Road diagnostic system, road diagnostic method, and recording medium

Abstract

This road diagnosis system includes a parameter acquisition unit, a weight acquisition unit, a calculation unit, and an output unit. The parameter acquisition unit acquires a parameter selection result of a selection performed on a screen displaying candidates for parameters that are used for calculating the priority of performing the repair of a road. The weight acquisition unit acquires the weight of each parameter input on a screen displaying the selected parameters and an input field for the weight of each parameter. The calculation unit calculates the priority on the basis of the parameters acquired by the parameter acquisition unit, the weight of each parameter acquired by the weight acquisition unit, and information pertaining to the road. The output unit outputs the calculated priority.

Description

Road diagnosis system, road diagnosis method, and recording medium

[0001] The present disclosure relates to a road diagnosis system and the like.

[0002] A road administrator, for example, checks the condition of a road by patrolling the road. Furthermore, the road administrator, for example, determines the priority of road repairs based on the road condition checked by patrolling. The road administrator then carries out road repairs based on the priority. Furthermore, patrolling the roads under management and checking the condition of each point can require a lot of work time. For this reason, road conditions are sometimes checked using photographed images of the road.

[0003] The road maintenance management system of Patent Document 1 acquires inspection information such as crack rates using image data of roads, and calculates the priority of road repairs based on the inspection information.

[0004] JP 2016-89593 A

[0005] With the technology described in Patent Document 1, it may be difficult to appropriately calculate the priority for carrying out road repairs.

[0006] In order to solve the above-mentioned problems, the present disclosure aims to provide a road diagnosis system etc. that can easily calculate the priority for carrying out road repairs.

[0007] In order to solve the above problems, the road diagnosis system of the present disclosure comprises a parameter acquisition means for acquiring the selection results of parameters selected on a screen displaying candidate parameters to be used in calculating the priority of carrying out road repairs, a weight acquisition means for acquiring weights for each parameter entered on a screen displaying input fields for the selected parameters and their respective weights, a calculation means for calculating a priority based on the parameters acquired by the parameter acquisition means and the weights for each parameter acquired by the weight acquisition means, and information related to the road condition, and an output means for outputting the calculated priority.In the road diagnosis method of the present disclosure, the road diagnosis system of the present disclosure acquires the selection results of parameters selected on a screen displaying candidate parameters to be used in calculating the priority of carrying out road repairs, acquires the weights for each parameter entered on the screen displaying input fields for the selected parameters and their respective weights, calculates a priority based on the acquired parameters and the weights for each parameter acquired by the weight acquisition means, and information related to the road condition, and outputs the calculated priority.

[0008] The recording medium of the present disclosure non-temporarily records a road diagnosis program that causes a computer to execute the following processes: a process of acquiring the selection results of parameters selected on a screen that displays candidate parameters to be used in calculating the priority of carrying out road repairs; a process of acquiring weights for each parameter that are input on a screen that displays input fields for the selected parameters and their respective weights; a process of calculating a priority based on the acquired parameters and their respective weights and information related to the road condition; and a process of outputting the calculated priority.

[0009] According to the present disclosure, the priority for carrying out road repairs can be easily calculated.

[0010] FIG. 1 is a diagram illustrating an example of the configuration of a road management system according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating an example of the configuration of a road diagnosis system according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating an example of parameter candidates according to an embodiment of the present disclosure. FIG. 4 is a diagram illustrating an example of a display screen according to an embodiment of the present disclosure. FIG. 5 is a diagram illustrating an example of a display screen according to an embodiment of the present disclosure. FIG. 6 is a diagram illustrating an example of parameter settings according to an embodiment of the present disclosure. FIG. 7 is a diagram illustrating an example of a display screen according to an embodiment of the present disclosure. FIG. 8 is a diagram illustrating an example of an operation flow of a road diagnosis system according to an embodiment of the present disclosure. FIG. 9 is a diagram illustrating an example of an operation flow of a road diagnosis system according to an embodiment of the present disclosure. FIG. 10 is a diagram illustrating an example of a hardware configuration according to an embodiment of the present disclosure.

[0011] An embodiment of the present disclosure will be described in detail with reference to the drawings. FIG. 1 is a diagram illustrating an example of the configuration of a road management system. The road management system includes a road diagnosis system 10, an on-vehicle device 20, and a terminal device 30. The road diagnosis system 10 is connected to the on-vehicle device 20 via a network. The road diagnosis system 10 is also connected to the terminal device 30 via the network. Data input / output between the road diagnosis system 10 and the on-vehicle device 20 may be performed via a storage device. For example, data input / output between the road diagnosis system 10 and the on-vehicle device 20 may be performed via a non-volatile semiconductor storage device. There may be a plurality of on-vehicle devices 20 and a plurality of terminal devices 30. The number of on-vehicle devices 20 and the number of terminal devices 30 are set as appropriate.

[0012] A road management system, for example, calculates the priority of carrying out road repairs. For example, the road management system calculates the priority of carrying out road repairs based on parameters used to calculate the priority, the weights of each parameter, and information related to the road condition. The priority of carrying out road repairs is, for example, an index used by a road administrator to determine the order in which road repairs should be carried out. For example, the priority of carrying out road repairs indicates that the higher the priority value, the greater the need to prioritize repairs. For example, the priority of carrying out road repairs increases as the degree of deterioration of the road increases. Furthermore, for example, the priority of carrying out road repairs increases as the importance of the road increases.

[0013] Furthermore, the parameters are, for example, parameters related to the road condition. For example, the parameters are factors that can affect the priority of carrying out road repairs. Factors that can affect the priority are, for example, the degree of road deterioration and the importance of the road. That is, the parameters are, for example, indicators related to the degree of road deterioration and the importance of the road. The weight of each parameter is an indicator indicating how much importance each parameter is to be given in calculating the priority. Furthermore, the parameters are not limited to the above. Furthermore, the information related to the road condition is information indicating the actual road condition in the indicator set as the parameter. That is, the information related to the road condition is, for example, the degree of road deterioration calculated based on measurement values ​​for each road that is the subject of priority calculation. Furthermore, the information related to the road condition may be, for example, a predicted value of the degree of road deterioration. The information related to the road condition is, for example, information indicating the importance of each road that is the subject of priority calculation. The information related to the road condition is not limited to the above.

[0014] The road management system calculates the priority of road repairs using, for example, parameters and parameter weights set by a person in charge of road repairs. The person in charge of road repairs is, for example, a person who belongs to the organization of the road administrator or a person who has been entrusted by the road administrator with calculating the priority of road repairs. The road administrator is, for example, a road management office, a road public corporation, or a local government. The road administrator is not limited to the above.

[0015] The road deterioration level is, for example, an index indicating the degree of deterioration of the road surface. The road management system estimates the road deterioration level based on, for example, video footage of the road. The road is photographed using, for example, a drive recorder. Road deterioration refers to, for example, a condition of the road surface that impedes traffic. If the road surface deterioration is cracks, the road surface deterioration level is, for example, the crack rate. The crack rate is, for example, a value indicating the ratio of the area of ​​the deterioration included in an image photographed at a certain point to the area of ​​the road included in the image. If the road surface deterioration is rutting, the deterioration level is, for example, the amount of rutting. If the road surface deterioration is abnormal flatness, the deterioration level is, for example, the International Roughness Index (IRI). The IRI is an index indicating the flatness of the road. The IRI may be estimated based on the vertical acceleration of the vehicle. The measured value of vertical acceleration reflects, for example, the vertical vibration of a vehicle when traveling over ruts. The vertical acceleration is measured, for example, by an acceleration sensor attached to the vehicle. Furthermore, the maintenance control index (MCI) may be used as the deterioration level. The MCI is a composite deterioration index estimated from, for example, the crack rate, the amount of rutting, and flatness.

