WO2021098161A1 - Pavement flatness measurement method and system - Google Patents
Pavement flatness measurement method and system Download PDFInfo
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- WO2021098161A1 WO2021098161A1 PCT/CN2020/089615 CN2020089615W WO2021098161A1 WO 2021098161 A1 WO2021098161 A1 WO 2021098161A1 CN 2020089615 W CN2020089615 W CN 2020089615W WO 2021098161 A1 WO2021098161 A1 WO 2021098161A1
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- pavement
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
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- the present invention relates to the technical field, in particular to a road surface smoothness detection method and system.
- the driving speed of the high-speed loop of the trial (race) yard is generally as high as 240Km/h, which requires extremely high road surface smoothness.
- the traditional road surface flatness detection method is to use a 3-meter lean ruler or an eight-wheel flatness tester.
- the high-speed loop curve section of the test (race) yard generally adopts a double-twisted hyperbolic pelvic pavement, and its high-speed lane inclination is as high as 45° or more.
- the eight-wheel flatness instrument cannot be used due to its own weight sliding or rolling due to its excessively large inclination angle. At the same time, due to its double-twisted hyperbolic structure, it cannot be detected by a 3-meter ruler.
- the test (race) high-speed loop flatness detection generally uses a total station to collect discrete points, and then compare the designed elevation to obtain the difference, and then calculate the mean square error as the basis for measuring flatness.
- This method uses the mean square error as the basis for measuring the flatness, while the standard flatness is based on the "mid-vector" change value as the basis for measuring the flatness. There is no correlation between the two.
- the total station is used to calculate the points on the flat road.
- the difference between the mean square error and the detection results of the eight-wheel flatness meter can reach dozens of times, and there is no standard basis for calculating the mean square error by collecting discrete points with a total station (none at home and abroad). It is a difficult problem to detect the flatness of high-speed loop curved roads and large inclined roads, and there is no related technology and method published in the relevant literature at present.
- the purpose of the present invention is to overcome the deficiencies or defects in the prior art, so as to provide a flatness detection method and system suitable for basin-shaped curved roads and large slope roads, and can also be used for conventional road flatness detection.
- the present invention adopts a road smoothness detection method, which includes the following steps:
- the eight-wheel flatness meter model is moved on the specified characteristic line of the road surface entity model, and the road surface unevenness deviation value of each road surface measurement section is obtained;
- the smoothness value of the road surface is calculated.
- the method further includes:
- the designated characteristic line is a position where flatness detection is required.
- the eight-wheel smoothness meter model is moved on the designated characteristic line of the road surface solid model to obtain the road surface unevenness deviation value of each road surface measurement section, including:
- the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness meter model is tracked, and the motion curve is drawn, and then the road surface unevenness deviation value is obtained.
- the "tracking" technology is a commonly used technical means in motion simulation, which can track the motion trajectory of an object.
- the calculating the road surface roughness value according to the road surface unevenness value includes:
- ⁇ i represents the calculated value of the roughness of each road surface measurement section
- d i represents the road surface unevenness deviation value of each road surface measurement section
- d represents the average value of the road surface unevenness deviation value
- n represents the flatness used to calculate the road surface measurement section. The number of test data.
- the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
- a road surface smoothness detection system which includes an acquisition module, a road surface entity model building module, a smoothness meter model building module, a road unevenness deviation value calculation module, and a smoothness calculation module;
- the acquisition module is used to acquire three-dimensional point cloud data of the road surface
- the pavement entity model building module is used for reverse modeling using point cloud data to obtain the pavement entity model
- the flatness instrument model building module is used to create an eight-wheel flatness instrument model in equal proportions, and constrain the connection relationship of each component;
- the road surface roughness deviation value calculation module is used to use the motion simulation technology to make the eight-wheel smoothness meter model move on the designated characteristic line of the road surface solid model to obtain the road surface roughness deviation value of each road surface measurement section;
- the flatness calculation module is used to calculate the flatness value of the road surface according to the unevenness value of the road surface.
- it also includes a processing module connected to the acquisition module, and the processing module is used to filter and classify the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure road surface point cloud data.
- road surface unevenness deviation value calculation module is specifically used for:
- the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness meter model is tracked, and the motion curve is drawn to obtain the road surface unevenness deviation value of each road surface measurement section.
- the flatness calculation module is specifically used for:
- ⁇ i represents the calculated value of the roughness of each road surface measurement section
- d i represents the road surface unevenness deviation value of each road surface measurement section
- d represents the average value of the road surface unevenness deviation value
- n represents the flatness used to calculate the road surface measurement section.
- the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
- the present invention uses the three-dimensional point cloud data of the road to be inspected to construct a physical model of the road surface through reverse modeling, and creates an eight-wheel flatness meter model in equal proportions, and constrains
- the connection relationship between the components uses the eight-wheel flatness meter model to move on the road surface solid model
- the "tracking" technology of motion simulation is used to track the distance between the distance measuring wheel and the main frame of the eight-wheel flatness meter model.
- the motion trajectory is drawn into a motion curve, and then the road surface unevenness deviation value of each road surface measurement section is obtained, so as to calculate the road surface smoothness.
- the road surface flatness detection scheme provided by the present invention has simple principles, strong feasibility and strong practicability, and can meet the requirements of various road surface flatness detection, in particular, it can effectively solve the double-twist and hyperbolic basin-shaped curved road surface and large slopes.
- the difficulty of road surface flatness detection provides a practical solution for the surface flatness detection of high-speed loop roads in the test (race) field, and can also be used for the flatness detection of conventional road surfaces such as expressways.
- Figure 1 is a schematic flow diagram of a road surface smoothness detection method
- Figure 2 shows the high-precision three-dimensional point cloud of the road surface obtained by the 3D scanner
- Figure 3 is the pure pavement point cloud obtained after processing
- Figure 4 is a road surface entity model obtained from point cloud data reverse modeling
- Figure 5 is a schematic diagram of the constructed eight-wheel flatness meter model
- Figure 6 is a schematic diagram of the motion curve between the distance measuring wheel and the main frame, which is received in real time by the computer motion simulation "tracking" technology;
- Figure 7 is a schematic diagram of the structure of a road smoothness detection system.
- this embodiment discloses a road smoothness detection method, which includes the following steps S1 to S5:
- the way to obtain the three-dimensional point cloud data of the road surface includes, but is not limited to, 3D scanners, backpack scanners, and total stations to measure dense points.
