JP2016113814A - Data output program, data output method and data output device - Google Patents

Abstract

A data output program, a data output method, and a data output apparatus capable of realizing comparison of road surface condition data under different measurement conditions are provided. A server device 10 includes first and second road surface condition data measured by first and second vehicles respectively traveling on a first road surface and a second road surface, and first and second road surface data. Of the reference road condition data measured by the reference vehicle traveling on both road surfaces, the measurement result of the road condition of the first vehicle based on the first road condition data and the reference road condition data related to the first road surface The first correction parameter is calculated to approximate the measurement result of the road surface condition of the reference vehicle, and the measurement result of the road condition of the second vehicle based on the second road condition data and the reference road condition data related to the second road surface The second correction parameter that approximates the measurement result of the road surface condition of the reference vehicle is calculated, and the first and second road surface condition data are corrected and output using the first and second correction parameters, respectively. [Selection] Figure 3

Description

  The present invention relates to a data output program, a data output method, and a data output device.

  The road surface of the road deteriorates due to traffic load and natural environment. Such deterioration of the road surface is preferably detected at an early stage in terms of driving safety and repair costs. For this reason, as an example, the measurement result of the acceleration sensor may be used for estimating the road surface condition.

JP 2009-281799 A

  However, if the measurement conditions such as the type of vehicle, tire condition, number of passengers, etc. are different, even if the acceleration is the same value, the road surface condition is not always the same, and the road surface condition is the same. However, the acceleration measured there is not necessarily the same. Therefore, it is difficult to compare road surface condition data including acceleration under different measurement conditions, for example, a road surface deterioration index obtained from acceleration.

  In one aspect, an object of the present invention is to provide a data output program, a data output method, and a data output device capable of comparing road surface condition data with different measurement conditions.

  The data output program according to the aspect includes the first road surface condition data measured by the first vehicle traveling on the first road surface portion and the second road measured by the second vehicle traveling on the second road surface portion. Of the road surface condition data and the reference road surface condition data measured by the reference vehicle traveling on both the first road surface part and the second road surface part, the first road surface related to the first road surface part. Based on the situation data and the reference road condition data relating to the first road surface portion, a first correction parameter for calculating a measurement result of the road condition of the first vehicle close to a measurement result of the road condition of the reference vehicle is calculated. The measurement result of the road surface condition of the second vehicle is based on the second road surface condition data on the second road surface part and the reference road surface data on the second road surface part. To execute a process of calculating the second correction parameter to approximate to the measurement result of the road surface condition of quasi-vehicle computer. Further, the data output program corrects and outputs the first road surface condition data using the calculated first correction parameter, and uses the calculated second correction parameter to output the second road surface condition. A process of correcting and outputting data is executed by the computer.

  Comparison of road surface condition data with different measurement conditions can be realized.

FIG. 1 is a diagram illustrating the configuration of the road surface survey system according to the first embodiment. FIG. 2 is a diagram illustrating an example of travel routes of the reference vehicle and the measurement vehicle. FIG. 3 is a block diagram illustrating a functional configuration of the server apparatus according to the first embodiment. FIG. 4 is a diagram illustrating an example of road surface condition data. FIG. 5 is a diagram illustrating an example of a method for calculating a representative value of acceleration. FIG. 6 is a diagram illustrating an example of a method for correcting road surface condition data. FIG. 7 is a diagram illustrating a display example of a survey result screen. FIG. 8 is a diagram illustrating a display example of a survey result screen. FIG. 9 is a flowchart of a correction parameter calculation process according to the first embodiment. FIG. 10 is a flowchart illustrating the procedure of the data output process according to the first embodiment. FIG. 11 is a diagram illustrating an application example 1 of the correction method. FIG. 12A is a diagram illustrating an application example 2 of the correction method. FIG. 12B is a diagram illustrating an application example 2 of the correction method. FIG. 12C is a diagram illustrating an application example 2 of the correction method. FIG. 13 is a diagram illustrating a hardware configuration example of a computer that executes the data output program according to the first embodiment and the second embodiment.

  Hereinafter, a data output program, a data output method, and a data output apparatus according to the present application will be described with reference to the accompanying drawings. Note that this embodiment does not limit the disclosed technology. Each embodiment can be appropriately combined within a range in which processing contents are not contradictory.

  FIG. 1 is a diagram illustrating the configuration of the road surface survey system according to the first embodiment. The road surface survey system 1 shown in FIG. 1 is based on the measurement results of acceleration of sensors 30 mounted on a reference vehicle 3 and the measurement results of acceleration of sensors 50A to 50N mounted on a plurality of measurement vehicles 5A to 5N. It provides a road surface survey service for investigating the road surface condition.

  As part of this road surface survey service, the road surface survey system 1 uses the acceleration measurement results of the sensors 50A to 50N mounted on the plurality of measurement vehicles 5A to 5N to determine the acceleration of the sensors 30 mounted on the reference vehicle 3. Correct based on measurement results. Accordingly, the road surface survey system 1 aims to process the acceleration measurement results measured by the sensors 50A to 50N under different measurement conditions when the measurement vehicles 5A to 5N travel into information that can be compared with each other. In the following description, when the vehicles of the plurality of measurement vehicles 5A to 5N are collectively referred to as “measurement vehicle 5”, the sensors 50A to 50N mounted on the measurement vehicle 5 are collectively referred to as “sensors”. May be described as "Class 50".

[Vehicle classification]
The reference vehicle 3 and the measurement vehicle 5 are both vehicles that travel on the road for the purpose of measuring acceleration, but the ranges to be traveled are different from each other. Among these, the reference | standard vehicle 3 points out the vehicle which drive | works comprehensively the road of each area included in the service provision range. On the other hand, the measurement vehicle 5 refers to a vehicle that travels on a road to which a charge is assigned in an area where a service provision range is divided. Here, as an example, it is assumed that the reference vehicle 3 is operated by a service provider, while the measurement vehicle 5 is operated by a service subscriber, for example, a local government that performs a road pavement plan and a repair plan. .

