Information processing apparatus

Abstract

To provide a technique for reducing time and effort for creating a repair history of a road surface.SOLUTION: An information processing apparatus according to a first aspect of the present disclosure acquires a first measured value in a first period, a second measured value in a second period before the first period, and a third measured value in a third period after the first period for a feature amount related to road surface damage in a target area. Determining whether or not the road surface damage in the target area has increased from the second period to the first period based on the first measured value and the second measured value, determining whether or not the road surface damage in the target area has decreased from the first period to the third period based on the first measured value and the third measured value, and detecting repair of the road surface in the target area when it is determined that the road surface damage in the target area has increased from the second period to the first period and the road surface damage in the target area has decreased from the first period to the third period; The detection result is output.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device. [Background technology]

[0002] A road damage detection device is proposed in Patent Document 1. The proposed road damage detection device detects road damage based on the maximum value of fluctuation per unit time of a physical quantity that indicates the behavior of each of a plurality of vehicles. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-178770 Summary of the Invention [Problem to be solved by the invention]

[0004] One of the objects of the present disclosure is to provide a technology for reducing the effort required to create a road surface repair history. [Means for solving the problem]

[0005] An information processing device according to a first aspect of the present disclosure includes a control unit configured to: acquire, for a feature quantity related to road surface damage in a target area, a first measurement value in a first time period, a second measurement value in a second time period that is earlier than the first time period, and a third measurement value in a third time period that is later than the first time period; determine, based on the first and second measurement values, whether road surface damage in the target area has increased between the second and first time periods; determine, based on the first and third measurement values, whether road surface damage in the target area has decreased between the first and third time periods; detect that road surface repairs have been performed in the target area when it is determined that road surface damage in the target area has increased between the second and first time periods and decreased between the first and third time periods; and output the detection results. [Effects of the Invention]

[0006] According to the present disclosure, it is expected that the effort required to create a road surface repair history can be reduced. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 schematically illustrates an example of a situation to which the present disclosure is applied. [Figure 2] FIG. 2 is a diagram showing an example of a scene in which the feature amount of wheel speed fluctuation is acquired. [Figure 3] FIG. 3 is a schematic diagram showing an example of a scene in which wheel speed data is collected. [Figure 4] FIG. 4 shows a schematic example of a scene in which the overall maximum value for each wheel is extracted. [Figure 5] FIG. 5 is a diagram illustrating an example of a hardware configuration of a server. [Figure 6] FIG. 6 shows an example of a processing procedure for detecting road surface damage by the server. DETAILED DESCRIPTION OF THE INVENTION

[0008] For example, conventional systems such as those disclosed in Patent Document 1 detect physical quantities that indicate the behavior of each of a plurality of vehicles, and determine the presence or absence of road surface damage based on feature quantities calculated from the fluctuations per unit time of the detected physical quantities. In this way, detecting road surface damage can encourage repairs to be made to damaged areas. However, the present inventors have found that conventional systems have the following problems. That is, in conventional systems, the repair history of road surface damage is manually recorded. Therefore, creating a repair history can be time-consuming.

[0009] In contrast, an information processing device according to a first aspect of the present disclosure includes a control unit configured to acquire, for feature quantities related to road surface damage in a target area, a first measurement value in a first period, a second measurement value in a second period before the first period, and a third measurement value in a third period after the first period, determine, from the first and second measurement values, whether road surface damage in the target area has increased between the second and first periods, determine, from the first and third measurement values, whether road surface damage in the target area has decreased between the first and third periods, detect the implementation of road surface repairs in the target area when it is determined that road surface damage in the target area has increased between the second and first periods and decreased between the first and third periods, and output the detection results.

[0010] When road surface damage occurs, the feature quantities related to road surface damage, such as wheel speed fluctuations, change toward an increase in the degree of road surface damage. On the other hand, when road surface repairs are performed and the road surface damage is eliminated, the feature quantities related to road surface damage change toward a decrease in the degree of road surface damage. Therefore, based on the feature quantities related to road surface damage, this configuration detects areas where road surface damage increased from the second period to the first period and decreased from the first period to the third period as areas where road surface repairs were performed. This enables repair history to be created accurately and automatically, and as a result, it is expected that the effort required to create repair history will be reduced.