- the acquired road surface point cloud data is shown in Figure 2.
- the point cloud is encapsulated and constructed into a surface through reverse modeling of the point cloud to obtain an actual road surface model.
- the method of performing reverse modeling on the point cloud data to obtain the solid model of the road surface includes, but is not limited to, the method of encapsulating and then smoothing, the method of extracting characteristic lines and then constructing the surface, the method of constructing the body from the surface, etc., and those skilled in the art can according to the actual The situation chooses the appropriate reverse modeling method, the goal is to obtain the actual road surface solid model, as shown in Figure 4.
- the model is created in the same proportions as the eight-wheel flatness instrument, as shown in Figure 5.
- the traction part 1 is mainly composed of connecting plugs and tie rods, and is connected to the front axle 2 through nuts; the front axle 2 is installed on the eight wheels.
- the pneumatic tire type motorcycle wheel is on the eight-wheel system 3 formed by the front and rear frame, and the wheels are connected by the wheel frame 9; the displacement sensor 4, namely the frequency modulation inductive displacement measurement system, is installed on the main frame 6, and the main frame 6 is installed on the front.
- the main frame 6 includes a telescopic square tube, a guiding structure and a rear frame; the locking mechanism 5 is used to control the stop and movement of the eight-wheeler.
- a measuring wheel 7 is also installed on the main frame 6 body, which is composed of a pressure spring, a lifting mechanism, a rubber wheel, and a distance sensor. Among them, the constraining distance measuring wheel 7 and the main frame 6 are connected by vertical sliding, the distance measuring wheel 7 can move freely relative to the frame in the vertical direction, and the rest are fixed connection methods.
- the motorcycle wheels of the eight-wheel system 3 can be along the direction of the main frame. scroll.
- step S1 obtaining the road surface three-dimensional point cloud data, and then further comprising: filtering and classifying the road surface three-dimensional point cloud data to obtain pure road surface point cloud data as shown in FIG. 3.
- the method of processing point cloud data in this embodiment includes but is not limited to several processing methods such as filtering and classification. According to the amount of data, segmentation and block processing can also be performed. The purpose of the processing is to obtain pure road points. cloud.
- step S4 using motion simulation technology to make the eight-wheel flatness meter model move on the specified characteristic line of the road surface entity model, and using the "tracking" technology of motion simulation to track the eight-wheel flatness
- the movement trajectory between the measuring wheel 7 and the main frame 6 of the instrument model is drawn into a movement curve, and then the road surface unevenness deviation value is obtained.
- the specified characteristic line of the road surface solid model refers to the actual position of the road surface that needs to be detected, generally when driving Wheel rolling area.
- the cumulative value is a curve diagram of the midpoint road surface deviation based on the 3m frame.
- step S5 calculating the road surface roughness value according to the road surface unevenness value, specifically:
- ⁇ i represents the calculated value of the roughness of each road surface measurement section
- d i represents the road surface unevenness deviation displacement value of each road surface measurement section
- d represents the average value of the road surface unevenness deviation displacement value
- n represents the road surface measurement section used for calculation The number of flatness test data.
- the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
- this embodiment discloses a road smoothness detection system, which includes an acquisition module 10, a road surface entity model building module 20, a smoothness meter model building module 30, a road unevenness deviation value calculation module 40, and a smoothness calculation module.
- Module 50 includes an acquisition module 10, a road surface entity model building module 20, a smoothness meter model building module 30, a road unevenness deviation value calculation module 40, and a smoothness calculation module.
- Module 50 includes an acquisition module 10, a road surface entity model building module 20, a smoothness meter model building module 30, a road unevenness deviation value calculation module 40, and a smoothness calculation module.
- the obtaining module 10 is used to obtain three-dimensional point cloud data of the road surface
- the pavement entity model building module 20 is used for reverse modeling using point cloud data to obtain a pavement entity model
- the flatness meter model construction module 30 is used to create an eight-wheel flatness meter model in equal proportions, and constrain the connection relationship of various components;
- the road surface unevenness deviation value calculation module 40 is configured to use the motion simulation technology to make the eight-wheel smoothness meter model move on the specified characteristic line of the road surface solid model to obtain the road surface unevenness deviation value of each road surface measurement section;
- the flatness calculation module 50 is configured to calculate the flatness value of the road surface according to the unevenness value of the road surface.
- it also includes a processing module connected to the acquisition module, and the processing module is used to filter and classify the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure pavement point cloud data;
- the pavement point cloud data is reversely modeled to obtain the pavement entity model.
- the road surface unevenness deviation value calculation module 40 is specifically used to track the motion trajectory between the distance measuring wheel 7 and the main frame 6 of the eight-wheel flatness meter model using the "tracking" technology of motion simulation, Draw it into a motion curve, and then measure the road surface unevenness deviation value of each road surface section.
- the flatness calculation module 50 is specifically configured to:
- ⁇ i represents the calculated value of the roughness of each road surface measurement section
- d i represents the road surface unevenness deviation value of each road surface measurement section
- d represents the average value of the road surface unevenness deviation value
- n represents the flatness used to calculate the road surface measurement section. The number of test data.
Abstract
A pavement flatness measurement method and system. The measurement method comprises: acquiring three-dimensional point cloud data of a pavement; performing reverse modeling by using the point cloud data to obtain a pavement entity model; building an eight-wheel flatness measuring instrument model in equal proportion, and constraining the connection relationship of all parts; enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the pavement entity model by using a motion simulation technology, to obtain a pavement irregularity deviation value of each pavement measurement interval; and calculating the flatness value of the pavement according to the pavement irregularity deviation value. The system comprises an acquisition module (10), a pavement entity model building module (20), a flatness measuring instrument model building module (30), a pavement irregularity deviation value calculation module (40), and a flatness calculation module (50). The measurement method and system can be applied to curved pavements and large-slope pavements, and has strong feasibility and practicability.
Description
本发明涉及技术领域，特别涉及一种路面平整度检测方法及系统。The present invention relates to the technical field, in particular to a road surface smoothness detection method and system.