  Further, the “service provision range” refers to a range in which a service provider provides the road surface survey service. For example, the whole country can be included as a category, and a wide area such as Kanto, Tohoku, Chubu, and Kansai, a prefecture, or a municipality can be included as a category.

  Here, the travel routes of the reference vehicle 3 and the measurement vehicles 5A to 5N are set so that at least a part thereof overlaps. That is, a part of the travel route on which the reference vehicle 3 travels in each area included in the service provision range and the travel route on which the measurement vehicles 5A to 5N travel in the area in charge overlap. Hereinafter, overlapping sections of the travel routes of the reference vehicle 3 and the measurement vehicles 5A to 5N may be referred to as “overlapping sections”.

  FIG. 2 is a diagram illustrating an example of travel routes of the reference vehicle 3 and the measurement vehicle 5. FIG. 2 shows three travel routes of the reference vehicle 3, the measurement vehicle 5A, and the measurement vehicle 5B. Further, in FIG. 2, the travel route 3R of the reference vehicle 3 is indicated by a solid line, the travel route 5RA of the measurement vehicle 5A is indicated by a one-dot chain line, and the travel route 5RB of the measurement vehicle 5B is indicated by a two-dot chain line. In the example shown in FIG. 2, the measurement vehicle 5A is in charge of the measurement of the area E1, and the measurement vehicle 5B is in charge of the measurement of the area E2.

As shown in FIG. 2, the reference vehicle 3 is set with a travel route 3R that crosses an area E1 that is an area in charge of the measurement vehicle 5A and an area E2 that is an area in charge of the measurement vehicle 5B. Under the travel route shown in FIG. 2, reference vehicle 3, with overlap overlapping interval I _A and the travel path 5RA measuring wheel 5A, it overlaps the travel route 5RB measuring wheel 5B by overlapping interval I _B, the same The road surface will be run. In these overlapping intervals I _A and overlapping interval I _B, the reference vehicle 3 and the measuring wheel 5A or, the type of the vehicle between the reference vehicle 3 and the measuring wheel 5B, the weight of the vehicle, the tire condition and boarding persons such measuring conditions The difference appears as a difference in acceleration measurement results. Further, the travel route 3R of the reference vehicle 3 has overlapping sections on both the travel route 5RA of the measurement vehicle 5A and the travel route 5RB of the measurement vehicle 5B.

From these facts, the difference or ratio of the measurement result in the overlap interval I _A and the correction parameters, while bringing the measuring wheel 5A acceleration measurement result of the acceleration of the measurement results of the reference vehicle 3, the result measured in the overlap interval I _B By using the difference or the ratio of the correction parameter as a correction parameter, the acceleration measurement result of the measurement vehicle 5B is brought close to the acceleration measurement result of the reference vehicle 3, so that the acceleration can be achieved even between the measurement vehicle 5A and the measurement vehicle 5B. The measurement result can be processed in a state where the magnitude can be compared.

  Thus, the type of road set as the travel route can be arbitrarily determined. For example, when a local government such as a prefecture is assumed as a service subscriber, the prefectural road including a national highway can be included in the travel route, and a local government such as a municipality can be included as a service subscriber. In the case where it is envisaged, national roads and municipal roads can be included in the travel route.

[System configuration]
As shown in FIG. 1, the road surface survey system 1 includes a server device 10, sensors 30 mounted on a reference vehicle 3, sensors 50A-50N mounted on a plurality of measurement vehicles 5A-5N, and a client. A terminal 70 is accommodated. Although FIG. 1 illustrates the case where three measurement vehicles and one client terminal 70 are accommodated, the road surface survey system 1 can accommodate any number of measurement vehicles and client terminals.

  The server device 10 is connected to the sensors 30, the sensors 50, and the client terminal 70 via the network 9 so that they can communicate with each other. The network 9 can employ any type of communication network, such as the Internet (Internet), LAN (Local Area Network), and VPN (Virtual Private Network), whether wired or wireless. For example, the server device 10 and the sensors 30 and 50 are connected via a mobile communication network, and the server device 10 and the client terminal 70 are connected via a LAN or the Internet. .

  Each of the sensors 30 and the sensors 50 is a sensor or a sensor group that senses a predetermined sensor value.

  Of these sensors 30 and 50, the sensors 30 are mounted on the reference vehicle 3 while the sensors 50 are mounted on the measurement vehicle 5, but the sensors mounted on both are the same. It doesn't matter.

  As one embodiment, the sensors 30 and 50 include at least an acceleration sensor. As an example of such an acceleration sensor, an X-axis, Y-axis, and Z-axis, that is, a 3-axis acceleration sensor that measures acceleration in the front-rear, left-right, up-down, and up-down directions can be used. You can also. The sensors 30 and 50 may include a GPS (Global Positioning System) receiver that measures positions such as latitude and longitude. Further, the sensors 30 and 50 may include a speed sensor that measures the speed at which the vehicle travels. In the following, an acceleration sensor, a GPS receiver, and a speed sensor are illustrated as an example of the sensors 30 and 50, but the GPS receiver and the speed sensor may not necessarily be included in the sensors 30 and 50. Absent.

  These acceleration, position, and speed are uploaded to the server device 10 as road surface condition data. Here, as an example, a case where the road surface condition data is uploaded in a state in which the acceleration, position, and speed of the time are associated with each other is illustrated as an example, but time series data of acceleration, time series data of speed, and speed It is also possible to upload each time series data individually.

  For example, the road surface condition data can be uploaded to the server device 10 via the network 9. In this case, road surface condition data can be uploaded in real time every time acceleration, position or speed is measured, and is accumulated over a predetermined period, for example, a period from the start to the end of traveling on a planned route. You can also upload road surface data. In addition, road surface condition data can be uploaded to the server device 10 via various removable media such as a memory card. In this case, road surface condition data measured by the sensors 30 or 50 is recorded on various removable media such as a memory card, read by a reader attached to or built in the client terminal 70, and then from the client terminal 70. Uploaded to the server device 10.