[0011] As another form of the information processing device according to the above aspect, one aspect of the present disclosure may be an information processing method that realizes all or part of the above components, a program, or a storage medium that stores such a program and is readable by a machine such as a computer. A storage medium that is readable by a machine such as a computer is a medium that stores information such as a program by electrical, magnetic, optical, mechanical, or chemical action.

[0012] [1 Application example] FIG. 1 schematically illustrates an example of a scenario in which the present disclosure is applied. The server 1 according to this embodiment is one or more computers configured to detect the implementation of road surface repairs from wheel speed data WD for each wheel acquired from multiple vehicles M. The server 1 according to this embodiment is an example of an information processing device. First, the server 1 acquires a first measurement value in a first period, a second measurement value in a second period, and a third measurement value in a third period for feature quantities related to road surface damage in a target area. The second period is a period before the first period (a past period). The third period is a period after the first period (a future period). The server 1 determines, based on the first and second measurement values, whether road surface damage in the target area has increased between the second and first periods. The server 1 determines, based on the first and third measurement values, whether road surface damage in the target area has decreased between the first and third periods. If it is determined that road surface damage in the target area has increased between the second period and the first period and that road surface damage in the target area has decreased between the first period and the third period, it is detected that road surface repairs have been carried out in the target area.Then, the server 1 outputs the result of detecting whether or not road surface repairs have been carried out.

[0013] <Road damage> Road surface damage is a localized damage that occurs on the road surface. In one example, road surface damage can be caused by chemical / physical changes in the road surface due to aging or natural phenomena. Road surface damage can include, for example, potholes, subsidence, cracks, peeling, etc.

[0014] The first period is a period in which road surface repairs are to be detected. The second period is a period that precedes the first period. The first period is between the first and second periods, and the third period is a period further in the future than the first period. Each period may be arbitrarily specified. The length of each period is not particularly limited and may be determined appropriately depending on the embodiment. In one example, each period may be a period during which a considerable number of vehicles M may pass, and may be defined as a length of, for example, several hours, one day, one week, etc. Each period may be defined so that they partially overlap, or so that they do not overlap. Furthermore, each period may be continuous or separate.

[0015] The target area for determining the presence or absence of road surface damage may also be specified arbitrarily. In one example, map data may be divided into meshes, and the target area may be defined as an area that includes one or more meshes. The size and shape of the meshes may each be determined arbitrarily.

[0016] <Features> The type of the feature quantity measured in each period is not particularly limited as long as it is related to road surface damage, and may be appropriately selected depending on the embodiment. In one example, the feature quantity related to road surface damage may include a feature quantity related to wheel speed fluctuation calculated from wheel speed data WD collected from multiple vehicles M. For convenience of explanation, the following description will be given assuming that the feature quantity of wheel speed fluctuation is used as the feature quantity for each period.

[0017] 2 schematically illustrates an example of a scene in which a feature quantity of wheel speed fluctuation is acquired. The server 1 may acquire designated target wheel speed data WD in each of the first to third periods and calculate a feature quantity IWF of wheel speed fluctuation for each vehicle M. The server 1 may calculate a feature quantity WWF of wheel speed fluctuation for all vehicles from the calculated feature quantity IWF. The server 1 may acquire the calculated measured value of the feature quantity WWF in each period as each of the first to third measured values.

[0018] <Wheel speed fluctuation> In the present disclosure, wheel speed fluctuation refers to the amount of fluctuation in wheel speed. Wheel speed relates to the rotation speed of the wheels. In one example, the wheel speed may be measured by a wheel speed sensor WS mounted on the vehicle M. The wheel speed sensor WS may be a known sensor such as an encoder. Furthermore, the amount of fluctuation in wheel speed may be expressed as a differential value of the wheel speed, a change (difference) between wheel speed samples, or the like.

[0019] <Wheel speed data> The content included in the wheel speed data WD may be determined appropriately depending on the embodiment. In one example, the wheel speed data WD may include time-series data related to wheel speed fluctuations. The server 1 may collect the wheel speed data WD from each of one or more vehicles M. The method of collecting the wheel speed data WD is not particularly limited and may be determined appropriately depending on the embodiment. In one example, each vehicle M may be equipped with a communication means and may transmit the wheel speed data WD to the server 1 via the communication means. In this way, the wheel speed data WD may be collected in the server 1.