试(赛)车场高速环道的行车速度一般高达240Km/h，对路面平整度要求极高。传统的路面平整度检测方法是采用3米靠尺或八轮平整度仪检测。但是试(赛)车场高速环道曲线段一般采用双扭双曲的盆腔式路面，其高速车道倾角高达45°以上。因其倾角过大八轮平整度仪因自重下滑或者翻滚无法使用，同时因其双扭双曲的结构，更无法采用3米靠尺检测。The driving speed of the high-speed loop of the trial (race) yard is generally as high as 240Km/h, which requires extremely high road surface smoothness. The traditional road surface flatness detection method is to use a 3-meter lean ruler or an eight-wheel flatness tester. However, the high-speed loop curve section of the test (race) yard generally adopts a double-twisted hyperbolic pelvic pavement, and its high-speed lane inclination is as high as 45° or more. The eight-wheel flatness instrument cannot be used due to its own weight sliding or rolling due to its excessively large inclination angle. At the same time, due to its double-twisted hyperbolic structure, it cannot be detected by a 3-meter ruler.
目前，试(赛)车场高速环道平整度检测一般采用全站仪采集离散点，然后对比设计高程获取差值，进而计算均方差作为衡量平整度的依据。该方法以均方差作为衡量平整度的依据，而规范平整度是以“中矢”变化值作为衡量平整度的依据，两者无相关关系，根据实验在平直路面上采用全站仪采点计算均方差和八轮平整度仪检测结果相差可达数十倍，且以全站仪采集离散点计算均方差没有任何规范依据(国内外皆无)。高速环道曲面路面及大斜面路面平整度检测是一个难题，目前没有相关文献公布相关技术及方法。At present, the test (race) high-speed loop flatness detection generally uses a total station to collect discrete points, and then compare the designed elevation to obtain the difference, and then calculate the mean square error as the basis for measuring flatness. This method uses the mean square error as the basis for measuring the flatness, while the standard flatness is based on the "mid-vector" change value as the basis for measuring the flatness. There is no correlation between the two. According to the experiment, the total station is used to calculate the points on the flat road. The difference between the mean square error and the detection results of the eight-wheel flatness meter can reach dozens of times, and there is no standard basis for calculating the mean square error by collecting discrete points with a total station (none at home and abroad). It is a difficult problem to detect the flatness of high-speed loop curved roads and large inclined roads, and there is no related technology and method published in the relevant literature at present.
发明内容Summary of the invention
本发明的目的在于克服现有技术存在的不足或缺陷，以提供一种适用于盆腔式曲面路面及大斜面路面的平整度检测方法及系统，也可用于常规道路平整度的检测。The purpose of the present invention is to overcome the deficiencies or defects in the prior art, so as to provide a flatness detection method and system suitable for basin-shaped curved roads and large slope roads, and can also be used for conventional road flatness detection.
为实现以上目的，本发明采用一种路面平整度检测方法，包括如下步骤：In order to achieve the above objectives, the present invention adopts a road smoothness detection method, which includes the following steps:
获取路面三维点云数据；Obtain 3D point cloud data of the road surface;
利用所述点云数据进行逆向建模，得到路面实体模型；Use the point cloud data to perform reverse modeling to obtain a road surface entity model;
等比例创建八轮平整度仪模型，并约束各部件的连接关系；Create an eight-wheel flatness instrument model in equal proportions, and constrain the connection relationship of each component;
利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值；Using the motion simulation technology, the eight-wheel flatness meter model is moved on the specified characteristic line of the road surface entity model, and the road surface unevenness deviation value of each road surface measurement section is obtained;
根据所述路面凹凸偏差值，计算路面的平整度值。According to the unevenness value of the road surface, the smoothness value of the road surface is calculated.
进一步地，在所述获取路面三维点云数据之后，还包括：Further, after the obtaining the road surface three-dimensional point cloud data, the method further includes:
对所述路面三维点云数据进行滤波、分类处理，得到纯净的路面点云数据；Filtering and classifying the road surface three-dimensional point cloud data to obtain pure road surface point cloud data;
利用得到的纯净路面点云数据进行逆向建模，得到路面实体模型。Use the obtained pure pavement point cloud data for reverse modeling to obtain a pavement entity model.
进一步地，所述指定特征线为需要进行平整度检测的位置。Further, the designated characteristic line is a position where flatness detection is required.
进一步地，利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值，包括：Further, using the motion simulation technology, the eight-wheel smoothness meter model is moved on the designated characteristic line of the road surface solid model to obtain the road surface unevenness deviation value of each road surface measurement section, including:
所述利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值，具体包括：The use of motion simulation technology to make the eight-wheel smoothness meter model move on the designated characteristic line of the road surface solid model to obtain the road surface unevenness deviation value of each road surface measurement section specifically includes:
利用运动仿真的追踪技术，追踪所述八轮平整度仪模型的测距轮和主架之间的运动轨迹，绘制成运动曲线，进而获得路面凹凸偏差值。Using the tracking technology of motion simulation, the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness meter model is tracked, and the motion curve is drawn, and then the road surface unevenness deviation value is obtained.
所述“追踪”技术，是运动仿真中常用的一种技术手段，可以追踪物体的运动轨迹。The "tracking" technology is a commonly used technical means in motion simulation, which can track the motion trajectory of an object.
进一步地，所述根据所述路面凹凸偏差值，计算路面的平整度值，包括：Further, the calculating the road surface roughness value according to the road surface unevenness value includes:
根据所述路面凹凸偏差值，计算每个路面测定区间的平整度：According to the unevenness of the road surface, calculate the flatness of each road surface measurement section:
其中，σ
i表示各路面测定区间的平整度计算值，d
i表示每个路面测定区间的路面凹凸偏差值，d表示各路面凹凸偏差值的平均值，n表示用于计算路面测定区间平整度的测试数据个数。
Among them, σ i represents the calculated value of the roughness of each road surface measurement section, d i represents the road surface unevenness deviation value of each road surface measurement section, d represents the average value of the road surface unevenness deviation value, and n represents the flatness used to calculate the road surface measurement section. The number of test data.
