Information processing apparatus

Abstract

To provide a technique for improving detection accuracy of road surface damage.SOLUTION: An information processing apparatus according to a first aspect of the present disclosure acquires, for each wheel, a maximum value of a wheel speed fluctuation in each of one or more vehicles passing through a target area in a target period, and extracts, for each wheel, a maximum value of a wheel speed fluctuation among all vehicles as an overall maximum value from the acquired maximum value of the wheel speed fluctuation in each of the one or more vehicles. The difference between the maximum value and the minimum value among the extracted overall maximum values for each wheel is calculated, the presence or absence of the road surface damage in the target area in the target period is determined according to the calculated difference between the maximum value and the minimum value, and the determination result of the presence or absence of the road surface damage is output.SELECTED DRAWING: Figure 5

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 multiple 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 technique for improving the accuracy of detecting road surface damage. [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 each wheel, a maximum value of wheel speed fluctuations for one or more vehicles passing through a target area during a target period; extract, for each wheel, a maximum value of wheel speed fluctuations across all vehicles from the acquired maximum values ​​of wheel speed fluctuations for each of the one or more vehicles as an overall maximum value; calculate a difference between a maximum value and a minimum value among the overall maximum values ​​extracted for each wheel; determine the presence or absence of road surface damage in the target area during the target period based on the calculated difference between the maximum and minimum values; and output a result of the determination of the presence or absence of road surface damage. [Effects of the Invention]

[0006] According to the present disclosure, it is expected that the accuracy of detecting road surface damage can be improved. [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 schematic diagram showing an example of a scene in which wheel speed data is collected. [Figure 3] FIG. 3 shows a schematic example of a scene in which the overall maximum value for each wheel is extracted. [Figure 4] FIG. 4 is a diagram illustrating an example of a hardware configuration of a server. [Figure 5] FIG. 5 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 multiple vehicles and determine the presence or absence of road damage based on feature quantities calculated from fluctuations in the detected physical quantities per unit time. This allows road damage to be detected with a certain degree of accuracy. However, the present inventors have discovered the following problem with conventional systems. Specifically, in conventional systems, each vehicle extracts the maximum value of wheel speed fluctuations of four wheels (left front wheel, right front wheel, left rear wheel, and right rear wheel) as the maximum wheel speed fluctuation value. Therefore, the behavior of the three wheels other than the wheel with the maximum wheel speed fluctuation is ignored in detecting road damage. This may make it difficult to improve the accuracy of road damage detection.

[0009] In contrast, an information processing device according to a first aspect of the present disclosure includes a control unit that acquires, for each wheel, a maximum value of wheel speed fluctuations for one or more vehicles passing through a target area during a target period, extracts, for each wheel, a maximum value of wheel speed fluctuations across all vehicles from the acquired maximum values ​​of wheel speed fluctuations for each of the one or more vehicles as an overall maximum value, calculates a difference between the maximum and minimum values ​​of the extracted overall maximum values ​​for each wheel, determines whether or not road surface damage exists in the target area during the target period based on the calculated difference between the maximum and minimum values, and outputs a determination result of whether or not road surface damage exists.

[0010] Generally, road damage often occurs on a portion of the road surface in the vehicle width direction. When damage occurs on a portion of the road surface, some of the vehicle's wheels run on the road damage, while the remaining wheels run on a normal road surface. In this case, the difference in wheel speed fluctuations between the some of the wheels running on the road damage and the remaining wheels running on a normal road surface becomes large. Therefore, in this configuration, the presence or absence of road damage is determined based on the difference between the maximum and minimum values ​​of the overall maximum values ​​for each wheel. As a result, it is possible to evaluate whether an event has occurred in which some of the wheels run on the road damage and the remaining wheels run on a normal road surface based on the difference between the maximum and minimum values ​​of the overall maximum values, and to detect the presence or absence of road damage based on this evaluation. Therefore, with this configuration, the behavior of multiple wheels can be reflected in the detection of road damage as described above, which is expected to improve the accuracy of road damage detection.

