KR100794591B1 - Automatic measuring system for vehicle information - Google Patents

Abstract

An automatic vehicle information measurement system is provided to control efficiently overloaded vehicles on a bridge by using vehicle information. A vehicle detection sensor unit(10) senses traveling information of a vehicle and outputs vehicle traveling information. A weight detection sensor unit(20) outputs vehicle weight information to a data processing unit(30). The data processing unit processes the vehicle traveling information sensed through the vehicle detection sensor unit, detected automatically kinds of vehicles on a bridge, records and stores statistical traffic data and the vehicle weight information and history information about dynamic characteristic information of the bridge by processing the vehicle weight information sensed through the weight detection sensor unit.

Description

Automatic Measuring System for Vehicle Information

1 is a block diagram of a vehicle information automatic measurement system according to the present invention.

Figure 2 is a detailed block diagram of a vehicle information automatic measurement system according to the present invention.

3 is a flowchart of an information processing process using an influence line of an automatic vehicle information measuring system according to the present invention;

Figure 4 is a theoretical diagram of the axis weight calculation using the influence line of the vehicle information automatic measurement system according to the present invention.

5 is a flowchart of an information processing procedure using an artificial neural network of an automatic vehicle information measuring system according to the present invention;

6 is a dynamic strain signal peak detection diagram of a vehicle information automatic measurement system according to the present invention.

Figure 7 is a passage difference discrimination diagram for the artificial neural network learning data of the vehicle information automatic measurement system according to the present invention.

8 is a configuration diagram using the axis sensor (SN1) consisting of two per lane as a driving information measuring sensor for measuring vehicle driving information according to the present invention.

Figure 9 is a waveform diagram of the response signal of the axis sensor (SN1) consisting of two driving information measuring sensors per lane for vehicle driving information measurement according to the present invention.

10 is a response signal waveform of the dynamic strain sensor used as a load sensor for measuring the vehicle load information and the axis sensor (SN1) consisting of two per lane used as a driving information measuring sensor for measuring the vehicle driving information according to the present invention Degree.

11 is a configuration diagram of two loop sensors SN2 and one axis sensor SN1 per lane used as a driving information measuring sensor for measuring vehicle driving information according to the present invention.

12 is a response signal waveform diagram of two loop sensors and one axis sensor used as driving information measuring sensors for measuring vehicle driving information according to the present invention;

FIG. 13 is a response signal waveform diagram of two loop sensors, one axis sensor, and a dynamic strain sensor used as a load information measuring sensor for measuring vehicle load information for measuring vehicle driving information according to the present invention; FIG. .

14 is a configuration diagram of two strain sensor groups per lane used as a driving information measuring sensor for measuring vehicle driving information according to the present invention;

15 is a waveform diagram of response signals of two strain sensor groups used as driving information measuring sensors for measuring vehicle driving information according to the present invention;

16 is a block diagram of a strain sensor group installed on the bridge girders used as a load information measuring sensor for measuring vehicle load information according to the present invention.

17 is a configuration diagram of a dynamic strain sensor installed on the bridge crossbeam for measuring vehicle load information according to the present invention.

18 is a waveform diagram of a response signal of a dynamic strain sensor installed in a bridge crossbeam for measuring vehicle load information according to the present invention.

19 is a conceptual diagram illustrating the peak duration time to reflect the influence of the distance between the axles when calculating the vehicle load using the artificial neural network according to the present invention.