进一步地，还包括：Further, it also includes:
根据所述每个路面测定区间的平整度，计算各路面测定区间内路段的平整度的平均值、平整度的标准差以及变异系数。According to the flatness of each road surface measurement interval, the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
另一方面，采用一种路面平整度检测系统，包括获取模块、路面实体模型构建模块、平整度仪模型构建模块、路面凹凸偏差值计算模块以及平整度计算模块；On the other hand, a road surface smoothness detection system is adopted, which includes an acquisition module, a road surface entity model building module, a smoothness meter model building module, a road unevenness deviation value calculation module, and a smoothness calculation module;
获取模块用于获取路面三维点云数据；The acquisition module is used to acquire three-dimensional point cloud data of the road surface;
路面实体模型构建模块用于利用点云数据进行逆向建模，得到路面实体模型；The pavement entity model building module is used for reverse modeling using point cloud data to obtain the pavement entity model;
平整度仪模型构建模块用于等比例创建八轮平整度仪模型，并约束各部件的连接关系；The flatness instrument model building module is used to create an eight-wheel flatness instrument model in equal proportions, and constrain the connection relationship of each component;
路面凹凸偏差值计算模块用于利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值；The road surface roughness deviation value calculation module is used to use the motion simulation technology to make the eight-wheel smoothness meter model move on the designated characteristic line of the road surface solid model to obtain the road surface roughness deviation value of each road surface measurement section;
平整度计算模块用于根据所述路面凹凸偏差值，计算路面的平整度值。The flatness calculation module is used to calculate the flatness value of the road surface according to the unevenness value of the road surface.
进一步地，还包括与所述获取模块连接的处理模块，该处理模块用于对所述获取模块获取的路面三维点云数据进行滤波、分类处理，得到纯净的路面点云数据。Further, it also includes a processing module connected to the acquisition module, and the processing module is used to filter and classify the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure road surface point cloud data.
进一步地，所述路面凹凸偏差值计算模块具体用于：Further, the road surface unevenness deviation value calculation module is specifically used for:
利用运动仿真的追踪技术，追踪所述八轮平整度仪模型的测距轮和主架之间的运动轨迹，绘制成运动曲线，进而获得每个路面测定区间的路面凹凸偏差值。Using the tracking technology of motion simulation, the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness meter model is tracked, and the motion curve is drawn to obtain the road surface unevenness deviation value of each road surface measurement section.
进一步地，所述平整度计算模块具体用于：Further, the flatness calculation module is specifically used for:
根据所述路面凹凸偏差值，计算每个路面测定区间的平整度：According to the unevenness of the road surface, calculate the flatness of each road surface measurement section:
其中，σ
i表示各路面测定区间的平整度计算值，d
i表示每个路面测 定区间的路面凹凸偏差值，d表示各路面凹凸偏差值的平均值，n表示用于计算路面测定区间平整度的测试数据个数；
Among them, σ i represents the calculated value of the roughness of each road surface measurement section, d i represents the road surface unevenness deviation value of each road surface measurement section, d represents the average value of the road surface unevenness deviation value, and n represents the flatness used to calculate the road surface measurement section. The number of test data;
根据所述每个路面测定区间的平整度，进而计算各路面测定区间内路段的平整度的平均值、平整度的标准差以及变异系数。According to the flatness of each road surface measurement interval, the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
与现有技术相比，本发明存在以下技术效果：本发明通过利用待检测路面的三维点云数据进行逆向建模构建出该路面的实体模型，等比例创建八轮平整度仪模型，并约束各部件间的连接关系，通过利用八轮平整度仪模型在路面实体模型上运动，利用运动仿真的“追踪”技术，追踪所述八轮平整度仪模型的测距轮和主架之间的运动轨迹，绘制成运动曲线，进而获得每个路面测定区间的路面凹凸偏差值，从而计算出路面的平整度。本发明提供的路面平整度检测方案，原理简单、可行性强且实用性强，能够满足各种路面平整度检测的要求，尤其是可有效的解决双扭双曲的盆腔式曲面路面及大斜面路面平整度检测的难题，为试(赛)车场高速环道路面平整度检提供一个切实可行的方案，也可用于高速公路等常规路面的平整度检测。Compared with the prior art, the present invention has the following technical effects: the present invention uses the three-dimensional point cloud data of the road to be inspected to construct a physical model of the road surface through reverse modeling, and creates an eight-wheel flatness meter model in equal proportions, and constrains The connection relationship between the components uses the eight-wheel flatness meter model to move on the road surface solid model, and the "tracking" technology of motion simulation is used to track the distance between the distance measuring wheel and the main frame of the eight-wheel flatness meter model. The motion trajectory is drawn into a motion curve, and then the road surface unevenness deviation value of each road surface measurement section is obtained, so as to calculate the road surface smoothness. The road surface flatness detection scheme provided by the present invention has simple principles, strong feasibility and strong practicability, and can meet the requirements of various road surface flatness detection, in particular, it can effectively solve the double-twist and hyperbolic basin-shaped curved road surface and large slopes. The difficulty of road surface flatness detection provides a practical solution for the surface flatness detection of high-speed loop roads in the test (race) field, and can also be used for the flatness detection of conventional road surfaces such as expressways.
下面结合附图，对本发明的具体实施方式进行详细描述：The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings:
图1是一种路面平整度检测方法的流程示意图；Figure 1 is a schematic flow diagram of a road surface smoothness detection method;
图2是3D扫描仪获得高精度的路面三维点云；Figure 2 shows the high-precision three-dimensional point cloud of the road surface obtained by the 3D scanner;
图3是经过处理得到的纯净路面点云；Figure 3 is the pure pavement point cloud obtained after processing;
图4是由点云数据逆向建模得到的路面实体模型；Figure 4 is a road surface entity model obtained from point cloud data reverse modeling;
图5是构建的八轮平整度仪模型示意图；Figure 5 is a schematic diagram of the constructed eight-wheel flatness meter model;
图6是由计算机运动仿真“追踪”技术实时回执的测距轮相对主架间的运动曲线示意图；Figure 6 is a schematic diagram of the motion curve between the distance measuring wheel and the main frame, which is received in real time by the computer motion simulation "tracking" technology;
图7是一种路面平整度检测系统的结构示意图。Figure 7 is a schematic diagram of the structure of a road smoothness detection system.
为了更进一步说明本发明的特征，请参阅以下有关本发明的详细说 明与附图。所附图仅供参考与说明之用，并非用来对本发明的保护范围加以限制。In order to further explain the features of the present invention, please refer to the following detailed description of the present invention and the accompanying drawings. The attached drawings are for reference and explanation purposes only, and are not used to limit the protection scope of the present invention.