[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 determine the presence or absence of road surface damage 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 specifies a target period and a target area for determining the presence or absence of road surface damage. The server 1 acquires wheel speed data WD collected by wheel speed sensors WS of multiple vehicles M in the target area during the specified target period, and calculates a feature value IWF of wheel speed fluctuations for each vehicle M. In addition, the server 1 calculates a feature value WWF of wheel speed fluctuations for all vehicles from the calculated feature value IWF. The server 1 may determine the presence or absence of road surface damage in the target area during the target period based on the calculated feature value WWF. The server 1 may then output a determination result of the presence or absence of road surface damage.

[0013] <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.

[0014] <Wheel speed data> The content of 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 relating to wheel speed fluctuations. The server 1 may collect the wheel speed data WD from each of one or more vehicles M. The collection method is not particularly limited and may be determined appropriately depending on the embodiment. In one example, each vehicle M may be provided with a communication means and 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.

[0015] 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.

[0016] <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.

[0017] The target period is a period for determining whether or not there is road surface damage. The target period may be arbitrarily specified. Furthermore, the length of the target period is not particularly limited and may be determined appropriately depending on the embodiment. In one example, the target 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.

[0018] 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.

[0019] <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 ease of explanation, the following description will assume 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 the overall maximum value, average value of maximum averages, average value of maximum averages, overall average value, etc., which will be described later.

[0020] (Overall maximum value) In one example, the feature WWF may include an overall maximum value for each wheel. The overall maximum value is a value calculated for each wheel. 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. 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 obtain the overall maximum value for each wheel by extracting, for each wheel, the maximum value of the maximum values ​​for all vehicles M.

[0021] 2 and 3 are diagrams showing an example of a scene in which wheel speed data WD is collected and the overall maximum value for each wheel is extracted. As shown in FIG. 2, the server 1 receives data from one or more vehicles M. The server 1 may acquire wheel speed data WD (time-series data) for each wheel from the acquired wheel speed data WD. Next, as shown in FIG. 3, 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 value 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 value is stored in table format. However, the format for storing the calculated maximum value 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 acquire the maximum value of wheel speed fluctuation for each wheel from each vehicle M, and extract the maximum value for each wheel from the acquired maximum values ​​to obtain the overall maximum value for each wheel.

[0022] (average of maximum values) In one example, the feature WWF may include an average value of maximum values. 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. The server 1 may then calculate, for each wheel, the average value of maximum wheel speed fluctuation values ​​for each wheel by dividing the sum of the maximum wheel speed fluctuation values ​​obtained from each vehicle M for each wheel by the number of vehicles. The server 1 may also calculate the average value of maximum wheel speed fluctuation values ​​for all wheels by dividing the sum of the maximum wheel speed fluctuation values ​​for each vehicle M and each wheel by the total number of wheels on all vehicles. The range over which the average value is calculated may be changed as desired. For example, the average value of maximum wheel speed fluctuation values ​​for left wheels may be calculated by dividing the sum of the maximum values ​​for the left front wheel and the left rear wheel by the total number of left wheels on all vehicles. Similarly, the average value of maximum wheel speed fluctuation values ​​for two or more wheels that meet certain conditions may be calculated for the right wheel, drive wheels, driven wheels, etc.

[0023] (average of maximum average) In one example, the feature WWF may include an average value of maximum averages. 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 of wheel speed fluctuation values ​​in the 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 obtain 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 average value of maximum averages.

[0024] (overall average value) In one example, the feature WWF may include an overall average value. 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 of wheel speed fluctuation values ​​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. The range for calculating the average value may be changed as desired. For example, the overall average value for the left wheels may be calculated by dividing the sum of the average values ​​for the left front wheels and the left rear wheels by the total number of left wheels of all vehicles. Similarly, the overall average values ​​for two or more wheels that meet the conditions may be calculated for the right wheels, driving wheels, driven wheels, etc. Note that the method for 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 obtain the average value of the wheel speed fluctuation for each wheel from each vehicle M, calculate the average value of the wheel speed fluctuation for each wheel, and perform subsequent calculations to calculate the overall average value for each wheel.