[0020] The time-series data regarding wheel speed fluctuations may be data that has been subjected to a predetermined pre-processing. For example, the predetermined pre-processing may be a process for suppressing noise components contained in the raw data. As described in Patent Document 1, the raw data regarding wheel speed fluctuations may contain noise components that may cause erroneous detection of road surface damage. Therefore, by performing a process for suppressing the noise components, it is possible to effectively extract wheel speed fluctuations detected due to road surface damage. A known method may be used for the process for suppressing the noise components. For example, the process for suppressing the noise components may be a filtering process. For example, the filtering process may smooth the raw data by applying a filter such as a moving average, a Gaussian filter, or an LSTM, thereby suppressing the noise components.

[0021] <Features of wheel speed fluctuations> The wheel speed fluctuation feature quantities (IWF, WWF) may be appropriately defined so as to be associated with road surface damage. For convenience of explanation, the following description will be made assuming that the vehicle M is a four-wheel vehicle. In one example, the wheel speed fluctuation feature quantities may include the wheel speed fluctuation feature quantities WWF for each wheel (right front wheel, left front wheel, right rear wheel, left rear wheel) of all the vehicles M. The feature quantities WWF may be statistics calculated from the wheel speed fluctuation feature quantities IWF for each vehicle M. For example, the feature quantities IWF may be the maximum value, average value, etc. of the wheel speed data WD. For example, the feature quantities WWF may be the maximum value, median value, average value, n-th percentile value, etc. of the feature quantities IWF. In one example, the feature quantities WWF may include at least one of the first to eighth feature quantities described below.

[0022] (First feature) In one example, the feature quantity related to road surface damage may include an overall maximum value for each wheel as the first feature quantity. The overall maximum value is a value calculated for each wheel. For example, the server 1 may acquire, for each wheel, the maximum value of wheel speed fluctuation for each of one or more vehicles M passing through the target area during the target period. The server 1 may calculate, from the acquired maximum values ​​of wheel speed fluctuation for each of the one or more vehicles M, the maximum value of wheel speed fluctuation across all vehicles as the overall maximum value. As a specific example, the server 1 may calculate, for each wheel of each vehicle M, the maximum value of wheel speed fluctuation in the time-series data included in the wheel speed data WD. Then, the server 1 may obtain the overall maximum value for each wheel by extracting, for each wheel, the maximum value of the maximum values ​​for all vehicles M.

[0023] 3 and 4 schematically illustrate an example of a situation in which wheel speed data WD is collected and an overall maximum value for each wheel is extracted. As shown in FIG. 3, the server 1 may acquire wheel speed data WD (time-series data) for each wheel from one or more vehicles M. Next, as shown in FIG. 4, the server 1 may calculate a maximum value for each wheel for each vehicle M from the acquired wheel speed data WD for each wheel, and store the calculated maximum values ​​for each vehicle M and wheel in an arbitrary storage area. The arbitrary storage area may be, for example, a memory resource within the server 1 or an external computer. In the example of FIG. 3, the calculated maximum values ​​are stored in table format. However, the format for storing the calculated maximum values ​​is not limited to this example and may be selected appropriately depending on the embodiment. The server 1 may extract the maximum value for each wheel from the stored maximum values ​​for each vehicle M and acquire the result as the overall maximum value for each wheel. Note that the method for obtaining the overall maximum value is not limited to this example. In another example, the maximum value of wheel speed fluctuation for each wheel of each vehicle M may be calculated for each vehicle M. The server 1 may obtain the maximum value of wheel speed fluctuation for each wheel from each vehicle M, and extract the maximum value for each wheel from the obtained maximum values, thereby obtaining the overall maximum value for each wheel.

[0024] (Second feature) In one example, the feature quantity related to road surface damage may include, as the second feature quantity, the difference between the maximum and minimum values ​​of the overall maximum values ​​(maximum values ​​of wheel speed fluctuations) for each wheel of all vehicles. For example, the server 1 may extract the overall maximum values ​​for each wheel in each period using the above method. Then, the server 1 may extract the maximum and minimum values ​​from the extracted overall maximum values ​​for each wheel, and calculate the difference between the extracted maximum and minimum values ​​to obtain the second feature quantity.