如图1所示，本实施例公开了一种路面平整度检测方法，包括如下步骤S1至S5：As shown in Fig. 1, this embodiment discloses a road smoothness detection method, which includes the following steps S1 to S5:
S1、获取路面三维点云数据；S1. Obtain 3D point cloud data of the road surface;
需要说明的是，路面三维点云数据的获取方式包括但不局限于3D扫描仪、背包式扫描仪、全站仪测量密集点等方法，获取的路面点云数据如图2所示。It should be noted that the way to obtain the three-dimensional point cloud data of the road surface includes, but is not limited to, 3D scanners, backpack scanners, and total stations to measure dense points. The acquired road surface point cloud data is shown in Figure 2.
S2、利用所述点云数据进行逆向建模，得到路面实体模型；S2. Use the point cloud data to perform reverse modeling to obtain a road surface entity model;
需要说明的是，本实施例通过对点云逆向建模，将点云封装构建成面，获得贴合实际的路面模型。其中对点云数据进行逆向建模得到路面实体模型的方法包括但不限于先封装后平滑的方法、也可提取特征线然后构建面、由面构成体的方法等，本领域技术人员可根据实际情况选用合适的逆向建模方法，目的是获得贴合实际的路面实体模型，如图4所示。It should be noted that, in this embodiment, the point cloud is encapsulated and constructed into a surface through reverse modeling of the point cloud to obtain an actual road surface model. Among them, the method of performing reverse modeling on the point cloud data to obtain the solid model of the road surface includes, but is not limited to, the method of encapsulating and then smoothing, the method of extracting characteristic lines and then constructing the surface, the method of constructing the body from the surface, etc., and those skilled in the art can according to the actual The situation chooses the appropriate reverse modeling method, the goal is to obtain the actual road surface solid model, as shown in Figure 4.
S3、等比例创建八轮平整度仪模型，并约束各部件的连接关系；S3. Create an eight-wheel flatness instrument model in equal proportions, and constrain the connection relationship of each component;
具体的说：是按八轮平整度仪实体等比例创建模型，如图5所示：牵引部分1主要由连接插头与拉杆组成，通过螺母与前桥2相连；前桥2安装在由八个充气轮胎型式的摩托车轮通过前后架构成的八轮系统3上，各车轮之间通过轮架9连接；位移传感器4即调频电感式位移测量系统安装在主架6上，主架6安装在前桥2和后桥8上，主架6包括伸缩方管、导向结构和后架；锁止机构5用于控制八轮车停止与运动。主架6本体上还安装有测量轮7，由加压弹簧及提升机构、橡胶轮、距离传感器组成。其中约束测距轮7与主架6为连接方式为竖向滑动，测距轮7在竖向相对架体可以自由运动，其余为固定连接方式，八轮系统3的摩托车轮可沿主架方向滚动。Specifically: the model is created in the same proportions as the eight-wheel flatness instrument, as shown in Figure 5. The traction part 1 is mainly composed of connecting plugs and tie rods, and is connected to the front axle 2 through nuts; the front axle 2 is installed on the eight wheels. The pneumatic tire type motorcycle wheel is on the eight-wheel system 3 formed by the front and rear frame, and the wheels are connected by the wheel frame 9; the displacement sensor 4, namely the frequency modulation inductive displacement measurement system, is installed on the main frame 6, and the main frame 6 is installed on the front. On the bridge 2 and the rear axle 8, the main frame 6 includes a telescopic square tube, a guiding structure and a rear frame; the locking mechanism 5 is used to control the stop and movement of the eight-wheeler. A measuring wheel 7 is also installed on the main frame 6 body, which is composed of a pressure spring, a lifting mechanism, a rubber wheel, and a distance sensor. Among them, the constraining distance measuring wheel 7 and the main frame 6 are connected by vertical sliding, the distance measuring wheel 7 can move freely relative to the frame in the vertical direction, and the rest are fixed connection methods. The motorcycle wheels of the eight-wheel system 3 can be along the direction of the main frame. scroll.
S4、利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模 型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值；S4. Using the motion simulation technology, make the eight-wheel flatness meter model move on the designated characteristic line of the road surface entity model, and obtain the road surface unevenness deviation value of each road surface measurement section;
S5、根据所述路面凹凸偏差值，计算路面的平整度值。S5. Calculate the smoothness value of the road surface according to the unevenness value of the road surface.
优选地，在上述步骤S1：获取路面三维点云数据，之后还包括：对所述路面三维点云数据进行滤波、分类等处理，得到纯净的路面点云数据如图3所示。Preferably, in the above step S1: obtaining the road surface three-dimensional point cloud data, and then further comprising: filtering and classifying the road surface three-dimensional point cloud data to obtain pure road surface point cloud data as shown in FIG. 3.
需要说明的是，本实施例中处理点云数据的方法包括但不限于滤波、分类等几种处理方法，根据数据量也可以进行分割、分区块处理等，处理的目的是获得纯净的路面点云。It should be noted that the method of processing point cloud data in this embodiment includes but is not limited to several processing methods such as filtering and classification. According to the amount of data, segmentation and block processing can also be performed. The purpose of the processing is to obtain pure road points. cloud.
具体来说，在上述步骤S4：利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，利用运动仿真的“追踪”技术，追踪所述八轮平整度仪模型的测距轮7和主架6之间的运动轨迹，绘制成运动曲线，进而获路面凹凸偏差值，所述路面实体模型的指定特征线指路面实际需要检测的位置，一般是行车时的车轮碾压区域。一般为：Specifically, in the above step S4: using motion simulation technology to make the eight-wheel flatness meter model move on the specified characteristic line of the road surface entity model, and using the "tracking" technology of motion simulation to track the eight-wheel flatness The movement trajectory between the measuring wheel 7 and the main frame 6 of the instrument model is drawn into a movement curve, and then the road surface unevenness deviation value is obtained. The specified characteristic line of the road surface solid model refers to the actual position of the road surface that needs to be detected, generally when driving Wheel rolling area. Generally:
(1)运用运动仿真技术将创建的八轮平整度模型在构建的路面模型指定特征线上运动。运动速度以5km/h为宜，不得超过12km/h。(1) Use motion simulation technology to move the created eight-wheel flatness model on the specified characteristic line of the constructed road surface model. The moving speed should be 5km/h and should not exceed 12km/h.