  Here, the sensors 30 and 50 can be realized by mounting a dedicated acceleration sensor, a GPS receiver, and a speed sensor on the reference vehicle 3 and the measurement vehicle 5, but a mobile terminal represented by a smartphone. It is also possible to use an acceleration sensor, a GPS receiver, a speed sensor, or the like provided as standard equipment in a device, a digital tachometer, a so-called “digital tachometer”, a drive recorder, a so-called “Doraco”, or the like. In addition, a digital communication device such as BLE (Bluetooth (registered trademark) Low Energy) is connected between a digital terminal or dorareco and a mobile terminal device such as a smartphone, and a mobile communication network to which the mobile terminal device can be connected is used. Thus, communication between the sensor and the server can be realized.

  The client terminal 70 is a computer that receives provision of the road surface survey service from the server device 10.

  As an embodiment, the client terminal 70 may be a desktop or notebook personal computer. In addition, the mobile terminal device may be a mobile terminal such as a smart phone, a mobile phone or a PHS (Personal Handyphone System), or a slate terminal such as a PDA (Personal Digital Assistants). it can.

  For example, the client terminal 70 is used by a service subscriber who subscribes to the above road surface survey service. An example of such a service subscriber is a local government that performs a road pavement plan and a repair plan. The server device 10 provides a road surface survey result to the client terminal 70 used by such a service subscriber. As a result, the service subscriber can receive the above road surface survey service.

  The server device 10 is a computer that provides the above road surface survey service to the client terminal 70.

  As an embodiment, the server device 10 can be implemented by installing a data output program for realizing the road surface survey service as package software or online software on a desired computer. For example, the server device 10 may be implemented as a Web server that provides the above road surface survey service, or may be implemented as a cloud that provides the above road surface survey service by outsourcing.

[Configuration of Server Device 10]
FIG. 3 is a block diagram illustrating a functional configuration of the server apparatus 10 according to the first embodiment. As illustrated in FIG. 3, the server device 10 includes a communication I / F (interface) unit 11, a storage unit 13, and a control unit 15. The server device 10 may include various functional units included in known computers, for example, functional units such as various input devices and audio output devices, in addition to the functional units illustrated in FIG.

  The communication I / F unit 11 is an interface that performs communication control with other devices such as the sensors 30, the sensors 50, and the client terminal 70.

  As an embodiment, a network interface card such as a LAN card can be adopted as one aspect of the communication I / F unit 11. For example, the communication I / F unit 11 receives the above road surface condition data from the sensors 30 and 50, and receives a request for browsing the road surface survey results from the client terminal 70. Further, the communication I / F unit 11 transmits a road surface survey result to the client terminal 70.

  The storage unit 13 is a storage device that stores data used for various programs such as the above-described data output program including an OS (Operating System) executed by the control unit 15.

  As an embodiment, the storage unit 13 is implemented as a main storage device in the server device 10. For example, various semiconductor memory elements such as a RAM (Random Access Memory) and a flash memory can be employed for the storage unit 13. The storage unit 13 can also be implemented as an auxiliary storage device. In this case, an HDD (Hard Disk Drive), an optical disk, an SSD (Solid State Drive), or the like can be employed.

  The storage unit 13 stores road surface condition data 13a and corrected road surface condition data 13b as an example of data used in a program executed by the control unit 15. In addition to the road surface condition data 13a and the corrected road surface condition data 13b, other electronic data, for example, information related to the vehicle and the driver, service provision range, map data of each area, schedule data related to the travel route, and the like are also stored. You can also. The description of the road surface condition data 13a and the corrected road surface condition data 13b will be described later at the stage where each data is registered or referred to.

  The control unit 15 has an internal memory for storing various programs and control data, and executes various processes using these.

  As an embodiment, the control unit 15 is implemented as a central processing unit, a so-called CPU (Central Processing Unit). Note that the control unit 15 is not necessarily implemented as a central processing unit, and may be implemented as an MPU (Micro Processing Unit). The control unit 15 can also be realized by a hard wired logic such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  The control unit 15 virtually implements the following processing unit by executing various programs. For example, as shown in FIG. 3, the control unit 15 includes a registration unit 15a, a detection unit 15b, a calculation unit 15c, a correction unit 15d, and an output unit 15e.

  The registration unit 15 a is a processing unit that registers information uploaded from the sensors 30 of the reference vehicle 3 or the sensors 50 of the measurement vehicle 5.

  As one embodiment, the registration unit 15 a performs the following process when road surface condition data is uploaded from the sensors 30 or 50. That is, the registration unit 15a stores the road surface state data in the storage unit 13 in a state where the computer can identify the travel route corresponding to the road surface state data and the travel date and time when the travel of the travel route was performed. The identification information “travel ID (IDentifier)” associated with the pair of travel route and travel date is assigned. Such travel IDs are given different ID values when the travel dates are different even on the same travel route. In addition, the registration unit 15a registers road surface condition data in the storage unit 13 in a state in which the travel IDs thus assigned are associated with each other.

  FIG. 4 is a diagram illustrating an example of road surface condition data 13a. In FIG. 4, as an example, road surface condition data measured when the measurement vehicle 5A traveled on the travel route 5RA on January 1, 2014 are extracted and shown. As shown in FIG. 4, the road surface condition data 13a is data in which items such as a travel ID, a vehicle classification, a date, a time, a position, an acceleration, and a speed are associated with each other. In the example of the road surface condition data 13a shown in FIG. 4, the “speed”, the “acceleration” of the front and rear G, the left and right G, and the vertical G, and the “position” of latitude and longitude are measured at a cycle of 0.1 second. Means that. In addition, although the case where the sampling cycle of position, acceleration, and speed was the same was illustrated in FIG. 4, the sampling cycle does not necessarily have to be the same. For example, when the sampling period differs between the position, acceleration, and speed, the position, acceleration, and speed may be stored in accordance with the sampling interval that has the longest sampling period among the position, acceleration, and speed.