20 is a response waveform diagram of a strain signal of a bottom plate slab which is a driving information sensor used for axial weight distribution when a vehicle load is calculated using an artificial neural network according to the present invention;

* Description of the symbols for the main parts of the drawings

10: vehicle detection sensor unit 11: driving information measuring sensor

20: load detection sensor 21: vehicle load measurement sensor

30: data processing unit 31: signal amplifier

32: signal converter 33: central processing unit (CPU)

34: RAM / HDD 35: Monitor

36: printer 37: interface

SN1: Axis Sensor SN2: Loop Sensor

SN3: Dynamic Strain Sensor for Axis Detection SN4: Dynamic Strain Sensor for Vehicle Weight Calculation

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a measurement system for automatically acquiring traffic and load information of a vehicle driving on a bridge in real time. More particularly, the present invention relates to a vehicle and a bridge in determining safety and service life of a bridge on a road. Interactions and the degree of maintenance are important factors.In order to clarify the information, information on the gross weight, the axle weight, the number of axles, the axle spacing, the speed of the vehicle, the dynamic behavior of the bridge, the head spacing, the running rate, The present invention relates to a vehicle information automatic measurement system for grasping and statistically processing driving characteristics such as heavy vehicle mixing rate.

In the conventional vehicle information measuring apparatus, the information acquisition method mainly grasps the characteristics of the vehicle load using only the information based on the vehicle information measured by the toll gate, etc., but accurately predicts the characteristics and traffic characteristics of the vehicles passing through the actual bridge. There was a limit.

Recently, due to the development of Intelligent Transport System (ITS), the dual traffic information is measured through VDS (Vehicle Detector Systems), but the information on vehicle load has not been provided yet. Efforts to solve this problem are urgently required.

In particular, the conventional vehicle information measuring apparatus mainly relies on the data of the expressway office or the short-term data measured by manpower to obtain such information, and thus data collection in such a manner has a problem that the reliability is deteriorated due to the small amount of measurement. There was this.

Accordingly, the present invention has been proposed to solve the above problems of the prior art, the main purpose of which is to accurately grasp the driving characteristics and load characteristics of the traveling vehicle to predict and evaluate the static and dynamic behavior characteristics of the bridge by the vehicle load It is to provide a vehicle information automatic measurement system for securing the safety and common life of the bridge.

The present invention for achieving the above object, in the vehicle information measuring system, the vehicle detection sensor unit for outputting the detected vehicle traffic information to the data processing unit, the load detection for outputting the weight information of the detected vehicle to the data processing unit Automatically classify the vehicle type by detecting the sensor unit, the number of shafts and the axis spacing of the vehicle, and processing the traffic information of the vehicle detected by the vehicle detection sensor unit to process the speed, the number of axes, the axis of the vehicle driving the bridge on the road It records and stores the interval, head gap, entrainment rate, heavy vehicle mixing rate, etc., and processes and processes the weight information of the vehicle detected by the load sensor, and thus the information about the vehicle load such as the total weight and the axle weight of the traffic vehicle, and the bridge. And a data processor for recording and storing information on the dynamic characteristics of the apparatus.

The data processor may further include a signal amplifier for amplifying the analog signals sensed by the vehicle sensor and the load sensor to an appropriate level, a signal converter for converting the analog signal output from the signal amplifier into a digital signal, And a central processing unit (CPU) for receiving a digital signal from a signal converter and outputting the processed information to a monitor, a printer, and an interface.

In addition, by using the strain signal obtained from the vehicle load measuring sensor attached to the bottom of the girder of the bridge and processing the longitudinal line of the influence line, the vehicle load information such as the axial weight and the total weight of the vehicle not only in single driving but also in series and parallel driving. It is characterized by calculating.

In addition, by calculating the strain signal obtained by the vehicle load sensor attached to the lower part of the bridge girder by artificial neural network technique, vehicle load information such as axial weight and gross weight of the vehicle is calculated not only in single driving but also in series and parallel driving. It is characterized by.

In addition, it is characterized in that the vehicle load information such as the axial weight, the total weight of the vehicle by calculating the strain signal obtained through the vehicle load measuring sensor attached to the lower cross beam of the bridge by the artificial neural network method.

In addition, it is characterized in that the traffic information of the driving vehicle is obtained by processing the strain signal obtained through the traveling information measuring sensor attached to the bottom plate slab of the bridge by an artificial neural network technique.

