CN114858214A - Urban road performance monitoring system - Google Patents

Abstract

The invention discloses an urban road performance monitoring system, which relates to the technical field of road monitoring. Then different types of monitoring equipment are investigated, and the monitoring equipment is transversely compared at multiple angles of performance, cost and the like to determine the model of the equipment which is most suitable for the monitoring equipment. On the basis, the distribution condition of the existing roadside monitoring equipment is combined, and the arrangement intervals of different monitoring equipment are determined by determining a design experiment from the monitoring range and cost of the monitoring equipment. Considering the high cost of arranging the monitoring system on all road sections in the city, the invention also provides a method for judging whether the road side equipment should be arranged on the road sections.

Description

Urban road performance monitoring system

Technical Field

The invention relates to the technical field of road monitoring, in particular to an urban road performance monitoring system.

Background

In order to clarify the reconstruction condition of the existing road by the monitoring system for building the road condition, the existing monitoring system for the urban road needs to be evaluated at first.

At present, except for smart road project sections built in part of cities, monitoring devices arranged in the cities comprise traffic cameras, speed measuring instruments and rain monitoring stations, but the monitoring devices lack devices necessary for a road surface performance monitoring system such as laser radars and vehicle-road communication devices, and the monitoring devices such as the cameras should be properly encrypted according to project requirements.

The traffic cameras and the speed measuring instrument are mainly distributed near the intersection and in the key road section, so that traffic violation behaviors can be accurately captured, and reliable basis is provided for law enforcement of traffic control departments. In addition, the statistics of the traffic flow at the intersection can be completed by taking pictures through the camera, and a basis is provided for intersection traffic light timing design and new construction or road reconstruction design. Although the existing traffic cameras are sufficient to meet the requirements for the above functions, the distribution density and pixels are not sufficient to fully realize the road performance monitoring for the automatic facing driving. According to research, the distribution density of traffic cameras in cities is low, for example, the distribution density of cameras in Nanjing city is about 2.3 km/place, and the monitoring coverage rate is only 8.7% when a single camera monitors a 200m road section. In addition, in order to meet the requirement of identifying tiny road surface diseases, the pixels of the camera are not less than 800 ten thousand pixels, and the pixel difference of the existing traffic camera is large due to different types, generally between 500 ten thousand pixels and 1000 ten thousand pixels, and only part of the cameras meet the requirement.

The rainfall monitoring station in the city is set by a meteorological department and is used for estimating the total rainfall of the area and sending out a strong rainfall warning. However, the distribution density of the rainfall monitoring stations is low, the average density is about 80 square kilometers per station, the precision of the rainfall of the road in a region far away from the monitoring stations is low, and in order to meet the requirements of a detection system, a proper amount of rainfall stations are required to be added to improve the distribution density.

In order to obtain detailed data required by an urban road performance evaluation model and accurately evaluate the performance state of a road, monitoring equipment is required to cover all areas of all road sections of the urban road, but the implementation cost is high, and the implementation is poor. According to the current national conditions of China, the acquisition of data required by a model is implemented step by step, equipment is installed on a representative road section with large flow, large equivalent area, high importance and the like, and the data is gradually expanded after running for a period of time. The equipment is distributed on the most representative road section, the road section where the equipment is to be distributed needs to be inspected and demonstrated on the spot, unnecessary road sections and unreasonable road sections are eliminated, the waste of resources caused by the repetition of collected information is avoided, and the function of the equipment is fully exerted.

Disclosure of Invention

The invention aims to provide an urban road performance monitoring system which is built on an urban road network and consists of three modules, namely a sensor module, a data center and a communication module.

The sensor module comprises a camera, a laser radar, a temperature sensor, a rainfall sensor and a vehicle detector; the camera is used for monitoring the road surface performance; the laser radar is used for monitoring, identifying and collecting road traffic data; the temperature sensor is used for monitoring the temperature condition of the road; the rainfall sensor is used for monitoring the rainfall on the road; the vehicle detector is used for monitoring the traffic volume;

the data center consists of a data processing module and a storage module;

the data processing module is used for analyzing the received data and the stored data, and specifically comprises road performance evaluation and road performance prediction;

the storage module is used for storing the monitoring data according to time and the road sections to which the monitoring data belong in a classified manner, wherein the data obtained after the data storage processing module of the camera and the laser radar is used for processing the data, and the data of other sensors and the data of the transverse force coefficient detection vehicle are directly stored;

the communication module comprises roadside communication equipment and communication optical fibers for connecting each sensor module and the data center and connecting the data center and the roadside communication equipment; the roadside communication equipment is connected with the data center through optical fibers and used for receiving the evaluation result of the road section where the roadside communication equipment is located and providing data service for the requester when the intelligent automobile initiates a service request to the roadside communication equipment.

