技术领域Technical Field
本发明属于道路检测技术领域，尤其是路面和桥梁技术状况检测方法。The invention belongs to the technical field of road detection, in particular to a method for detecting the technical conditions of road surfaces and bridges.
背景技术Background technique
根据国家相关法律、规范的规定，需要对各等公路的路面和桥梁进行定期的技术状况检测，以判断公路健康状况，开展维修养护工作，确保公路安全运营。公路的检测主要分为路面检测和桥梁检测两部分，桥梁动力特性检测是桥梁技术状况检测的重要内容；According to the relevant national laws and regulations, it is necessary to conduct regular technical condition inspections on the pavement and bridges of various highways to determine the health of the highway, carry out maintenance work, and ensure the safe operation of the highway. Highway inspections are mainly divided into two parts: pavement inspection and bridge inspection. Bridge dynamic characteristics inspection is an important part of bridge technical condition inspection;
目前路面检测解决方案为日常巡查和定期检测相结合：日常巡查是定期(例如每天)安排巡检技术人员乘坐巡检车辆，通过目视检查来发现显著的病害情况；定期检测是定期(例如每年)使用搭载了激光、摄像头、传感器的路面检测车对路面进行详细定量检测；At present, the road surface inspection solution is a combination of daily inspection and periodic inspection. Daily inspection is to arrange inspection technicians to take inspection vehicles regularly (for example, every day) to find significant defects through visual inspection. Periodic inspection is to use road surface inspection vehicles equipped with lasers, cameras, and sensors to conduct detailed quantitative inspections of the road surface regularly (for example, every year).
目前桥梁动力特性检测的解决方案有两种：一是人工检测法，首先封闭桥面紧急车道，技术人员将多个加速度传感器放置在桥面特定位置，将所有传感器与上位机连接，打开上位机数据采集软件进行数据采集和保存，然后通过加速度数据分析桥梁动力特性；二是实时监测法，将传感器永久安装在桥梁上，通过构建物联网系统，实现数据实时采集和分析；There are currently two solutions for bridge dynamic characteristics detection: one is the manual detection method, which first closes the emergency lane on the bridge deck, and technicians place multiple acceleration sensors at specific locations on the bridge deck, connect all sensors to the host computer, open the host computer data acquisition software to collect and save data, and then analyze the bridge dynamic characteristics through acceleration data; the other is the real-time monitoring method, which permanently installs sensors on the bridge and builds an Internet of Things system to achieve real-time data collection and analysis;
已有的路面检测解决方案中，日常巡查需要投入专业技术人员和专用车辆，实施人工和设备成本较高，同时日常巡查只能以定性检查为主，检查精细化程度不足。定期检测需要使用昂贵的路面检测车，检测成本较高，且两次检测间隔时间较久，时效性不足；In the existing road inspection solutions, daily inspections require professional technicians and special vehicles, which have high labor and equipment costs. At the same time, daily inspections can only be based on qualitative inspections, and the level of refinement is insufficient. Regular inspections require the use of expensive road inspection vehicles, which have high inspection costs, and the interval between two inspections is long, which is not timely enough.
已有的桥梁动力特性检测方法中，人工检测法需要长时间封闭交通、需要购置若干加速度传感器，检测成本高、实施风险大且不具有实时性，实时监测法需要购置和安装传感器、通信设备、供电设备、采集设备等，实施周期长且实施成本高。Among the existing methods for detecting the dynamic characteristics of bridges, the manual detection method requires long-term traffic closure and the purchase of a number of acceleration sensors. The detection cost is high, the implementation risk is great and it is not real-time. The real-time monitoring method requires the purchase and installation of sensors, communication equipment, power supply equipment, data collection equipment, etc., and the implementation cycle is long and the implementation cost is high.
发明内容Summary of the invention
发明目的：提供路面和桥梁技术状况检测方法，以解决现有技术存在的上述问题。Purpose of the invention: To provide a method for detecting the technical condition of a road surface and a bridge to solve the above-mentioned problems existing in the prior art.