[0025] (Third feature) In one example, the feature quantity related to road surface damage may include, as a third feature quantity, an integrated value of the overall maximum value (maximum value of wheel speed fluctuation) of each wheel of all vehicles. For example, the server 1 may extract the overall maximum value for each period for each wheel using the above method. Then, the server 1 may obtain the integrated value of the overall maximum value (third feature quantity) by integrating the extracted overall maximum values ​​for each wheel. The integration may include, for example, sum, average, etc. The sum may be a simple sum, a weighted sum, etc. The average may be a simple average, a weighted average, etc. When a weighted sum or a weighted average is adopted , each weight may be set arbitrarily.

[0026] (Fourth feature) In one example, the feature quantity related to road surface damage may include an average value of the maximum values ​​for all wheels as the fourth feature quantity. For example, the server 1 may calculate, for each wheel of each vehicle M, the maximum value of wheel speed fluctuation in the time-series data included in the wheel speed data WD. Then, the server 1 may calculate, for each wheel, the average value of the maximum values ​​of wheel speed fluctuation by dividing the sum of the maximum values ​​of wheel speed fluctuation obtained from each vehicle M for each wheel by the number of vehicles. The server 1 may calculate the average value of the maximum values ​​of wheel speed fluctuation for all wheels (the fourth feature quantity) by dividing the sum of the maximum values ​​of wheel speed fluctuation for each vehicle M and each wheel by the total number of wheels for all vehicles.

[0027] (5th feature) In one example, the feature quantity related to road surface damage may include an average value of maximum averages across all vehicles as a fifth feature quantity. For example, the server 1 may calculate an average value of wheel speed fluctuations for each wheel of each vehicle M by calculating an average value of wheel speed fluctuations in time-series data included in the wheel speed data WD. The server 1 may extract the maximum value of the average values ​​for each wheel as the maximum average for each vehicle M. The server 1 may calculate the average value of maximum averages by dividing the sum of the extracted maximum averages for all vehicles by the total number of vehicles. Note that the method of calculating the average value of maximum averages is not limited to this example. In another example, the average value of wheel speed fluctuations for each wheel may be calculated for each vehicle M. The server 1 may acquire the average value of wheel speed fluctuations for each wheel from each vehicle M, calculate the average value of wheel speed fluctuations for each wheel, and perform subsequent calculations to calculate the average value of maximum averages.

[0028] (6th feature) In one example, the feature amount related to road surface damage may include an overall average value (average value of wheel speed fluctuations for all wheels) as a sixth feature amount. For example, the server 1 may calculate the average value of wheel speed fluctuations for each wheel of each vehicle M by calculating the average value of wheel speed fluctuations in the time-series data included in the wheel speed data WD. The server 1 may calculate the overall average value for each wheel by dividing the sum of the calculated average values ​​by the total number of vehicles. The server 1 may calculate the overall average value for all wheels by dividing the sum of the calculated average values ​​for each wheel of each vehicle M by the total number of wheels of all vehicles. Note that the method of calculating the overall average value is not limited to this example. In another example, the average value for each wheel of each vehicle M may be calculated for each vehicle M. The server 1 may acquire the average value of wheel speed fluctuations for each wheel from each vehicle M, calculate the average value of wheel speed fluctuations for each wheel, and perform the subsequent calculations to calculate the overall average value for each wheel.

[0029] (7th feature) In one example, the feature quantities related to road surface damage may include the feature quantities of the left wheels of all vehicles as the seventh feature quantities. The server 1 may include at least one of the feature quantities of the left front wheel and the feature quantities of the left rear wheel. The feature quantities may be maximum values, median values, average values, n-th percentile values, etc. When the seventh feature quantities include the feature quantities of the left front wheel and the feature quantities of the left rear wheel, the seventh feature quantity may be, for example, the maximum value, average value, etc. of the feature quantities of the two left wheels. For example, the feature quantities related to road surface damage may include at least one of the maximum value of the wheel speed fluctuation of the left front wheel (overall maximum value) and the maximum value of the wheel speed fluctuation of the left rear wheel (overall maximum value) of all vehicles as the seventh feature quantity. The overall maximum values ​​of the left front wheel and the left rear wheel may be calculated in the same manner as the first feature quantity. When both the overall maximum values ​​of the left front wheel and the left rear wheel are used, the overall maximum values ​​of the left front wheel and the left rear wheel may be used individually to determine the repair history, or may be used together like the third feature quantity.