[0025] <Determining whether road surface is damaged or not> The server 1 may determine the presence or absence of road surface damage in a target area during a target period based on a feature of wheel speed fluctuation calculated from wheel speed data WD of one or more vehicles M. In one example, the server 1 may determine the presence or absence of road surface damage depending on whether the feature of wheel speed fluctuation satisfies a predetermined condition. The determination conditions may be defined appropriately using the feature of wheel speed fluctuation. The feature may be at least one of the overall maximum value, the average of maximum values, the average of maximum averages, and the overall average value. In one example, at least one of the following first to sixth determination conditions using the feature WWF may be employed.

[0026] (1st judgment condition) In one example, the first determination condition for determining whether or not road surface damage exists may be defined based on the difference between the maximum and minimum values ​​among the overall maximum values ​​for each wheel in the target area during the target period. That is, when the first determination condition is adopted, the server 1 may calculate the difference between the maximum and minimum values ​​among the overall maximum values ​​for each extracted wheel. The presence or absence of road surface damage in the target area during the target period may be determined based on the difference between the calculated maximum and minimum values. For example, the first determination condition may be defined to determine whether or not road surface damage exists based on whether the difference between the maximum and minimum values ​​among the overall maximum values ​​for each wheel is greater than a predetermined threshold. The predetermined threshold may be determined appropriately depending on the embodiment. Road surface damage may occur on either the right or left side of the traveling direction of the vehicle M. In this case, the overall maximum value of one of the left wheels (left front wheel, left rear wheel) and the right wheels (right front wheel, right rear wheel) will be large, but the overall maximum value of the other wheel will be small. As a result, the difference between the maximum and minimum values ​​among the overall maximum values ​​for each wheel will be large. Therefore, by determining whether or not there is road damage based on the difference between the maximum and minimum values ​​of the overall maximum values ​​for each wheel, it is possible to expect that road damage will be detected appropriately.

[0027] (Second judgment condition) In one example, the second determination condition for the presence or absence of road surface damage may be defined according to the average value of the maximum averages in the target area for the target period. That is, when the second determination condition is adopted, the presence or absence of road surface damage in the target area for the target period may be determined according to the calculated average value of the maximum averages. For example, the second determination condition may be defined to determine the presence or absence of road surface damage according to whether the average value of the maximum averages is greater than a predetermined threshold. The predetermined threshold may be determined appropriately depending on the embodiment. When a road surface damage location is passed, the wheel speed fluctuation of the passing wheels increases. Accordingly, the average value of the maximum averages also increases. Therefore, by determining the presence or absence of road surface damage according to the average value of the maximum averages, appropriate detection of road surface damage can be expected.