技术方案：路面和桥梁技术状况检测方法，所述方法包括如下步骤：Technical solution: A method for detecting the technical condition of a road surface and a bridge, the method comprising the following steps:
1)获得众多自动驾驶车辆定位信息与车速数据；1) Obtain positioning information and speed data of many autonomous driving vehicles;
2)由车载计算单元计算车速均值和方差，并根据均值和方差筛选符合要求的车辆；2) The vehicle-mounted computing unit calculates the mean and variance of the vehicle speed, and selects vehicles that meet the requirements based on the mean and variance;
3)由车载加速度传感器、摄像头、激光雷达分别获取加速度数据、摄像头图像数据、激光雷达扫描数据，并储存于车载储存单元；3) The vehicle-mounted acceleration sensor, camera, and laser radar respectively obtain acceleration data, camera image data, and laser radar scanning data, and store them in the vehicle-mounted storage unit;
4)由车载计算单元对加速度数据、摄像头图像数据、激光雷达扫描数据进行分析，并通过车载通信单元上传至中心服务器；4) The vehicle-mounted computing unit analyzes the acceleration data, camera image data, and lidar scanning data, and uploads them to the central server through the vehicle-mounted communication unit;
5)由中心服务器分别对加速度数据分析结果、摄像头图像数据分析结果、激光雷达扫描数据分析结果进行融合分析；5) The central server performs fusion analysis on the acceleration data analysis results, camera image data analysis results, and lidar scanning data analysis results respectively;
6)由中心服务器的数据存储单元对分析结果进行存储。6) The data storage unit of the central server stores the analysis results.
优选的，众多自动驾驶车辆定位信息包括：分析桥梁动力特性下的车辆定位信息与分析路面病害下的车辆定位信息；Preferably, the plurality of autonomous driving vehicle positioning information includes: vehicle positioning information under analysis of bridge dynamic characteristics and vehicle positioning information under analysis of road surface diseases;
其中，分析桥梁动力特性下的车辆定位信息，包括：将某段时间通过桥梁的某辆车编号为i，i＝1、2、3……n，获得i车定位信息；The analysis of vehicle positioning information under the dynamic characteristics of the bridge includes: numbering a vehicle passing through the bridge in a certain period of time as i, i=1, 2, 3...n, and obtaining the positioning information of vehicle i;
分析路面病害下的车辆定位信息，包括：将路面根据车道和长度分块，记为itk，t为长度方向顺序号，t＝1、2、3……n，k为车道编号，k＝1、2、3、4，i为某段时间通过某路面区块的某辆车编号,i＝1、2、3……n。Analyze the vehicle positioning information under pavement defects, including: divide the pavement into blocks according to lanes and lengths, denoted as itk, t is the length sequence number, t=1, 2, 3...n, k is the lane number, k=1, 2, 3, 4, i is the number of a vehicle passing through a pavement block in a certain period of time, i=1, 2, 3...n.
优选的，由车载加速度传感器获取加速度数据，包括：Preferably, the acceleration data is obtained by a vehicle-mounted acceleration sensor, including:
通过定位信息判断车辆经过桥梁的起讫时间，分别截取车辆位于桥面和位于路面的加速度数据，并对加速度数据进行滤波处理。The starting and ending times of the vehicle passing the bridge are determined by the positioning information, and the acceleration data of the vehicle on the bridge deck and on the road are intercepted respectively, and the acceleration data are filtered.
优选的，由车载计算单元对加速度数据进行分析，包括：Preferably, the acceleration data is analyzed by the vehicle-mounted computing unit, including:
采用傅里叶变换将路面加速度数据从时域转换至频域，提取前三阶频率记为f1、f2、f3；Fourier transform is used to convert the road acceleration data from the time domain to the frequency domain, and the first three frequencies are extracted and recorded as f1 , f2 , and f3 ;
采用带阻滤波器对桥面加速度数据进行滤波处理，阻带频率范围分别为：[f1×0.95，f1×1.05]，[f2×0.95，f2×1.05]，[f3×0.95，f3×1.05]；The bridge deck acceleration data were filtered using a band-stop filter, and the stop-band frequency ranges were: [f1 ×0.95, f1 ×1.05], [f2 ×0.95, f2 ×1.05], [f3 ×0.95, f3 ×1.05];
将滤波后的数据采用傅里叶变换将数据从时域转换至频域，提取前三阶频率记为The filtered data is converted from the time domain to the frequency domain using Fourier transform, and the first three frequencies are extracted and recorded as
优选的，由车载计算单元对摄像头图像数据进行分析，包括：Preferably, the camera image data is analyzed by the vehicle-mounted computing unit, including:
获取车载摄像头视频流数据，基于图像识别技术进行路面病害识别。Obtain video stream data from the vehicle-mounted camera and identify road defects based on image recognition technology.