[0016] The importance of a road is, for example, an index showing the social importance of the road. For example, a trunk road with a large volume of traffic is a road with a high importance. Also, for example, a road with few alternative routes is a road with a high importance. Also, for example, a road with highly public facilities nearby, a road designated as an emergency transportation route, and a road designated as a school route are roads with a high importance.

[0017] Here, an example of the configuration of the road diagnosis system 10 will be described. Fig. 2 is a diagram showing an example of the configuration of the road diagnosis system 10. The road diagnosis system 10 basically includes a parameter acquisition unit 102, a weight acquisition unit 104, a calculation unit 110, and an output unit 112. The road diagnosis system 10 may further include, for example, a candidate output unit 101, a parameter output unit 103, a detailed parameter output unit 105, a detailed parameter acquisition unit 106, a detailed weight acquisition unit 107, an acquisition unit 108, a deterioration level estimation unit 109, a candidate estimation unit 111, and a storage unit 113.

[0018] The candidate output unit 101 outputs candidate parameters recommended for selection based on, for example, at least one of the attributes of the road and the attributes of the road administrator. The parameters are, for example, parameters used for calculating priority. The parameters are, for example, information indicating the degree of road deterioration and the importance of the road. The parameters may be information indicating either the degree of road deterioration or the importance of the road. Furthermore, the parameters may be, for example, parameters related to a predicted value of the degree of road deterioration.

[0019] The road attributes are, for example, information on one or more of the following: road classification, vehicle traffic volume, road structure, road location, and road positioning. The road classification is, for example, a road classification in road management. For example, the road classification is information indicating whether the road corresponds to an expressway, a motorway, a national highway, a prefectural road, a municipal road, a farm road, or a forest road. The road classification is not limited to the above. The vehicle traffic volume is, for example, the number of vehicles traveling on the road whose priority is to be calculated. For example, the vehicle traffic volume is the number of vehicles traveling per unit time on the road whose priority is to be calculated. The road structure is, for example, information indicating the characteristic structure of the road. For example, if the road whose priority is to be calculated has a large number of tunnels, the road structure is "tunnels." The road structure is not limited to the above. The road location is information indicating the characteristic topography of the road whose priority is to be calculated. For example, if the road whose priority is to be calculated has a large number of seaside sections, the road location is, for example, "coast." The road location is not limited to the above. The positioning of a road is, for example, the role that the road plays. The role of a road is, for example, an emergency transportation route or a school route. The positioning of a road is not limited to the above. The attributes of a road are not limited to the above.

[0020] The road administrator's attribute is, for example, information indicating the organization managing the road. The road administrator's attribute is, for example, information indicating whether the road administrator is a Japan Highway Public Corporation, a national highway management office, a prefecture, or a city, town, or village. The road administrator's attribute may also be information indicating the location tendency of roads under their management. For example, the road administrator's attribute may be information indicating whether the road administrator manages more urban roads or more mountain roads. For example, if the road administrator manages more mountain roads, the candidate output unit 101 outputs, as parameter candidates, parameter candidates suitable for estimating the priority of mountain roads. The relationship between at least one of the road attributes and the road administrator attributes and the parameter candidates is set, for example, as table data. The candidate output unit 101 identifies the road administrator based on, for example, login information. Then, the candidate output unit 101 outputs parameter candidates based on, for example, at least one of the attributes of the roads managed by the identified road administrator or the attributes of the identified road administrator.

[0021] Parameters used to calculate the priority for road repair include, for example, MCI, crack rate, IRI, rutting depth, potholes, crack width, tortoiseshell crack size, pothole size, pothole occurrence prediction, crack prediction, management level, traffic volume, emergency transportation route, school route, and citizen reports.Citizen reports are, for example, reports about road abnormalities from residents along the road or drivers of passing vehicles.The parameters used to calculate the priority are not limited to the above.

[0022] The candidate output unit 101 may output parameter candidates estimated by the candidate estimation unit 111 using the candidate estimation model. For example, the candidate output unit 101 outputs parameters that the candidate estimation model estimates as suitable parameters with a certainty equal to or higher than a standard as parameter candidates. For example, the candidate output unit 101 may output parameter candidates in descending order of the certainty that the candidate estimation model estimates as suitable parameters. How the parameters estimated by the candidate estimation model are output can be set as appropriate.

[0023] FIG. 3 shows an example of a display screen for parameter candidates used in calculating the priority for carrying out road repair. The candidate output unit 101 outputs, for example, a screen displaying parameter candidates such as the example display screen of FIG. 3. In the example display screen of FIG. 3, parameter candidates used in calculating the priority are displayed as "priority judgment item candidates." A person in charge of road repair selects a parameter to be used in calculating the priority by, for example, selecting a parameter on the display screen of FIG. 3. In the example display screen of FIG. 3, "PH size" is the size of a pothole. Also, in the example display screen of FIG. 3, "PH occurrence prediction" is a prediction of the occurrence of a pothole. For example, on the display screen shown in the example of FIG. 3, a person in charge of road repair selects a parameter to be used in calculating the priority from the parameter candidates.

[0024] The parameter acquisition unit 102 acquires a selection result of a parameter selected on a screen displaying candidate parameters used to determine the priority of road repairs. For example, when a parameter is moved from a candidate parameter display field to a selected parameter display field by dragging and dropping on the screen, the parameter acquisition unit 102 acquires a selection result indicating that the moved parameter has been selected. For example, when a parameter is moved from a selected parameter display field to a candidate parameter display field by dragging and dropping on the screen, the parameter acquisition unit 102 acquires a selection result indicating that the selection of the moved parameter has been canceled. Furthermore, when a parameter is clicked, double-clicked, or touched in either the candidate parameter display field or the selected parameter display field, the parameter may be moved to the other display field. The parameter selection method may be set as appropriate. The parameter acquisition unit 102 acquires the selection result of a parameter from, for example, the terminal device 30.

[0025] The parameter acquisition unit 102 acquires, for example, information for changing a selected parameter. For example, when a selected parameter is changed after a priority has been calculated in order to change the parameter and recalculate the priority, the parameter acquisition unit 102 acquires, for example, the changed parameter. The parameter acquisition unit 102 may acquire the parameter before the change and the parameter after the change.

[0026] The parameter output unit 103 outputs, for example, parameters in a selected state. That is, the parameter output unit 103 outputs the parameters acquired by the parameter acquisition unit 102 so that they can be visually recognized as parameters in a selected state. The parameter output unit 103 also outputs the weights of each parameter in association with the parameters in a selected state. The weights of each parameter output by the parameter output unit 103 are, for example, initial setting values ​​or weight values ​​acquired by the weight acquisition unit 104. The initial setting value is, for example, a value obtained by equally distributing a value set as the total value of the weights to each selected parameter. The initial setting value is not limited to the above.

