A method for assessing road safety conditions based on precise three-dimensional analysis

Abstract

This invention provides a method for assessing road safety conditions based on precise three-dimensional modeling data, belonging to the field of road inspection technology. The method includes: acquiring road safety information based on precise three-dimensional modeling data; the safety information includes: road cross slope, road longitudinal slope, road transverse texture, road longitudinal texture, road microscopic anomaly areas, road macroscopic anomaly areas, and attribute information of the road macroscopic anomaly areas; and assessing the safety of the road based on the safety information. The road safety condition assessment method provided by this invention, based on precise three-dimensional modeling data, acquires safety information for evaluating the road's safety condition and assesses road safety based on this information, enabling a more accurate assessment of road safety.

Description

Technical Field

[0001] This invention relates to the field of road surface inspection technology, and in particular to a method for assessing road surface safety conditions based on precise three-dimensional analysis. Background Technology

[0002] With the rapid development of road traffic, road traffic safety has become a new social issue, and people are paying more and more attention to it.

[0003] Road safety data collection has always been a core component of road safety monitoring and a fundamental aspect of road traffic safety risk assessment. Therefore, it is essential to collect comprehensive and accurate data, evaluate the data to ensure its security and validity, conduct safety assessments based on road traffic risks, improve road usability, and effectively monitor road safety data.

[0004] When a vehicle is driving on the road, the wheels are in direct contact with the road surface, and the condition of the road surface directly affects road traffic safety. Most of the existing mainstream road surface detection methods use two-dimensional image recognition technology, which is difficult to comprehensively detect road surface safety defects, resulting in low accuracy of safety assessment. Summary of the Invention

[0005] This invention provides a method for assessing road safety conditions based on precise three-dimensional analysis, which addresses the shortcomings of existing technologies that struggle to comprehensively detect road safety defects and have low accuracy in safety assessments.

[0006] In a first aspect, the present invention provides a method for assessing road safety conditions based on precise three-dimensional modeling data of the road surface, comprising: obtaining safety information of the road surface based on precise three-dimensional modeling data of the road surface; the safety information includes: attribute information of road surface cross slope, road surface longitudinal slope, road surface transverse texture, road surface longitudinal texture, road surface micro-anomaly area, road surface macro-anomaly area and road surface macro-anomaly area; and assessing the safety of the road surface based on the safety information.

[0007] According to the present invention, a method for assessing road safety conditions based on precise three-dimensional modeling data of the road surface is provided to obtain the road surface cross slope and road surface longitudinal slope based on precise three-dimensional modeling data of the road surface, including: obtaining the road surface cross slope based on cross section data in the three-dimensional modeling data; and obtaining the road surface longitudinal slope based on longitudinal section data in the three-dimensional modeling data.

[0008] According to the present invention, a method for assessing road safety conditions based on precise three-dimensional modeling data of the road surface is provided. The method acquires the transverse and longitudinal textures of the road surface based on precise three-dimensional modeling data, including: filtering the cross-sectional data to obtain a cross-sectional control profile; calculating the first absolute distance from each measuring point in the cross-sectional data to the corresponding cross-sectional control profile point, and obtaining first distribution information of the first absolute distance; calculating the transverse texture of each measuring point in the cross-sectional data based on the first distribution information of the first absolute distance; and filtering the longitudinal profile data to obtain a longitudinal profile control profile; calculating the second absolute distance from each measuring point in the longitudinal profile data to the corresponding longitudinal profile control profile point, and obtaining second distribution information of the second absolute distance; and calculating the longitudinal texture of each measuring point in the longitudinal profile data based on the second distribution information of the second absolute distance.

[0009] According to the present invention, a method for assessing road safety conditions based on precise three-dimensional modeling data of the road surface is provided to obtain microscopic anomaly regions of the road surface. The method includes: determining transverse microscopic anomaly points based on the transverse texture of the road surface at each measuring point and a pre-set transverse texture anomaly threshold; determining longitudinal microscopic anomaly points based on the longitudinal texture of the road surface at each measuring point and a pre-set longitudinal texture anomaly threshold; constructing a binary map of the microscopic anomaly regions using morphology based on the transverse and longitudinal microscopic anomaly points; performing noise reduction processing on the binary map of the microscopic anomaly regions based on the length of connected regions in the binary map; and determining the microscopic anomaly regions of the road surface based on the noise-reduced binary map.