优选的，基于图像识别技术进行路面病害识别，包括：Preferably, the road surface disease identification is performed based on image recognition technology, including:
构建路面病害图片数据集；Construct a pavement damage image dataset;
基于数据集训练图像识别算法；Train image recognition algorithms based on datasets;
将图像识别算法应用于车载摄像头视频。Apply image recognition algorithms to vehicle camera videos.
优选的，由车载计算单元对激光雷达扫描数据进行分析，包括：Preferably, the on-board computing unit analyzes the laser radar scanning data, including:
通过车载激光雷达扫描数据建立路面区块高程点云；Establish the elevation point cloud of road blocks through vehicle-mounted LiDAR scanning data;
根据纵向高程差估算该区块国际平整度指数IRI，根据横向高程差判断是否有车辙，根据局部高程差判断是否有局部凸起或凹陷。The international roughness index (IRI) of the block is estimated based on the longitudinal elevation difference, whether there are ruts is determined based on the lateral elevation difference, and whether there are local protrusions or depressions is determined based on the local elevation difference.
优选的，由中心服务器对加速度数据分析结果进行融合分析，包括：Preferably, the central server performs a fusion analysis on the acceleration data analysis results, including:
剔除较大的5％和较小的5％数据，计算剩余车辆的均值得到h1、h2、h3作为该桥梁某段时间的特征频率。Eliminate the larger 5% and smaller 5% data and calculate the remaining vehicles The average values of h1 , h2 , and h3 are obtained as the characteristic frequencies of the bridge for a certain period of time.
优选的，由中心服务器对摄像头图像数据分析结果进行融合分析，包括：Preferably, the central server performs a fusion analysis on the camera image data analysis results, including:
当85％的车辆数据分析结果支持同一结论时，则采纳该结论，结论包括是否存在病害以及病害类型和尺寸。When 85% of the vehicle data analysis results support the same conclusion, the conclusion is adopted, including whether there is a disease and the type and size of the disease.
优选的，由中心服务器对激光雷达扫描数据分析结果进行融合分析，包括：Preferably, the central server performs a fusion analysis on the laser radar scanning data analysis results, including:
定量计算指标：剔除较大的5％和较小的5％数据，计算剩余车辆的均值作为该区块指标的代表值；Quantitative calculation of indicators: Eliminate the larger 5% and smaller 5% of the data, and calculate the average of the remaining vehicles as the representative value of the block indicator;
定性分析指标：当85％的车辆数据分析结果支持同一结论时，则采纳该结论。Qualitative analysis indicator: When 85% of the vehicle data analysis results support the same conclusion, the conclusion is adopted.
综上所述，本发明的有益效果是：In summary, the beneficial effects of the present invention are:
1、实时性大幅度提升；对于路面技术状况检测，定量检测原来的频率是1次/年，本发明可以实时定量跟踪路面病害发生和发展趋势；对于桥梁动力特性检测频率约为5年/次，本发明可以实时检测桥梁动力特性；1. Real-time performance is greatly improved. For road technical condition detection, the original frequency of quantitative detection is once a year. The present invention can quantitatively track the occurrence and development trend of road diseases in real time. For bridge dynamic characteristics detection, the frequency is about once every five years. The present invention can detect bridge dynamic characteristics in real time.