[0027] The weights of the parameters output by the parameter output unit 103 may be weight values ​​recommended for use in calculating the priority. For example, the parameter output unit 103 outputs recommended weights as recommended settings based on at least one of the selected parameters, road attributes, and road administrator attributes. The parameter output unit 103 outputs the weights estimated by the candidate estimation unit 111 as the recommended weights.

[0028] Furthermore, the parameter output unit 103 may output a recommended combination of parameters and the weights of each of the parameters. The recommended combination of parameters and the weights of each of the parameters are stored as data in a table associated with the attributes of the road or the attributes of the road administrator. The recommended combination of parameters and the weights of each of the parameters may be stored as data in a table associated with the attributes of the road and the attributes of the road administrator. Furthermore, the parameter output unit 103 may output a recommended combination of parameters and the weights of each of the parameters.

[0029] The weight acquisition unit 104 acquires the weights of the respective parameters that are input on a screen that displays the selected parameters and input fields for the weights of the respective parameters. The weight acquisition unit 104 acquires the weights of the respective parameters, for example, so that the sum of the weights of the respective parameters becomes the maximum value of the priority. For example, if the maximum value of the priority is 100, the weight acquisition unit 104 acquires the weights of the respective parameters so that the sum of the weights of the respective parameters becomes 100.

[0030] The weights of the respective parameters are input, for example, by a road repair technician to the terminal device 30. Then, the weight acquisition unit 104 acquires the weights of the respective parameters, for example, from the terminal device 30. Furthermore, when the set values ​​of the weights are changed, the weight acquisition unit 104 acquires, for example, the weights of the respective parameters after the change.

[0031] The detailed parameter output unit 105 outputs detailed parameter candidates, which are detailed parameters of the parameters, based on, for example, the parameters acquired by the parameter acquisition unit 102. The detailed parameters are, for example, parameters for setting conditions for reflecting the weight of the parameter in the priority calculation result. For example, the detailed parameters are set as conditions for determining the relationship between the number of indicators exceeding a standard and the weight value reflected in the priority. The relationship between the parameters and the detailed parameter candidates is set, for example, as table data.

[0032] For example, suppose that the "control level" in the example of parameters in Fig. 3 is set as a parameter composed of one or more indices from among the indices of cracks, IRI, and rutting amount. In this case, the detailed parameter output unit 105 outputs, for example, cracks, IRI, and rutting amount as candidates for the detailed parameters.

[0033] The detailed parameter acquiring unit 106 acquires, for example, the selection result of a detailed parameter selected on a screen displaying candidate detailed parameters. For example, assume that three indicators, namely, crack, IRI, and rutting depth, are displayed as candidate detailed parameters for "control level" in the example of parameters in FIG. 3 . In this case, the result of selecting which of crack, IRI, and rutting depth to select as a detailed parameter is acquired. The selection result of the detailed parameter is input to the terminal device 30 by, for example, a person in charge of road repair. Then, the detailed parameter acquiring unit 106 acquires the selection result of the detailed parameter from, for example, the terminal device 30.

[0034] The detail weight acquisition unit 107 acquires the input result of the weight of the detailed parameter, which is input, for example, on a screen displaying candidates for the detailed parameter. The detail weight acquisition unit 107 acquires the weight of the detailed parameter, for example, so that the maximum value of the weight of the detailed parameter becomes the weight set for the parameter. For example, in the example of FIG. 3 , if the weight of "control level" in the example of parameters in FIG. 3 is set to "30," the detail weight acquisition unit 107 acquires the weight of the detailed parameter, for example, so that the maximum value of the weight of the detailed parameter becomes "30." The weight of the detailed parameter is input, for example, to the terminal device 30 by a person in charge of road repair. Then, the detail parameter acquisition unit 106 acquires the weight of the detailed parameter from, for example, the terminal device 30.

[0035] FIG. 4 shows an example of a display screen for setting parameters used to calculate the priority of road repair work. The example of the display screen in FIG. 4 displays three display fields: "Priority Judgment Items," "Priority Judgment Table," and "Detailed Settings." In the example of the display screen in FIG. 4, the "Priority Judgment Items" display field displays parameter candidates output by the candidate output unit 101, as in the example of FIG. 3. The "Priority Judgment Table" display field displays selected parameters and their respective weights. In the example of the display screen in FIG. 4, the "Priority Judgment Table" display field is output, for example, by the parameter output unit 103. In the example of the display screen in FIG. 4, "Control Level" is selected as a parameter, and the weight of the "Control Level" is input as "20." In the example of the display screen in FIG. 4, the "Detailed Settings" display field displays a selection field for detailed parameters and an input field for weights. In the example of the display screen in FIG. 4, the "Detailed Settings" display field is output, for example, by the detailed parameter output unit 105.

[0036] In the example of the display screen of FIG. 4 , the "target index" is a selection field for selecting, for example, which index to use as the "control level." In the example of the display screen of FIG. 4 , three parameters, namely, cracks, IRI, and ruts, are selected. In the example of the display screen of FIG. 4 , the "crack" parameter is, for example, the crack rate. In the example of the display screen of FIG. 4 , the "rut" parameter is, for example, the amount of rutting. In addition, in the example of the display screen of FIG. 4 , the "point allocation" parameter is an input field for setting the weight to be actually assigned to the "control level" when, for example, the conditions set for each target index are met. In the example of the display screen of FIG. 4 , for example, "exceeds all three indexes" indicates that when all three parameters, namely, cracks, IRI, and ruts, exceed the standards, the weight assigned to the "control level" is "20," which is set as the weight for the "control level" in the "priority judgment table." In other words, the weight set as the weight for the "control level" in the "priority judgment table" becomes the upper limit of the weight for the detailed parameters. In the example display screen of Figure 4, for example, "Exceeds two indicators" indicates that if two of the parameters of cracks, IRI, and ruts exceed the standard, the weight assigned to the "control level" will be "10," which is lower than the upper limit of the weight setting for the "control level."

[0037] FIG. 5 is an example of a display screen in which the setting of parameters used for priority calculation has been completed in the example of the display screen of FIG. 4 . In FIG. 5 , the display fields for "Priority Judgment Items" and "Detailed Settings" are displayed in the same manner as in the example of the display screen of FIG. 4 . In the example of the display screen of FIG. 5 , six parameters, namely, "Control Level," "Traffic Volume," "Emergency Transport Route," "MCI," "Pothole," and "Pothole Occurrence Prediction," are selected in the display field for "Priority Judgment Table." In the example of the display screen of FIG. 5 , the display field for "Priority Judgment Table" is output, for example, by the parameter output unit 103. In the example of the display screen of FIG. 5 , the weights of the respective parameters in the display field for "Priority Judgment Table" are set to "20," "30," "20," "10," "10," and "10," and the total weight value is "100." For example, when the highest priority value is "100," the display screen of FIG. 5 shows a state in which the setting of parameters used for priority calculation has been completed because the total weight value is "100." For example, when a button for completing the settings (not shown) is pressed in the state of the example display screen of FIG. 5, the calculation unit 110 calculates the priority for carrying out road repairs.