[0028] (Third judgment condition) In one example, the third determination condition for determining whether or not road surface damage exists may be defined based on a measurement value of a feature amount of at least one of the front and rear left wheels. That is, when the third determination condition is adopted, the server 1 may acquire a measurement value of a feature amount related to road surface damage of at least one of the front and rear left wheels of one or more vehicles M passing through a target area during a target period. The presence or absence of road surface damage in a target area during a target period may be determined based on the acquired measurement value of the feature amount of the left wheel. In one example, the server 1 may compare the acquired measurement value of the feature amount of the left wheel with a threshold. The server 1 may determine the presence or absence of road surface damage in a target area during a target period based on the result of comparing the measurement value of the feature amount of the left wheel with the threshold. For example, if the feature amount of the left wheel is defined such that the measurement value increases as road surface damage forms, the third determination condition may be defined to determine the presence or absence of road surface damage based on whether the measurement value of the feature amount of the left wheel is greater than a predetermined threshold. The predetermined threshold may be determined appropriately depending on the embodiment. Roads may slope from the center strip to the shoulder, with the shoulder side being lower. If the shoulder is low, the vehicle's weight is more likely to be placed on the shoulder, which may increase the likelihood of road damage occurring on the shoulder. If traffic drives on the left side of the road, there is a high possibility that the left wheels of vehicle M will pass over the damaged area. Therefore, by determining the presence or absence of road damage based on the comparison result between the measured value of the feature amount of the left wheel and a threshold, it is possible to expect appropriate detection of road damage. The feature amount of the left wheel is not particularly limited and may be determined appropriately depending on the embodiment. In one example, at least one of the feature amounts of wheel speed fluctuation described above may be adopted as the feature amount of the left wheel. The feature amount of the left wheel may be composed of an overall maximum value of at least one of the left front wheel and the left rear wheel, an integrated value of the overall maximum values ​​of the left wheel, an average value of the maximum values ​​of at least one of the left front wheel and the left rear wheel, an average value of the maximum averages of the left wheel, an overall average value of the left wheel, or a combination thereof.

[0029] The integration may include, for example, summing, averaging, etc. The summing may be a simple summing, a weighted summing, etc. The averaging may be a simple average, a weighted average, etc. When a weighted summing or weighted average is used, each weight may be set arbitrarily. The integrated value of the overall maximum value of the left wheel may be calculated by integrating the overall maximum values ​​of the left front wheel and the left rear wheel. Similarly, the integrated value of the overall maximum values ​​of two or more wheels that meet the conditions may be calculated for the right wheel, driving wheel, driven wheel, etc.

[0030] (4th judgment condition) In one example, the fourth determination condition for determining whether or not road surface damage exists may be defined based on a measurement value of a feature amount of at least one of the front and rear right wheels. That is, when the fourth determination condition is adopted, the server 1 may acquire a measurement value of a feature amount related to road surface damage of at least one of the front and rear right wheels of one or more vehicles M passing through a target area during a target period. The presence or absence of road surface damage in a target area during a target period may be determined based on the acquired measurement value of the feature amount of the right wheel. In one example, the server 1 may compare the acquired measurement value of the feature amount of the right wheel with a threshold. The server 1 may determine the presence or absence of road surface damage in a target area during a target period based on the result of comparing the measurement value of the feature amount of the right wheel with the threshold. For example, if the feature amount of the right wheel is defined such that the measurement value increases as road surface damage is formed, the fourth determination condition may be defined to determine the presence or absence of road surface damage based on whether the measurement value of the feature amount of the right wheel is greater than a predetermined threshold. The predetermined threshold may be determined appropriately depending on the embodiment. For the above reasons, road surface damage may be more likely to occur on the shoulder side. When driving on the right side of the road, there is a high possibility that the right wheels of the vehicle M will pass through a damaged area on the road. Therefore, by determining whether or not there is road damage based on the comparison result between the measured value of the feature amount of the right wheel and a threshold, it is possible to expect appropriate detection of road damage. The feature amount of the right wheel is not particularly limited and may be determined appropriately depending on the embodiment. In one example, the feature amount of the right wheel may be at least one of the feature amounts of wheel speed fluctuations described above. The feature amount of the right wheel may be composed of the overall maximum value of at least one of the right front wheel and the right rear wheel, the integrated value of the overall maximum values ​​of the right wheel, the average value of the maximum values ​​of at least one of the right front wheel and the right rear wheel, the average value of the maximum averages of the right wheel, the overall average value of the right wheel, or a combination thereof.

