of the road centerline section; L MPD is the depth value of the left section of the road; R Constructs depth values ​​for the right-of-road section.

6. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: In step 4, the displacement process of the detection vehicle is modeled according to the three-line detection method, and the wear rate of the detection vehicle after displacement of different trajectories is simulated. The specific process is: The displacement process is modeled by the displacement parameters of the three-line structural depth detection vehicle in the average section structural depth MPD thermal map of the road section. When the detection vehicle is offset horizontally by a distance d, the laser transmitter is also offset by the same distance d along the cross-sectional direction. The new three-line road section structural depth is recorded as MPD′. mn , calculate the road wear rate after the detection vehicle trajectory deviates d 7. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The absolute error in step 5 and relative error The calculation formula is as follows: Where: To detect the wear rate when the vehicle track is not offset; To detect the wear rate after the vehicle track is offset; absolute error The calculation result is the algebraic difference, relative error The calculation result is an absolute value.

8. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The specific process of performing error correction on the detection vehicle track deviation in step 6 is as follows: Summary of relative error of pavement wear rate The calculated results are used as the reliability of the detection results at different trajectory deviation levels of the detection vehicle; the absolute error of the road wear rate at different deviation levels The calculation result is used as the correction value of the detection vehicle trajectory deviation for the road section, and the error of the detection vehicle trajectory deviation is corrected.

9. According to claim 1, a method for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: In step 7, the system error of the detection vehicle is corrected The specific process is: Analyze the detection results of the vehicle trajectory not deviating in each cross section of the road section The maximum wear rate of the road section The difference between them is taken as the system error of the detection vehicle. Summarize the systematic errors of each cross-section inspection vehicle The error correction value of the wear detection system of the detection vehicle is obtained through fitting analysis; among them, the maximum wear rate value of the road section It is calculated by the minimum and maximum MPD values ​​in each cross section of the road section. The calculation process is as follows: Where: MPD max is the maximum MPD value of the cross section; MPD min is the minimum MPD value of the cross section.

10. A system for analyzing and correcting the error of vehicle track deviation on asphalt pavement wear, characterized in that: The error analysis and correction method according to any one of claims 1 to 9 comprises: Inspection vehicle, model construction module, drawing module, calculation module and error correction module; The inspection vehicle is used to obtain elevation data point cloud of asphalt pavement; The model construction module is used to reconstruct the three-dimensional model of asphalt pavement wear and establish a model of the detection vehicle's deviation process; The drawing module is used to draw a road section average section structural depth MPD thermal map; The calculation module is used to calculate the wear rate of the detection vehicle track without deviation Detect the wear rate of the vehicle after the track is deviated The absolute error of the road wear rate before and after the different trajectory deviations of the detection vehicle is calculated. and relative error The error correction module is used to correct the detection vehicle track deviation error and the detection vehicle wear detection system error.

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