[0038] The acquisition unit 108 acquires video of a road captured by a device mounted on the vehicle and position information of the vehicle at the time the video was captured. For example, the acquisition unit 108 acquires time-series video of a road captured from a traveling vehicle and data related to the position of the vehicle at the time the video was captured. The acquisition unit 108 may acquire video of a road to which information about the location where the video was captured is added. Furthermore, the acquisition unit 108 may acquire video captured by traveling the same section of road multiple times and position information of the vehicle at the time each video was captured. Furthermore, the acquisition unit 108 may acquire acceleration of the vehicle measured by a device mounted on the vehicle and position information of the vehicle at the time the acceleration was measured.

[0039] The acquisition unit 108 acquires, for example, from the in-vehicle device 20, time-series images of a road captured from a traveling vehicle and position information of the vehicle at the time of capture. The captured images of the road are, for example, images showing the road surface, and the acquisition unit 108 may further acquire information on the speed and acceleration of the vehicle at the time the road was captured. The acquisition unit 108 may acquire the captured images of the road to be diagnosed from the in-vehicle device 20 via a storage medium. For example, a non-volatile semiconductor storage device is used as the storage medium. The storage medium is not limited to the above. The acquisition unit 108 may also acquire, from a server connected to a network, images of the road to be subjected to road deterioration degree calculation.

[0040] The acquisition unit 108 may acquire information about the road for which priority is to be calculated. The information about the road for which priority is to be calculated is, for example, road attributes and citizen reports. The road attributes are, for example, information about one or more items of road classification, vehicle traffic volume, road structure, road location, and road positioning. The information about the road for which priority is to be calculated is not limited to the above. The acquisition unit 108 may also acquire information about the road condition. The information about the road condition for which priority is to be calculated is information about the deterioration type occurring on the road for which priority is to be calculated and the deterioration degree indicating the deterioration level for each deterioration type. The deterioration type is, for example, the deterioration state.

[0041] The deterioration level estimation unit 109 estimates the deterioration level of the road at each point on the road for which priority is to be calculated, for example, based on a video of the road and location information. The deterioration level estimation unit 109 estimates, for example, the deterioration type and the deterioration level indicating the degree of deterioration as the deterioration level of the road.

[0042] For example, the degradation level estimation unit 109 compares the shooting time added to the video with the time at which the location information added to the vehicle's location information was acquired to estimate the shooting location of each frame included in the video. When the shooting time does not match the time at which the location information was acquired, the degradation level estimation unit 109 may estimate the shooting location of each frame, for example, based on the shooting time of each of multiple consecutive frames and the time at which each of multiple consecutive pieces of location information was acquired and the location information. For example, the degradation level estimation unit 109 may compare the shooting time added to the video with the time at which the location information added to the vehicle's location information was acquired to estimate the shooting location of each of the frames included in the video. The shooting time may include information indicating the date on which the video was shot. The time at which the location information was acquired may include information indicating the date on which the location information was acquired. Furthermore, the shooting location and the vehicle's location information are indicated using, for example, latitude and longitude.

[0043] The deterioration level estimation unit 109 estimates the deterioration level of a road using, for example, a deterioration level estimation model. The deterioration level estimation model, for example, inputs frames included in a video showing a road and estimates the deterioration level of the road shown in the frame. The deterioration level estimation unit 109, for example, estimates the deterioration level of the road for each managed section. A managed section is, for example, a section of a length that serves as a unit for road repairs performed by a road administrator. For example, the deterioration level estimation unit 109 estimates a statistical value of the deterioration level estimated from frames captured in each managed section as the deterioration level of the road in each managed section. The statistical value is, for example, an average value, a maximum value, a median value, or a minimum value. The statistical value is not limited to the above. The deterioration level estimation unit 109 may estimate the deterioration level of the road for each section obtained by dividing the area to be managed into a mesh pattern. Furthermore, the deterioration level estimation unit 109 may estimate the deterioration level of the road for each point at which a frame was captured.

[0044] The deterioration level estimation model is, for example, a learning model using image recognition technology. The deterioration level estimation model is generated, for example, by machine learning. The deterioration level estimation model is generated, for example, by deep learning using a neural network. The deterioration level estimation model is generated, for example, by learning the relationship between frames showing the road and the degree of deterioration. The deterioration level estimation model is generated, for example, in a system external to the road diagnosis system 10.

[0045] The deterioration level estimation unit 109 may estimate the deterioration level using a different deterioration level estimation model for each road deterioration type. The deterioration level estimation unit 109 estimates the deterioration level using a deterioration level estimation model with different learning data for each road deterioration type. For example, the deterioration level estimation unit 109 estimates the deterioration level related to a crack in the road surface using a deterioration level estimation model that estimates the deterioration level related to a crack in the road surface captured in a video. The deterioration level estimation model that estimates the deterioration level related to a crack in the road surface is, for example, a model that detects cracks in the road surface from a video and estimates the deterioration level related to the crack based on the width and length of the crack. The deterioration level estimation model that estimates the deterioration level related to a crack in the road surface is generated, for example, by learning the relationship between an image in which a crack is captured and the deterioration level.

[0046] When estimating the degree of deterioration using a different deterioration estimation model for each road deterioration type, for example, the deterioration estimation unit 109 estimates the number of potholes using a deterioration estimation model that estimates the number of potholes on a road. The deterioration estimation model that estimates the number of potholes is generated by learning the relationship between an image showing potholes and the presence or absence of potholes or the number of potholes. Furthermore, the deterioration estimation unit 109 may estimate the degree of deterioration using a deterioration estimation model that is generated using a different machine learning algorithm for each road deterioration type. Furthermore, the deterioration estimation model is generated, for example, outside the road diagnosis system 10.

[0047] The calculation unit 110 calculates the priority of carrying out road repairs based on the weights of the parameters acquired by the parameter acquisition unit 102 and the parameters acquired by the weight acquisition unit 104, and information on the road condition. For example, the calculation unit 110 calculates the priority of carrying out road repairs using the road deterioration level as information on the road condition.

[0048] The calculation unit 110 calculates the priority by, for example, adding weights of parameters that satisfy a set condition among the selected parameters. Alternatively, the calculation unit 110 may calculate the priority using a function in which the parameters are explanatory variables and the priority is a target variable. For example, the calculation unit 110 may calculate the priority by using the weights of each parameter as coefficients of the explanatory variables corresponding to each parameter in the function for calculating the priority. Alternatively, the calculation unit 110 may calculate the priority using a priority calculation model that calculates the priority using each parameter as input data. When calculating the priority using the priority calculation model, the weights of each parameter are used, for example, as the weights of each parameter in the process of calculating the priority by the priority calculation model.

[0049] The calculation unit 110 determines the priority further based on, for example, the detailed parameters acquired by the detailed parameter acquisition unit 106. The calculation unit 110 determines the priority further based on, for example, weights based on the number of detailed parameters exceeding a reference value among the detailed parameters acquired by the detailed weight acquisition unit 107. The calculation unit 110 calculates the priority by, for example, adding weights corresponding to conditions that are satisfied by information on road conditions among the conditions set in the detailed parameters for each parameter. For example, for each of five parameters, the weights determined based on the detailed parameters are W1, W2, W3, W4, W5, W6, W7, W8, W9, W10, W11, W12, W13, W14, W15, W16, W17, W18, W19, W20, W21, W22, W23, W24, W25, W26, W27, W28, W29, W30, W31, W32, W33, W34, W35, W36, W37, W38, W39, W40, W41, W42, W43, W44, W45, W46, W47, W48, W50, W51, W52, W53, W54, W55, W56, W57, W58, W59, W60, W61, W62, W63, W64, W65, W66, W70, W71, W72, W73, W74, W75, W85, W86, W87, W88, W91, W92, W93, W94, W95, W95, W96, W97, W98, W99, W99, W99, W99, W99, W99, W99, W99, W99, W99, W99, ２ , W3, W4, and W5, the calculation unit 110 calculates the priority P as follows: P=W1+W ２ + W3 + W4 + W5. In this case, W1, W ２ , W3, W4, and W5 are the weights set for the respective parameters.