2、成本大幅度下降；已有传统技术需要投入专门的人力、设备和时间来完成路面和桥梁技术状况检测工作，以高速公路为例，目前路面技术状况检测每年需要投入约1.3万元/公里，本发明每年仅需要投入约0.5万元/公里，目前桥梁动力特性检测投入平均约3万元/桥/次，本发明仅需投入约0.1万元/桥/次；2. The cost is greatly reduced. The existing traditional technology requires special manpower, equipment and time to complete the road and bridge technical condition detection work. Taking the expressway as an example, the current road technical condition detection requires an annual investment of about 13,000 yuan/km, while the present invention only requires an annual investment of about 5,000 yuan/km. The current bridge dynamic characteristics detection investment is an average of about 30,000 yuan/bridge/time, while the present invention only requires an investment of about 1,000 yuan/bridge/time;
3、社会总体安全风险下降；一方面减少路面交通管制，减少路面作业次数和范围，作业事故数量将明显降低；另一方面，由于路桥技术状况检测的实时性提高，有助于及时发现路桥安全隐患，避免恶性事故的发生。3. The overall social safety risk is reduced; on the one hand, road traffic control is reduced, the number and scope of road operations are reduced, and the number of operation accidents will be significantly reduced; on the other hand, the real-time performance of road and bridge technical condition detection is improved, which helps to timely discover road and bridge safety hazards and avoid the occurrence of serious accidents.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明提供的路面和桥梁技术状况检测方法的流程图；FIG1 is a flow chart of a method for detecting the technical condition of a road surface and a bridge provided by the present invention;
图2是本发明提供的路面和桥梁技术状况检测方法的网络架构图。FIG2 is a network architecture diagram of the road surface and bridge technical condition detection method provided by the present invention.
具体实施方式Detailed ways
在下文的描述中，给出了大量具体的细节以便提供对本发明更为彻底的理解。然而，对于本领域技术人员而言显而易见的是，本发明可以无需一个或多个这些细节而得以实施。在其他的例子中，为了避免与本发明发生混淆，对于本领域公知的一些技术特征未进行描述。In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.
为了使本技术领域的人员更好地理解本发明方案，下面结合具体实施例进一步说明本发明的技术方案。In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with specific embodiments.
实施1Implementation 1
如图1和图2，本实施例提供的桥梁技术状况检测方法，所述方法包括如下步骤：As shown in FIG1 and FIG2 , the bridge technical condition detection method provided in this embodiment includes the following steps:
1)获得众多自动驾驶车辆定位信息与车速数据；1) Obtain positioning information and speed data of many autonomous driving vehicles;
其中，获得众多自动驾驶车辆定位信息包括将某段时间通过桥梁的某辆车编号为i，i＝1、2、3……n，获得i车定位信息；Among them, obtaining the positioning information of many autonomous driving vehicles includes numbering a certain vehicle passing through the bridge in a certain period of time as i, i=1, 2, 3...n, and obtaining the positioning information of vehicle i;
速度数据可通过自动驾驶车辆上的速度传感器获得。Speed data can be obtained through speed sensors on autonomous vehicles.
2)由车载计算单元计算车速均值和方差，并根据均值和方差筛选符合要求的车辆；2) The vehicle-mounted computing unit calculates the mean and variance of the vehicle speed, and selects vehicles that meet the requirements based on the mean and variance;
3)由车载加速度传感器获取加速度数据，并储存于车载储存单元；3) Acquire acceleration data from the vehicle-mounted acceleration sensor and store it in the vehicle-mounted storage unit;
其中，由车载加速度传感器获取加速度数据包括通过定位信息判断车辆经过桥梁的起讫时间，分别截取车辆位于桥面和位于路面的加速度数据，并对加速度数据进行滤波处理。Among them, obtaining acceleration data by the vehicle-mounted acceleration sensor includes determining the start and end times of the vehicle passing the bridge through positioning information, intercepting the acceleration data of the vehicle on the bridge deck and on the road surface respectively, and filtering the acceleration data.