[0030] (8th feature) In one example, the feature amount related to road surface damage may include the feature amount of the right wheel of all vehicles as the eighth feature amount. The feature may be a maximum value, a median value, an average value, an n-th percentile value, or the like. When the eighth feature includes a feature of the right front wheel and a feature of the right rear wheel, the eighth feature may be, for example, a maximum value, an average value, or the like of the feature of the two right wheels. For example, the feature related to road surface damage may include, as the eighth feature, at least one of the maximum value of wheel speed fluctuation of the right front wheel (overall maximum value) and the maximum value of wheel speed fluctuation of the right rear wheel (overall maximum value) for all vehicles. The overall maximum value of the right front wheel and the right rear wheel may be calculated in the same manner as the first feature. When both the overall maximum values ​​of the right front wheel and the right rear wheel are used, the overall maximum values ​​of the right front wheel and the right rear wheel may be used individually to determine the repair history, or may be used in combination like the third feature.

[0031] <Determining Increased Road Damage> The server 1 may determine whether road surface damage in the target area has increased between the second period and the first period based on the first measurement value of the feature WWF for the first period and the second measurement value of the feature WWF for the second period. In one example, the server 1 may determine whether road surface damage has increased depending on whether the first measurement value and the second measurement value satisfy a predetermined condition. The predetermined condition may be defined as appropriate using the feature WWF of wheel speed fluctuation. In one example, the predetermined condition may be defined as a difference between the first measurement value and the second measurement value of the feature WWF. For example, if the feature is defined so that its value increases as road surface damage increases, the predetermined condition may be defined to determine whether road surface damage has increased depending on whether the difference between the first measurement value and the second measurement value of the feature WWF is greater than a predetermined threshold. The predetermined threshold may be determined as appropriate depending on the embodiment. At least one of the first to eighth feature values ​​described above may be used as the feature WWF.

[0032] <Determining reduction in road surface damage> The server 1 may determine whether road damage in the target area has decreased between the first and third periods based on the first measurement value of the feature WWF for the first period and the third measurement value of the feature WWF for the third period. In one example, the server 1 may determine whether road damage has decreased depending on whether the first measurement value and the third measurement value satisfy a predetermined condition. The predetermined condition may be defined as appropriate using the feature WWF of wheel speed fluctuation. In one example, the predetermined condition may be defined as a difference between the first and third measurement values ​​of the feature WWF. For example, if the feature is defined so that its value increases as road damage increases, the predetermined condition may be defined to determine whether road damage has decreased depending on whether the difference between the first and third measurement values ​​of the feature WWF is greater than a predetermined threshold. The predetermined threshold may be determined as appropriate depending on the embodiment. At least one of the first to eighth feature values ​​described above may be used as the feature WWF.

[0033] <Detection of repair work> The server 1 detects that road surface repairs have been performed in the target area if it is determined that road surface damage in the target area increased between the second time period and the first time period and decreased between the first time period and the third time period. For example, if the feature is defined so that its value increases as road surface damage increases, and the measurement value of the feature is greater in the first time period than in the second time period (the difference between the first measurement value and the second measurement value exceeds a threshold), and the measurement value of the feature is smaller in the third time period than in the first time period (the difference between the first measurement value and the third measurement value exceeds a threshold), the server 1 may determine that repairs have been performed in the target area during the first time period. Otherwise, the server 1 may determine that repairs have not been performed during the first time period. This allows the server 1 to detect that repairs have been performed.

[0034] (summary) In one example, the feature quantity related to road surface damage may include at least one of the first to eighth feature quantities. The feature quantity WWF used to detect whether road surface repair has been performed may be selected by any method. The feature quantity WWF used may be selected by the user or may be defined within the program.

[0035] In one example, the second feature value may be used for the feature value WWF related to road surface damage. Accordingly, the server 1 may determine whether road surface repairs have been performed in the target area based on the difference between the maximum and minimum values ​​of the overall maximum values ​​for each wheel calculated for each period. Road surface damage may occur on either the right or left side of the vehicle M's traveling direction. In this case, the overall maximum value for either the left wheel (left front wheel, left rear wheel) or the right wheel (right front wheel, right rear wheel) will be large, but the overall maximum value for the other wheel will be small. This increases the difference between the maximum and minimum values ​​of the overall maximum values ​​for each wheel. Therefore, by determining whether road surface damage is increasing or decreasing based on the difference between the maximum and minimum values ​​of the overall maximum values ​​for each wheel, it is possible to appropriately detect whether road surface repairs have been performed, which is expected to reduce the effort required to create a road surface repair history.