(2)利用运动仿真的“追踪”技术，追踪所述八轮平整度仪模型的测距轮7和主架6之间的运动轨迹，绘制成运动曲线。(2) Using the "tracking" technology of motion simulation, track the motion trajectory between the distance measuring wheel 7 and the main frame 6 of the eight-wheel flatness meter model, and draw it into a motion curve.
(3)按每10cm间距采集的位移值自动计算100m计算区间的平整度标准差，记录测试长度、曲线振幅大于某一定值(3mm、5mm、8mm、10mm等)的次数、曲线振幅的单向(凸起或凹下)累计值，以3m机架为基准的中点路面偏差值曲线图。(3) Automatically calculate the flatness standard deviation of the 100m calculation interval according to the displacement value collected every 10cm interval, record the test length, the number of times the curve amplitude is greater than a certain value (3mm, 5mm, 8mm, 10mm, etc.), and the one-way curve amplitude The cumulative value (convex or recess) is a curve diagram of the midpoint road surface deviation based on the 3m frame.
具体来说，上述步骤S5：根据所述路面凹凸偏差值，计算路面的平整度值，具体为：Specifically, the above step S5: calculating the road surface roughness value according to the road surface unevenness value, specifically:
根据所述路面凹凸偏差值，计算每个路面测定区间的平整度：According to the unevenness of the road surface, calculate the flatness of each road surface measurement section:
其中，σ
i表示各路面测定区间的平整度计算值，d
i表示每个路面测定区间的路面凹凸偏差位移值，d表示各路面凹凸偏差位移值的平均值，n表示用于计算路面测定区间平整度的测试数据个数。
Among them, σ i represents the calculated value of the roughness of each road surface measurement section, d i represents the road surface unevenness deviation displacement value of each road surface measurement section, d represents the average value of the road surface unevenness deviation displacement value, and n represents the road surface measurement section used for calculation The number of flatness test data.
具体来说：还包括：Specifically: It also includes:
根据所述每个路面测定区间的平整度，计算各路面测定区间内路段的平整度的平均值、平整度的标准差以及变异系数。According to the flatness of each road surface measurement interval, the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
如图7所示，本实施例公开了一种路面平整度检测系统，包括获取模块10、路面实体模型构建模块20、平整度仪模型构建模块30、路面凹凸偏差值计算模块40以及平整度计算模块50；As shown in FIG. 7, this embodiment discloses a road smoothness detection system, which includes an acquisition module 10, a road surface entity model building module 20, a smoothness meter model building module 30, a road unevenness deviation value calculation module 40, and a smoothness calculation module. Module 50;
获取模块10用于获取路面三维点云数据；The obtaining module 10 is used to obtain three-dimensional point cloud data of the road surface;
路面实体模型构建模块20用于利用点云数据进行逆向建模，得到路面实体模型；The pavement entity model building module 20 is used for reverse modeling using point cloud data to obtain a pavement entity model;
平整度仪模型构建模块30用于等比例创建八轮平整度仪模型，并约束各部件连接关系；The flatness meter model construction module 30 is used to create an eight-wheel flatness meter model in equal proportions, and constrain the connection relationship of various components;
路面凹凸偏差值计算模块40用于利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值；The road surface unevenness deviation value calculation module 40 is configured to use the motion simulation technology to make the eight-wheel smoothness meter model move on the specified characteristic line of the road surface solid model to obtain the road surface unevenness deviation value of each road surface measurement section;
平整度计算模块50用于根据所述路面凹凸偏差值，计算路面的平整度值。The flatness calculation module 50 is configured to calculate the flatness value of the road surface according to the unevenness value of the road surface.
具体来说，还包括与所述获取模块连接的处理模块，该处理模块用于对所述获取模块获取的路面三维点云数据进行滤波、分类处理，得到纯净的路面点云数据；利用纯净的路面点云数据进行逆向建模，得到路面实体模型。Specifically, it also includes a processing module connected to the acquisition module, and the processing module is used to filter and classify the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure pavement point cloud data; The pavement point cloud data is reversely modeled to obtain the pavement entity model.
具体来说，所述路面凹凸偏差值计算模块40具体用于：利用运动仿真的“追踪”技术，追踪所述八轮平整度仪模型的测距轮7和主架6之间的运动轨迹，绘制成运动曲线，进而每个路面测定区间的路面凹凸偏差值。Specifically, the road surface unevenness deviation value calculation module 40 is specifically used to track the motion trajectory between the distance measuring wheel 7 and the main frame 6 of the eight-wheel flatness meter model using the "tracking" technology of motion simulation, Draw it into a motion curve, and then measure the road surface unevenness deviation value of each road surface section.
具体来说，所述平整度计算模块50具体用于：Specifically, the flatness calculation module 50 is specifically configured to:
根据所述路面凹凸偏差值，计算每个路面测定区间的平整度：According to the unevenness of the road surface, calculate the flatness of each road surface measurement section:
其中，σ
i表示各路面测定区间的平整度计算值，d
i表示每个路面测定区间的路面凹凸偏差值，d表示各路面凹凸偏差值的平均值，n表示用于计算路面测定区间平整度的测试数据个数。
Among them, σ i represents the calculated value of the roughness of each road surface measurement section, d i represents the road surface unevenness deviation value of each road surface measurement section, d represents the average value of the road surface unevenness deviation value, and n represents the flatness used to calculate the road surface measurement section. The number of test data.
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection of the present invention. Within range.
Claims (10)
- 一种路面平整度检测方法，其特征在于，包括：A method for detecting road surface roughness, which is characterized in that it comprises:获取路面三维点云数据；Obtain 3D point cloud data of the road surface;利用所述点云数据进行逆向建模，得到路面实体模型；Use the point cloud data to perform reverse modeling to obtain a road surface entity model;等比例创建八轮平整度仪模型，并约束各部件的连接关系；Create an eight-wheel flatness instrument model in equal proportions, and constrain the connection relationship of each component;利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值；Using the motion simulation technology, the eight-wheel flatness meter model is moved on the specified characteristic line of the road surface entity model, and the road surface unevenness deviation value of each road surface measurement section is obtained;根据所述路面凹凸偏差值，计算路面的平整度值。According to the unevenness value of the road surface, the smoothness value of the road surface is calculated.