Preferably, the monitoring camera adopts a CCD camera, and the transverse monitoring range of the camera comprises all lanes of the area to be monitored; the camera can clearly shoot the performance of the road surface within at least 100m along the lane direction; the camera pixels should not be less than 200 ten thousand; cameras are installed on the road side, and the installation interval of the cameras is 80 m.

Preferably, the laser radar is a semi-solid laser radar; the horizontal field angle of the laser radar is 30 degrees, and the transverse monitoring distance of the laser radar is not smaller than 7.5m when the laser radar is arranged to monitor two lanes; the installation height of the laser radar is 14m, the installation interval of the laser radar is 50m for a 16-line laser radar, and the installation interval of the laser radar is 110m for a 32-line laser radar.

Preferably, the temperature sensors are IC temperature sensors, and the temperature sensors are arranged at intervals of 6km in urban areas, and at intervals of 2-3km in suburban transition areas.

Preferably, the rainfall sensor adopts a radar type rain gauge or a piezoelectric type rain gauge, and the arrangement interval of the rainfall sensor is 4-5 km/station.

Preferably, the vehicle detector is a microwave vehicle detector; in a single road section, a vehicle detector is installed at a middle position of the road section.

Preferably, the roadside communication device adopts a vehicle-road cooperative communication device of an LTE-V technology.

Based on the above, another object of the present invention is to provide a road performance evaluation method, comprising the steps of:

s1: classifying the received sensor data according to the sending time and the road section where the sensor is located;

s2: preprocessing image data and laser point cloud data to obtain data such as transverse and longitudinal crack length, pit area and the like;

s3: and inputting the data belonging to the same road section into the evaluation model, and calculating and obtaining the grade and the driving suggestion of the road section by the evaluation model.

In view of the above, another object of the present invention is to provide a road surface performance prediction method, comprising the steps of:

s1: respectively calculating data such as average score and annual rainfall of the road section all the year round;

s2: and inputting the data into a prediction model, and calculating the road surface performance score of the road section in the coming years as a basis for judging whether the road section needs to be maintained or not.

Based on the above, in view of the cost of arranging monitoring systems on all road segments in a city, a further object of the present invention is to provide a method for determining whether road-side equipment should be arranged on a road segment, wherein the road segment on which the sensor module (i.e. the monitoring equipment) is arranged is scored according to the road segment importance, and the calculation formula of the road segment importance score is as follows:

T＝0.5ω+0.5Q

in the formula:

ω: grade evaluation value of a certain road;

q: a traffic flow normalization value of a certain road;

t: road importance;

the calculation formula of the traffic flow normalized value of each road is as follows:

in the formula:

q: a traffic flow normalization value of a certain road;

q: a traffic flow value on a certain road;

q _max : the maximum traffic flow of the roads in the area;

q _min : the minimum traffic flow of the roads in the region.

The grade evaluation value omega of a certain road belongs to qualitative indexes, the importance degrees of every two indexes need to be compared, and an analytic hierarchy process can be adopted for determining.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 is a schematic diagram of a road performance monitoring system;

FIG. 2 is a laser cloud point diagram with a laser radar mounted horizontally;

FIG. 3 is a laser cloud point with the lidar mounted vertically;

FIG. 4 is a diagram of laser radar lateral monitoring distances under different conditions;

FIG. 5 is a diagram of different conditions lidar longitudinal monitored distances;

fig. 6 is a difference in traffic volume at the entrance and exit of the road.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.

Examples

Aiming at data required by a road performance evaluation system and a prediction model, the embodiment of the application aims at determining monitoring equipment and means of various data. Then, in order to enable data to be communicated between vehicles and equipment, components of the urban road performance monitoring system are determined, selected equipment is investigated, and the type selection and the performance requirements of various types of equipment are determined. In addition, in order to ensure that construction funds are fully utilized, the existing roadside equipment is analyzed, and a design scheme of monitoring system arrangement is provided on the basis.

Urban road performance monitoring demand analysis

According to the requirements of the evaluation model and the prediction model, the urban road performance of the equipment needing to be installed is monitored, and data required by the evaluation model and the prediction model are obtained. Considering that the partial diseases have similar characteristics, the same equipment can be adopted for monitoring, and according to the similarity between data, the monitoring contents can be divided into the following categories:

(1) apparent class data: the method comprises the steps of evaluating the area of a reticular crack and the length of a transverse crack and a longitudinal crack required by the crack condition; the pit length, width, and position information required to evaluate the pit condition, and the area of the obstacle. The color and brightness difference between the disease occurrence area and the surrounding area is obvious, the disease area is determined by performing color difference analysis on road surface pictures shot by cameras arranged on the road side based on a deep learning algorithm, and then the type of the regional disease is determined according to the shape and the color difference characteristics of the area and data is obtained.