  The detection unit 15 b is a processing unit that detects an overlapping section in which the same road surface is traveled between the road surface state data regarding the reference vehicle 3 and the road surface state data regarding the measurement vehicle 5. Here, as an example, a case where road surface condition data regarding each measurement vehicle 5 is uploaded as needed in a state where the road surface condition data regarding the reference vehicle 3 is stored in the storage unit 13 is illustrated. It is not limited. For example, when the road surface condition data related to the reference vehicle 3 is uploaded later than the road surface condition data related to the measurement vehicle 5, the road surface state data related to the reference vehicle 3 and the road surface state data related to each registered measurement vehicle 5. Then, the same processing as described below may be executed.

  As one embodiment, when the road surface condition data of the measurement vehicle 5 is registered in the storage unit 13, the detection unit 15 b reads the road surface state data of the reference vehicle 3 stored in the storage unit 13. Then, the detection unit 15 b performs map matching of the road surface condition data of the reference vehicle 3 and map matching of the road surface condition data of the measurement vehicle 5. Thereby, the locus of the position included in the road surface condition data of the reference vehicle 3, that is, the travel route is developed on the map, and the locus of the position included in the road surface condition data of the measurement vehicle 5, that is, the travel route is displayed on the map. Will be deployed. Then, the detection unit 15b detects a portion where the travel routes of the reference vehicle 3 and the measurement vehicle 5 overlap on the map as an overlapping section.

  The calculation unit 15c is a processing unit that calculates a correction parameter that brings the measurement result of the acceleration of the measurement vehicle 5A close to the measurement result of the acceleration of the reference vehicle 3.

  As one embodiment, the calculation unit 15 c derives a representative value of acceleration measured in the overlapping section detected by the detection unit 15 b for each of the reference vehicle 3 and the measurement vehicle 5. At this time, as an example, the calculation unit 15c may extract the maximum value from the accelerations measured in the overlapping sections, or may calculate the average value of the accelerations measured in the overlapping sections. In addition, the calculation unit 15c specifies a point where the speed difference between the reference vehicle 3 and the measurement vehicle 5 is the smallest in the overlapping section, and determines the acceleration value measured at the specified point as a representative value of the reference vehicle 3 and It may be extracted as a representative value of the measuring wheel 5. The reason why the acceleration measured at the point where the speed difference is minimum is used as the representative value is to reduce an error that occurs when the road surface condition data of the measurement vehicle 5 is corrected. This is because even when traveling on the same road surface, a larger vibration is generated as the speed is higher, so that an error occurring during correction is larger as the speed difference between the two is larger.

  In addition, it is not always necessary to derive the representative acceleration value in the entire overlapping section. FIG. 5 is a diagram illustrating an example of a method for calculating a representative value of acceleration. As shown in FIG. 5, the overlapping section includes sections (A) to (E) formed with a predetermined unit distance, for example, 20 m or 100 m. In this case, the calculation unit 15c obtains a cumulative value of the speed difference in each of the sections (A) to (E), and extracts the maximum value of the acceleration measured in the section where the cumulative value of the speed difference is minimum, An average value can also be calculated.

  Thus, after the representative value of the acceleration measured in the overlapping section is calculated for each of the reference vehicle 3 and the measurement vehicle 5, the calculation unit 15c performs correction as to the measurement vehicle 5 by executing the following calculation. Calculate the parameters. That is, the calculation unit 15c performs the calculation of the ratio of the representative value of the measurement vehicle 5 to the representative value of the reference vehicle 3, that is, the calculation that divides the representative value of the reference vehicle 3 by the representative value of the measurement vehicle 5. A correction parameter for the measurement wheel 5 is calculated.

  The correction unit 15d is a processing unit that corrects road surface condition data regarding the measurement vehicle 5 using the correction parameter calculated by the calculation unit 15c.

  As an embodiment, the correction unit 15d multiplies the acceleration time series data included in the road surface condition data regarding the measurement vehicle 5 by the correction parameter calculated by the calculation unit 15c. As a result, the road surface condition data relating to the measurement vehicle 5 is corrected to the road surface state data when the measurement is performed by the reference vehicle 3. The road surface condition data after being corrected in this way may be referred to as “corrected road surface condition data”. Thereafter, the correction unit 15d registers the corrected road surface condition data 13b obtained by the correction in the storage unit 13.

  FIG. 6 is a diagram illustrating an example of a method for correcting road surface condition data. 6 shows acceleration time-series data related to the reference vehicle 3, that is, a time waveform of acceleration, while the lower left-hand side of FIG. 6 shows acceleration time-series data related to the measurement vehicle 5A and FIG. The time series data of the acceleration relating to the measurement vehicle 5B is shown on the lower right side. FIG. 6 shows, as an example, a case where the maximum value of acceleration measured in the overlapping section is used as the representative value of the overlapping section.

As shown in FIG. 6, the correction parameter related to the measurement wheel 5A is a representative value of the acceleration of the reference vehicle 3 and the measuring wheel 5A overlap interval I standards were measured at _A vehicle 3 of the "1.0" in overlapping interval I _A It is obtained by calculation divided by the representative value “0.5” of the measured measuring wheel 5A. The reference vehicle 3 and the representative value of the redundant interval I _B in the measured representative value of the acceleration of the measured reference vehicle 3 to "1.5" in overlapping interval I _B measurement wheel 5B measuring wheel 5B "1.0 Is calculated by dividing by "." Then, by multiplying the time series data of acceleration included in the road surface condition data related to the measurement vehicle 5A by the correction parameter “1.0 / 0.5” related to the measurement vehicle 5A, road surface condition data related to the measurement vehicle 5A. Is corrected to road surface condition data when measurement is performed by the reference vehicle 3. Further, by multiplying the time series data of the acceleration included in the road surface condition data related to the measurement vehicle 5B by the correction parameter “1.5 / 1.0” related to the measurement vehicle 5B, the road surface condition data related to the measurement vehicle 5B becomes the reference. The road surface condition data when the measurement is performed by the vehicle 3 is corrected. With these corrections, the magnitudes of the acceleration measurement results can be compared even between the measurement vehicle 5A and the measurement vehicle 5B in which there are no overlapping sections on the travel route.