In addition, it is characterized in that the traffic information of the driving vehicle is obtained by digital image processing the image signal obtained through the digital video camera installed on the bridge.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a vehicle information automatic measurement system according to the present invention. As shown, the vehicle sensor 10 for outputting the detected vehicle traffic information to the data processor 30, and the detection Load sensor 20 for outputting the weight information of the vehicle to the data processing unit 30, the vehicle number by detecting the number of shafts and the axis interval of the vehicle to automatically classify the vehicle, and detects through the vehicle sensor 10 Process and process the traffic information of the vehicle to record and store the speed, number of axes, axis interval, head gap, running rate, heavy vehicle mixing rate, etc. of the vehicle driving the bridge on the road and at the same time through the load sensor 20 And a data processor 30 for processing the sensed weight information of the vehicle to record and store information on the vehicle load such as the total weight of the traffic vehicle, the axle weight, and the dynamic characteristics of the bridge.

Figure 2 shows a detailed block diagram of a vehicle information automatic measurement system according to the present invention, as shown, the vehicle sensor 10 is provided with a driving information measuring sensor 11, the load sensor ( 20 is provided with a vehicle load measuring sensor 21.

In addition, the data processing unit 30 outputs from the signal amplifier 31 and a signal amplifier 31 for amplifying the analog signal sensed by the driving information measuring sensor 11 and the load sensing sensor unit 20 to an appropriate level. The signal converter 32 converts the analog signal into a digital signal, and receives the digital signal from the signal converter 32 and outputs the processed information to the monitor 35, the printer 36, and the interface 37. And a central processing unit (CPU) 33.

3 is a flowchart illustrating a data processing process using an influence line of an automatic vehicle information measuring system according to the present invention, and calculates a speed of a vehicle after detecting an axis (S1) by a driving information measuring sensor (S2), and makes a model and an axis number. Calculate the distance between the axis, the carry rate and the distance between the vehicle (S3). Subsequently, the influence line length is calculated (S4), the strain filtering and the maximum strain are determined (S5), the moment diagram is created with the maximum moment (S6), the moment is calculated according to the vehicle position (S7), and the total weight , Axle weight and heavy vehicle mixing rate is calculated (S8). The above-described load calculation calculates the load by selecting two points within the bridge section, comparing the theoretical moment value of the strain sensor installation point with the measured moment value, and then distributing the load.

Figure 4 shows the calculation theory of the axis of the vehicle information automatic measurement system according to the present invention. It is difficult to formulate the data without using the strain data acquired from the actual bridge because of the dynamic change. Therefore, after processing the data using the moving average, the maximum moment that is actually generated is multiplied by the factor (theoretic maximum moment / actual maximum moment) to represent the moment line as a vertex in a straight line. Find the point where the moment line and the theoretical moment line intersect in this way, extract the two moments needed for the calculation from the moment line and calculate the gross weight of all vehicles assumed to be 2 axis using Equation (1) below.

- - (1)

- - (One)

Ⅰ _ij : Influence line vertical of the j-th wheel of the vehicle at the i-th position

              Pi: j-th shaft weight

              Mi: Moment generated at the i position by the vehicle

On the basis of the total weight calculated as described above, the weight of each vehicle is calculated from the distribution ratio among the vehicles. Generally, bridges on highways have more than three lanes for the passengers and two or more vehicles travel in series or in parallel. Even in this case, the weight of the vehicle entering the bridge can be calculated by the calculation method described above.

FIG. 5 is a flowchart illustrating a data processing procedure using an artificial neural network of an automatic vehicle information measuring system according to the present invention. Initialization of a measurement sensor (H1) is for zero adjustment of a dynamic strain signal. Will also be performed.

The data preprocessing process (H2) is a process for converting / storing to a file of any form by selecting only a portion necessary for calculating vehicle information from the measured data file.

Axle number determination H3 is determined using a peak detection algorithm of the dynamic strain signal of the travel information measuring sensor.

Axle distance determination (H4) is obtained by using the dynamic strain signal of the travel information measuring sensor and using the relative time difference of the peak position using a peak detection algorithm.