(2) Height difference type data: comprises evaluating the depth of track; the maximum gap height of the road surface required for evaluation of the flatness and the height of the obstacle. Certain height difference exists between the disease areas of the diseases and the surrounding areas, and the certain height difference is obtained by analyzing point cloud data obtained by scanning the laser radar. When the laser radar works, whether obstacles exist in a scanning area is judged by combining a camera picture, when the obstacles exist, the height of the obstacles is determined by calculation according to the height change of the point cloud, the point cloud of the obstacles is removed, then the subsequent analysis is carried out, and when the obstacles do not exist, the subsequent analysis is directly carried out. In subsequent analysis, the height change of the point cloud along the road advancing direction and the vertical direction needs to be calculated respectively to obtain rut depth and maximum gap height data of the road surface.

(3) Data required for evaluation of anti-skid properties: the system comprises temperature and a road surface transverse force coefficient, wherein temperature data are automatically monitored by adopting a temperature sensor, the transverse force coefficient is short of a mature automatic monitoring means, and the data are acquired by adopting a transverse force coefficient detection vehicle in a monthly detection mode.

(4) Other types of data: in order to meet the demand of the prediction model, rainfall and traffic volume data are acquired in addition to the data. Wherein the rainfall data is obtained by arranging a rain gauge at the roadside and the traffic volume is acquired by a vehicle detector.

Second, the system architecture and functions of the present embodiment

The road performance monitoring system is built on an urban road network, the characteristics of urban roads and the monitoring content of the detection system are combined, and the whole system consists of a sensor module, a communication module and a data center, wherein the components of the system are shown in figure 1.

(1) Sensor module

The monitoring system comprises five sensors, namely a camera, a laser radar, a temperature sensor, a rainfall sensor and a vehicle detector. In order to meet the requirement of the detection system on acquisition precision and reduce the cost as much as possible, the cost, the performance and other aspects of the sensor need to be investigated and comprehensively compared, and the sensor applied to the system is determined.

Camera

According to the difference between the photo-sensing chips, the cameras can be classified into Charge-coupled device (CCD) cameras and Complementary Metal Oxide Semiconductor (CMOS) cameras, and the comparison between the two cameras in terms of performance is shown in table 1:

table 1: comparison of CCD and CMOS Performance

According to table 1, the CCD camera performs better than the CMOS camera in terms of both imaging quality and minimum illumination intensity, and is inferior to the CMOS camera only in terms of noise amount, thereby having an effect that the CCD camera takes a larger amount of data of a picture and occupies more memory under the same definition condition. However, the superior performance of the CCD camera in terms of imaging quality and minimum illumination enables the CCD camera to have higher shooting precision and longer working time than the CMOS camera, and achieve higher road surface performance monitoring accuracy, so the CCD camera is recommended to be used as a monitoring camera.

In order to meet the monitoring requirement, the performance of the selected CCD camera should meet the following requirements: (1) in order to ensure that no blind area exists on the road, the transverse monitoring range of the camera comprises all lanes of the area to be monitored; (2) the camera can clearly shoot the performance of the road surface within at least 100m along the lane direction; (3) in order to ensure that the shot picture can clearly reflect the pavement cracks, the pixel of the camera is not less than 200 ten thousand.

② laser radar

The laser radar has more parts, and the difference of the technical selection of each part causes the difference of the effect and the cost, which leads to the diversification of the technical route of the laser radar. The laser radar can be decomposed into five core technologies from a ranging mode, a transmitting mode, a light beam operation mode, a detecting mode and a data processing mode, each core technology has different technical branches, the laser radars of different branches are different in the aspects of performance, cost, current mass production difficulty and the like, and the different branch technologies on the five core technologies are selected, so that the technical routes of the laser radars of all families and enterprises are different.

The laser radar has various classification modes, wherein the mainstream method is divided into three categories according to scanning components, namely a mechanical type, a semi-solid type and a solid type, and the details are as follows:

i) mechanical: the mechanical part (scanning module) and the electronic part (laser transceiver module) are both in motion, and are driven by a motor to rotate for 360 degrees;

ii) semi-solid formula: the laser transceiver module does not move, and only the scanning module moves;

iii) solid state formula: not only does the laser transceiver module not move, but the scanning module also does not move mechanically.

The semi-solid laser radar can be divided into three types of MEMS, rotating mirror type and prism type according to the movement mode of the scanning module, and the solid laser radar is divided into two types of OPA phase control and Flash. A comparison of the various types of lidar is shown in table 2.

Table 2: comparison of various lidar types

Based on the above table 2, of the three types of laser radars, the mechanical type laser radar has the advantages of the highest technical maturity and the best ranging accuracy, but has the disadvantages of huge volume and high cost, and under the condition of the same line number, the cost of the mechanical type laser radar is several times that of the semisolid laser radar and is ten times that of the semisolid laser radar. However, the related research technology of the solid-state laser radar is not mature at present and is not put into mass production. Compared with the former two, the semi-solid laser radar technology is mature, and the existing semi-solid laser radar technology starts mass production in part of enterprises and is applied to intelligent automobiles. Although the semi-solid laser radar is inferior to the mechanical laser radar in the precision of single-beam laser ranging, the number of laser transmitters of the semi-solid laser radar is more than one time of that of the mechanical laser radar at the same price, the density of formed point cloud is higher, and the overall detection effect is better. In view of the above, a semi-solid lidar was chosen as the monitoring device.