  The output unit 15e is a processing unit that outputs the corrected road surface condition data corrected by the correction unit 15d. The “output” mentioned here includes display output, audio output, print output, signal output, and the like in its category. Hereinafter, as an example, display output of corrected road surface condition data will be described.

  As an embodiment, the output unit 15 e outputs, to the client terminal 70, corrected road surface condition data regarding each area included in the service providing range when a browsing request regarding the road surface survey result is received from the client terminal 70. For example, the output unit 15e can display time series data of acceleration included in the corrected road surface condition data of the measurement vehicle 5 corresponding to each area, that is, time waveforms of acceleration in the vertical direction (gravity direction). In addition, the output unit 15e calculates, for each section included in each area, for example, a section formed with a unit distance such as 20 m or 100 m, an index related to acceleration in the gravity direction measured in the section, for example, a degradation index, It is also possible to display a map in which the degradation index is mapped by area and section. For example, the output unit 15e can calculate a degradation index corresponding to the level range depending on which level range of the plurality of level ranges the maximum acceleration value in the section belongs to. The output unit 15e can also calculate a degradation index corresponding to the level range depending on which level range of the plurality of level ranges the number of times that exceeds a predetermined threshold in the section. In addition to these deterioration indexes, the deterioration index can be calculated using acceleration dispersion (standard deviation), acceleration other than the direction of gravity, and the like. In addition, although the case where correction road surface condition data is displayed on demand is illustrated here, the display which updates the time waveform and deterioration index of an acceleration whenever correction road surface state data of the measurement vehicle 5 is registered into the memory | storage part 13 is shown. It can also be.

  7 and 8 are diagrams illustrating display examples of the survey result screen. FIG. 7 and FIG. 8 extract and show the display of the degradation index related to the areas E1 and E2 among the areas included in the service providing range. 7 shows a display example when the area E1 and the area E2 are adjacent to each other, while FIG. 8 shows a display example when the area E1 and the area E2 are areas not adjacent to each other. Show. Further, in the example of FIGS. 7 and 8, it is assumed that road surface condition data in the area E1 is measured by the measurement vehicle 5A and road surface condition data in the area E2 is measured by the measurement vehicle 5B. In FIGS. 7 and 8, the degree of road surface deterioration is represented by a three-stage deterioration index of 0 to 2, and the deterioration index of the section in which the right-handed fill is mapped on the road is “1”. This means that the degradation index of the section where the black fill is mapped on the road is “2”, and the degradation index of the section where the fill is not mapped on the road is “0”. It means to be there.

  In the example of the survey result screen 200 shown in FIG. 7, there are many narrow roads in the area E1, and deterioration of about the deterioration index “1” is observed occasionally. The viewer can be made aware that only one location is found. On the other hand, the road in the area E2 is only a main road, and it is possible for the viewer to grasp that the deterioration has progressed to the deterioration index “2” in four places. By comparing these present conditions, it is possible to make the viewer judge that it is better to give priority to repairing the area E2 where many deteriorations exist on the main road.

  In the example of the survey result screen 210 shown in FIG. 8, it becomes easy to compare the areas E1 and E2 that are not adjacent to each other. Both of these areas E1 and E2 are districts centering on arterial roads, and both have two degradation indexes “1” and three degradation indexes “2”, indicating the degree of degradation. Is about the same. However, since there are important facilities such as hospitals and fire stations in the area E2, it is possible for the viewer to make a decision that priority should be given to the repair of the area E2.

  In the example of FIG. 8, a hospital or a fire department is illustrated as an example of an important facility, but the same judgment can be made even when there is a police or a school, and the vehicle type such as small, medium, and large However, it is possible to use a lot of traffic volume, especially the traffic volume of large vehicles. Further, the above determination may be incorporated in the degradation index calculation logic without leaving it to the viewer. For example, the deterioration index of a section where there is an important facility near the road is calculated to be higher than the deterioration index of a section where there is no important facility, or the deterioration index of a section where the traffic volume is greater than a predetermined amount It is also possible to calculate with a value higher than the degradation index of the section.

[Process flow]
Next, the flow of processing of the road surface survey system 1 according to the present embodiment will be described. Here, after (1) correction parameter calculation processing executed by the server device 10 is described, (2) data output processing is described.

(1) Correction Parameter Calculation Processing FIG. 9 is a flowchart illustrating a correction parameter calculation processing procedure according to the first embodiment. In this process, every time the road surface condition data of the measurement vehicle 5 is registered in the storage unit 13, the following steps S <b> 102 to S <b> 106 are repeatedly executed.

  As illustrated in FIG. 9, when the road surface condition data of the measurement vehicle 5 is registered in the storage unit 13 (Yes in Step S <b> 101), the detection unit 15 b detects the difference between the road surface state data regarding the reference vehicle 3 and the road surface state data regarding the measurement vehicle 5. In step S102, an overlapping section in which the same road surface is traveled is detected.

  Subsequently, the calculation unit 15c derives a representative value of acceleration measured in the overlapping section detected in step S102 for each of the reference vehicle 3 and the measurement vehicle 5 (step S103). In addition, the calculation unit 15 c performs calculation of the ratio of the representative value of the measurement vehicle 5 to the representative value of the reference vehicle 3, that is, calculation to divide the representative value of the reference vehicle 3 by the representative value of the measurement vehicle 5. Then, a correction parameter for the measurement vehicle 5 is calculated (step S104).