The vehicle model discrimination H5 is automatically made when the number of vehicle axes and the distance between the axes are obtained. New vehicle types can be distinguished, taking into account the total length and number of shafts as well as the distance between the shafts.

Determination of passing lane (H6) is realized by the neural network, which is an M? 1 matrix composed of the maximum values of the strain signals of a series of driving information sensors installed on the bridge deck, and the target output value is between 1 and N. Is an integer. M is the number of sensors and N is the number of lanes. For example, if 16 driving information measuring sensors are installed on the 6-lane round-trip bridge, the input layer used in the discrimination neural network is 16-1 and the output value is an integer between 1 and 6. In addition, Q nodes are formed in P hidden layers to form a neural network. For the neural network training, training is performed using a training set composed of data setters of any data group. Iterative calculation allows convergence to fall within the convergence limit. In order to check whether the learning is completed normally, input the learning data into the learned neural network again and check whether an output value matching the target output value is obtained. To do this, the neural network file and the learning data used for learning are read again, and the accuracy of comparing the output value with the target value is output.

In order to quantitatively express the accuracy of the neural network output value, a correctness index (correctness index) was used. The accuracy index is composed of a combination of relative evaluation index and absolute evaluation index. The relative evaluation index indicates how large the output value of the target node is compared to the output value of other nodes, and the absolute evaluation index indicates how close the output value of the target node is to 1. Indicates. The severity rate of the relative and absolute valuation indices can be determined by the user's judgment, and 0.8 and 0.2 were used, respectively.

- - (2)

- - (2)

Where O _t is the output value of the target node.

_I is the maximum value among the output values of nodes except target nodes

a and b are the light weight ratios of C ₁ and C ₂ , respectively

The passage speed calculation (H7) can be calculated from the time difference between the signals of the two channels of the lane and the distance between the sensors connected to each channel when the lane through which the vehicle passes is determined using the passage lane discrimination neural network. . In order to calculate the time difference between two channels, one channel is fixed, the other channel is moved on the time axis, and the method of finding the point of time when the error between the two channels is minimum is used. When the parallax is obtained using this method, the passage speed is calculated from equation (3) using the distance L between the two sensors and the obtained parallax Δt.

- - (3)

--(3)

Total weight detection (H8) uses the dynamic strain signal measured by the vehicle load sensor. At this time, the vehicle load measuring sensor is a dynamic strain signal attached to the bottom of the bridge girders or crossbeams, unlike the strain signal of the bottom plate on which the driving information measuring sensor is installed, the signal measured as a part that is less sensitive to the wheel load of the passing vehicle. Can be effectively used for total weight detection. The analysis algorithm used mainly uses three input variables. 1) the dynamic strain signal obtained from the vehicle load sensor installed at the passage line, 2) vehicle passage speed, and 3) peak duration. Here, the peak duration is the time that 60% or more of the peak value for the total dynamic strain signal sum of the vehicle load sensor is used to reflect the influence of the distance between the axes.

Axle weight calculation (H9) is a process of distributing the total weight obtained above to each axle weight. As the peak values of the dynamic strain signal detected by the driving information measuring sensor are proportional to each axle weight, the total weight is determined as the peak value. Calculated in proportion to each shaft weight.

Vehicle information statistical processing (H10) is a process of statistically processing the daily traffic volume, such as weekly, monthly, yearly traffic volume by vehicle type, gross weight, and shaft weight obtained above. This includes developing statistically representative representative vehicle load models for the area.

6 shows a peak detection algorithm of the automatic vehicle information measuring system according to the present invention. A window of constant width moves at Δt intervals and records the position of the maximum value every hour, and when the same number of peak positions as the window width is continuously recorded, the point is recognized as a peak. In order to improve the accuracy of peak recognition, a cutoff level is set so that a peak value smaller than this value is not recognized as a peak. The accuracy of peak recognition depends on the width and the cutoff level of the window, which is set up to determine the maximum width and cutoff level of the window.