③ temperature sensor

Temperature sensors commonly used on the market are resistance temperature detectors, thermocouples, thermistors and IC temperature sensors. The comparison between them is shown in table 3 below:

table 3: comparison of various temperature sensors

According to the above table 3, the temperature measuring ranges of the four sensors are all in accordance with the requirements of the present invention. Among the four sensors, the accuracy of the resistance temperature detector is optimal; the thermistor and the IC temperature sensor are slightly inferior in precision, generally about 0.1 ℃, and can meet the monitoring requirement; and the measurement accuracy of the thermocouple depends on the voltage measurement accuracy. From the view of the complexity and cost of the circuit diagram, the IC temperature sensor is far superior to the rest three, and has extremely low cost and simple circuit diagram structure. In addition, due to the simple circuit diagram, the volume of the IC temperature sensor is the smallest of the four, and the IC temperature sensor is most convenient to install. And integrating all conditions, selecting the IC temperature sensor to monitor the temperature condition of the road, wherein the IC temperature sensor is most suitable.

Pluviometer

From the structural point of view, the rain gauge can be classified into a mechanical type and a non-mechanical type. The mechanical rain gauge comprises a tipping bucket rain gauge, a siphon rain gauge, a double-valve capacity grid rain gauge and the like, and the non-mechanical rain gauge comprises a radar rain gauge, a pressure sensing rain gauge and a laser rain gauge. A comparison of the respective types of rain gauges is shown in table 4 below.

Table 4: comparison of types of rain gauges

The mechanical rain gauge is usually required to be maintained frequently, has a large volume, is very easy to shield vehicles when being installed on the road side so as to induce traffic accidents, and is not suitable for the invention. The non-mechanical rain gauges are approximately similar in accuracy, which is sufficient for monitoring. The radar type rain gauge and the piezoelectric type rain gauge can be used for a long time without regular maintenance, so that the radar type rain gauge or the piezoelectric type rain gauge is adopted.

Vehicle detector

At present, the equipment for detecting the traffic volume in China is various, and the equipment is widely applied to coil type vehicle detectors, microwave type vehicle detectors, video vehicle detectors and the like.

Coil type vehicle detector

The coil type vehicle detector is a vehicle detector based on the principle of electromagnetic induction, and a coil buried under a road surface is used as a sensor of the vehicle detector.

After decades of development, the coil type vehicle detector is quite mature, is widely applied to the highway industry, and has the advantages of high speed measurement precision, high traffic counting precision, good working stability, strong interference resistance and the like, and is not influenced by weather and traffic environments; but need cut the road surface and bury wherein after bituminous paving construction is accomplished, not only damage seriously to the road surface, its result of use can be influenced to road surface subsidence, crack etc. moreover, rolling, coil ageing, environmental change etc. of oversize vehicle can influence vehicle detector performance, arrange with later stage operation cost of maintenance high.

(ii) microwave-type vehicle detector

The microwave type vehicle detector is a traffic data product using digital radar microwave detection technology, and sends out microwave beams to a detection area in real time through a transmitting antenna installed on a portal frame or a roadside upright column. When the vehicle passes through the detection area, the microwave receiving module receives microwaves with different frequencies, and the transmitter and the receiver of the microwave type vehicle detector measure the frequency change to measure the passing and the existence of the vehicle, so as to obtain data such as traffic flow, vehicle speed, vehicle type and the like.

The microwave type vehicle detector has the advantages of simple installation, convenient debugging, contribution to later-stage operation and maintenance management and all-weather work. The device has the disadvantage of being greatly influenced by the environment, and if an obstacle or other signal emitting devices exist nearby, the device can influence the detection precision, so that the device cannot be installed at the same position as the laser radar.

(iii) video vehicle detector

Video vehicle detectors are a computer processing system that uses image processing techniques to achieve the detection of traffic targets. The method realizes automatic statistics of the number of motor vehicles running on a traffic road section, calculation of the speed of running vehicles, identification and division of various related traffic parameters such as the classes of the running vehicles and the like by detecting the road traffic condition information and the traffic target in real time.

The video vehicle detector is similar to the microwave vehicle detector, and has the advantages of no need of damaging road surface, convenient mounting and dismounting of the detector, wireless performance and large detection range. However, the measurement accuracy is very limited by the field lighting, and the vehicle cannot work at night and cannot detect a stationary vehicle.

Compared with the three detectors, the coil type vehicle detector needs to embed the detection coil into the road, which can damage the road surface, and if the detection coil is overhauled, the detection coil can cause secondary damage to the road surface, so that the detection coil is not suitable for use; the video vehicle detector cannot work at night, the monitoring data is incomplete, and accurate traffic data cannot be obtained; only the microwave type vehicle detector can continuously work without damaging the road surface, and all the requirements of the invention on traffic detection are met. Therefore, a microwave-type vehicle detector is selected to monitor the traffic volume.