- 如权利要求1所述的路面平整度检测方法，其特征在于，在所述获取路面三维点云数据之后，还包括：The road surface smoothness detection method according to claim 1, wherein after said obtaining the road surface three-dimensional point cloud data, the method further comprises:对所述路面三维点云数据进行滤波、分类处理，得到纯净的路面点云数据；利用得到的纯净路面点云进行逆向建模，得到路面实体模型。Filtering and classifying the road surface three-dimensional point cloud data to obtain pure road surface point cloud data; using the obtained pure road surface point cloud to perform reverse modeling to obtain a road surface entity model.
- 如权利要求1所述的路面平整度检测方法，其特征在于，所述指定特征线为需要进行平整度检测的位置。The road surface flatness detection method according to claim 1, wherein the designated characteristic line is a location where flatness detection is required.
- 如权利要求1所述的路面平整度检测方法，其特征在于，所述利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值，具体包括：The road surface roughness detection method according to claim 1, wherein the motion simulation technology is used to make the eight-wheel flatness meter model move on the designated characteristic line of the road surface solid model to obtain each road surface measurement interval. The road surface unevenness deviation value includes:利用运动仿真的追踪技术，追踪所述八轮平整度仪模型的测距轮和主架之间的运动轨迹，绘制成运动曲线，进而获得路面凹凸偏差值。The tracking technology of motion simulation is used to track the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness meter model, and draw it into a motion curve, and then obtain the road surface unevenness deviation value.
- 如权利要求1所述的路面平整度检测方法，其特征在于，所述根据所述路面凹凸偏差值，计算路面的平整度值，包括：The method for detecting road surface roughness according to claim 1, wherein said calculating the road surface roughness value according to said road surface roughness deviation value comprises:根据所述路面凹凸偏差值，计算每个路面测定区间的平整度：According to the unevenness of the road surface, calculate the flatness of each road surface measurement section:
其中，σ i表示各路面测定区间的平整度计算值，d i表示每个路面测定区间的路面凹凸偏差位移值，d表示各路面凹凸偏差位移值的平均值，n表示用于计算路面测定区间平整度的测试数据个数。 Among them, σ i represents the calculated value of the roughness of each road surface measurement section, d i represents the road surface unevenness deviation displacement value of each road surface measurement section, d represents the average value of the road surface unevenness deviation displacement value, and n represents the road surface measurement section used for calculation The number of flatness test data. - 如权利要求5所述的路面平整度检测方法，其特征在于，还包 括：The road smoothness detection method according to claim 5, further comprising:根据所述每个路面测定区间的平整度，计算各路面测定区间内路段的平整度的平均值、平整度的标准差以及变异系数。According to the flatness of each road surface measurement interval, the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
- 一种路面平整度检测系统，其特征在于，包括获取模块、路面实体模型构建模块、平整度仪模型构建模块、路面凹凸偏差值计算模块以及平整度计算模块；A road surface smoothness detection system, which is characterized by comprising an acquisition module, a road surface entity model building module, a smoothness meter model building module, a road surface unevenness deviation value calculation module, and a smoothness calculation module;获取模块用于获取路面三维点云数据；The acquisition module is used to acquire three-dimensional point cloud data of the road surface;路面实体模型构建模块用于利用点云数据进行逆向建模，得到路面实体模型；The pavement entity model building module is used for reverse modeling using point cloud data to obtain the pavement entity model;平整度仪模型构建模块用于等比例创建八轮平整度仪模型，并约束各部件的连接关系；The flatness instrument model building module is used to create an eight-wheel flatness instrument model in equal proportions, and constrain the connection relationship of each component;路面凹凸偏差值计算模块用于利用运动仿真技术，使得八轮平整度仪模型在所述路面实体模型的指定特征线上运动，获得每个路面测定区间的路面凹凸偏差值；The road surface roughness deviation value calculation module is used to use the motion simulation technology to make the eight-wheel smoothness meter model move on the designated characteristic line of the road surface solid model to obtain the road surface roughness deviation value of each road surface measurement section;平整度计算模块用于根据所述路面凹凸偏差值，计算路面的平整度值。The flatness calculation module is used to calculate the flatness value of the road surface according to the unevenness value of the road surface.
- 如权利要求7所述的路面平整度检测系统，其特征在于，还包括与所述获取模块连接的处理模块，该处理模块用于对所述获取模块获取的路面三维点云数据进行滤波、分类处理，得到纯净的路面点云数据。The road smoothness detection system according to claim 7, further comprising a processing module connected to the acquisition module, and the processing module is used to filter and classify the road surface three-dimensional point cloud data acquired by the acquisition module Processing to obtain pure pavement point cloud data.
- 如权利要求7所述的路面平整度检测系统，其特征在于，所述路面凹凸偏差值计算模块具体用于：8. The road smoothness detection system according to claim 7, wherein the road surface unevenness deviation value calculation module is specifically used for:利用运动仿真的追踪技术，追踪所述八轮平整度仪模型的测距轮和主架之间的运动轨迹，绘制成运动曲线，进而获得每个路面测定区间的路面凹凸偏差值。Using the tracking technology of motion simulation, the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness meter model is tracked, and the motion curve is drawn to obtain the road surface unevenness deviation value of each road surface measurement section.