4)由车载计算单元对加速度数据进行分析，并通过车载通信单元上传至中心服务器；4) The vehicle-mounted computing unit analyzes the acceleration data and uploads it to the central server through the vehicle-mounted communication unit;
其中，由车载计算单元对加速度数据进行分析包括：采用傅里叶变换将路面加速度数据从时域转换至频域，提取前三阶频率记为f1、f2、f3；采用带阻滤波器对桥面加速度数据进行滤波处理，阻带频率范围分别为：[f1×0.95，f1×1.05]，[f2×0.95，f2×1.05]，[f3×0.95，f3×1.05]；将滤波后的数据采用傅里叶变换将数据从时域转换至频域，提取前三阶频率记为The on-board computing unit analyzes the acceleration data, including: using Fourier transform to convert the road acceleration data from the time domain to the frequency domain, extracting the first three frequencies and recording them as f1 , f2 , and f3 ; using a band-stop filter to filter the bridge deck acceleration data, and the stopband frequency ranges are: [f1 ×0.95, f1 ×1.05], [f2 ×0.95, f2 ×1.05], [f3 ×0.95, f3 ×1.05]; using Fourier transform to convert the filtered data from the time domain to the frequency domain, extracting the first three frequencies and recording them as
示例性的，若采样频率为Fs，采样点数为N，频率分辨率为则自动提取前三阶的算法为：For example, if the sampling frequency is Fs , the number of sampling points is N, and the frequency resolution is The algorithm for automatically extracting the first three orders is:
选取部分数据手动分析确定第一阶段频率的估计值f'1；Select some data for manual analysis to determine the estimated value f'1 of the first-stage frequency;
自动分析数据，进行高通滤波，设置截止频率为f'1/2；Automatically analyze the data, perform high-pass filtering, and set the cutoff frequency to f'1 /2;
进行快速傅里叶变换将数据从时域转换至频域，得到频域曲线y＝H(ωi)Perform fast Fourier transform to convert data from time domain to frequency domain, and obtain frequency domain curve y = H (ωi )
其中，ωi是离散频域曲线上第i个点的频率值，H(ωi)是第i个点的幅值，Where ωi is the frequency value of the i-th point on the discrete frequency domain curve, H(ωi ) is the amplitude of the i-th point,
绘制频域曲线的趋势图，找出所有趋势点：Draw a trend graph of the frequency domain curve and find all trend points:
首先将频域曲线第一个点(ω1,H(ω1))作为趋势线起点，记为(x1,q1)；First, the first point of the frequency domain curve (ω1 ,H(ω1 )) is taken as the starting point of the trend line, recorded as (x1 ,q1 );
计算趋势线其他点的方法如下：The method for calculating other points of the trend line is as follows:
计算移动窗大小公式为：The formula for calculating the moving window size is:
其中int为取整函数；Where int is the rounding function;
计算频域曲线(ω1,ωw]范围内，即一个移动窗范围内的所有幅值点和ω1幅值点连线的斜率：Calculate the slope of the line connecting all amplitude points within the range of the frequency domain curve (ω1 ,ωw ], that is, within a moving window range and the ω1 amplitude point:
取所有ki最大值对应的(ωi,H(ωi))作为趋势线第二个点，记为(x2,q2)；Take (ωi ,H(ωi )) corresponding to the maximum value of all ki as the second point of the trend line, recorded as (x2 ,q2 );
以(x2,q2)为起点寻找下一个移动窗范围的趋势点(x3,q3)，以此类推，确定其他趋势点(x4,q4)……(xm,qm)。Taking (x2 ,q2 ) as the starting point, find the trend point (x3 ,q3 ) of the next moving window range, and so on, to determine other trend points (x4 ,q4 )…(xm ,qm ).
寻找特征频率：Find the eigenfrequencies:
计算(xi,qi)与相邻趋势点(xi-1,qi-1)及(xi+1,qi+1)的斜率：Calculate the slope of (xi ,qi ) and the adjacent trend points (xi-1 ,qi-1 ) and (xi+1 ,qi+1 ):
若ki-1>0且ki<0，则计xi为特征频率，取前三个特征频率为前三阶频率。If ki-1 >0 and ki <0, then xi is taken as the characteristic frequency, and the first three characteristic frequencies are taken as the first three-order frequencies.