(2) Data center

The data center consists of a data processing module and a storage module.

The data processing module is responsible for analyzing the received data and the stored data, and can be specifically divided into road performance evaluation and road performance prediction.

The road performance evaluation steps are as follows:

s1: classifying the received sensor data according to the sending time and the road section where the sensor is located;

s2: preprocessing image data and laser point cloud data to obtain data such as transverse and longitudinal crack length, pit area and the like;

s3: and inputting the data belonging to the same road section into the evaluation model, and calculating and obtaining the grade and the driving suggestion of the road section by the evaluation model.

The steps for predicting the road surface performance are as follows:

s1: respectively calculating data such as average score and annual rainfall of the road section all the year round;

s2: and inputting the data into a prediction model, and calculating the road surface performance score of the road section in the coming years as a basis for judging whether the road section needs to be maintained or not.

The storage module is responsible for storing the monitoring data according to time and the belonged road section in a classified mode, wherein the data obtained after the data storage processing module of the camera and the laser radar is used for processing the data, and the data of other sensors and the transverse force coefficient detection vehicle are directly stored.

(3) Communication module

The communication module comprises roadside communication equipment and communication optical fibers for connecting each sensor and the data center and connecting the data center and the roadside communication equipment.

The road side communication equipment is connected with the data center through optical fibers and is responsible for receiving the evaluation result of the road section where the road side communication equipment is located and providing data service for a requester when the intelligent automobile initiates a service request to the intelligent automobile.

From the global perspective, in the field of vehicle-to-road communication, two different technical routes of LTE-V (4GLTE communication) and DSRC (WiFi-based short-range communication for vehicles) mainly exist at present. Wherein LTE-V is mainly promoted by domestic enterprises (including Datang, Huashi, etc.), and DSRC is promoted by the U.S. dominance. The DSRC develops over ten years, the technology tends to be mature, and the complete standard of the DSRC enables the DSRC to occupy the first opportunity when being popularized and deployed; however, DSRC employs lower frequency signals that are less penetrating than LTE-V at the higher frequencies. LTE-V provides higher bandwidth, higher transmission rates, greater coverage, and can reuse existing cellular infrastructure and spectrum.

In addition, from the technical perspective, the LTE-V fully uses the experience and the deficiency of DSRC in the design process, and has obvious performance advantages in the aspects of system capacity, coverage range and the like; the LTE-V can fully utilize the advantages of an LTE cellular network, ensure the continuity and reliability of services, can also utilize the connection of a base station and a hosted cloud server to carry out high-speed data transmission such as high-definition video and audio, and has certain advantages.

From the industrial perspective, LTE-V is a communication technology with independent intellectual property rights, is beneficial to domestic enterprises to avoid patent risks, has low investment in network deployment and maintenance, and can be realized by upgrading based on the existing LTE network base station equipment and a safety mechanism.

In summary, the embodiment of the present application selects the vehicle-road cooperative communication device that is produced by domestic enterprises and adopts the LTE-V technology.

Third, arrangement scheme of urban road monitoring equipment

(1) Monitoring equipment layout space design

I) Camera arrangement Interval design

When the focal length and the pixel are fixed, the road surface definition is lower when the distance between the camera and the camera in the shooting picture is longer. In order to accurately identify and acquire the road surface disease information, the image in the picture shot by the camera needs to meet the precision requirement of image identification software. Therefore, the factors in the aspect need to be comprehensively considered when the camera is installed, the installation height, the angle, the focal length and other information of the camera are comprehensively determined, and a wider road surface range is monitored as far as possible on the premise of meeting the requirement on monitoring precision. In order to determine the optimal installation requirement of the camera, an orthogonal experiment method is adopted to arrange experiments for research. The orthogonal experiment method is a design method for researching multiple factors and multiple levels, selects part of representative horizontal combinations from comprehensive experiments according to the Galois theory to carry out experiments, and analyzes the results to find out the optimal horizontal combination. The orthogonal experiment method can reduce the experiment times and achieve better effect.

The focal length of a traffic camera in a city is 8mm or 12mm, the mounting height is 6.5m at most, and the mounting angle is 75 degrees. And the distance between the portal traffic sign pole and the road surface can not be less than 5m according to the relevant specification requirements. Determining four levels of 6mm, 8mm, 12mm and 16mm for the focal length of the camera by referring to the conditions; the installation height adopts four levels of 5m, 7 m, 9 m and 11 m; the installation angle is tested by adopting four levels of 65 degrees, 70 degrees, 75 degrees and 80 degrees, and L16 (4) is utilized ³ ) The orthogonal table of (2) was designed experimentally. In order to ensure accurate experimental results and avoid interference of irrelevant factors, the cameras for experiments all adopt 800 ten thousand pixels and are carried out at noon in a fine day under the condition that a picture shot by the cameras is not shielded. The obtained experimental results are shown in table 5 below, where the near point distance refers to the minimum monitoring range of the camera, and the far point distance refers to the maximum monitoring range of the camera.