[0050] FIG. 6 shows an example of conditions set for each parameter. In the example of conditions in FIG. 6, "parameter," "condition," and "weight" are associated with each other. In the example of FIG. 6, "parameter" is, for example, the parameter selected in the example of the display screen in FIG. 5. Also, in the example of FIG. 6, "condition" corresponds to the detailed parameter in the example of the display screen in FIG. 5. For example, in the example of FIG. 6, for the "control level," a weight is set based on the number of items exceeding the standard among cracks, IRI, and rutting amount. In the example of FIG. 6, the calculation unit 110 adds up the weights of the conditions that apply to each parameter and calculates the total value as the priority. For example, when conditions are set as in the example of Fig. 6, if the information on road conditions indicates that the traffic volume is "B," the emergency transportation route is "YES," the control level is "exceeded by three indicators," the MCI is "2," the pinholes are "none," and the PH occurrence prediction is "no prediction," the weights corresponding to each of these are "20," "20," "20," "5," "0," and "0." Therefore, the calculation unit 110 adds up the weights corresponding to each of these and calculates the priority as "65."

[0051] The candidate estimation unit 111 estimates candidate parameters using a candidate estimation model that estimates recommended parameters from at least one of road attributes and road administrator attributes. The candidate estimation model is, for example, a machine learning model that estimates road repair priorities using attributes of managed roads or attributes of road administrators as input. The candidate estimation model may also be a machine learning model that estimates road repair priorities using attributes of managed roads and attributes of road administrators as input. The candidate output unit 101 estimates candidate parameters for estimating road repair priorities using the candidate estimation model using at least one of attributes of managed roads and attributes of road administrators as input. The candidate estimation model is generated, for example, by learning the relationship between the attributes of managed roads or attributes of road administrators and the parameters used to estimate repair priorities. The candidate estimation model is generated, for example, by deep learning using a neural network. The candidate estimation model may also be generated, for example, by inverse reinforcement learning. The machine learning algorithm for generating the candidate estimation model is not limited to the above. Moreover, the candidate estimation model is generated, for example, in a system external to the road diagnosis system 10 .

[0052] The candidate estimation unit 111 estimates the recommended weights using, for example, a weight estimation model that estimates the recommended weights from at least one of the selected parameters, road attributes, and road administrator attributes. The weight estimation model is generated, for example, by machine learning using the parameters and their respective weights as learning data. The weight estimation model is generated, for example, by deep learning using a neural network. The candidate estimation model may also be generated by inverse reinforcement learning. The machine learning algorithm that generates the weight estimation model is not limited to the above. The weight estimation model may also be generated, for example, in a system external to the road diagnosis system 10.

[0053] The output unit 112 outputs the priorities calculated by the calculation unit 110. The output unit 112 outputs, for example, identification information for each point on the road to be managed and the priority of each point. The identification information for each point is, for example, one or more pieces of information selected from the latitude and longitude of each point, an identification number, and a point name. The identification information for each point is not limited to the above. The output unit 112 may also output identification information and priority values ​​for points whose priorities exceed a standard for requiring repair. The output unit 112 may also output the identification information and priority of each point in descending order of priority. The output unit 112 may also output the priority of each point using the level to which the priority corresponds from among multiple levels.

[0054] The output unit 112 may output the priority information by superimposing it on a map. For example, the output unit 112 outputs the priority information of each location on a map in a manner corresponding to the priority. For example, the output unit 112 may output the priority information by superimposing it on a map using a numerical value, a symbol, or a color corresponding to the priority. For example, the output unit 112 may output the priority information by superimposing it on a map using two or more of a numerical value, a symbol, and a color corresponding to the priority. The output unit 112 may output the priority information by changing the thickness of an arrow corresponding to the length of the management unit according to the priority. Furthermore, the output unit 112 may output the priority of each section when the map is divided into a mesh. For example, the output unit 112 changes the color of each section when the map is divided into a mesh according to the priority, thereby superimposing the priority of each section on the map. Furthermore, the color change may include changing the hue within the same classification range. For example, the color change may include changing the hue within a range classified as red.

[0055] When some or all of the parameters used to calculate the priority for carrying out road repairs are changed and the priority is recalculated, the output unit 112 may output information regarding the difference in priority before and after the parameter change. The parameter change may include a change in detailed parameters. The output unit 112 may output information regarding the difference in priority before and after the parameter change using priority levels. For example, the output unit 112 may output information regarding the difference in priority before and after the parameter change using the number of locations for each priority level. For example, the output unit 112 outputs the number of locations for each priority level as information regarding the difference in priority before and after the parameter change so that the number of locations before and after the parameter change can be compared.

[0056] FIG. 7 shows an example of a display screen showing the calculation results of the priority for carrying out road repairs. In the example of the display screen of FIG. 7, the priority calculated for each managed section of the road is displayed superimposed on a map. The output unit 112 outputs the calculation results of the priority, for example, as shown in the example of the display screen of FIG. 7. A managed section is, for example, a section that is a unit for managing a road by a road administrator. In the example of the display screen of FIG. 7, the length of the arrow corresponds to the length of the unit section. Furthermore, in the example of the display screen of FIG. 7, the priority for carrying out repairs is indicated by the thickness of the arrow. In the example of the display screen of FIG. 7, the thicker the arrow, the higher the priority for carrying out repairs.

[0057] FIG. 8 shows an example of a display screen that displays the priority of road repairs for each section divided into a mesh. In the example of the display screen in FIG. 8 , the priority of roads within each section is shown. The output unit 112 outputs the priority calculation results, for example, as shown in the example of the display screen in FIG. 8 . In the example of the display screen in FIG. 8 , for example, the lane section has the highest priority, the black dot section has the second highest priority, and the white section has the lowest priority. In the example of the display screen in FIG. 8 , the priority is shown in three levels, but the priority may be in levels other than three. Furthermore, the output unit 112 may display the priority by superimposing it on a map using colors set for each level. The manner in which the priority is shown may be set as appropriate.

[0058] The memory unit 113 stores data related to the process of determining the priority for carrying out road repairs. The memory unit 113 stores, for example, candidate parameters. The memory unit 113 stores, for example, information related to roads. For example, the memory unit 113 stores video footage of roads. The memory unit 113 also stores, for example, recommended parameters associated with at least one of road attributes or road administrator attributes. The memory unit 113 also stores, for example, weights for each recommended parameter. The memory unit 113 also stores, for example, recommended detailed parameters associated with at least one of road attributes or road administrator attributes. The memory unit 113 also stores, for example, weights for recommended detailed parameters. The memory unit 113 also stores, for example, selection results for parameters and detailed parameters. The memory unit 113 also stores, for example, weights for each parameter and set values ​​for weights for detailed parameters. The memory unit 113 also stores, for example, calculation results for the priority for carrying out road repairs. The storage unit 113 also stores, for example, a candidate estimation model, a weight estimation model, a degradation level estimation model, and a priority calculation model. The candidate estimation model, the weight estimation model, the degradation level estimation model, and the priority calculation model may be stored in a storage unit other than the storage unit 113.

