CN114858214B - Urban road performance monitoring system - Google Patents

Abstract

The application discloses an urban road performance monitoring system, which relates to the technical field of road monitoring, wherein the system firstly divides various data into apparent data, height difference data, anti-skid performance evaluation data and other data according to the characteristics of the data, and respectively selects the types of monitoring equipment according to the characteristics. And then, different types of monitoring equipment are studied, and the monitoring equipment is transversely compared in a plurality of angles such as performance, cost and the like, so that the equipment model most suitable for the application is determined. On the basis, the distribution situation of the existing road side monitoring equipment is combined, the design experiment is determined from the monitoring range of the monitoring equipment and the angle of the cost, and the arrangement interval of different monitoring equipment is determined. In view of the high cost of the overall road segment placement monitoring system in a city, the present application also provides a method of determining whether a road segment should be placed with road side equipment.

Description

Urban road performance monitoring system

Technical Field

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

Background

In order to clearly construct a road condition monitoring system to reform the existing road, the existing urban road monitoring system needs to be evaluated at first.

At present, except for intelligent road project sections built in part of cities, monitoring equipment arranged in the cities comprises traffic cameras, velocimeters and rainfall monitoring stations, but the equipment necessary for road surface performance monitoring systems such as laser radars, vehicle road communication equipment and the like is lacked, and the monitoring equipment such as the cameras and the like should be encrypted appropriately according to project requirements.

The traffic cameras and the velocimeters are mainly distributed near intersections and in key road sections, can accurately capture traffic violation behaviors, and provide reliable basis for traffic management departments to law enforcement. In addition, the statistics of the traffic flow of the intersection can be completed by shooting pictures by the camera, and a basis is provided for the timing design and the newly-built or reconstructed road design of the traffic lights of the intersection. Although the existing traffic cameras are sufficient to meet the requirements of the functions, the distribution density and pixels are not sufficient to fully realize the monitoring of the road performance facing the automatic driving. According to investigation, the distribution density of traffic cameras in cities is low, for example, the distribution density of cameras in Nanjing cities 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 the tiny diseases on the road surface, the pixel of the camera is not lower than 800 ten thousand pixels, and the pixel difference of the existing traffic cameras is large due to different types of selection, and usually, only part of cameras meet the requirement between 500 ten thousand pixels and 1000 ten thousand pixels.

The rainfall monitoring stations in the city are set by the meteorological department for estimating the total rainfall of the area and giving out a strong rainfall warning. However, the rainfall monitoring stations have lower distribution density, the average density is about 80 square kilometers per unit, and the road rainfall precision in the area far away from the monitoring stations is lower, so that in order to meet the requirements of a detection system, a proper amount of rainfall stations are required to be additionally arranged to improve the distribution density.

In order to obtain detailed data required for the urban road performance evaluation model, to accurately evaluate the performance state of the road, a monitoring device is required to cover the whole area 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 the data required by the model is implemented step by step, equipment is firstly installed on a representative road section with large flow, large equivalent area and high importance, and the equipment is gradually expanded after a period of operation. The equipment is distributed on the most representative road section, the road section to be laid with the equipment is required to be inspected and demonstrated in the field, unnecessary road sections and unreasonable road sections are eliminated, the repeated information collection is avoided, the waste of resources is caused, and the function of the equipment is fully exerted.

Disclosure of Invention

An object of the present application is to provide an urban road performance monitoring system, which is built on an urban road network and comprises 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 of the road; 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 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 the time and the road section, wherein the data obtained after the processing of the data storage processing module of the camera and the laser radar is finished, and the data of other sensors and the transverse force coefficient detection vehicle are directly stored;

the communication module comprises road side communication equipment and communication optical fibers for connecting each sensor module with the data center and connecting the data center with the road side communication equipment; the road side communication equipment is connected with the data center through optical fibers and is used 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.

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 pavement performance within the range of at least 100m along the direction of the traffic lane; the pixel of the camera should not be lower than 200 ten thousand; cameras are installed on the road side, and the installation interval of the cameras is 80m.

Preferably, the laser radar is a semi-solid laser radar; the horizontal view 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 laser radar mounting height is 14m, for 16-line laser radar, the mounting interval is 50m, and for 32-line laser radar, the mounting interval is 110m.