[0010] According to the present invention, a method for assessing road safety conditions based on precise three-dimensional modeling data of the road surface is provided. The method acquires macroscopic anomaly regions of the road surface based on precise three-dimensional modeling data, including: extracting low-frequency signal data of the road surface from the three-dimensional modeling data; performing jump detection on the low-frequency signal data to acquire lateral and longitudinal anomalies; constructing a binary map of the macroscopic anomaly regions using morphology based on the lateral and longitudinal anomalies; performing noise reduction processing on the binary map of the macroscopic anomaly regions according to the length of connected regions in the binary map; and determining the macroscopic anomaly regions of the road surface based on the noise-reduced binary map. The attribute information of the macroscopic anomaly regions includes: the average elevation difference of the macroscopic anomaly regions, the standard deviation of the elevation difference of the macroscopic anomaly regions, the average elevation difference of adjacent normal road surface areas, the length of the macroscopic anomaly regions, the width of the macroscopic anomaly regions, the aspect ratio of the macroscopic anomaly regions, the type of the macroscopic anomaly regions, and the position of the macroscopic anomaly regions in the width direction within the lane.

[0011] According to the present invention, a method for assessing road safety conditions based on precise three-dimensional information is provided. The method for assessing the safety of the road surface based on the safety information includes: assessing the safety of the road surface based on the road surface cross slope, road surface longitudinal slope, road surface transverse texture, road surface longitudinal texture, and road surface microscopic anomaly areas; and assessing the macroscopic safety of the road surface based on the road surface macroscopic anomaly areas and their attribute information.

[0012] According to the present invention, a method for assessing road safety based on precise three-dimensional analysis is provided. The method assesses the safety of the road surface based on its cross slope, longitudinal slope, transverse texture, longitudinal texture, and microscopic anomaly areas. The method includes: determining a transverse texture hazard threshold based on the cross slope; determining a longitudinal texture hazard threshold based on the longitudinal slope; calculating a first ratio and a second ratio for each measuring point in the microscopic anomaly area, and using the larger of the first and second ratios as the safety level of each measuring point; determining the safety level of the microscopic anomaly area based on the safety level and location distribution characteristics of each measuring point within the microscopic anomaly area; wherein the first ratio is the ratio of the transverse texture hazard threshold to the transverse texture, and the second ratio is the ratio of the longitudinal texture hazard threshold to the longitudinal texture.

[0013] According to the present invention, a method for assessing road safety conditions based on precise three-dimensional measurements is provided. The step of determining the safety level of the road micro-anomaly area based on the safety level and location distribution characteristics of each measuring point within the road micro-anomaly area includes: calculating the minimum distance between each measuring point within the road micro-anomaly area and the positions of the left and right wheel tracks; and calculating the safety level of the road micro-anomaly area based on the safety level of each measuring point and the minimum distance.

[0014] According to a method for assessing road safety conditions based on precise three-dimensional analysis provided by the present invention, when the representative elevation difference of a macroscopic anomaly area of ​​the road surface is greater than or equal to the average elevation difference of an adjacent normal road surface area, the method assesses the macroscopic safety of the road surface based on the macroscopic anomaly area and its attribute information. This includes: determining a first abnormal elevation difference threshold for the macroscopic anomaly area based on its width and the maximum speed limit of the road area; and determining the safety level of the macroscopic anomaly area based on the first abnormal elevation difference threshold, the representative elevation difference, and the position of the anomaly area in the width direction within the lane. The representative elevation difference is determined based on the average elevation difference and the standard deviation of the elevation differences in the macroscopic anomaly area. The average elevation difference is the difference between the higher and lower average elevations of the macroscopic anomaly area along its length.