  And the correction | amendment part 15d correct | amends the road surface condition data regarding the measurement vehicle 5 using the correction parameter calculated by step S104 (step S105). As a result, the road surface condition data relating to the measurement vehicle 5 is corrected to the road surface state data when the measurement is performed by the reference vehicle 3. Thereafter, the correction unit 15d registers the corrected road surface condition data 13b obtained by the correction in step S105 in the storage unit 13 (step S106), and returns to the process of step S101.

(2) Data Output Process FIG. 10 is a flowchart illustrating the procedure of the data output process according to the first embodiment. This process is executed as an example only when a browsing request regarding a road surface survey result is received from the client terminal 70.

  As illustrated in FIG. 10, the output unit 15 e executes the following process when receiving a browsing request regarding the road surface survey result from the client terminal 70 of the measurement vehicle 5. That is, the output unit 15e reads the corrected road surface condition data regarding each area included in the service providing range from the storage unit 13 (step S301).

  Subsequently, the output unit 15e selects one area from the areas included in the service providing range (step S302). Then, the output unit 15e selects one section in the area selected in step S302 (step S303).

  Thereafter, the output unit 15e determines whether or not the maximum acceleration value included in the section selected in step S303 is equal to or greater than the first threshold value (step S304). At this time, if it is equal to or greater than the first threshold (Yes at Step S304), the output unit 15e classifies the degradation index of the section selected at Step S303 into “2” (Step S305).

  If it is less than the first threshold value (No in step S304), the output unit 15e determines whether the maximum acceleration value included in the section selected in step S303 is equal to or greater than a second threshold value that is smaller than the first threshold value. It is determined whether or not (step S306). At this time, when it is equal to or greater than the second threshold, that is, when the second threshold ≦ the maximum value of acceleration <the first threshold (step S306 Yes), the output unit 15e determines the deterioration index of the section selected in step S303. Are classified into “1” (step S307).

  Further, when it is less than the second threshold value, that is, when the maximum acceleration value is smaller than the second threshold value (No in Step S306), the output unit 15e classifies the deterioration index of the section selected in Step S303 as “0”. (Step S308).

  After the classification in step S305, step S307, or step S308 is performed, the processing from step S303 to step S308 is repeatedly executed until all sections in the area selected in step S302 are selected (No in step S309). .

  When all sections in the area selected in step S302 are selected (step S309 Yes), the output unit 15e determines whether all areas included in the service providing range have been selected (step S310). At this time, when all the areas included in the service providing range have not been selected (No in step S310), the processes from step S302 to step S309 are repeatedly executed.

  When all the areas included in the service providing range are selected (Yes at Step S310), the output unit 15e maps the deterioration index of each section included in the area on the map of each area (Step S311). After that, the output unit 15e displays on the client terminal 70 a map of a plurality of areas among the areas where the degradation index is mapped by section in step S311 and arranged in a predetermined position, for example, left and right or up and down (step S312). The process is terminated.

[One aspect of effect]
As described above, the server device 10 according to the present embodiment converts the road surface condition data of the measurement vehicles A and B whose traveling routes do not overlap into the road surface state data of the reference vehicle traveling along the same route as the measurement vehicles A and B. Each road surface condition data is output with the approaching correction. Therefore, according to the server apparatus 10 which concerns on a present Example, the comparison of road surface condition data between the measurement vehicles A and B is realizable.

  Although the embodiments related to the disclosed apparatus have been described above, the present invention may be implemented in various different forms other than the above-described embodiments. Therefore, another embodiment included in the present invention will be described below.

[location information]
In the first embodiment, the case where the positions of the reference vehicle 3 and the measurement vehicle 5 are measured by the GPS receiver is exemplified, but the position measurement may not necessarily be realized by the GPS receiver. For example, the speed is measured by designating the travel route of the reference vehicle 3 or the measurement vehicle 5 and inputting the time when the start point of the travel route starts and the time when the vehicle arrives at the end point, and time-integrating the time series data of the speed. Alternatively, the locus of the position at each time point when the acceleration is measured may be estimated by calculating the travel distance at each time point. This makes it possible to estimate time-series data of positions without using a position detection sensor such as a GPS receiver or an IC tag.

[Application example 1 of correction method]
In the first embodiment, the case where the road surface state data related to the measurement vehicle 5A in the area E1 and the road surface state data related to the measurement vehicle 5B in the area E2 is corrected to be close to the road surface state data of the reference vehicle 3 is exemplified. When a plurality of situation data are registered, if there is an overlapping section between the road surface situation data, the road surface situation data can be corrected in the area E1 or the area E2 without using the reference vehicle 3.

  FIG. 11 is a diagram illustrating an application example 1 of the correction method. In FIG. 11, three travel routes 5RA1, 5RA2, and 5RA3 are illustrated in the area E1, and three travel routes 5RB1, 5RB2, and 5RB3 are illustrated in the area E2. Traveling route 5RA1 corresponds to 5RA shown in FIG. 2, and traveling route 5RB1 corresponds to 5RB shown in FIG. Further, the travel routes 5RA1, 5RA2, and 5RA3 may be measured by the same measurement vehicle 5, or may be measured by different measurement vehicles 5. The same applies to the travel routes 5RB1, 5RB2, and 5RB3.

  As shown in FIG. 11, the travel route 5RA1 in the area E1 has an overlapping section between both of the other two travel routes 5RA2 and 5RA3. For this reason, the correction road surface condition data regarding the travel route 5RA2 and the travel route 5RA3 can be obtained by calculating the correction parameter that approaches the road surface condition data regarding the travel route 5RA1 for each of the travel route 5RA2 and the travel route 5RA3. This makes it possible to compare the corrected road surface condition data regarding the three travel routes 5RA1, 5RA2, and 5RA3.