5)由中心服务器对加速度数据分析结果进行融合分析；5) The central server performs fusion analysis on the acceleration data analysis results;
剔除较大的5％和较小的5％数据，计算剩余车辆的均值得到h1、h2、h3作为该桥梁某段时间的特征频率。Eliminate the larger 5% and smaller 5% data and calculate the remaining vehicles The average values of h1 , h2 , and h3 are obtained as the characteristic frequencies of the bridge for a certain period of time.
6)由中心服务器的数据存储单元对分析结果进行存储。6) The data storage unit of the central server stores the analysis results.
本实施例提供的桥梁技术状况检测方法：让众多自动驾驶的车辆行驶通过桥梁，通过加速度传感器获取并截取车辆位于桥面和位于路面的加速度数据，对加速度数据进行分析，计算得出前三阶频率的均值，作为该桥梁某段时间的特征频率，以对桥梁动力特性进行检测，可实时检测桥梁动力特性，及时发现桥梁安全隐患，避免恶性事故发生，无需投入专门的人力、设备和时间来进行桥梁技术状况检测工作，成本大幅度下降，同时可以减少路面交通管制，减少路面作业次数和范围，作业事故数量将明显降低。The method for detecting the technical condition of a bridge provided in this embodiment is as follows: a plurality of self-driving vehicles are allowed to drive across the bridge, and acceleration data of the vehicles on the bridge deck and on the road surface are acquired and intercepted by acceleration sensors, and the acceleration data are analyzed to calculate the average of the first three frequencies, which is used as the characteristic frequency of the bridge for a certain period of time, so as to detect the dynamic characteristics of the bridge. The dynamic characteristics of the bridge can be detected in real time, and potential safety hazards of the bridge can be discovered in time to avoid serious accidents. There is no need to invest special manpower, equipment and time in the detection of the technical condition of the bridge, and the cost is greatly reduced. At the same time, road traffic control can be reduced, the number and scope of road operations can be reduced, and the number of operational accidents will be significantly reduced.
实施例2Example 2
如图1和图2，本实施例提供的路面技术状况检测方法，所述方法包括如下步骤：As shown in FIG1 and FIG2 , the road surface technical condition detection method provided in this embodiment includes the following steps:
1)获得众多自动驾驶车辆定位信息与车速数据；1) Obtain positioning information and speed data of many autonomous driving vehicles;
其中，获得众多自动驾驶车辆定位信息包括将路面根据车道和长度分块，记为itk，t为长度方向顺序号，t＝1、2、3……n，k为车道编号，k＝1、2、3、4，i为某段时间通过某路面区块的某辆车编号,i＝1、2、3……n；Among them, obtaining the positioning information of many autonomous driving vehicles includes dividing the road surface into blocks according to lanes and lengths, recorded as itk, t is the length direction sequence number, t = 1, 2, 3...n, k is the lane number, k = 1, 2, 3, 4, i is the number of a certain vehicle passing through a certain road surface block in a certain period of time, i = 1, 2, 3...n;
速度数据可通过自动驾驶车辆上的速度传感器获得。Speed data can be obtained through speed sensors on autonomous vehicles.
2)由车载计算单元计算车速均值和方差，并根据均值和方差筛选符合要求的车辆；2) The vehicle-mounted computing unit calculates the mean and variance of the vehicle speed, and selects vehicles that meet the requirements based on the mean and variance;
3)由车载摄像头获取摄像头图像数据并储存于车载储存单元；3) Obtaining camera image data from the vehicle-mounted camera and storing it in the vehicle-mounted storage unit;
4)由车载计算单元对摄像头图像数据进行分析，并通过车载通信单元上传至中心服务器；4) The on-board computing unit analyzes the camera image data and uploads it to the central server through the on-board communication unit;
其中，由车载计算单元对摄像头图像数据进行分析包括获取车载摄像头视频流数据，基于图像识别技术进行路面病害识别。The on-board computing unit analyzes the camera image data, including acquiring the on-board camera video stream data and identifying road surface defects based on image recognition technology.