[0031] (5th judgment condition) In one example, the fifth determination condition for determining whether or not road surface damage exists may be defined based on a measurement value of a feature quantity related to road surface damage for at least one of the left and right front wheels and the rear wheels. That is, when the fifth determination condition is adopted, the server 1 may acquire a measurement value of a feature quantity related to road surface damage for at least one of the left and right front wheels and the rear wheels of one or more vehicles M passing through a target area during a target period. The server 1 may compare the acquired measurement value of the feature quantity for the front wheels with a first threshold. The server 1 may compare the acquired measurement value of the feature quantity for the rear wheels with a second threshold. The presence or absence of road surface damage in a target area during a target period may be determined based on a result of comparing the measurement value of the front wheels with the first threshold and a result of comparing the measurement value of the rear wheels with the second threshold. For example, if the feature quantities are defined so that the measurement value increases as road surface damage is formed, the fifth determination condition may be defined to determine whether or not road surface damage exists based on whether the measurement value of the feature quantity for the front wheels is greater than the first threshold and whether the measurement value of the feature quantity for the rear wheels is greater than the second threshold. The first threshold and the second threshold may be determined appropriately depending on the embodiment. In one example, the vehicle The vehicle may be configured so that the front wheels are heavier. In this case, the rear wheels may be more affected by road damage than the front wheels. Therefore, if the feature is defined so that the measurement value increases as more road damage is formed, the second threshold may be set to a value greater than the first threshold. By setting the second threshold to a value greater than the first threshold and determining the presence or absence of road damage based on the comparison result between the measurement value of the front wheels and the first threshold and the comparison result between the measurement value of the rear wheels and the second threshold, it is possible to expect appropriate detection of road damage.

[0032] The feature quantities of the front and rear wheels are not particularly limited and may be determined appropriately depending on the embodiment. In one example, at least one of the above-described feature quantities of wheel speed fluctuations may be used as each of the feature quantities of the front and rear wheels. The feature quantity of the front wheels may be composed of the overall maximum value of at least one of the left front wheel and the right front wheel, the integrated value of the overall maximum values ​​of the front wheels, the average value of the maximum averages of at least one of the left front wheel and the right front wheel, the average value of the front wheel maximums, the overall average value of the front wheels, or a combination thereof. The feature quantity of the rear wheels may be composed of the overall maximum value of at least one of the left rear wheel and the right rear wheel, the integrated value of the overall maximum values ​​of the rear wheels, the average value of the maximum averages of at least one of the left rear wheel and the right rear wheel, the average value of the rear wheel maximums, the overall average value of the rear wheels, or a combination thereof.

[0033] (6th judgment condition) In one example, the sixth determination condition for the presence or absence of road surface damage may be defined based on a measurement value of a feature quantity related to road surface damage for at least one of the left and right drive wheels and the driven wheels. That is, when the sixth determination condition is adopted, the server 1 may acquire a measurement value of a feature quantity related to road surface damage for at least one of the left and right drive wheels and the driven wheels of one or more vehicles M passing through a target area during a target period. The server 1 may compare the acquired measurement value of the feature quantity of the drive wheels with a third threshold. The server 1 may compare the acquired measurement value of the feature quantity of the driven wheels with a fourth threshold. The presence or absence of road surface damage in a target area during a target period may be determined based on a result of comparing the measurement value of the drive wheels with the third threshold and a result of comparing the measurement value of the driven wheels with the fourth threshold. For example, if the feature quantities are defined so that the measurement value increases as road surface damage is formed, the sixth determination condition may be defined to determine the presence or absence of road surface damage based on whether the measurement value of the feature quantity of the drive wheels is greater than the third threshold and whether the measurement value of the feature quantity of the driven wheels is greater than the fourth threshold. The drive wheels are configured to receive driving force directly from a power source (engine, motor, etc.). On the other hand, the driven wheels are configured to rotate in response to the movement of the vehicle due to the driving force of the drive wheels, without receiving driving force from the power source. The third and fourth thresholds may be determined appropriately depending on the embodiment. In one example, since the drive wheels receive driving force from the power source, the amount of fluctuation of the drive wheels due to road damage may be smaller than that of the driven wheels. Therefore, if the feature is defined so that the measurement value increases as road damage is formed, the fourth threshold may be set larger than the third threshold. By setting the fourth threshold to a value larger than the third threshold and determining the presence or absence of road damage based on the comparison result between the measurement value of the drive wheels and the third threshold and the comparison result between the measurement value of the driven wheels and the fourth threshold, appropriate detection of road damage can be expected.