[0015] According to a method for assessing road safety conditions based on precise three-dimensional analysis provided by the present invention, when the representative elevation difference of a macroscopic anomaly area of ​​the road surface is less than the average elevation difference of an adjacent normal road surface area, the method for assessing the macroscopic safety of the road surface based on the macroscopic anomaly area and its attribute information further includes: obtaining a second abnormal elevation difference threshold for the macroscopic anomaly area of ​​the road surface according to the maximum speed limit of the road area; and determining the safety level of the macroscopic anomaly area of ​​the road surface based on the second abnormal elevation difference threshold, the average elevation difference of an adjacent normal road surface area, and the position of the road surface anomaly area in the width direction within the lane.

[0016] The road safety condition assessment method provided by this invention obtains safety information for evaluating the safety condition of the road based on precise three-dimensional modeling data of the road surface, and assesses the safety of the road surface based on the safety information, thereby achieving a more accurate assessment of road safety. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the road safety condition assessment method provided by the present invention;

[0019] Figure 2 This is a flowchart illustrating the method for obtaining macroscopic anomaly areas of the road surface provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] It should be noted that, in the description of the embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0022] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more.

[0023] The following is combined Figures 1-2 This invention describes the road safety condition assessment method provided by embodiments of the present invention.

[0024] Figure 1 This is a flowchart illustrating the road safety condition assessment method provided by the present invention, as shown below. Figure 1 As shown, including but not limited to the following steps:

[0025] Step 101: Based on the precise three-dimensional modeling data of the road surface, obtain the safety information of the road surface.

[0026] Optionally, the present invention uses line-scan three-dimensional measurement technology to obtain precise three-dimensional data of the entire width of the road surface.

[0027] A three-dimensional model of the road surface is created based on full-width precision three-dimensional data, thereby obtaining precise three-dimensional modeling data of the road surface.

[0028] The safety information includes: road cross slope, road longitudinal slope, road transverse texture, road longitudinal texture, road micro-anomaly areas, road macro-anomaly areas, and attribute information of road macro-anomaly areas.

[0029] Step 102: Based on the safety information, assess the safety of the road surface.

[0030] Optionally, the evaluation method includes: evaluating the safety of the road surface based on the road surface cross slope, road surface longitudinal slope, road surface transverse texture, road surface longitudinal texture, and road surface micro-anomaly areas; and evaluating the macro-safety of the road surface based on the road surface macro-anomaly areas and their attribute information.

[0031] The road safety condition assessment method provided by this invention obtains safety information for evaluating the safety condition of the road based on precise three-dimensional modeling data of the road surface, and assesses the safety of the road surface based on the safety information, thereby achieving a more accurate assessment of road safety.

[0032] As an optional embodiment, the road safety condition assessment method provided by the present invention obtains the road cross slope and road longitudinal slope based on the precise three-dimensional modeling data of the road surface, including: obtaining the road cross slope based on the cross section data in the three-dimensional modeling data; and obtaining the road longitudinal slope based on the longitudinal section data in the three-dimensional modeling data.

[0033] Based on the above embodiments, as an optional embodiment, the method of obtaining the horizontal and vertical textures of the road surface based on the precise three-dimensional modeling data of the road surface according to the present invention will be described below.

[0034] The road surface transverse texture refers to the road surface texture along the road width direction. Based on the cross-sectional data in the precise 3D modeling data of the road surface, the undulation of the road surface texture along the road width direction is calculated. Specifically, the cross-sectional data is filtered to obtain the cross-sectional control contour; the first absolute distance from each measuring point in the cross-sectional data to the corresponding cross-sectional control contour point is calculated point by point, and the first distribution information of the first absolute distance is obtained; based on the first distribution information of the first absolute distance, the road surface transverse texture at each measuring point in the cross-sectional data is calculated.

[0035] It is understandable that for each measuring point, the first absolute distance set is formed by taking the first absolute distances corresponding to the measuring points with a distance less than R1 along the road width direction from the cross section data where the measuring point is located, and the first distribution information is obtained from the numerical distribution of the first absolute distance set; based on the first distribution information, the road transverse texture of each measuring point is calculated.