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

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

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

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

Based on the above, another object of the present application is to provide a road performance evaluation method, which includes the following steps:

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 an evaluation model, and calculating and obtaining the score and the driving suggestion of the road section by the evaluation model.

Based on the above, another object of the present application is to provide a pavement performance prediction method, which comprises the following steps:

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

s2: and inputting the data into a prediction model, and calculating road surface performance scores of road sections for the next years to serve as a basis for whether maintenance is needed for the road sections.

Based on the above, considering the cost of arranging the monitoring system for all road segments in the city, it is still another object of the present application to provide a method for determining whether a road segment should be arranged with a road side device, wherein the road segment arranged with the sensor module (i.e. the monitoring device) is calculated according to the road segment importance score, and the road segment importance score calculation formula is as follows:

T＝0.5ω+0.5Q

wherein:

omega: a 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 normalization value of each road is as follows:

wherein:

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

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

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

q _min : the road in the region has the minimum traffic flow.

The grade evaluation value omega of a certain road belongs to a qualitative index, the importance degree of every two indexes needs to be compared, and the grade evaluation value omega can be determined by adopting an analytic hierarchy process.

Drawings

The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. In the drawings:

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

FIG. 2 is a cloud of laser points when the lidar is mounted horizontally;

FIG. 3 is a cloud of laser points when the lidar is mounted vertically;

FIG. 4 is a graph of laser radar lateral surveillance distance for different conditions;

FIG. 5 is a graph of longitudinal range monitoring for different condition lidar;

fig. 6 is a road junction traffic volume difference.

Detailed Description

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

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 exchanged between vehicles and equipment, the components of the urban road performance monitoring system are determined, the selected equipment is researched, and the type selection and performance requirements of various equipment are determined. In addition, in order to ensure that construction funds are fully utilized, the existing road side equipment is analyzed, and a design scheme for monitoring system arrangement is provided on the basis of the analysis.

1. Urban road performance monitoring demand analysis

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

(1) Appearance class data: including the mesh fracture area, transverse and longitudinal fracture length required to evaluate fracture conditions; pit length, width and location information required to evaluate pit conditions and the area of the obstacle. The color and brightness difference between the disease occurrence area and the surrounding area is obvious, the color difference analysis is carried out on the road surface pictures shot by the cameras arranged on the road side based on the deep learning algorithm to determine the disease area, and then the disease type of the area is determined according to the shape and the color difference characteristics of the area, and the data are obtained.

(2) Height difference class data: including the rut depth required to evaluate rut conditions; the road surface maximum clearance height and the obstacle height required for the flatness were evaluated. The disease areas of the diseases have certain height difference with surrounding areas, and the point cloud data obtained by laser radar scanning are analyzed to obtain the point cloud data. When the laser radar works, firstly, whether an obstacle exists in a scanning area is judged by combining a camera picture, when the obstacle exists, the height of the obstacle is calculated and determined according to the change of the point cloud height, the point cloud of the obstacle is removed, then the subsequent analysis is carried out, and when the obstacle does not exist, the subsequent analysis is directly carried out. In the subsequent analysis, the point cloud height changes of the point cloud in the travelling direction and the vertical direction along the road are required to be calculated respectively to obtain the rut depth and the maximum gap height data of the road surface.

(3) Data required for the evaluation of the anti-skid property: the system comprises temperature and road surface transverse force coefficients, wherein temperature data are automatically monitored by adopting a temperature sensor, and the transverse force coefficients are not yet mature automatic monitoring means, and the data are acquired by adopting a mode of monthly detection by adopting a transverse force coefficient detection vehicle.

(4) Other classes of data: in order to meet the requirements of the prediction model, rainfall and traffic data are acquired in addition to the data. Wherein the rainfall data is obtained by arranging a rain gauge on the road side, and the traffic volume is obtained by a vehicle detector.

2. System architecture and functionality of the present embodiment

The road performance monitoring system is built on an urban road network, and the whole system is composed of a sensor module, a communication module and a data center by combining the characteristics of the urban road and the monitoring content of the detection system, wherein the composition of the sensor module, the communication module and the data center is 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 acquisition precision requirement of the detection system and reduce the cost as much as possible, various aspects such as the cost, the performance and the like of the sensor need to be researched and comprehensively compared to determine the sensor applied to the system.