Table 5: orthogonal experiment result table

According to the experimental statistical data, when the focal length of the camera is 16mm, the installation height is 6m, and the installation vertical angle is 80 degrees, the monitored road area of the camera is the largest and is 80 m. Therefore, it is recommended to mount cameras on the roadside at the parameters, with the mounting interval of the cameras taken to be 80 m.

Ii) laser radar layout spacing design

When the laser radar is fixed on a road, the point cloud generated by a single laser beam is an arc-shaped curve, and the curve formed by the point cloud is different according to different deployment modes. When horizontal installation is adopted, the obtained point cloud image is an outward-diffused circular ring as shown in fig. 2; when vertical installation is adopted, the obtained point cloud image is a cluster of hyperbolas, as shown in fig. 3. Compared with vertical installation, the horizontal installation has a larger monitoring range, but the point cloud density is lower, and a monitoring blind area exists in the area below the laser radar, and the blind area is increased along with the increase of the installation height of the laser radar, so that the evaluation result is greatly interfered. Therefore, the monitoring effect of the vertical installation of the laser radar is better, and the vertical installation is recommended in the embodiment of the application.

Market research results show that the vertical field angle of the laser radar is mostly 30 degrees or 40 degrees, the laser line beams have 8 beams, 16 beams, 32 beams and the like, the more the laser line beams, the higher the point cloud density, but the higher the cost. According to the graph of fig. 3, when the laser radar is vertically installed, the point cloud density under the laser radar is the highest, the monitoring width is the narrowest, then the point cloud density gradually decreases along the laser scanning direction, the laser beams irradiated on the road surface gradually decrease, the distance between the laser beams gradually increases, and the monitoring blind area gradually expands. The lane width is 3.75m, and the laser intervals when 3, 4 and 5 laser beams are irradiated on a single lane are respectively 1.25, 0.94 and 0.75. When the laser beam is less than 4, the laser interval is far larger than 1m, most of diseases cannot be detected, and therefore, the road surface area with the laser beam less than 4 is considered to be out of the monitoring range of the laser radar.

When the laser radar is vertically arranged in a road area, the longitudinal detection range (L) and the transverse monitoring range (W) of 16-beam laser radar and 32-beam laser radar at different horizontal field angles and installation heights can be respectively calculated. Wherein L refers to the monitoring range of the lidar along the direction of the roadway and W refers to the monitoring range perpendicular to the direction of the roadway at the position directly below the lidar. The calculation results are shown in table 6 below.

Table 6: laser radar monitoring range

As shown in fig. 4, the lateral monitoring range of the laser radar is greatly affected by the horizontal field angle and the installation height of the laser radar, and the lateral monitoring distance can be increased by increasing the installation height when a smaller horizontal field angle is adopted. According to fig. 5, the range of the longitudinal monitoring range of the laser radar is small with the change of the installation height, the influence is large with the number of laser beams and the horizontal angle of view, and the longitudinal monitoring range is about 1.5 times of the range of 40 ° when the horizontal angle of view is 30 °, so that the laser radar with the horizontal angle of view of 30 ° is recommended. Considering that the lateral monitoring distance should not be less than 7.5m when the lidar is set to monitor two lanes, the installation height can be 14m with reference to table 6, the installation interval should be 50m for the 16-line lidar and 110m for the 32-line lidar.

Iii) temperature sensor arrangement interval design

Along with the rapid development of social economy, the scale of cities is rapidly expanded, and the heat island effect caused by the rapid expansion is more and more obvious, which is particularly characterized in that the temperature of urban areas is obviously higher than that of suburban areas. In order to reasonably determine the arrangement interval of the temperature sensors and reduce the error of road temperature measurement, the rule of the temperature change along with the space needs to be determined.

In order to determine the rule of temperature variation with places in cities, temperature sensors are sequentially arranged at intervals of 3km, and the arrangement positions of the sensors are gradually transited from urban areas to suburban areas. Wherein points a, b and c are located in the urban area, and points d, e and f are located in the transition area between the urban area and the suburban area. The data for the temperature sensors at 8:00, 14:00, 18:00, and 22:00 were read sequentially as shown in table 7 below:

table 7: temperature sensor readings

According to the above table 7, the temperature difference between the measuring points in the urban area is significantly smaller than the temperature difference between the suburb past areas, the temperature difference between the adjacent measuring points in the urban area is not greater than 1 ℃, and the variation is relatively random, while in the transition area from the urban area to the suburb area, the temperature difference between the measuring points is generally about 2 ℃, and the temperature gradually decreases. Therefore, temperature sensors should be spaced at 6km intervals in urban areas, and at 2-3km intervals in suburban transition areas.