  In addition, the travel route 5RB1 in the area E2 has an overlapping section between both of the other two travel routes 5RB2 and 5RB3. For this reason, the correction road surface condition data regarding the travel route 5RB2 and the travel route 5RB3 can be obtained by calculating the correction parameter that approaches the road surface condition data regarding the travel route 5RB1 for each of the travel route 5RB2 and the travel route 5RB3. This makes it possible to compare the corrected road surface condition data regarding the three travel routes 5RB1, 5RB2, and 5RB3.

  Further, the corrected road surface condition data related to the travel route 5RA2 and the travel route 5RA3 are aligned with the corrected road surface state data related to the travel route 5RA1, and the corrected road surface state data related to the travel route 5RB2 and the travel route 5RB3 are corrected road surfaces related to the travel route 5RB1. Aligned with status data. For this reason, as shown in FIG. 6, by using the road surface condition data related to the reference vehicle 3, the correction parameters related to the travel route 5RA1 and the correction parameters related to the travel route 5RB1 are obtained, so that the six travel routes 5RA1, the travel route 5RA2, It becomes possible to compare the corrected road surface condition data regarding the travel route 5RA3, the travel route 5RB1, the travel route 5RB2, and the travel route 5RB3.

[Application example 2 of correction method]
In the first embodiment, the case of correcting the time series data of acceleration, that is, the raw data of the sensor is exemplified. However, the deterioration index is obtained from the acceleration of each section at the stage of obtaining the correction parameter, and the time series data of the acceleration is obtained. Instead of the above, the deterioration index of each section may be used as road surface condition data, and the correction parameter may be obtained.

12A to 12C are diagrams illustrating an application example 2 of the correction method. FIG. 12A shows three travel routes similar to the travel route shown in FIG. 2, and also shows deterioration indexes in the overlapping section and other sections. When the overlapping sections of the three travel routes and the other sections have the deterioration index shown in FIG. 12A, as shown in FIG. 12B, the corrected deterioration index for each section of the measurement vehicle 5A and the corrected deterioration index for each section of the measurement car 5B. Can be obtained. That is, the correction parameters relating to measured wheel 5A, the reference vehicle 3 and deterioration index of the overlap interval I _A with the measured reference vehicle 3 of deterioration index "4" overlap interval I _A with the measured measured wheel 5A of the measuring wheel 5A It is obtained by calculation dividing by “3”. Further, the calculation is divided by the reference vehicle 3 and deterioration index of the overlap interval I _B in the measured degradation index "3" of the measured reference vehicle 3 in an overlapping interval I _B measurement wheel 5B measuring wheel 5B "2" Desired. After that, the road surface condition data regarding the measurement vehicle 5A is obtained by multiplying the deterioration index of each section included in the road surface condition data regarding the measurement vehicle 5A by the correction parameter “4/3” regarding the measurement vehicle 5A. Is corrected to road surface condition data when measurement is performed. Further, the road condition data relating to the measurement vehicle 5B is measured by the reference vehicle 3 by multiplying the deterioration index of each section included in the road condition data relating to the measurement vehicle 5B by the correction parameter “3/2” relating to the measurement vehicle 5B. Is corrected to road surface condition data when With these corrections, the magnitudes of the acceleration measurement results can be compared even between the measurement vehicle 5A and the measurement vehicle 5B in which there are no overlapping sections on the travel route.

  Further, the correction parameters related to the measurement vehicle 5A and the correction parameters related to the measurement vehicle 5B are obtained as shown in FIG. 12B. The time series data of the acceleration of the measurement vehicle 5A and the time series data of the acceleration of the measurement vehicle 5B are shown in FIG. As shown in FIG. That is, after the correction parameter “4/3” for the measurement vehicle 5A and the correction parameter “3/2” for the measurement vehicle 5B are obtained, as illustrated in FIG. By multiplying the correction parameter “4/3” related to the measurement vehicle 5A, the time series data of the acceleration related to the measurement vehicle 5A is corrected to the time series data of the acceleration when the measurement is performed by the reference vehicle 3. In addition, the time series data of the acceleration related to the measurement vehicle 5B is multiplied by the correction parameter “3/2” related to the measurement vehicle 5B, and the time series data of the acceleration related to the measurement vehicle 5B is measured by the reference vehicle 3. The acceleration is corrected to time series data.

[Distribution and integration]
In addition, each component of each illustrated apparatus does not necessarily have to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. For example, the registration unit 15a, the detection unit 15b, the calculation unit 15c, the correction unit 15d, or the output unit 15e may be connected as an external device of the server device 10 via a network. In addition, the registration unit 15a, the detection unit 15b, the calculation unit 15c, the correction unit 15d, or the output unit 15e is provided in each of the other devices, and the functions of the server device 10 described above are realized by cooperation through a network connection. You may do it. The functions of the registration unit 15a, detection unit 15b, calculation unit 15c, correction unit 15d, and output unit 15e are shown in FIGS. Processing can also be executed.

[Data output program]
The various processes described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer or a workstation. In the following, an example of a computer that executes a data output program having the same function as that of the above embodiment will be described with reference to FIG.

  FIG. 13 is a diagram illustrating a hardware configuration example of a computer that executes the data output program according to the first embodiment and the second embodiment. As illustrated in FIG. 13, the computer 100 includes an operation unit 110 a, a speaker 110 b, a camera 110 c, a display 120, and a communication unit 130. Further, the computer 100 includes a CPU 150, a ROM 160, an HDD 170, and a RAM 180. These units 110 to 180 are connected via a bus 140.

  As shown in FIG. 13, the HDD 170 includes a data output program 170a that exhibits the same functions as the registration unit 15a, the detection unit 15b, the calculation unit 15c, the correction unit 15d, and the output unit 15e described in the first embodiment. Remembered. The data output program 170a may be integrated or separated as in the case of the components of the registration unit 15a, detection unit 15b, calculation unit 15c, correction unit 15d, and output unit 15e shown in FIG. That is, the HDD 170 does not necessarily have to store all the data shown in the first embodiment, and data used for processing may be stored in the HDD 170.