[0036] The longitudinal texture of the road surface represents the road surface texture along the driving direction. Based on the longitudinal profile data from the precise 3D modeling data of the road surface, the undulation of the road surface texture along the driving direction is calculated. Specifically, the longitudinal profile data is filtered to obtain the longitudinal profile control contour. Then, the second absolute distance from each measuring point in the longitudinal profile data to the corresponding longitudinal profile control contour point is calculated point by point, and the distribution information of the second absolute distance is obtained. Finally, based on the distribution information of the absolute distance, the longitudinal texture of the road surface at each measuring point is calculated.

[0037] It is understandable that for each measuring point, the second absolute distance is formed by taking the second absolute distance corresponding to the measuring point with a distance less than R2 along the road driving direction from the longitudinal profile data where the measuring point is located, and the second distribution information is obtained from the numerical distribution of the second absolute distance set; based on the first distribution information, the longitudinal texture of the road surface at each measuring point is calculated.

[0038] Based on the above embodiments, as an optional embodiment, the method for obtaining microscopic anomaly areas of the road surface based on precise three-dimensional modeling data of the road surface according to the present invention will be described below.

[0039] Based on the road surface transverse texture at each measuring point and the pre-set transverse texture anomaly threshold, transverse micro-anomalies are determined; based on the road surface longitudinal texture at each measuring point and the pre-set longitudinal texture anomaly threshold, longitudinal micro-anomalies are determined.

[0040] Optionally, the present invention can utilize the road surface cross slope to determine the transverse texture anomaly threshold T. Tex1 and make the horizontal texture smaller than T Tex1 The points are marked as lateral micro anomalies.

[0041] Specifically, confirm the horizontal texture anomaly threshold T. Tex1 The formula is:

[0042] T Tex1 =k t1 *S t +b t1

[0043] Where, k t1 b t1 S is a constant. t This refers to the cross slope of the road surface.

[0044] Using the road surface longitudinal slope, the longitudinal texture anomaly threshold T was determined. Tex2 and make the vertical texture smaller than T Tex2 The points are marked as longitudinal micro anomalies.

[0045] Specifically, confirm the vertical texture anomaly threshold T. Tex2 The formula is:

[0046] T Tex2 =k v1 *S v +b v1

[0047] Where, k v1 b v1 S is a constant. v This refers to the longitudinal slope of the road surface.

[0048] After identifying the lateral and longitudinal micro-anomalies, a first initial binary image can be constructed based on these points. Then, using morphological processing methods, employing a dilation-then-erosion approach, the defects in the first initial binary image are extended to obtain a binary image of the micro-anomaly region. Further, based on the length of the connected regions in the micro-anomaly region binary image, noise reduction processing is performed on the image; for example, based on the length characteristics of the connected regions, shorter noise regions are removed. Finally, the road surface micro-anomaly region is determined based on the noise-reduced binary image.

[0049] Figure 2 This is a flowchart illustrating the method for obtaining macroscopic anomaly areas of the road surface provided by the present invention; as shown. Figure 2 As shown, the steps include:

[0050] Step 201: Extract the road surface low-frequency signal data from the three-dimensional modeling data.

[0051] This invention can use signal separation methods to process 3D modeling data; wherein, the signal separation method used can be data processing methods such as Fourier analysis and wavelet analysis.

[0052] Step 202: Perform jump detection on the road surface low-frequency signal data to obtain lateral and longitudinal anomalies.

[0053] Optionally, based on cross-sectional data from low-frequency road surface signal data, the elevation difference between the lowest and highest points within a radius TR is statistically analyzed point by point. If the elevation difference is greater than a threshold T, the result is considered. h Then mark the current point as a lateral anomaly; based on the longitudinal profile data in the road low-frequency signal data, count the elevation difference between the lowest and highest points within the radius DR point by point; if the elevation difference is greater than the threshold T v If so, the current point will be marked as a vertical outlier.