[0036] In one example, the third feature may be used for the feature WWF related to road surface damage. In response to this, the server 1 may determine whether road surface repairs have been performed in the target area based on the integrated value of the overall maximum values ​​calculated for each period. When road surface damage is formed, multiple wheels of the vehicle M may pass through the damaged area. For example, if road surface damage exists on the left side of the vehicle M's traveling direction, the left front wheel and the left rear wheel of the vehicle M may pass through the damaged area. When multiple wheels of the vehicle M pass through the damaged area, the maximum value of wheel speed fluctuations of each passing wheel increases. Accordingly, the overall maximum value of each passing wheel also increases. If the overall maximum value for multiple wheels increases, the integrated value of the overall maximum value also increases. Therefore, by determining whether road surface damage is increasing or decreasing based on the integrated value of the overall maximum value, it is possible to appropriately detect whether road surface repairs have been performed, which is expected to reduce the effort required to create a road surface repair history.

[0037] In one example, the seventh feature value may be used for the feature value WWF related to road surface damage. Accordingly, the server 1 may determine whether road surface repairs have been performed in the target area based on the overall maximum value of the left wheel calculated for each period. Roads may slope from the center strip to the shoulder, with the shoulder side being lower. If the shoulder side is lower, the vehicle's weight is more likely to be applied to the shoulder side, which may increase the likelihood of road surface damage occurring on the shoulder side. If traffic is on the left side of the road, the left wheel of vehicle M is likely to pass through an area with road surface damage. Therefore, determining whether road surface damage has increased or decreased based on the feature value of the left wheel makes it possible to appropriately detect whether road surface repairs have been performed, which is expected to reduce the effort required to create a road surface repair history.

[0038] In one example, the eighth feature value may be used for the feature value WWF related to road surface damage. Accordingly, the server 1 may determine whether road surface repairs have been performed in the target area based on the overall maximum value of the right wheel calculated for each period. For the reasons described above, road surface damage may be more likely to occur on the shoulder side. If traffic is on the right side of the road, the right wheel of the vehicle M is likely to pass through a damaged area. Therefore, by determining whether road surface damage is increasing or decreasing based on the feature value of the right wheel, it is possible to appropriately detect whether road surface repairs have been performed, which is expected to reduce the effort required to create a road surface repair history.

[0039] [2 Configuration Examples] 5 schematically illustrates an example of a hardware configuration of the server 1 of the present disclosure. The server 1 according to this embodiment is a computer in which a control unit 11, a storage unit 12, a communication interface 13, an input device 14, an output device 15, and a drive 16 are electrically connected.

[0040] The control unit 11 includes a CPU (Central Processing Unit), a RAM (Random Access Memory ), ROM (Read Only Memory), etc., and is configured to execute any information processing. The storage unit 12 may be configured, for example, by a hard disk drive, a solid state drive, etc. In this embodiment, the storage unit 12 stores a program 81. The program 81 is a program for causing the server 1 to execute information processing (see FIG. 6 described below) relating to the detection of road surface damage. The program 81 includes a series of instructions for the information processing.

[0041] The communication interface 13 is configured to perform wired or wireless data communication via a network. The communication interface 13 may be configured, for example, by a wired LAN (Local Area Network) module, a wireless LAN module, etc. In this embodiment, the server 1 may use the communication interface 13 to perform data communication via the network with another computer (for example, an on-board device of the vehicle M, etc.).

[0042] The input device 14 is a device for inputting, for example, a mouse, keyboard, joystick, microphone, operator, etc. The output device 15 is a device for outputting, for example, a display, speaker, etc. The input device 14 and the output device 15 may be integrally configured, for example, by a touch panel display, etc.

[0043] The drive 16 is a device for reading various information such as a program stored in a storage medium 91. The program 81 may be stored in the storage medium 91 instead of or together with the storage unit 12. The storage medium 91 is configured to store various information (such as the stored program) by electrical, magnetic, optical, mechanical, or chemical action so that a machine such as a computer can read the information. The server 1 may obtain the program 81 from the storage medium 91. The storage medium 91 may be a disk-type storage medium such as a CD or DVD, or may be a non-disk-type storage medium such as a semiconductor memory (e.g., a flash memory). The type of the drive 16 may be selected appropriately depending on the type of the storage medium 91.