Figure 7 shows the result of the passage difference determination for the artificial neural network learning data of the vehicle information automatic measurement system according to the present invention. When the accuracy index of Equation (2) is 60 or more, it can be seen that the correct discrimination has been made. In the case of FIG. 7, the accuracy index is all 80 or more, which shows a very accurate determination result, which indicates that the neural network has been successfully learned. Indicates.

FIG. 8 is a block diagram illustrating an axis sensor sensor SN1 configured as two driving information measuring sensors per lane for measuring vehicle driving information according to the present invention.

Where L is the distance between the driving information sensors used for speed calculation.

       SL: Distance between the driving sensor and the starting point of the bridge.

FIG. 9 is a waveform diagram illustrating response signals of an axis sensor SN1 configured as two driving lanes as a driving information measuring sensor for measuring vehicle driving information according to the present invention. Calculate the speed by the time difference when the peaks of the two driving information sensors LIP1 and LIP2 occur.

10 is a response signal waveform of the dynamic strain sensor used as a load sensor for measuring the vehicle load information and the axis sensor (SN1) consisting of two per lane used as a driving information measuring sensor for measuring the vehicle driving information according to the present invention As shown in the figure, the relationship between the driving information measuring sensor and the vehicle load measuring sensor response signal when the vehicle passes the bridge is shown. Where SL represents the time related to the distance between the driving sensor and the bridge start point, CL / 2 represents the center of gravity of the vehicle, and 1/2 represents the length of the vehicle, and BL / 2 represents 1/2 the length of the bridge. Represents the time involved. That is, the maximum peak of the dynamic strain signal appears after CL / 2 + SL + BL / 2 has passed since the vehicle detected the first axis of the piezoelectric sensor.

FIG. 11 shows a configuration diagram of two loop sensors SN2 and one axis sensor SN1 per lane used as a driving information measuring sensor for measuring vehicle driving information according to the present invention. Where L is the distance between the loop sensor (SN2) used for the speed calculation, SL is the distance between the driving information measuring sensor and the bridge starting point used when obtaining the dynamic strain signal.

FIG. 12 is a waveform diagram illustrating response signals of two loop sensors and one axis sensor used as driving information measuring sensors for measuring vehicle driving information according to the present invention. It is possible to calculate the speed by the time difference between two loop sensors (L3-LP1 and L3-LP2).

FIG. 13 is a response signal waveform diagram of two loop sensors, one axis sensor, and a dynamic strain sensor used as a load information measuring sensor for measuring vehicle load information for measuring vehicle driving information according to the present invention; FIG. It is shown.

FIG. 14 is a diagram illustrating a configuration of two strain sensor groups per lane used as a driving information measuring sensor for measuring vehicle driving information according to the present invention, wherein a tape switch, a piezo sensor, and a loop sensor are placed on a pavement layer for measuring vehicle driving information. It is a configuration for acquiring vehicle driving information by installing two strain sensor groups SN3 per lane on the bottom surface of the bridge deck slab without installing a separate axis sensor.

15 is a response signal waveform diagram of two strain sensor groups used as driving information measuring sensors for measuring vehicle driving information according to the present invention, and the driving information measuring sensor as a sensor response waveform when a three-axis dump truck travels. And the mutual response waveform of the vehicle load sensor.

16 is a block diagram of a strain sensor group installed in a bridge girder used as a load information measuring sensor for measuring vehicle load information according to the present invention. Axle weight and gross weight of a traffic vehicle based on the theory that the external moment value obtained from the strain signal obtained by attaching strain sensors in the longitudinal direction to the girder of the bridge coincides with the internal moment value obtained from the longitudinal value of the static influence line. Information such as can be obtained. In addition, the dynamic strain signal obtained from the sensors can be applied to the neural network technique to obtain information such as the total weight and the axle weight of the traffic vehicle.

FIG. 17 is a diagram illustrating a configuration of a dynamic strain sensor installed in a bridge crossbeam for measuring vehicle load information according to the present invention. Dynamic strain signals obtained from the strain sensors installed in the transverse direction under the bridge crossbeams can be applied to the neural network to obtain information such as the total weight and the axle weight of a traffic vehicle.