[0059] The in-vehicle device 20 includes, for example, a camera that captures an image in front of the vehicle. The camera of the in-vehicle device 20 captures an image including the road surface. The camera of the in-vehicle device 20 may also capture an image behind the vehicle. The in-vehicle device 20, for example, adds information about the location where the image was captured to the captured image. The in-vehicle device 20 identifies the location of the vehicle at the time the image was captured using, for example, a Global Navigation Satellite System (GNSS). The in-vehicle device 20 may identify the location of the vehicle based on a beacon containing location information. The in-vehicle device 20 may identify the location where the image was captured based on map information and the travel distance from the location where the vehicle's location was identified. The in-vehicle device 20 may also output the captured image and vehicle location information at the time the image was captured to, for example, the acquisition unit 108 of the road diagnosis system 10. The in-vehicle device 20 may also measure the acceleration of the vehicle traveling on the road for which the priority is to be calculated. When measuring the acceleration of the vehicle, the in-vehicle device 20 outputs the acceleration of the vehicle and position information of the vehicle at the time of measuring the acceleration to, for example, the acquisition unit 108 of the road diagnosis system 10. For example, a drive recorder is used as the in-vehicle device 20. However, the in-vehicle device 20 is not limited to a drive recorder.

[0060] The terminal device 30 is, for example, a terminal device used by a person in charge of road repair. The terminal device 30 acquires, for example, from the output unit 112 of the road diagnosis system 10, the priority of carrying out road repair calculated by the road diagnosis system 10. The terminal device 30 then outputs the acquired priority of carrying out road repair to a display device (not shown).

[0061] The terminal device 30 acquires parameter candidates from, for example, the candidate output unit 101 of the road diagnosis system 10. Then, the terminal device 30 outputs the parameter candidates to a display device (not shown). The terminal device 30 acquires, for example, a parameter selection result input by an operation of a person in charge. Then, the terminal device 30 outputs the parameter selection result to, for example, the parameter acquisition unit 102 of the road diagnosis system 10. Furthermore, the terminal device 30 acquires, for example, a weight for each parameter input by an operation of the person in charge. Then, the terminal device 30 outputs the weight for each parameter to, for example, the weight acquisition unit 104 of the road diagnosis system 10.

[0062] The terminal device 30 acquires parameter candidates from, for example, the detailed parameter output unit 105 of the road diagnosis system 10. Then, the terminal device 30 outputs the detailed parameter candidates to a display device (not shown). The terminal device 30 acquires, for example, a selection result of detailed parameters input by an operation of a person in charge. Then, the terminal device 30 outputs the selection result of detailed parameters to, for example, the detailed parameter acquisition unit 106 of the road diagnosis system 10. The terminal device 30 also acquires, for example, weights for the detailed parameters input by an operation of a person in charge. Then, the terminal device 30 outputs the weights for the detailed parameters to, for example, the detailed weight acquisition unit 107 of the road diagnosis system 10.

[0063] For example, a personal computer, a tablet computer, or a smartphone can be used as the terminal device 30. The terminal device 30 is not limited to the above examples.

[0064] The following describes the operation of calculating the priority for carrying out road repairs in the road diagnosis system 10. Fig. 9 is a diagram showing an example of a flow of processing for calculating the priority for carrying out road repairs in the road diagnosis system 10. In the example of Fig. 9, an example will be described in which detailed parameters and weights for the detailed parameters have already been set for each parameter.

[0065] The parameter acquisition unit 102 acquires the selection result of the parameter used to calculate the priority of carrying out road repairs, which is selected on the screen (step S11). The parameter is selected on the screen that displays parameter candidates.

[0066] The weight acquisition unit 104 also acquires the weight of each parameter (step S12). The weight of each parameter is input on a screen that displays the selected parameter and an input field for the weight of each parameter.

[0067] Once the weights for each parameter have been acquired, the calculation unit 110 calculates the priority for carrying out road repairs based on the weights of the parameters acquired by the parameter acquisition unit 102 and the parameters acquired by the weight acquisition unit 104, and information about the road (step S13).

[0068] Once the priority for carrying out road repairs is calculated, the output unit 112 outputs the calculated priority (step S14).

[0069] An explanation will be given of an operation for setting detailed parameters and calculating a priority for carrying out road repairs in the road diagnosis system 10. Fig. 10 is a diagram showing an example of a processing flow for setting detailed parameters and calculating a priority for carrying out road repairs in the road diagnosis system 10.

[0070] The parameter acquisition unit 102 acquires, for example, the selection result of parameters used to calculate the priority of carrying out road repairs (step S21).

[0071] Furthermore, the weight acquisition unit 104 acquires, for example, the weight of each parameter (step S22).

[0072] When the weights of the respective parameters are acquired, the detailed parameter output unit 105 outputs detailed parameter candidates, which are detailed parameters of the parameters, based on the parameters acquired by the parameter acquisition unit 102, for example (step S23).

[0073] After outputting the candidates for the detailed parameters, the detailed parameter acquisition unit 106 acquires, for example, the selection results of the detailed parameters selected on a screen displaying the candidates for the detailed parameters (step S24).

[0074] If detailed parameters have been acquired for all parameters (Yes in step S25), the calculation unit 110 calculates the priority for carrying out road repairs, for example, based on the weight of each parameter, the detailed parameters acquired by the detailed parameter acquisition unit 106, and information regarding the condition of the road (step S26).

[0075] When the priority for carrying out road repairs is calculated, the output unit 112 outputs, for example, the calculated priority (step S27).

[0076] In step S25, if there are parameters for which detailed parameters have not been acquired (No in step S25), the process returns to step S23, and detailed parameter candidates are output for the parameters for which detailed parameters have not yet been acquired.

[0077] The road diagnosis system 10 acquires the selection results of parameters used to calculate the priority for carrying out road repairs, selected on the screen. The road diagnosis system 10 also acquires weights for each parameter input on the screen. The road diagnosis system 10 then calculates the priority for carrying out road repairs based on the acquired parameters and the weights for each parameter. In this way, by calculating the priority using the parameters selected on the screen and the weights for each parameter, the road diagnosis system 10 can easily set parameters for calculating the priority for carrying out road repairs. Therefore, by using the road diagnosis system 10, the priority for carrying out road repairs can be easily calculated.

[0078] Furthermore, since the parameters and their respective weights can be set on the screen, for example, the road diagnosis system 10 can be used to easily make settings suited to the road administrator or the roads under management. Therefore, the person in charge of road repairs can easily make settings suited to the road administrator or the roads under management by using the road diagnosis system 10. Therefore, the use of the road diagnosis system 10 can improve the accuracy of calculation of priorities suited to the road administrator or the roads under management.

[0079] Furthermore, the road diagnosis system 10 outputs candidates for detailed parameters, which are detailed parameters of the parameters, based on, for example, the selected parameters. Furthermore, the road diagnosis system 10 acquires, for example, a selection result of the detailed parameters selected on a screen that displays candidates for the detailed parameters. Then, the road diagnosis system 10 calculates a priority for carrying out road repairs, for example, further based on the selected detailed parameters. By selecting detailed parameters on the screen in this way, it is possible to easily set the parameters more suited to the road administrator or the roads under management. Therefore, the road diagnosis system 10 can accurately calculate the priority for carrying out road repairs according to the road administrator or the roads under management.