(1) Camera head

Depending on the differences between the photosensitive chips, cameras can be classified into an electric coupling element camera (Charge-CoupledDevice, CCD) and a complementary metal oxide semiconductor (ComplementaryMetalOxideSemiconductor, CMOS) camera, and the comparison of the two in terms of performance is shown in table 1:

table 1: CCD and CMOS performance comparison

According to table 1, the CCD camera performs better than the CMOS camera in terms of both imaging quality and minimum illuminance, and is inferior to the CMOS camera only in terms of noise amount, and thus the effect is that the data amount of the picture photographed by the CCD camera is larger and more memory under the same sharpness 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 higher road surface performance monitoring accuracy can be achieved, so that 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 should comprise all lanes of the area to be monitored; (2) The camera can clearly shoot the pavement performance within the range of at least 100m along the direction of the traffic lane; (3) In order to ensure that the shot image can clearly reflect the road surface crack, the pixels of the camera should not be lower than 200 ten thousand.

(2) Laser radar

The number of lidar components is large, and the different choice of technology for each component results in different effects and costs, which lead to a diversification of the laser radar technology route. The laser radar can be decomposed into five core technologies from a ranging mode, a transmitting mode, a beam operation mode, a detecting mode and a data processing mode, each core technology has different technical branches, the laser radars of different branches have different performances, cost, current mass production difficulty and the like, and the selection of different branch technologies in the five core technologies also leads to the difference of technical routes of the laser radars of various enterprises.

There are various classification modes of lidar, wherein the main stream is classified into three major categories according to scanning components, namely, mechanical, semi-solid and solid, and the details are as follows:

i) mechanical: the mechanical part (scanning module) and the electronic part (laser transceiver module) are both moving-rotated by the motor by 360 degrees;

ii) semi-solid state: the laser transceiver module is not moved, and only the scanning module moves;

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

The semi-solid laser radar can be divided into MEMS, rotary mirror type and prism type according to the motion mode of the scanning module, and the solid laser radar is divided into OPA phase control and Flash. The comparison of the various lidars is shown in table 2.

Table 2: comparison of various laser radars

Based on the above table 2, among the three types of lidars, the mechanical lidar has the advantages of highest technical maturity and optimal ranging accuracy, but has the disadvantages of huge volume and high cost, and under the same line number condition, the cost is several times that of the semi-solid lidar and ten times that of the semi-solid lidar. Solid-state lidar is not yet mature in related research technology and is not put into mass production. In comparison with the semi-solid laser radar technology, the semi-solid laser radar technology is mature, and partial models are produced in mass and applied to intelligent automobiles in partial enterprises. Although the semi-solid laser radar is inferior to the mechanical laser radar in the aspect of the precision of single-beam laser ranging, the number of laser transmitters of the semi-solid laser radar is more than one time that of the mechanical laser radar at the same price, so that the formed point cloud density is higher, and the overall detection effect is better. In view of the above description, a semi-solid laser radar is selected as the monitoring device.

(3) Temperature sensor

Temperature sensors commonly used in the market are resistive 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 all meet the requirements of the present application. Among the four sensors, the accuracy of the resistance temperature detector is optimal; the thermistor and the IC temperature sensor are slightly poorer in precision, and are generally about 0.1 ℃ and enough to meet the monitoring requirement; and the measurement accuracy of the thermocouple depends on the voltage measurement accuracy. From the aspects of complexity and cost of the circuit diagram, the IC temperature sensor is far superior to the other three, and has extremely low cost and simple circuit diagram structure. In addition, the IC temperature sensor has the smallest volume among the four, and is most convenient to install due to the simple circuit diagram. The IC temperature sensor is most suitable for integrating all conditions, and is selected to monitor the temperature condition of the road.

(4) Rain gauge

From a construction 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 capacitance 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. The comparison of the types of rain gauges is shown in table 4 below.