[0054] Step 203: Based on the horizontal and vertical anomalies, construct a binary map of the macroscopic anomaly region using morphology.

[0055] Step 204: Based on the length of the connected regions in the binary image of the macroscopic anomaly region, perform noise reduction processing on the binary image of the macroscopic anomaly region.

[0056] First, a second initial binary map can be constructed based on the horizontal and vertical anomalies. Second, using morphological processing methods, a dilation-then-erosion approach is adopted to extend the defects in the second initial binary map to obtain a macroscopic anomaly region binary map. Furthermore, noise reduction processing is performed on the macroscopic anomaly region binary map based on the length of the connected regions in the macroscopic anomaly region binary map; for example, based on the length characteristics of the connected regions, shorter noise regions are removed.

[0057] Step 205: Determine the macroscopic anomaly region of the road surface based on the binary map of the macroscopic anomaly region after noise reduction processing.

[0058] Optionally, after detecting macroscopic anomaly areas, the macroscopic anomaly areas are divided into two categories, higher and lower, by combining the road surface 3D modeling data and the macroscopic anomaly area location results and using clustering methods.

[0059] To obtain attribute information about macroscopic anomaly areas on the road surface, the average elevation of the higher and lower areas can be calculated by combining the road surface 3D modeling data, the location of the higher area, and the location of the lower area, respectively. The average elevation difference of the macroscopic anomaly areas, the standard deviation of the elevation difference of the macroscopic anomaly areas, the length of the macroscopic anomaly areas, the width of the macroscopic anomaly areas, the aspect ratio of the macroscopic anomaly areas, and the position of the macroscopic anomaly areas in the width direction within the lane.

[0060] The average elevation difference of the road surface macroscopic anomaly area is the difference between the average elevation of the higher area and the average elevation of the lower area in the road surface macroscopic anomaly area, which is statistically analyzed along the length of the road surface macroscopic anomaly area.

[0061] Optionally, if the length direction of the road surface macroscopic anomaly area is transverse, then for the longitudinal section where the road surface macroscopic anomaly area is located, the difference between the average elevation of the higher area and the average elevation of the lower area in the longitudinal section is calculated for each longitudinal section; the longitudinal section is a subset of the road surface macroscopic anomaly area.

[0062] Optionally, if the length direction of the road surface macroscopic anomaly area is longitudinal, then for each cross section in the cross section where the road surface macroscopic anomaly area is located, the difference between the average elevation of the higher area and the average elevation of the lower area in the cross section is calculated; the cross section is a subset of the road surface macroscopic anomaly area.

[0063] This invention also analyzes adjacent normal road surface areas. Specifically, combining the three-dimensional road surface modeling data and the location results of macroscopic anomaly areas, the area formed by points with an outer radius less than R3 of the macroscopic anomaly area is defined as the adjacent normal road surface area. Along the length of the anomaly area, the adjacent normal areas are divided into two categories, either upper and lower or left and right, and the average elevation of the two categories of adjacent road surface areas and the average elevation difference between the two categories are calculated respectively.

[0064] This invention can determine the type of macroscopic anomaly in the road surface based on the average elevation of the higher area, the average elevation difference of the lower area, the length of the macroscopic anomaly area, the width of the macroscopic anomaly area, the aspect ratio of the macroscopic anomaly area, and the average elevation of the adjacent normal road surface area.

[0065] It should be noted that the types of macroscopic abnormal areas on the road surface include, but are not limited to: potholes, foreign objects, bumps, subsidence, vehicle bounce, and misalignment.

[0066] Based on the content of the above embodiments, as an optional embodiment, the safety of the road surface is evaluated, including: evaluating the safety of the road surface based on the road surface cross slope, road surface longitudinal slope, road surface transverse texture, road surface longitudinal texture and road surface micro-anomaly areas; and evaluating the macro-safety of the road surface based on the road surface macro-anomaly areas and the attribute information of the road surface macro-anomaly areas.