Iv) arrangement interval design of rainfall sensor

In order to determine the law of rainfall changing along with the space position, three rainfall sensors are arranged at intervals of 4km to monitor the rainfall, the rainfall in the process of rainfall is recorded at intervals of 5min, and the total recording time is 1 h. The data obtained are shown in Table 8 below, where the rate of change refers to the amount of U.S. increase or decrease in rainfall per kilometer, in mm/km.

Table 8: rainfall sensor reading

As can be seen from the data in table 8, the rainfall is the largest in the rainfall center region, and the rainfall becomes smaller as the distance from the center position is longer, so the rainfall at each point basically shows a change trend of increasing first and then decreasing, and therefore, for the position between the two rainfall sensors, the error can be effectively reduced by performing weighted average calculation using the data of the two rainfall sensors closest to the point with the distance as the weight. The change rate of the average 1km rainfall is 1.85% according to the data calculation in the table. According to rainfall observation specifications (SL 21-2006), the rainfall monitoring error is not more than 4%, and the arrangement interval of the obtained rainfall sensors is most suitable to be 4-5 km/station in combination with the average change rate of the rainfall.

V) vehicle Detector Placement Interval design

The traffic volume of different road sections in a city is often different greatly and has no regularity, the traffic volume change in the road sections is limited, and the traffic volume change in the road sections is mainly caused by partial vehicles entering and exiting a community or a parking lot. In the early peak hours, the number of vehicles exiting a cell or parking lot is much higher than the number of vehicles entering, so that fewer vehicles are present at the entrance of the road section than at the exit of the road section, and vice versa in the late peak hours. Therefore, in order to analyze the change rule of the traffic volume in the urban road system, the traffic volume at the entrance and the exit in the urban road section needs to be statistically analyzed. We have collected the morning and evening peak traffic volumes at the entrance and exit of each section of the liberation road. The data are shown in table 9 below. And on the basis, the traffic volume difference value of the inlet and outlet positions of each road section of the liberation road is analyzed, as shown in the following table 10 and fig. 6.

Table 9: traffic volume statistical table

Table 10: difference value of traffic volume at road entrance and exit

From the above calculation results, the traffic volume variation value of the early peak or the late peak in the road section is generally less than 150, and the traffic volume variation value of the sum of the traffic volumes of the early peak and the late peak in the road section is generally less than 80, which accounts for about 5% of the total traffic volume. Therefore, the monitoring error caused by arranging one vehicle detector in a single road section can be controlled within 5 percent and is smaller than the monitoring error of the vehicle detector, and the influence on the final statistical result is small. The traffic distribution in the road section is approximately in a linear relation, and the traffic at the middle position of the road section is closest to the average traffic in the road section. In addition, the vehicle running speed in the middle of a road section is most stable compared to the road section entrance and exit. Therefore, the error of the monitoring data obtained by installing the vehicle detector in the middle of the road section is minimized. In summary, a single road section needs to be provided with a vehicle detector and the vehicle detector is arranged at the middle position of the road section.

Fourthly, selecting arrangement road sections of monitoring equipment

In order to make better use of construction funds, all road segments in a city need to be compared, and the most important road segment arrangement monitoring system is selected. The importance of the urban road is mainly embodied in two aspects of road grade and road section traffic volume, the road grade and the road section traffic volume of each road section are respectively marked, and the road sections needing to be arranged are determined by sequencing the road sections according to the total scores.

(1) Rank indicator quantization

The road section grade index belongs to a qualitative index, the importance degrees of every two indexes need to be compared, and an analytic hierarchy process can be adopted for determining. The constructed importance judgment matrix is shown in table 11 below.

Table 11: importance determination matrix

The data in table 11 above are calculated to obtain the relative importance coefficients of each index, where the fast path is 0.535, the main path is 0.296, the secondary path is 0.109, and the branch path is 0.06.

(2) Traffic flow indicator quantification

Because the flow of each road in the road network is very different, the traffic flow of each road needs to be normalized so as to be combined with the road grade index to evaluate the importance of one road. The normalization method is shown in the following formula:

in the formula:

q: a traffic flow normalization value of a certain road;

q: a traffic flow value on a certain road;

q _max : the maximum traffic flow of the roads in the area;

q _min : the minimum traffic flow of the roads in the region.

(3) Evaluation of road segment importance

And combining the results, taking the weight with the same grade and traffic volume, and finally obtaining a road section importance score calculation formula:

T＝0.5ω+0.5Q

in the formula:

ω: grade evaluation value of a certain road;

q: a traffic flow normalization value of a certain road;

t: road importance.