Table 4: comparison of types of rain gauges

The mechanical rain gauge is often required to be maintained frequently, has huge volume, is extremely easy to shade vehicles when being installed on the road side, and is not suitable for being adopted by the application to induce traffic accidents. The non-mechanical rain gauge is generally similar in accuracy and sufficient for monitoring purposes. Wherein the radar type rain gauge and the piezoelectric type rain gauge can be used for a long time without periodic maintenance, so the radar type rain gauge or the piezoelectric type rain gauge is adopted.

(5) Vehicle detector

At present, the devices for traffic detection in China are various in variety, and the devices are 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 electromagnetic induction principle, a coil buried under a road surface is used as a sensor of the vehicle detector, when a vehicle passes through the coil, the coil causes the change of inductance in a coil loop, and the detector detects the relationship of the existence of the vehicle, the speed of the vehicle and the like according to the change.

Through decades of development, the coil type vehicle detector is quite mature, is widely applied to the expressway industry, and has the advantages of high speed measurement precision, high traffic counting precision, good working stability, no influence of weather and traffic environment, strong anti-interference capability and the like; but need cut the road surface and embed into it after the bituminous paving construction is accomplished, not only serious to the road surface damage, and pavement subsidence, crack etc. can influence its result of use moreover, and large-scale vehicle rolls, coil ageing, environmental change etc. can influence vehicle detector performance, and the arrangement and later operation cost of maintenance are extremely high.

(ii) microwave vehicle detector

The microwave vehicle detector is a traffic data product utilizing digital radar microwave detection technology, and transmits microwave beams to a detection area in real time through a transmitting antenna arranged on a portal or a road side upright post. When the vehicle passes through the detection area, the microwave receiving module receives microwaves with different frequencies, and the transmitting and receiving devices of the microwave type vehicle detector measure the passing and existing of the vehicle by measuring the frequency change, so as to obtain data of the vehicle flow, the vehicle speed, the vehicle type and the like.

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

(iii) video vehicle detector

The video vehicle detector is a computer processing system which utilizes image processing technology to realize traffic target detection. The method realizes the automatic statistics of the number of the motor vehicles running on the traffic road section, the calculation of the speed of the running vehicles, the identification and division of various related traffic parameters such as the category of the running vehicles and the like through the real-time detection of the road traffic condition information and the traffic targets.

The video vehicle detector is similar to the microwave vehicle detector, and has the advantages of no need of damaging the road surface, convenient installation and disassembly of the detector, wireless and larger detection range. However, the measurement accuracy is very limited by the field illumination, and the vehicle cannot work at night and still cannot be detected.

Compared with the three detectors, the coil type vehicle detector needs to embed the detection coil into the road, so that the road surface is damaged, and if overhauling occurs, secondary damage is caused to the road surface, so that the coil type vehicle detector is not suitable for being adopted; 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 on the premise of not damaging the road surface, and meets all requirements of the application for traffic detection. Therefore, a microwave type vehicle detector is selected to monitor traffic volume.

(2) Data center

The data center is composed 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 classified into road performance evaluation and road performance prediction.

The road performance evaluation method comprises the following steps:

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 an evaluation model, and calculating and obtaining the score and the driving suggestion of the road section by the evaluation model.

The pavement performance prediction method comprises the following steps:

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

s2: and inputting the data into a prediction model, and calculating road surface performance scores of road sections for the next years to serve as a basis for whether maintenance is needed for the road sections.

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

(3) Communication module

The communication module comprises road side communication equipment and communication optical fibers for connecting each sensor with the data center and connecting the data center with the road side 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 then data service is provided for a requester when the intelligent automobile initiates a service request to the intelligent automobile.

Worldwide, there are currently two main different technical routes of LTE-V (4 GLTE communication) and DSRC (WiFi-based short range communication for vehicles) in the field of vehicle-to-vehicle communication. LTE-V is driven by domestic enterprises (including large tangs, hua-ji, etc.), while DSRC is driven by us dominance. DSRC has evolved over ten years, technology has tended to mature, and in addition its complete standards have led to its occupation in advance when deployed; however, DSRC uses relatively less high-band penetration than low-frequency signals of LTE-V. LTE-V provides higher bandwidth, higher transmission rate, greater coverage, and reuse of existing cellular infrastructure and spectrum.