基于图像识别技术进行路面病害识别包括：Pavement disease identification based on image recognition technology includes:
构建路面病害图片数据集；Construct a pavement damage image dataset;
基于数据集训练图像识别算法；Train image recognition algorithms based on datasets;
将图像识别算法应用于车载摄像头视频。Apply image recognition algorithms to vehicle camera videos.
5)由中心服务器对摄像头图像数据分析结果进行融合分析；5) The central server performs fusion analysis on the camera image data analysis results;
当85％的车辆数据分析结果支持同一结论时，则采纳该结论，结论包括是否存在病害以及病害类型和尺寸。When 85% of the vehicle data analysis results support the same conclusion, the conclusion is adopted, including whether there is a disease and the type and size of the disease.
6)由中心服务器的数据存储单元对分析结果进行存储。6) The data storage unit of the central server stores the analysis results.
本实施例提供的路面技术状况检测方法：将路面根据车道和长度分块，让众多自动驾驶车辆在路面区块中行驶，并通过车载摄像头收集图像数据，并基于图像识别技术进行路面病害识别，可实时定量跟踪路面病害发生和发展趋势，及时发现安全隐患，避免恶性事故发生，无需投入专门的人力、设备和时间来进行路面技术状况检测工作，成本大幅度下降，同时可以减少路面交通管制，减少路面作业次数和范围，作业事故数量将明显降低。The road surface technical condition detection method provided in this embodiment divides the road surface into blocks according to lanes and lengths, allows a large number of autonomous driving vehicles to travel in the road surface blocks, collects image data through on-board cameras, and identifies road surface defects based on image recognition technology. It can quantitatively track the occurrence and development trend of road surface defects in real time, discover safety hazards in time, and avoid serious accidents. There is no need to invest special manpower, equipment and time to carry out road surface technical condition detection work, and the cost is greatly reduced. At the same time, it can reduce road traffic control, reduce the number and scope of road operations, and the number of operation accidents will be significantly reduced.
实施例3Example 3
如图1和图2，本实施例提供的路面技术状况检测方法，所述方法包括如下步骤：As shown in FIG1 and FIG2 , the road surface technical condition detection method provided in this embodiment includes the following steps:
1)获得众多自动驾驶车辆定位信息与车速数据；1) Obtain positioning information and speed data of many autonomous driving vehicles;
其中，获得众多自动驾驶车辆定位信息包括将路面根据车道和长度分块，记为itk，t为长度方向顺序号，t＝1、2、3……n，k为车道编号，k＝1、2、3、4，i为某段时间通过某路面区块的某辆车编号,i＝1、2、3……n；Among them, obtaining the positioning information of many autonomous driving vehicles includes dividing the road surface into blocks according to lanes and lengths, recorded as itk, t is the length direction sequence number, t = 1, 2, 3...n, k is the lane number, k = 1, 2, 3, 4, i is the number of a certain vehicle passing through a certain road surface block in a certain period of time, i = 1, 2, 3...n;
速度数据可通过自动驾驶车辆上的速度传感器获得。Speed data can be obtained through speed sensors on autonomous vehicles.
2)由车载计算单元计算车速均值和方差，并根据均值和方差筛选符合要求的车辆；2) The vehicle-mounted computing unit calculates the mean and variance of the vehicle speed, and selects vehicles that meet the requirements based on the mean and variance;
3)由车载激光雷达获取激光雷达扫描数据，并储存于车载储存单元；3) Obtaining laser radar scanning data from the vehicle-mounted laser radar and storing it in the vehicle-mounted storage unit;
4)由车载计算单元对激光雷达扫描数据进行分析，并通过车载通信单元上传至中心服务器；4) The on-board computing unit analyzes the laser radar scanning data and uploads it to the central server through the on-board communication unit;
其中，由车载计算单元对激光雷达扫描数据进行分析包括：The on-board computing unit analyzes the laser radar scanning data including:
通过车载激光雷达扫描数据建立路面区块高程点云；Establish the elevation point cloud of road blocks through vehicle-mounted LiDAR scanning data;
根据纵向高程差估算该区块国际平整度指数IRI，根据横向高程差判断是否有车辙，根据局部高程差判断是否有局部凸起或凹陷。The international roughness index (IRI) of the block is estimated based on the longitudinal elevation difference, whether there are ruts is determined based on the lateral elevation difference, and whether there are local protrusions or depressions is determined based on the local elevation difference.