[0044] Note that, with regard to the specific hardware configuration of the server 1, components can be omitted, replaced, or added as appropriate depending on the embodiment. For example, the control unit 11 may include multiple hardware processors. The hardware processor may be configured with a microprocessor, an FPGA (field-programmable gate array), a DSP (digital signal processor), a GPU (graphics processing unit), an ASIC (application specific integrated circuit), or the like. At least one of the communication interface 13, the input device 14, the output device 15, and the drive 16 may be omitted. The server 1 may be a computer designed specifically for the services provided, as well as a general-purpose computer, a terminal device, or the like.

[0045] [3 Example of operation] FIG. 6 shows an example of a processing procedure for detecting whether road surface repairs have been performed by the server 1 according to this embodiment. The following processing procedure is an example of an information processing method executed by a computer. The control unit 11 of the server 1 executes instructions included in the program 81 using the CPU. As a result, the server 1 operates as a computer capable of executing the following information processing. The following processing procedure is an example of a control method executed by a computer. However, the following processing procedure is merely an example, and each step may be changed as much as possible. Steps in the following processing procedure may be omitted, replaced, or added as appropriate depending on the embodiment.

[0046] (Step S101) In step S101, the control unit 11 acquires a first measurement value in a first period, a second measurement value in a second period, and a third measurement value in a third period for the feature quantity related to road surface damage in the target area. The target area and the first to third periods may be designated as appropriate. In one example, the feature quantity related to road surface damage may use the feature quantity WWF of wheel speed fluctuation. For example, the feature quantity related to road surface damage may include at least one of the first to eighth feature quantities. When the feature quantity WWF of wheel speed fluctuation is used as the feature quantity related to road surface damage, the control unit 11 may acquire wheel speed data WD collected from multiple vehicles M in order to acquire the measurement value of the feature quantity for each period. In one example, the wheel speed data WD is obtained by calculating the wheel speed data WD of the vehicle M that is the target in each period. The wheel speed data WD may include time-series data of wheel speed fluctuations over time as the vehicle passes through the area. The wheel speed data WD may be acquired for each wheel of each vehicle M. The control unit 11 may calculate, for each period, a feature value IWF of wheel speed fluctuations for each vehicle from the acquired wheel speed data WD for the first to third periods. The control unit 11 may calculate, for each period, a feature value WWF of wheel speed fluctuations for all vehicles from the calculated feature value IWF. This may obtain a measurement value of the feature value (feature value WWF) for each period. After obtaining the measurement value of the feature value for each period, the control unit 11 proceeds to the next step S102.

[0047] (Step S102) In step S102, the control unit 11 determines whether road surface damage in the target area has increased based on the acquired first and second measurement values. In one example, the control unit 11 may determine whether road surface damage has increased based on whether the first and second measurement values ​​satisfy the predetermined condition. If it is determined that road surface damage has increased, the control unit 11 proceeds to step S103. If it is determined that road surface damage has not increased, the control unit 11 proceeds to step S105.

[0048] (Step S103) In step S103, the control unit 11 determines whether the road surface damage in the target area has decreased based on the acquired first and third measurement values. In one example, the control unit 11 may determine whether the road surface damage has decreased based on whether the first and third measurement values ​​satisfy the predetermined condition. If it is determined that the road surface damage has decreased, the control unit 11 proceeds to step S104. If it is determined that the road surface damage has not decreased, the control unit 11 proceeds to step S105.

[0049] The order of steps S102 and S103 is not limited to this example and may be determined appropriately depending on the embodiment. In another example, step S103 may be performed before step S102. Furthermore, steps S102 and S103 may be performed at least partially in parallel.

[0050] (Step S104) In step S104, the control unit 11 detects that road surface repairs have been performed in the target area in response to the fact that road surface damage increased between the second time period and the first time period and decreased between the first time period and the third time period. The detection of the performance of repairs may be performed by setting a flag, etc. When the control unit 11 detects the performance of road surface repairs, the control unit 11 proceeds to step S105.