In addition, from the technical point of view, the LTE-V fully references 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 the LTE cellular network, ensure the continuity and reliability of the service, and can also utilize the connection between the base station and the hosted cloud server to perform high-speed data transmission such as high-definition video and audio, and the like, thereby having certain superiority.

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

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

3. Urban road monitoring equipment arrangement scheme

(1) Monitoring device arrangement interval design

I) Camera arrangement interval design

At a certain focal length and a certain pixel, the definition of the road surface in the picture shot by the camera is lower as the distance between the camera and the road surface is farther. In order to accurately identify and acquire pavement disease information, the image in the picture shot by the camera needs to meet the precision requirement of image identification software. Therefore, when the camera is installed, the factors are comprehensively considered, the installation height, angle, focal length and other information of the camera are comprehensively determined, and the wider pavement range is monitored as much as possible on the premise of meeting the requirement on monitoring precision. In order to determine the optimal installation requirement of the camera, the experiment is arranged by adopting an orthogonal experiment method for research. The orthogonal experiment method is a design method for researching multiple factors and multiple levels, and selects part of representative level combinations from the comprehensive experiments to perform experiments according to Galois theory, and analyzes the results to find out the optimal level combinations. The orthogonal experiment method can reduce the experiment times and has better effect.

The focal length of the traffic camera in the city is 8mm or 12mm, the installation height is 6.5m at most, and the installation angle is 75 degrees. And according to the related specification, the distance between the portal type traffic sign rod and the road surface cannot be lower than 5m. With reference to the conditions, determining that the focal length of the camera adopts four levels of 6, 8, 12 and 16 mm; the installation height adopts four levels of 5, 7, 9 and 11 m; the mounting angles were tested at four levels of 65 °, 70 °, 75 °, 80 °, and L16 (4) ³ ) Is designed for experiments. In order to ensure accurate experimental results and avoid interference of irrelevant factors, the experimental cameras are all 800 ten thousand pixels, and are carried out in the noon of sunny days under the condition that pictures shot by the cameras are not blocked. The experimental results obtained are shown in table 5 below, wherein 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 monitoring road area of the camera is the largest and is 80m. Therefore, it is recommended to install cameras on the road side according to this parameter, and the installation interval of the cameras is taken as 80m.

Ii) lidar arrangement interval design

When the laser radar is fixed on a road, point clouds generated by a single laser are arc-shaped curves, and the curves formed by the point clouds are different according to different deployment modes. When the 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 can be greatly interfered. Therefore, the monitoring effect of the laser radar vertical installation is better, and the embodiment of the application recommends the vertical installation.

Market research results show that the vertical field angle of the laser radar is mostly 30 degrees or 40 degrees, 8 beams, 16 beams, 32 beams and the like are formed in laser beams, and the more the laser beams are, the higher the point cloud density is, but the higher the cost is. According to fig. 3, when the laser radar is vertically installed, the point cloud density at the position right below the laser radar is highest, the monitoring width is narrowest, then the point cloud density is gradually reduced along the laser scanning direction, the laser beam irradiated onto the road surface is gradually reduced, the distance between the laser beams is gradually increased, and the monitoring blind area is gradually enlarged. The lane width is 3.75m, and the laser intervals when the single lane is irradiated by 3, 4 and 5 laser beams are respectively 1.25, 0.94 and 0.75. The laser interval when the laser beam is less than 4 is far more than 1m, most diseases cannot be detected, so the road surface area where the laser beam is less than 4 is regarded as being out of the monitoring range of the laser radar.

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

Table 6: laser radar monitoring range

As shown in fig. 4, the horizontal angle of view and the mounting height of the lidar are both greatly affected by the lateral monitoring range of the lidar, and the lateral monitoring distance can be increased by increasing the mounting height when a smaller horizontal angle of view is used. In contrast, according to fig. 5, the range of the longitudinal monitoring of the lidar varies little with the installation height, but the longitudinal monitoring range is 1.5 times as large as the number of laser beams and the horizontal angle of view, which is about 40 ° when the horizontal angle of view is 30 °, so that the lidar 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 provided to monitor two lanes, the available mounting height with reference to table 6 should be 14m, the mounting interval should be 50m for 16-line lidar and 110m for 32-line lidar.