In summary, in order to obtain monitoring data required by each evaluation index model of urban road performance, in the embodiment of the application, the monitoring data is firstly divided into apparent data, altitude difference data, anti-skid performance evaluation data and other types of data according to the characteristics of the various types of data, and the types of the monitoring devices are respectively selected according to the characteristics. Then different types of monitoring equipment are investigated, and the monitoring equipment is transversely compared at multiple angles of performance, cost and the like to determine the model of the equipment which is most suitable for the monitoring equipment. On the basis, the distribution condition of the existing roadside monitoring equipment is combined, and the arrangement intervals of different monitoring equipment are determined by determining a design experiment from the monitoring range and cost of the monitoring equipment. In consideration of high cost of arranging monitoring systems on all road sections in a city, the embodiment of the application provides a method for judging whether road side equipment should be arranged on the road sections.

Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An urban road performance monitoring system is characterized in that the system is built on an urban road network;

the system consists of three modules, namely a sensor module, a data center and a communication module,

the sensor module comprises a camera, a laser radar, a temperature sensor, a rainfall sensor and a vehicle detector;

the camera is used for monitoring the road surface performance; the laser radar is used for monitoring, identifying and collecting road traffic data; the temperature sensor is used for monitoring the temperature condition of the road; the rainfall sensor is used for monitoring the rainfall on the road; the vehicle detector is used for monitoring the traffic volume;

the data center consists of a data processing module and a storage module;

the data processing module is used for analyzing the received data and the stored data, and specifically comprises road performance evaluation and road performance prediction;

the storage module is used for storing the monitoring data according to time and the road sections to which the monitoring data belong in a classified manner, wherein the data obtained after the data storage processing module of the camera and the laser radar is used for processing the data, and the data of other sensors and the transverse force coefficient detection vehicle are directly stored;

the communication module comprises roadside communication equipment and communication optical fibers for connecting each sensor module and the data center and connecting the data center and the roadside communication equipment; the roadside communication equipment is connected with the data center through optical fibers and used for receiving the evaluation result of the road section where the roadside communication equipment is located and providing data service for the requester when the intelligent automobile initiates a service request to the roadside communication equipment.

2. The urban road performance monitoring system according to claim 1, wherein the monitoring camera employs a CCD camera, and a transverse monitoring range of the camera includes all lanes of an area to be monitored; the camera can clearly shoot the performance of the road surface within at least 100m along the lane direction; the camera pixels should not be less than 200 ten thousand; cameras are installed on the road side, and the installation interval of the cameras is 80 m.

3. The urban road performance monitoring system according to claim 1, wherein the lidar employs a semi-solid lidar; the horizontal field angle of the laser radar is 30 degrees, and the transverse monitoring distance of the laser radar is not smaller than 7.5m when the laser radar is arranged to monitor two lanes; the installation height of the laser radar is 14m, the installation interval of the laser radar is 50m for a 16-line laser radar, and the installation interval of the laser radar is 110m for a 32-line laser radar.

4. The system of claim 1, wherein the temperature sensors are IC temperature sensors, and are arranged at 6km intervals in urban areas and 2-3km intervals in suburban transition areas.

5. The system for monitoring urban road performance according to claim 1, wherein the rainfall sensor is radar type rainfall meter or piezoelectric type rainfall meter, and the arrangement interval of the rainfall sensor is 4-5 km/meter.

6. The urban road performance monitoring system according to claim 1, wherein the vehicle detector is a microwave-type vehicle detector; in a single road section, a vehicle detector is installed at a middle position of the road section.

7. The urban road performance monitoring system according to claim 1, wherein the roadside communication devices adopt vehicle-road cooperative communication devices of the LTE-V technology.

8. The urban road performance monitoring system according to claim 1, characterized in that the road performance evaluation steps are as follows:

s1: classifying the received sensor data according to the sending time and the road section where the sensor is located;

s2: preprocessing image data and laser point cloud data to obtain data such as transverse and longitudinal crack length, pit area and the like;

s3: and inputting the data belonging to the same road section into the evaluation model, and calculating and obtaining the grade and the driving suggestion of the road section by the evaluation model.

9. The urban road performance monitoring system according to claim 1, wherein the road surface performance prediction step is as follows:

s1: respectively calculating data such as annual average score and annual rainfall of the road section;

s2: and inputting the data into a prediction model, and calculating the road surface performance score of the road section in the coming years as a basis for judging whether the road section needs to be maintained or not.

10. The system of claim 1, wherein the sensor module is arranged on the road section according to a road section importance score, and the road section importance score is calculated by the following formula:

T＝0.5ω+0.5Q

in the formula:

ω: grade evaluation value of a certain road;

q: normalizing the traffic flow of a certain road;

t: road importance;

the calculation formula of the traffic flow normalized value of each road is as follows:

in the formula:

q: a traffic flow normalization value of a certain road;

q: a traffic flow value on a certain road;

q _max : the maximum traffic flow of the roads in the area;

q _min : the minimum traffic flow of the roads in the region.

The grade evaluation value omega of a certain road belongs to qualitative indexes, the importance degrees of every two indexes need to be compared, and an analytic hierarchy process can be adopted for determining.

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