[0080] Furthermore, by outputting recommended settings for parameters and weights, even when calculating repair priorities for roads for which the road administrator has few examples, the road diagnosis system 10 can be used to easily refer to the recommended settings and make settings appropriate for the roads under management.

[0081] Each process in the road diagnosis system 10 may be distributed and executed among a plurality of information processing devices connected via a network. For example, the processes in the candidate output unit 101, the parameter acquisition unit 102, the parameter output unit 103, the weight acquisition unit 104, the detailed parameter output unit 105, the detailed parameter acquisition unit 106, and the detailed weight acquisition unit 107, and the processes in the deterioration level estimation unit 109 and the calculation unit 110 may be executed in different information processing devices. It may be appropriately set which of the plurality of information processing devices executes each process in the road diagnosis system 10.

[0082] Each process in the road diagnosis system 10 can be realized by executing a computer program on a computer. Fig. 11 shows an example of the configuration of a computer 200 that executes a computer program that performs each process in the road diagnosis system 10. The computer 200 includes a CPU (Central Processing Unit) 201, a memory 202, a storage device 203, an input / output I / F (Interface) 204, and a communication I / F 205.

[0083] The CPU 201 reads and executes computer programs for performing each process from the storage device 203. The CPU 201 may be configured with a combination of multiple CPUs. Furthermore, the CPU 201 may be configured with a combination of a CPU and another type of processor. For example, the CPU 201 may be configured with a combination of a CPU and a graphics processing unit (GPU). The memory 202 is configured with a dynamic random access memory (DRAM) or the like, and temporarily stores computer programs executed by the CPU 201 and data being processed. The storage device 203 stores the computer programs executed by the CPU 201. The storage device 203 is configured with, for example, a non-volatile semiconductor storage device. Other storage devices such as a hard disk drive may also be used for the storage device 203. The input / output I / F 204 is an interface that receives input from an operator and outputs display data, etc. The communication I / F 205 is an interface that transmits and receives data between the in-vehicle device 20, the terminal device 30, and other information processing devices. Furthermore, the terminal device 30 may have a configuration similar to that of the computer 200.

[0084] The computer program used to execute each process can also be stored and distributed on a computer-readable recording medium that non-temporarily stores data. Examples of recording media that can be used include magnetic tapes for recording data and magnetic disks such as hard disks. Optical disks such as CD-ROMs (Compact Disc Read Only Memory) can also be used as recording media. Non-volatile semiconductor storage devices can also be used as recording media.

[0085] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0086] [Supplementary Note 1] A road diagnosis system comprising: a parameter acquisition means for acquiring a selection result of a parameter selected on a screen displaying candidate parameters used in calculating a priority for carrying out road repairs; a weight acquisition means for acquiring a weight for each of the selected parameters input on a screen displaying input fields for the selected parameters and their respective weights; a calculation means for calculating the priority based on the parameters acquired by the parameter acquisition means and the weights for each of the parameters acquired by the weight acquisition means, and information relating to the road condition; and an output means for outputting the calculated priority.

[0087] [Supplementary Note 2] The road diagnosis system according to Supplementary Note 1 further comprises: a detailed parameter output means for outputting detailed parameter candidates, which are detailed parameters of the parameters, based on the parameters acquired by the parameter acquisition means; and a detailed parameter acquisition means for acquiring a selection result of the detailed parameter selected on a screen displaying the detailed parameter candidates, wherein the calculation means calculates the priority further based on the detailed parameters acquired by the detailed parameter acquisition means.

[0088] [Supplementary Note 3] The road diagnosis system according to Supplementary Note 1 or 2, wherein the calculation means calculates the priority by adding weights of parameters that satisfy conditions set for the respective parameters.

[0089] [Supplementary Note 4] The road diagnosis system according to Supplementary Note 2, further comprising a detail weight acquisition means for acquiring input results of weights for the detailed parameters that are input on a screen that displays candidates for the detailed parameters, and the calculation means determines weights to be used in calculating the priority based on the weights for the detailed parameters acquired by the detail weight acquisition means and the number of detailed parameters that satisfy a criterion among the detailed parameters.

[0090] [Supplementary Note 5] The road diagnosis system according to Supplementary Note 4, wherein the maximum value of the weights in the detailed parameters is set to be the weight of the parameter.

[0091] [Supplementary Note 6] The road diagnosis system according to any one of Supplementary Notes 1 to 5, further comprising a parameter output means for outputting candidate parameters recommended for selection based on at least one of an attribute of the road and an attribute of the road administrator.

[0092] [Supplementary Note 7] The road diagnosis system according to Supplementary Note 6, wherein the parameter output means outputs recommended parameters and respective weights of the parameters as parameters to be used in calculating the priority based on at least one of attributes of the road and attributes of the road administrator.

[0093] [Supplementary Note 8] A road diagnosis system as described in any of Supplementary Notes 1 to 7, further comprising: an acquisition means for acquiring video of a road taken by a device mounted on a vehicle and location information of the vehicle at the time the video was taken; and a deterioration level estimation means for estimating a degree of deterioration of a road at each point on the road for which a priority is to be calculated, based on the video of the road and the location information, wherein the calculation means calculates the priority by using the estimated degree of deterioration of the road as information on the condition of the road.

[0094] [Supplementary Note 9] The road diagnosis system according to Supplementary Note 7, further comprising an estimation means for estimating recommended parameters using a candidate estimation model that estimates recommended parameters from at least one of road attributes and road administrator attributes, wherein the parameter output means outputs the estimated parameters as the recommended parameters.

[0095] [Supplementary Note 10] The road diagnosis system according to Supplementary Note 9, wherein the estimation means estimates the recommended weight using a weight estimation model that estimates the recommended weight from at least one of the selected parameter, an attribute of the road, and an attribute of the road administrator, and the parameter output means outputs the weight estimated by the estimation means as the recommended parameter weight.

[0096] [Supplementary Note 11] A road diagnosis system according to any one of Supplementary Notes 1 to 10, wherein the parameter acquisition means acquires information for changing the selected parameters; the weight acquisition means acquires weights for each of the changed parameters; the calculation means calculates the priority based on the changed parameters and the weights for each of the changed parameters; and the output means outputs information regarding a difference in the priority before and after the change.

[0097] [Supplementary Note 12] The road diagnosis system according to any one of Supplementary Notes 1 to 11, wherein the parameter candidates include a parameter related to a measurement result and a parameter related to a prediction result.

[0098] [Supplementary Note 13] The road diagnosis system according to any one of Supplementary Notes 1 to 12, wherein the output means outputs the priority by superimposing it on a map.

[0099] [Supplementary Note 14] The road diagnosis system according to Supplementary Note 11, wherein the output means outputs information relating to the difference in the priority before and after the change using a priority level.

[0100] [Supplementary Note 15] The road diagnosis system according to Supplementary Note 8, wherein the deterioration level estimation means estimates the deterioration level of the road for which priority is to be calculated, using a deterioration level estimation model that estimates the deterioration level of the road from an image of the road.