[0067] Optionally, the safety of the road surface is assessed based on the road surface cross slope, road surface longitudinal slope, road surface transverse texture, road surface longitudinal texture, and road surface micro-anomaly areas, including:

[0068] Based on the road surface cross slope, determine the transverse texture hazard threshold (which can be denoted as T). Tex3 Specifically:

[0069] T Tex3 =k t2 *S t +b t2

[0070] Where, k t2 b t2 S is a constant. t This refers to the cross slope of the road surface.

[0071] Based on the road surface longitudinal slope, determine the longitudinal texture hazard threshold (which can be denoted as T). Tex4 Specifically:

[0072] T Tex4 =k v2 *S v +b v2

[0073] Where, k v2 b v2 S is a constant. v For the longitudinal slope of the road surface, calculate the first ratio (denoted as RD) at each measuring point in the micro-anomaly region of the road surface. h The ratio of ) to the second (which can be denoted as RD) v The larger of the first ratio and the second ratio is taken as the safety level of each measuring point; wherein, the first ratio is the ratio of the transverse texture danger threshold to the transverse texture of the road surface, and the second ratio is the ratio of the longitudinal texture danger threshold to the longitudinal texture of the road surface.

[0074] The safety level of the road surface micro-anomaly area is determined based on the safety level and location distribution characteristics of each measuring point within the area.

[0075] Specifically, calculate the minimum distance D from each measuring point within the micro-anomaly area of ​​the road surface to the positions of the left and right wheel tracks;

[0076] The safety level of the road surface micro-anomaly area is calculated based on the safety level of each measuring point and the minimum distance; the specific formula is as follows:

[0077]

[0078] Where N is the number of measuring points within the microscopic anomaly area of ​​the road surface, and CTD i D represents the safety level of the i-th measuring point within the microscopic anomaly area of ​​the road surface. i DT1, DT2, k, and b are the minimum distances from the i-th measuring point within the micro-anomaly area of ​​the road surface to the positions of the left and right wheel tracks, and CTA is the safety level of the micro-anomaly area of ​​the road surface.

[0079] Based on the above embodiments, the following describes how to assess the macroscopic safety of road surfaces.

[0080] Optionally, when the representative elevation difference of a macroscopically abnormal road surface area is greater than or equal to the average elevation difference of an adjacent normal road surface area, the assessment of the macroscopic safety of the road surface based on the macroscopically abnormal road surface area and its attribute information includes:

[0081] The threshold for the first abnormal elevation difference of the road surface macroscopic anomaly region is determined based on the width of the region and the maximum speed limit of the road area; the specific formula is as follows:

[0082]

[0083] Among them, T d1The first abnormal elevation difference threshold; W is the width of the macroscopic abnormal area of ​​the road surface; V is the maximum speed limit of the road area; H1, b1, b m It is a constant.

[0084] Based on the first abnormal elevation difference threshold, the representative elevation difference, and the location of the abnormal road surface area in the width direction within the lane, the safety level of the macroscopic abnormal road surface area is determined; the specific formula is as follows:

[0085]

[0086] CDA represents the safety level of macroscopic anomaly areas on the road surface; T d1 H is the threshold for the first abnormal elevation difference; r D represents the elevation difference; min DT1 and DT2 are constants, representing the location of the abnormal road surface area in the width direction within the lane.

[0087] The representative elevation difference is determined based on the average elevation difference of the macroscopic anomaly area of ​​the road surface and the standard deviation of the elevation difference of the macroscopic anomaly area of ​​the road surface; the specific calculation formula is as follows:

[0088] H r =H a +k s *H s

[0089] Among them, H r H represents the elevation difference. a H represents the average elevation difference in macroscopically abnormal areas of the road surface. s k represents the standard deviation of the elevation difference in macroscopic anomaly areas of the road surface. s It is a constant.

[0090] Optionally, when the representative elevation difference of a macroscopically abnormal road surface area is less than the average elevation difference of an adjacent normal road surface area, the assessment of the macroscopic safety of the road surface based on the macroscopically abnormal road surface area and its attribute information includes:

[0091] Based on the maximum speed limit of the road area, the threshold for the second abnormal elevation difference of the macroscopic abnormal area of ​​the road surface is obtained; the specific formula is as follows:

[0092]

[0093] Among them, T d2 is the threshold for the second abnormal elevation difference; V is the maximum speed limit for the road area; H2 and b2 are constants.