Iii) temperature sensor arrangement spacing design

With the rapid development of socioeconomic performance, the scale of cities expands rapidly, and the heat island effect caused by the rapid expansion of the scale is more and more remarkable, and the heat island effect is embodied in that the urban air temperature is obviously higher than that of peripheral suburbs. In order to reasonably determine the arrangement interval of the temperature sensors and reduce the error of road temperature measurement, the rule of temperature along with the spatial change needs to be determined.

In order to determine the law of temperature variation with location in cities, temperature sensors were sequentially arranged at intervals of 3km, and the sensor arrangement positions were gradually transited from the urban area to the surrounding suburban area. The points a, b and c are positioned in urban areas, and the points d, e and f are positioned in urban and suburban transitional areas. The temperature sensors were sequentially read for data at 8:00, 14:00, 18:00, and 22:00, as shown in Table 7 below:

table 7: temperature sensor readings

According to Table 7, the temperature difference between the stations in the urban area is significantly smaller than the temperature difference between the stations in the suburban area, the temperature difference between the adjacent stations in the urban area is not greater than 1 ℃, the change is relatively random, and the temperature difference between the stations in the urban area to suburban area transition area is generally about 2 ℃ and the temperature gradually decreases. Therefore, when the temperature sensor is arranged in the urban area, it should be arranged at intervals of 6km, and in the suburban transition area, it should be arranged at intervals of 2-3 km.

Iv) rain sensor arrangement interval design

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

Table 8: rain sensor reading

As can be seen from the data in table 8, since the rainfall is maximum in the rainfall center region and the rainfall is smaller as the distance from the center position is longer, the rainfall at each point basically shows a trend of increasing and then decreasing, and thus, the error can be effectively reduced by weighted average calculation of the data of two rainfall sensors closest to the point with the distance as the weight for the position between the two rainfall sensors. The average 1km rainfall was found to have a rate of change of 1.85% based on the data in the table. According to the precipitation observation Specification (SL 21-2006), the error of precipitation monitoring should not be more than 4%, and the arrangement interval of the available rainfall sensors should be taken as 4-5 km/table most suitable in combination with the average change rate of precipitation.

V) vehicle detector arrangement spacing design

Traffic volumes in different road sections in cities often differ greatly and are not regular, and traffic volume changes in road sections are limited, which are mainly caused by part of vehicles entering and exiting a cell or a parking lot. In the early peak, the number of vehicles exiting the cell or parking lot is much higher than the number of vehicles entering, so that the vehicles at the entrance of the road are less than the vehicles at the exit of the road, and in the late peak, the opposite is true. Therefore, in order to analyze the change rule of traffic in the urban road system, it is necessary to perform statistical analysis on traffic at the entrance and exit in the urban road section. We collect the peak traffic in the morning and evening at the import and export of each section of the liberation road. The data are shown in Table 9 below. And on the basis of this, the traffic volume difference of the entrance and exit positions of each section of the free road is analyzed as shown in the following table 10 and fig. 6.

Table 9: traffic volume statistics

Table 10: traffic volume difference between road section entrance and exit

As can be seen from the calculation results, the traffic volume change value of the early peak or the late peak in the road section is generally less than 150, and the traffic volume change in the road section is generally less than 80, which is 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 to be within 5 percent, which is smaller than the monitoring error of the vehicle detector, and has little influence on the final statistical result. The traffic distribution in the road segments is approximately linear, and the traffic at the middle position of the road segments is closest to the average traffic in the road segments. In addition, the vehicle running speed in the middle of the road section is the most stable compared with 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 vehicle detector needs to be installed in a single road segment and installed in a middle position of the road segment.

4. Monitoring device placement road segment selection

In order to better utilize construction funds, all road segments in the city need to be compared, and the most important road segment arrangement monitoring system is selected. The importance of the urban roads is mainly represented in the two aspects of road grade and road section traffic volume, the road grade and road section traffic volume of each road section are respectively scored, and the road sections to be arranged are determined by sorting the road sections according to the score sum.

(1) Level index quantization

The road section grade index belongs to a qualitative index, the importance degree of every two indexes needs to be compared, and the road section grade index can be determined by adopting a hierarchical analysis method. The importance judgment matrix constructed is shown in the following table 11.