FIG. 18 is a waveform diagram of a response signal of a dynamic strain sensor installed on a crossbeam of a bridge for measuring vehicle load information according to the present invention. As shown in FIG. It shows a lateral distribution phenomenon in which the value decreases.

19 is a conceptual diagram illustrating peak duration time to reflect the influence of the distance between axles when calculating the vehicle load by using the artificial neural network according to the present invention. As an input value for load estimation, the sum of six channels in addition to the maximum value of strain is shown. Peak duration was used. Here, the peak duration means a time for which a value of 60% or more of the peak value lasts.

FIG. 20 is a response waveform diagram of a strain signal of a bottom plate slab, which is a driving information measuring sensor used for axial weight distribution when a vehicle load is calculated using an artificial neural network according to the present invention. The strain signal of the slab slab is sensitive to the axis of the passing vehicle, so it is used to distribute the weight of the axle and to calculate the total weight.

According to the present invention, in addition to the above-described method, a method of obtaining vehicle driving information by applying an image processing technique using an image signal of a driving vehicle obtained through a digital video camera, vehicle load information as well as vehicle driving information from a driving information measuring sensor At the same time, it is possible to employ a method of measuring vehicle information by applying a pattern recognition technique other than an artificial neural network to a signal measured from a vehicle load measuring sensor and the like.

In addition, if the data on the vehicle driving and load information collected through the present invention through statistical and probabilistic methods, it is used to develop a new design live load model suitable for the domestic situation required for the future design of the bridge, It will be used as important data for judgment and prediction of remaining life. It can also be used to crack down on overload vehicles, the main cause of bridge damage.

Although the present invention has been described in detail only with respect to the specific examples described, it will be apparent to those skilled in the art that various modifications and variations are possible within the spirit of the present invention, and such modifications and modifications belong to the appended claims.

As described above, using the vehicle information obtained through the vehicle information automatic measurement system according to the present invention it is possible to redefine the design vehicle live load model required for the design of the bridge to a model suitable for the domestic reality, there is a problem when maintaining the bridge There is an effect that can more efficiently crack down on the overloaded vehicle.

Claims (7)

In the vehicle information measuring system, A vehicle detection sensor unit 10 for detecting and outputting traffic information of the vehicle; A load sensor 20 for outputting weight information of the vehicle detected by the vehicle sensor 10 to the data processor 30; By processing the traffic information of the vehicle detected by the vehicle detection sensor unit 10 to detect the number of shafts and the axis of the vehicle driving the bridge on the road to automatically classify the vehicle type, the speed of the vehicle, the number of shafts, the axis interval And store and store traffic statistics information such as head gap, entrainment rate, and heavy vehicle mixing rate, and process the weight information of the vehicle detected by the load sensor 20 to process the total weight of the traffic vehicle, the axle weight, and the like. Vehicle information automatic measurement system comprising a data processing unit for recording and storing the information on the vehicle load and the historical information on the dynamic characteristics of the bridge. delete delete The method of claim 1, wherein the data processing unit 30 processes the strain signal obtained through the vehicle load measuring sensor SN4 attached to the lower portion of the bridge girders (GD) by artificial neural network method, as well as serial driving and parallel driving. Vehicle information automatic measurement system, characterized in that for calculating the vehicle load information, such as the axial weight, the total weight of the vehicle. The method of claim 1, wherein the data processing unit 30 processes the strain signal obtained through the vehicle load measuring sensor attached to the lower cross beam of the bridge by artificial neural network method to obtain vehicle load information such as the axial weight, the total weight of the vehicle Vehicle information automatic measurement system, characterized in that the calculation. The method of claim 1, wherein the data processing unit 30 is obtained by processing the strain signal obtained through the traveling information measuring sensor attached to the bottom plate slab of the bridge by artificial neural network method to obtain the traffic information of the driving vehicle Vehicle information automatic measurement system. delete

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