[0094] Based on the second abnormal elevation difference threshold, the average elevation difference between adjacent normal road surface areas, and the location of the abnormal road surface area in the width direction within the lane, the safety level of the macroscopic abnormal road surface area is determined; the specific calculation method is as follows:

[0095]

[0096] CDA represents the safety level of macroscopic anomaly areas on the road surface; T d2 H is the threshold for the second abnormal elevation difference. n D represents the average elevation difference between adjacent normal road surface areas. min DT1 and DT2 are constants, representing the location of the abnormal road surface area in the width direction within the lane.

[0097] The abnormal road surface area is located at position D in the width direction within the lane. min , is the minimum distance from all measuring points within the macroscopic anomaly area of ​​the road surface to the positions of the left and right wheel tracks.

[0098] The road safety condition assessment method provided by this invention obtains safety information for evaluating the safety condition of the road based on precise three-dimensional modeling data of the road surface, and assesses the safety of the road surface based on the safety information, thereby achieving a more accurate assessment of road safety.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating a road surface safety condition based on precision three dimensions, characterized by, include: Based on the precise three-dimensional modeling data of the road surface, the safety information of the road surface is obtained; The safety information includes: road cross slope, road longitudinal slope, road transverse texture, road longitudinal texture, road micro-anomaly area, road macro-anomaly area, and attribute information of the road macro-anomaly area. Based on the aforementioned safety information, the safety of the road surface is assessed, including: The safety of the road surface is assessed based on its cross slope, longitudinal slope, transverse texture, longitudinal texture, and microscopic anomalies; and... The macroscopic safety of the road surface is assessed based on the macroscopic anomaly regions and their attribute information. The safety assessment of the road surface based on its cross slope, longitudinal slope, transverse texture, longitudinal texture, and microscopic anomaly areas includes: Based on the road surface cross slope, determine the transverse texture hazard threshold; using the road surface longitudinal slope, determine the longitudinal texture hazard threshold. Calculate the first ratio and the second ratio for each measuring point in the micro-anomaly area of ​​the road surface, and take the larger value between the first ratio and the second ratio as the safety level of each measuring point; The safety level of the road surface micro-anomaly area is determined based on the safety level and location distribution characteristics of each measuring point within the area. Wherein, the first ratio is the ratio of the lateral texture hazard threshold to the road surface lateral texture, and the second ratio is the ratio of the longitudinal texture hazard threshold to the road surface longitudinal texture.

2. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 1, characterized in that, Based on precise 3D modeling data of the road surface, the cross slope and longitudinal slope of the road surface are obtained, including: Based on the cross-sectional data in the 3D modeling data, the cross slope of the road surface is obtained; and, Based on the longitudinal section data in the three-dimensional modeling data, the longitudinal slope of the road surface is obtained.

3. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 2, characterized in that, Based on precise 3D modeling data of the road surface, the horizontal and vertical textures of the road surface are obtained, including: The cross-sectional data is filtered to obtain the cross-sectional control profile; the first absolute distance from each measuring point in the cross-sectional data to the corresponding cross-sectional control profile point is calculated point by point, and the first distribution information of the first absolute distance is obtained; based on the first distribution information of the first absolute distance, the road surface transverse texture at each measuring point in the cross-sectional data is calculated; and... The longitudinal profile data is filtered to obtain the longitudinal profile control contour; the second absolute distance from each measuring point in the longitudinal profile data to the corresponding longitudinal profile control contour point is calculated point by point, and the second distribution information of the second absolute distance is obtained; based on the second distribution information of the second absolute distance, the road surface longitudinal texture of each measuring point in the longitudinal profile data is calculated.

4. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 3, characterized in that, Based on precise 3D modeling data of the road surface, the microscopic anomaly regions of the road surface are obtained, including: Based on the road surface transverse texture at each measuring point and the pre-set transverse texture anomaly threshold, determine the transverse micro-anomaly points; based on the road surface longitudinal texture at each measuring point and the pre-set longitudinal texture anomaly threshold, determine the longitudinal micro-anomaly points. Based on the horizontal and vertical micro-anomalies, a binary map of the micro-anomaly region is constructed using morphology. Based on the length of the connected regions in the binary image of the micro-anomaly region, noise reduction processing is performed on the binary image of the micro-anomaly region. The micro-anomaly region of the road surface is determined based on the binary map of the micro-anomaly region after noise reduction processing.

5. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 4, characterized in that, Based on precise 3D modeling data of the road surface, the macroscopic anomaly regions of the road surface are obtained, including: Extract the road surface low-frequency signal data from the three-dimensional modeling data; Jump detection is performed on the low-frequency signal data of the road surface to obtain lateral and longitudinal anomalies; Based on the horizontal and vertical anomalies, a binary map of the macroscopic anomaly region is constructed using morphology. The macroscopic anomaly region binary image is denoised based on the length of the connected regions in the macroscopic anomaly region binary image. The macroscopic anomaly region of the road surface is determined based on the binary map of the macroscopic anomaly region after noise reduction processing; The attribute information of the macroscopic anomaly area of ​​the road surface includes: the average elevation difference of the macroscopic anomaly area of ​​the road surface, the standard deviation of the elevation difference of the macroscopic anomaly area of ​​the road surface, the average elevation difference of adjacent normal road surface areas, the length of the macroscopic anomaly area of ​​the road surface, the width of the macroscopic anomaly area of ​​the road surface, the aspect ratio of the macroscopic anomaly area of ​​the road surface, the type of the macroscopic anomaly area of ​​the road surface, and the position of the macroscopic anomaly area of ​​the road surface in the width direction within the lane.

6. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 1, characterized in that, The step of determining the safety level of the road surface micro-anomaly area based on the safety level and location distribution characteristics of each measuring point within the road surface micro-anomaly area includes: Calculate the minimum distance from each measuring point within the microscopic anomaly area of ​​the road surface to the positions of the left and right wheel tracks; The safety level of the road surface micro-anomaly area is calculated based on the safety level of each measuring point and the minimum distance.

7. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 6, characterized in that, When the representative elevation difference of a macroscopically abnormal road surface area is greater than or equal to the average elevation difference of an adjacent normal road surface area, the assessment of the macroscopic safety of the road surface based on the macroscopically abnormal road surface area and its attribute information includes: The threshold for the first abnormal elevation difference of the road surface macroscopic anomaly area is determined based on the width of the road surface macroscopic anomaly area and the maximum speed limit of the road area. The safety level of the macroscopic abnormal area of ​​the road surface is determined based on the first abnormal elevation difference threshold, the representative elevation difference, and the position of the abnormal area of ​​the road surface in the width direction within the lane. The representative elevation difference is determined based on the average elevation difference of the road surface macro-abnormal area and the standard deviation of the elevation difference of the road surface macro-abnormal area. The average elevation difference of the road surface macro-abnormal area is the difference between the average elevation of the higher area and the average elevation of the lower area in the road surface macro-abnormal area, which is statistically analyzed along the length of the road surface macro-abnormal area.

8. The method for assessing road safety conditions based on precise three-dimensional analysis according to claim 7, characterized in that, When the representative elevation difference of a road surface macroscopic anomaly area is less than the average elevation difference of an adjacent normal road surface area, the assessment of the macroscopic safety of the road surface based on the road surface macroscopic anomaly area and its attribute information further includes: Based on the maximum speed limit of the road area, obtain the second abnormal elevation difference threshold of the macroscopic abnormal area of ​​the road surface; The safety level of the macroscopic abnormal road surface area is determined based on the second abnormal elevation difference threshold, the average elevation difference between adjacent normal road surface areas, and the location of the abnormal road surface area in the width direction within the lane.

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