[0101] [Supplementary Note 16] A road diagnosis method comprising: acquiring a selection result of the parameters selected on a screen displaying candidate parameters used in calculating a priority for carrying out road repairs; acquiring weights of the selected parameters input on a screen displaying input fields for the selected parameters and their respective weights; calculating the priority based on the acquired parameters and their respective weights, and information related to the road condition; and outputting the calculated priority.

[0102] [Supplementary Note 17] A recording medium that non-temporarily records a road diagnosis program that causes a computer to execute the following processes: a process of acquiring a selection result of a parameter selected on a screen that displays candidate parameters used in calculating a priority for carrying out road repairs; a process of acquiring weights for each of the selected parameters that are input on a screen that displays input fields for the selected parameters and their respective weights; a process of calculating the priority based on the acquired parameters and their respective weights, and information related to the road condition; and a process of outputting the calculated priority.

[0103] Furthermore, some or all of the configurations described in Supplementary Notes 2 to 15, which are dependent on Supplementary Note 1, may also be dependent on Supplementary Notes 16 and 17 in the same dependent relationship as Supplementary Notes 2 to 15. Furthermore, not limited to Supplementary Notes 1, 16, and 17, some or all of the configurations described as Supplements may be made dependent on various hardware, software, various recording means for recording software, or systems, within the scope of each of the above-mentioned embodiments.

[0104] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0105] REFERENCE SIGNS LIST 10 Road diagnosis system 101 Candidate output unit 102 Parameter acquisition unit 103 Parameter output unit 104 Weight acquisition unit 105 Detailed parameter output unit 106 Detailed parameter acquisition unit 107 Detailed weight acquisition unit 108 Acquisition unit 109 Deterioration degree estimation unit 110 Calculation unit 111 Candidate estimation unit 112 Output unit 113 Storage unit 20 In-vehicle device 30 Terminal device 200 Computer 201 CPU 202 Memory 203 Storage device 204 Input / output I / F 205 Communication I / F

Claims

1. A road diagnosis system comprising: a parameter acquisition means for acquiring the selection results of parameters selected on a screen displaying candidate parameters used in calculating the priority of carrying out road repairs; a weight acquisition means for acquiring weights for each of the parameters input on a screen displaying input fields for the selected parameters and their respective weights; a calculation means for calculating the priority based on the parameters acquired by the parameter acquisition means and the weights for each of the parameters acquired by the weight acquisition means, and information related to the road condition; and an output means for outputting the calculated priority.

2. A road diagnosis system as described in claim 1, further comprising: a detailed parameter output means for outputting candidate detailed parameters, which are detailed parameters of the parameters, based on the parameters acquired by the parameter acquisition means; and a detailed parameter acquisition means for acquiring a selection result of the detailed parameters selected on a screen displaying the candidate detailed parameters, wherein the calculation means calculates the priority further based on the detailed parameters acquired by the detailed parameter acquisition means.

3. The road diagnosis system according to claim 1 or 2, wherein the calculation means calculates the priority by adding weights of parameters that satisfy conditions set for each of the parameters.

4. The road diagnosis system according to claim 2, further comprising a detail weight acquisition means for acquiring the input results of weights for the detailed parameters that are input on a screen displaying the candidate detailed parameters, and the calculation means determines the weights to be used in calculating the priority based on the weights for the detailed parameters acquired by the detail weight acquisition means and the number of detailed parameters that satisfy a criterion among the detailed parameters.

5. The road diagnosis system according to claim 4, wherein the maximum value of the weights in the detailed parameters is set to be the weight of the parameter.

6. A road diagnosis system according to any one of claims 1 to 5, further comprising parameter output means for outputting candidate parameters recommended for selection based on at least one of road attributes and road administrator attributes.

7. The road diagnosis system according to claim 6, wherein the parameter output means outputs recommended parameters and respective weights of the parameters to be used in calculating the priority based on at least one of the attributes of the road and the attributes of the road administrator.

8. A road diagnosis system as described in any one of claims 1 to 7, further comprising: an acquisition means for acquiring video of a road taken by a device mounted on a vehicle and information on the vehicle's location at the time the video was taken; and a deterioration level estimation means for estimating the degree of deterioration of a road at each point on the road for which priority is to be calculated based on the video of the road and the location information, wherein the calculation means calculates the priority using the estimated degree of deterioration of the road as information on the condition of the road.

9. The road diagnosis system according to claim 7, further comprising an estimation means for estimating recommended parameters using a candidate estimation model that estimates recommended parameters from at least one of road attributes and road administrator attributes, and the parameter output means outputs the estimated parameters as the recommended parameters.

10. A road diagnosis system as described in claim 9, wherein the estimation means estimates the recommended weight using a weight estimation model that estimates the recommended weight from at least one of the selected parameter, road attributes, and road administrator attributes, and the parameter output means outputs the weight estimated by the estimation means as the recommended parameter weight.

11. A road diagnosis system as described in any one of claims 1 to 10, wherein the parameter acquisition means acquires information for changing the selected parameters, the weight acquisition means acquires weights for each of the changed parameters, the calculation means calculates the priority based on the changed parameters and the weights for each of the changed parameters, and the output means outputs information regarding the difference in priority before and after the change.

12. A road diagnosis system according to any one of claims 1 to 11, wherein the parameter candidates include parameters relating to measurement results and parameters relating to prediction results.

13. A road diagnosis system according to any one of claims 1 to 12, wherein the output means outputs the priority by superimposing it on a map.

14. The road diagnosis system according to claim 11, wherein the output means outputs information relating to the difference in the priority before and after the change using a priority level.

15. A road diagnosis system as described in claim 8, wherein the deterioration level estimation means estimates the deterioration level of the road for which priority is to be calculated using a deterioration level estimation model that estimates the deterioration level of the road from a photographed image of the road.

16. A road diagnosis method comprising: acquiring a selection result of a parameter selected on a screen displaying candidate parameters used in calculating a priority for carrying out road repairs; acquiring weights of each of the selected parameters input on a screen displaying input fields for the selected parameters and their respective weights; calculating the priority based on the acquired parameters and their respective weights and information related to the road condition; and outputting the calculated priority.

17. A road diagnosis method as described in claim 16, further comprising: outputting candidates for detailed parameters, which are detailed parameters of the parameters, based on the acquired parameters; acquiring a selection result of the detailed parameters selected on a screen displaying the candidate detailed parameters; and calculating the priority further based on the acquired detailed parameters.

18. A road diagnosis method according to claim 16 or 17, wherein the priority is calculated by adding up weights of parameters that satisfy conditions set for each of the parameters.

19. A road diagnosis method as described in claim 17, further comprising: acquiring the weights of the detailed parameters input on a screen displaying the candidate detailed parameters; and determining weights to be used in calculating the priority based on the acquired weights of the detailed parameters and the number of detailed parameters that satisfy a condition among the detailed parameters.

20. A recording medium that non-temporarily records a road diagnosis program that causes a computer to execute the following processes: a process of acquiring the selection results of parameters selected on a screen displaying candidate parameters used in calculating the priority of road repair; a process of acquiring weights for each of the selected parameters input on a screen displaying input fields for the selected parameters and their respective weights; a process of calculating the priority based on the acquired parameters and their respective weights and information related to the road condition; and a process of outputting the calculated priority.

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