  Under such an environment, the CPU 150 reads the data output program 170 a from the HDD 170 and expands it in the RAM 180. As a result, the data output program 170a functions as a data output process 180a as shown in FIG. The data output process 180a expands various data read from the HDD 170 to an area allocated to the data output process 180a in the storage area of the RAM 180, and executes various processes using the expanded various data. For example, as an example of processing executed by the data output process 180a, the processing shown in FIGS. Note that the CPU 150 does not necessarily operate all the processing units described in the first embodiment, and the processing unit corresponding to the process to be executed may be virtually realized.

  Note that the data output program 170a is not necessarily stored in the HDD 170 or the ROM 160 from the beginning. For example, each program is stored in a “portable physical medium” such as a flexible disk inserted into the computer 100, so-called FD, CD-ROM, DVD disk, magneto-optical disk, or IC card. Then, the computer 100 may acquire and execute each program from these portable physical media. In addition, each program is stored in another computer or server device connected to the computer 100 via a public line, the Internet, a LAN, a WAN, etc., and the computer 100 acquires and executes each program from these. It may be.

DESCRIPTION OF SYMBOLS 1 Road surface inspection system 3 Reference vehicle 5A, 5B, 5N Measurement vehicle 9 Network 10 Server apparatus 11 Communication I / F part 13 Storage part 13a Road surface condition data 13b Correction road surface condition data 15 Control part 15a Registration part 15b Detection part 15c Calculation part 15d Correction unit 15e Output unit 30 Sensors 50A, 50B, 50N Sensors 70 Client terminal

Claims (7)

First road surface condition data measured by a first vehicle traveling on a first road surface portion; second road surface condition data measured by a second vehicle traveling on a second road surface portion; Of the reference road surface condition data measured by the reference vehicle that has traveled both the one road surface portion and the second road surface portion, the first road surface state data and the first road surface regarding the first road surface portion. Calculating a first correction parameter for bringing the measurement result of the road surface condition of the first vehicle closer to the measurement result of the road surface condition of the reference vehicle based on the reference road surface condition data relating to the part, and relating to the second road surface part Based on the second road surface condition data and the reference road surface condition data regarding the second road surface portion, the measurement result of the road surface condition of the second vehicle is close to the measurement result of the road surface condition of the reference vehicle. Kicking calculates the second correction parameter,
The first road condition data is corrected and output using the calculated first correction parameter, and the second road condition data is corrected and output using the calculated second correction parameter. Data output program that causes a computer to execute   The data output program according to claim 1, wherein the computer executes a process of displaying the first road surface portion and the second road surface portion in a comparable manner. The data output program according to claim 2, wherein the computer executes a process of displaying the first road surface portion on the left side of the screen and displaying the second road surface portion on the right side of the screen. Accepting designations for the first and second regions,
Based on the measurement result of the road surface condition measured by the reference vehicle that traveled on both the road surface portion included in the designated first area and the road surface portion included in the designated second area, A first correction parameter of a measurement result of a vehicle that measures a road surface condition of a road surface part that is common to a road surface part that is included in the area of which the road surface condition is measured by the reference vehicle, and the reference that is included in the second area A data output program for causing a computer to execute a process of calculating a second correction parameter of a measurement result of a vehicle that measures a road surface condition of a road surface part that is common to a road surface part whose road surface condition is measured by the vehicle. Measurement results of road surface conditions measured by each of a plurality of vehicles that have traveled in the first area are based on the measurement results of road surface conditions measured by any of the plurality of vehicles. Correct the measurement results of road surface conditions by vehicles in
Measurement results of road surface conditions measured by each of a plurality of vehicles that have traveled in the second area are based on the measurement results of road surface conditions measured by any of the plurality of vehicles. Correct the measurement results of road surface conditions by vehicles in
Using the measurement result of the road surface condition measured by the vehicle that traveled on both the road surface included in the first region and the road surface included in the second region, between the first region and the second region A data output program that causes a computer to execute processing to correct measurement errors in road surface conditions. First road surface condition data measured by a first vehicle traveling on a first road surface portion; second road surface condition data measured by a second vehicle traveling on a second road surface portion; Of the reference road surface condition data measured by the reference vehicle that has traveled both the one road surface portion and the second road surface portion, the first road surface state data and the first road surface regarding the first road surface portion. Calculating a first correction parameter for bringing the measurement result of the road surface condition of the first vehicle closer to the measurement result of the road surface condition of the reference vehicle based on the reference road surface condition data relating to the part, and relating to the second road surface part Based on the second road surface condition data and the reference road surface condition data regarding the second road surface portion, the measurement result of the road surface condition of the second vehicle is close to the measurement result of the road surface condition of the reference vehicle. Kicking calculates the second correction parameter,
The first road condition data is corrected and output using the calculated first correction parameter, and the second road condition data is corrected and output using the calculated second correction parameter. Is a data output method executed by a computer. First road surface condition data measured by a first vehicle traveling on a first road surface portion; second road surface condition data measured by a second vehicle traveling on a second road surface portion; Of the reference road surface condition data measured by the reference vehicle that has traveled both the one road surface portion and the second road surface portion, the first road surface state data and the first road surface regarding the first road surface portion. Calculating a first correction parameter for bringing the measurement result of the road surface condition of the first vehicle closer to the measurement result of the road surface condition of the reference vehicle based on the reference road surface condition data relating to the part, and relating to the second road surface part Based on the second road surface condition data and the reference road surface condition data regarding the second road surface portion, the measurement result of the road surface condition of the second vehicle is close to the measurement result of the road surface condition of the reference vehicle. A calculation unit for calculating a kick second correction parameter,
The first road surface condition data is corrected and output using the calculated first correction parameter, and the second road surface data is corrected and output using the calculated second correction parameter. A data output device.

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