[0051] (Step S105) In step S105, the control unit 11 outputs the detection result of the implementation of road surface repair. The content of the information to be output and the output destination may each be selected appropriately depending on the embodiment. In one example, the control unit 11 may output the detection result of the implementation of road surface repair as is. In another example, the control unit 11 may perform any information processing according to the detection result. The control unit 11 may output the result of the information processing as information related to the detection result. The output of the result of the information processing may include, for example, creating a repair history or outputting a specific message according to the detection result. In one example, the control unit 11 may notify a database that stores map information of the location where the implementation of road surface repair was detected, thereby updating the information stored in the database. The output destination may be, for example, RAM, the memory unit 12, the output device 15, another computer, etc.

[0052] [Features] In one example of this embodiment, in steps S102 and S103, It is determined whether there has been an increase or decrease in road surface damage. Based on these determination results, it is possible to detect whether road surface repairs have been carried out. This is expected to reduce the effort required to create a road surface repair history.

[0053] [4 Variations] Although the embodiments of the present disclosure have been described in detail above, the above description is merely an example of the present disclosure in every respect. The processes and means described in the present disclosure can be freely combined and implemented as long as no technical contradictions arise. Furthermore, various improvements or modifications may be made to the above embodiments as appropriate. For example, the following modifications are possible. Note that, in the following, the same reference numerals are used for components similar to those in the above embodiments, and descriptions of the same points as those in the above embodiments are omitted as appropriate. The following modifications can be combined as appropriate.

[0054] In the above embodiment, the server 1 is an example of an information processing device. However, the form of the information processing device is not limited to this example and may be changed as appropriate depending on the embodiment. In another example, a terminal such as a PC (personal computer), a tablet terminal, or a mobile terminal may be used. , may be configured to execute the arithmetic processing of the server 1. As a result, the terminal may be an example of an information processing device.

[0055] In the above embodiment, the server 1 directly acquires data from each vehicle M, but the data exchange method is not limited to this example. The server 1 and the terminal may indirectly acquire data from each vehicle via an external computer, storage medium, etc. Also, part of the calculation processing of the server 1 may be executed in each vehicle.

[0056] In addition, in the above embodiment, a four-wheeled vehicle is used as the vehicle M. However, the number of wheels of the vehicle M is not limited to four, and may be three wheels, or five or more wheels. Accordingly, cases other than four-wheeled vehicles may also be used to detect the implementation of road surface repairs.

[0057] In the above embodiment, the feature amount of wheel speed fluctuation (feature amount WWF) is used as the feature amount related to road surface damage. However, the feature amount related to road surface damage does not have to be limited to that related to wheel speed fluctuation. Any feature amount other than wheel speed fluctuation may be used as the feature amount related to road surface damage. A known feature amount may be used as the feature amount related to road surface damage. [Explanation of symbols]

[0058] 1 server, 11. Control unit, 12. Storage unit, 13. Communication interface, 14. Input device, 15··Output device, 16··Drive, 81··Program, 91··Storage medium, M··Vehicle, WS··Wheel speed sensor WD: Wheel speed data, IWF: Wheel speed fluctuation feature for each vehicle WWF: Wheel speed fluctuation feature of all vehicles

Claims

1. An information processing device including a control unit, The control unit acquiring a first measurement value in a first period, a second measurement value in a second period before the first period, and a third measurement value in a third period after the first period, for a feature amount related to road surface damage in a target area; determining whether road surface damage in the target area has increased between the second period and the first period based on the first measurement value and the second measurement value; determining whether road surface damage in the target area has decreased between the first period and the third period based on the first measurement value and the third measurement value; Detecting that road surface repairs have been performed in the target area when it is determined that road surface damage in the target area has increased between the second time period and the first time period and that road surface damage in the target area has decreased between the first time period and the third time period; and outputting the detection result; To execute Information processing device.

2. The feature value is a difference between a maximum value and a minimum value among maximum values ​​of wheel speed fluctuations of each wheel in all vehicles. The information processing device according to claim 1 .

3. The feature value is an integrated value of the maximum value of wheel speed fluctuations of each wheel in all vehicles. The information processing device according to claim 1 .

4. the feature amount includes at least one of a maximum value of wheel speed fluctuation of a left front wheel and a maximum value of wheel speed fluctuation of a left rear wheel in all vehicles; The information processing device according to claim 1 .

5. the feature amount includes at least one of a maximum value of wheel speed fluctuation of a right front wheel and a maximum value of wheel speed fluctuation of a right rear wheel in all vehicles; The information processing device according to claim 1 .

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