5)由中心服务器对激光雷达扫描数据分析结果进行融合分析；5) The central server performs fusion analysis on the LiDAR scanning data analysis results;
对于定量计算指标：剔除较大的5％和较小的5％数据，计算剩余车辆的均值作为该区块指标的代表值；For quantitative calculation indicators: remove the larger 5% and smaller 5% of the data, and calculate the average of the remaining vehicles as the representative value of the block indicator;
对于定性分析指标：当85％的车辆数据分析结果支持同一结论时，则采纳该结论。For qualitative analysis indicators: when 85% of the vehicle data analysis results support the same conclusion, that conclusion is adopted.
6)由中心服务器的数据存储单元对分析结果进行存储。6) The data storage unit of the central server stores the analysis results.
本实施例提供的路面技术状况检测方法：将路面根据车道和长度分块，让众多自动驾驶车辆在路面区块中行驶，并通过车载激光雷达对路面进行扫描，建立路面区块高程点云，以检测路面变形类病害，可实时定量跟踪路面变形类病害发生和发展趋势，及时发现安全隐患，避免恶性事故发生，无需投入专门的人力、设备和时间来进行路面技术状况检测工作，成本大幅度下降，同时可以减少路面交通管制，减少路面作业次数和范围，作业事故数量将明显降低。The pavement technical condition detection method provided in this embodiment is as follows: the pavement is divided into blocks according to lanes and lengths, a large number of autonomous driving vehicles are allowed to travel in the pavement blocks, and the pavement is scanned by means of on-board laser radar to establish elevation point clouds of the pavement blocks to detect pavement deformation-related diseases. The occurrence and development trends of pavement deformation-related diseases can be tracked quantitatively in real time, potential safety hazards can be discovered in time, and serious accidents can be avoided. There is no need to invest special manpower, equipment, and time in the pavement technical condition detection work, and the cost is greatly reduced. At the same time, road traffic control can be reduced, the number and scope of road operations can be reduced, and the number of operation accidents will be significantly reduced.
以上结合附图详细描述了本发明的优选实施方式，但是，本发明并不限于上述实施方式中的具体细节，在本发明的技术构思范围内，可以对本发明的技术方案进行多种等同变换，这些等同变换均属于本发明的保护范围。The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical scheme of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
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| CN202311815179.1ACN118258568B (en) | 2023-12-27 | 2023-12-27 | Methods for testing the technical conditions of pavements and bridges |
| Application Number | Priority Date | Filing Date | Title |
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| CN202311815179.1ACN118258568B (en) | 2023-12-27 | 2023-12-27 | Methods for testing the technical conditions of pavements and bridges |
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| CN118258568Atrue CN118258568A (en) | 2024-06-28 |
| CN118258568B CN118258568B (en) | 2025-07-11 |
| Application Number | Title | Priority Date | Filing Date |
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| CN202311815179.1AActiveCN118258568B (en) | 2023-12-27 | 2023-12-27 | Methods for testing the technical conditions of pavements and bridges |
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| US20080184785A1 (en)* | 2007-02-01 | 2008-08-07 | Wee Seong-Dong | Apparatus for automatically inspecting road surface pavement condition |
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| CN112229355A (en)* | 2020-12-14 | 2021-01-15 | 湖南大学 | A method and system for bridge movement detection based on signal processing and trailer response |
| CN112965077A (en)* | 2021-02-09 | 2021-06-15 | 上海同陆云交通科技有限公司 | Road inspection system and method based on vehicle-mounted laser radar |
| CN113706737A (en)* | 2021-10-27 | 2021-11-26 | 北京主线科技有限公司 | Road surface inspection system and method based on automatic driving vehicle |
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| CN118258568B (en) | 2025-07-11 |
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