- 如权利要求7所述的路面平整度检测系统，其特征在于，所述平整度计算模块具体用于：8. The road smoothness detection system according to claim 7, wherein the smoothness calculation module is specifically configured to:根据所述路面凹凸偏差值，计算每个路面测定区间的平整度：According to the unevenness of the road surface, calculate the flatness of each road surface measurement section:
其中，σ i表示各路面测定区间的平整度计算值，d i表示每个路面测定区间的路面凹凸偏差值，d表示各路面凹凸偏差值的平均值，n表示用于计算路面测定区间平整度的测试数据个数； Among them, σ i represents the calculated value of the roughness of each road surface measurement section, d i represents the road surface unevenness deviation value of each road surface measurement section, d represents the average value of the road surface unevenness deviation value, and n represents the flatness used to calculate the road surface measurement section. The number of test data;根据所述每个路面测定区间的平整度，进而计算各路面测定区间内路段的平整度的平均值、平整度的标准差以及变异系数。According to the flatness of each road surface measurement interval, the average value of the flatness, the standard deviation of the flatness and the coefficient of variation of the road sections in each road surface measurement interval are calculated.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689565A (en) * | 2021-10-21 | 2021-11-23 | 北京中科慧眼科技有限公司 | Road flatness grade detection method and system based on binocular stereo vision and intelligent terminal |
| CN113957774A (en) * | 2021-10-27 | 2022-01-21 | 安徽理工大学 | Road surface roughness detection device based on singlechip information feedback formula |
| CN116289443A (en) * | 2023-02-01 | 2023-06-23 | 兰陵县昌通公路工程有限公司 | Road and bridge construction flatness measuring device |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111021206B (en) * | 2019-11-20 | 2021-06-25 | 中铁四局集团第一工程有限公司 | Road surface flatness detection method and system |
| CN112665535A (en) * | 2020-12-04 | 2021-04-16 | 中冶天工集团有限公司 | Method for measuring wall surface flatness |
| CN114908649B (en) * | 2022-06-16 | 2024-01-26 | 西安长安大学工程设计研究院有限公司 | Roadbed and pavement flatness measuring device |
| CN115538253A (en) * | 2022-09-01 | 2022-12-30 | 中国路桥工程有限责任公司 | Intelligent paving system based on laser guide |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19910967C1 (en) * | 1999-03-12 | 2000-09-21 | Avl Deutschland Gmbh | Method for simulating the behavior of a vehicle on a road |
| CN101840449A (en) * | 2010-04-13 | 2010-09-22 | 北京农业信息技术研究中心 | Tyre stress simulation method and system thereof |
| CN208121529U (en) * | 2018-04-10 | 2018-11-20 | 四川瑞通工程建设集团有限公司 | A kind of wheel Road surface level instrument of continous way eight removed obstacles automatically |
| CN208219371U (en) * | 2018-04-12 | 2018-12-11 | 浙江裕立检测科技有限公司 | Continuous pavement eight takes turns smoothness measuring equipment |
| CN109547960A (en) * | 2018-11-16 | 2019-03-29 | 万翼科技有限公司 | A kind of intelligent detecting method and system |
| CN110084116A (en) * | 2019-03-22 | 2019-08-02 | 深圳市速腾聚创科技有限公司 | Pavement detection method, apparatus, computer equipment and storage medium |
| JP6561854B2 (en) * | 2016-01-15 | 2019-08-21 | 住友ゴム工業株式会社 | Tire simulation method |
| CN110318327A (en) * | 2019-06-10 | 2019-10-11 | 长安大学 | A kind of surface evenness prediction technique based on random forest |
| CN111021206A (en) * | 2019-11-20 | 2020-04-17 | 中铁四局集团第一工程有限公司 | Road surface flatness detection method and system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2545543B2 (en) * | 1987-06-26 | 1996-10-23 | 雅生 犬塚 | Wheel trampling device |
| CN104313986B (en) * | 2014-09-11 | 2016-06-08 | 交通运输部公路科学研究所 | Surface evenness detection system and method |
| CN104573343B (en) * | 2014-12-25 | 2017-06-16 | 长安大学 | A kind of Asphalt Pavement Surface Evenness comfortableness field evaluation method and method of tire |
| US20190197201A1 (en) * | 2016-09-30 | 2019-06-27 | Faraday&Future Inc. | Vehicle durability modeling |
| CN107905073A (en) * | 2017-11-09 | 2018-04-13 | 南京中高知识产权股份有限公司 | The method knead dough smoothness measuring equipment of straight line laser profile scanning surface evenness |
| CN108221603B (en) * | 2018-01-08 | 2019-08-09 | 重庆大学 | A kind of road surface three-dimensional information detection device, the method and system of road |
-
2019
- 2019-11-20 CN CN201911143562.0A patent/CN111021206B/en active Active
-
2020
- 2020-05-11 WO PCT/CN2020/089615 patent/WO2021098161A1/en active Application Filing
- 2020-05-11 AU AU2020386611A patent/AU2020386611A1/en active Pending
- 2020-05-11 DE DE112020000229.2T patent/DE112020000229T5/en active Pending
- 2020-05-11 AU AU2020104486A patent/AU2020104486A4/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19910967C1 (en) * | 1999-03-12 | 2000-09-21 | Avl Deutschland Gmbh | Method for simulating the behavior of a vehicle on a road |
| CN101840449A (en) * | 2010-04-13 | 2010-09-22 | 北京农业信息技术研究中心 | Tyre stress simulation method and system thereof |
| JP6561854B2 (en) * | 2016-01-15 | 2019-08-21 | 住友ゴム工業株式会社 | Tire simulation method |
| CN208121529U (en) * | 2018-04-10 | 2018-11-20 | 四川瑞通工程建设集团有限公司 | A kind of wheel Road surface level instrument of continous way eight removed obstacles automatically |
| CN208219371U (en) * | 2018-04-12 | 2018-12-11 | 浙江裕立检测科技有限公司 | Continuous pavement eight takes turns smoothness measuring equipment |
| CN109547960A (en) * | 2018-11-16 | 2019-03-29 | 万翼科技有限公司 | A kind of intelligent detecting method and system |
| CN110084116A (en) * | 2019-03-22 | 2019-08-02 | 深圳市速腾聚创科技有限公司 | Pavement detection method, apparatus, computer equipment and storage medium |
| CN110318327A (en) * | 2019-06-10 | 2019-10-11 | 长安大学 | A kind of surface evenness prediction technique based on random forest |
| CN111021206A (en) * | 2019-11-20 | 2020-04-17 | 中铁四局集团第一工程有限公司 | Road surface flatness detection method and system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689565A (en) * | 2021-10-21 | 2021-11-23 | 北京中科慧眼科技有限公司 | Road flatness grade detection method and system based on binocular stereo vision and intelligent terminal |
| CN113689565B (en) * | 2021-10-21 | 2022-03-18 | 北京中科慧眼科技有限公司 | Road flatness grade detection method and system based on binocular stereo vision and intelligent terminal |
| CN113957774A (en) * | 2021-10-27 | 2022-01-21 | 安徽理工大学 | Road surface roughness detection device based on singlechip information feedback formula |
| CN116289443A (en) * | 2023-02-01 | 2023-06-23 | 兰陵县昌通公路工程有限公司 | Road and bridge construction flatness measuring device |
| CN116289443B (en) * | 2023-02-01 | 2023-11-10 | 兰陵县昌通公路工程有限公司 | Road and bridge construction flatness measuring device |
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| Publication number | Publication date |
|---|---|
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