Table 11: importance degree judgment matrix

The relative importance coefficient of each index can be obtained by calculation from the data in the table 11, the expressway is 0.535, the trunk is 0.296, the secondary trunk is 0.109, and the branch is 0.06.

(2) Traffic flow index quantification

Because the traffic flow of each road in the road network has great difference, the traffic flow of each road needs to be normalized so as to evaluate the importance of one road by combining with the road grade index. The normalization method is as follows:

wherein:

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

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

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

q _min : the road in the region has the minimum traffic flow.

(3) Evaluation of importance of road section

And taking weights with equal grades and traffic volumes according to the results, and finally obtaining a road section importance score calculation formula:

T＝0.5ω+0.5Q

wherein:

omega: a grade evaluation value of a certain road;

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

t: road importance.

To sum up, in order to obtain the monitoring data required in each evaluation index model of urban road performance, the embodiment of the application firstly divides the data into apparent type data, height difference type data, anti-skid performance evaluation data and other type data according to the characteristics of various data, and respectively selects the type of monitoring equipment according to the characteristics. And then, different types of monitoring equipment are studied, and the monitoring equipment is transversely compared in a plurality of angles such as performance, cost and the like, so that the equipment model most suitable for the application is determined. On the basis, the distribution situation of the existing road side monitoring equipment is combined, the design experiment is determined from the monitoring range of the monitoring equipment and the angle of the cost, and the arrangement interval of different monitoring equipment is determined. In view of the high cost of the overall road segment placement monitoring system in a city, embodiments of the present application provide a method of determining whether a road segment should be placed with road side equipment.

The foregoing description is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present application has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (9)

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

the system consists of 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 pavement 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 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 the time and the road section, wherein the data obtained after the processing of the data storage processing module of the camera and the laser radar is finished, and the data of other sensors and the transverse force coefficient detection vehicle are directly stored;

the communication module comprises road side communication equipment and communication optical fibers for connecting each sensor module with the data center and connecting the data center with the road side communication equipment; the road side communication equipment is connected with the data center through optical fibers and is used 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 road side communication equipment;

the road section importance score calculation formula of the sensor module is as follows according to the road section importance score:

wherein:

omega: a 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 normalization value of each road is as follows:

wherein:

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

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

q _max : the maximum traffic flow of the road;

q _min : the minimum traffic flow of the road;

the grade evaluation value omega of a certain road belongs to a qualitative index, the importance degree of every two indexes is required to be compared, and the grade evaluation value omega can be determined by adopting an analytic hierarchy process.

2. The urban road performance monitoring system according to claim 1, wherein the camera adopts a CCD camera, and the lateral monitoring range of the camera comprises all lanes of the area to be monitored; the camera can clearly shoot the pavement performance within the range of at least 100m along the direction of the traffic lane; the pixel of the camera should not be lower than 200 ten thousand; cameras are installed on the road side, and the installation interval of the cameras is 80m.

3. The urban road performance monitoring system according to claim 1, wherein the lidar is a semi-solid lidar; the horizontal view 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 laser radar mounting height is 14m, for 16-line laser radar, the mounting interval is 50m, and for 32-line laser radar, the mounting interval is 110m.

4. The urban road performance monitoring system according to claim 1, wherein the temperature sensor is an IC temperature sensor, and is arranged at intervals of 6km in urban areas and 2-3km in suburban transition areas.

5. The urban road performance monitoring system according to claim 1, wherein the rain sensor is a radar type rain gauge or a piezoelectric type rain gauge, and the rain sensor is arranged at a spacing of 4-5 km/table.

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

7. The urban road performance monitoring system of claim 1, wherein the roadside communication device employs a vehicle-to-vehicle cooperative communication device of LTE-V technology.

8. The urban road performance monitoring system according to claim 1, characterized in that the step of road performance evaluation is 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 an evaluation model, and calculating and obtaining the score 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 step of road surface performance prediction is as follows:

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

s2: and inputting the data into a prediction model, and calculating road surface performance scores of road sections for the next years to serve as a basis for whether maintenance is needed for the road sections.