[0034] The drive wheels and driven wheels may be either the front wheels or the rear wheels of the vehicle M, respectively. In one example, the drive wheels may be the front wheels and the driven wheels may be the rear wheels. In this case, the sixth judgment condition may be treated similarly to the fifth judgment condition. In another example, the drive wheels may be the rear wheels and the driven wheels may be the front wheels. The feature quantities of the drive wheels and the driven wheels are not particularly limited and may be determined appropriately depending on the embodiment. In one example, at least one of the feature quantities of the wheel speed fluctuations described above may be adopted as the feature quantities of the drive wheels and the driven wheels, respectively. The feature quantity of the drive wheels may be composed of an overall maximum value of at least one of the left drive wheel and the right drive wheel, an integrated value of the overall maximum values ​​of the drive wheels, an average value of the maximum values ​​of at least one of the left drive wheel and the right drive wheel, an average value of the maximum averages of the drive wheels, an overall average value of the drive wheels, or a combination thereof. The feature quantities of the driven wheels include the overall maximum value of at least one of the left and right driven wheels, the integrated value of the overall maximum values ​​of the driven wheels, the average value of the maximum values ​​of at least one of the left and right driven wheels, the maximum value of the driven wheels, and the average value of the maximum values ​​of the driven wheels. The average value may be the average of the largest values, the average of the largest average of the driven wheels, the overall average value of the driven wheels, or a combination thereof.

[0035] (summary) In one example, one or more judgment conditions may be selected from the first to sixth judgment conditions. If all of the selected one or more judgment conditions are met, it may be determined that road damage exists. If at least one of the one or more judgment conditions is not met, it may be determined that no road damage exists. The judgment conditions used to detect road damage may be selected by any method. The judgment conditions to be used may be selected by the user or may be defined within the program.

[0036] In one example, the above-mentioned first judgment condition may be used to detect road surface damage. In response to this, the server 1 may judge the presence or absence of road surface damage in the target area during the target period based on the difference between the maximum and minimum values ​​among the calculated overall maximum values. For the reasons described above, according to one example of the present embodiment, by using the difference between the maximum and minimum values ​​among the overall maximum values ​​as a judgment index, it is expected that the accuracy of detecting road surface damage can be improved.

[0037] In addition, in one example, in addition to the first judgment condition, a third judgment condition may be further used to detect road damage. Accordingly, determining the presence or absence of road damage in the target area during the target period may be configured by determining the presence or absence of road damage in the target area during the target period based on the difference between the maximum and minimum values ​​among the calculated overall maximum values ​​and the measurement value of the feature amount of the left wheel. For the reasons described above, according to one example of the present embodiment, using the measurement value of the feature amount of the left wheel as a judgment index can be expected to improve the accuracy of detecting road damage.

[0038] In addition, in one example, in addition to the first judgment condition, a fourth judgment condition may be further used to detect road damage. Accordingly, determining the presence or absence of road damage in the target area during the target period may be configured by determining the presence or absence of road damage in the target area during the target period based on the difference between the maximum and minimum values ​​among the calculated overall maximum values ​​and the measurement value of the feature amount of the right wheel. For the reasons described above, according to one example of the present embodiment, using the measurement value of the feature amount of the right wheel as a judgment index can be expected to improve the accuracy of detecting road damage.

[0039] In addition, in one example, in addition to the first judgment condition, a fifth judgment condition may be further used to detect road damage. Accordingly, determining the presence or absence of road damage in the target area during the target period may be configured to determine the presence or absence of road damage in the target area during the target period based on the difference between the maximum and minimum values ​​among the calculated overall maximum values, the result of comparing the measurement value of the front wheels with a first threshold value, and the result of comparing the measurement value of the rear wheels with a second threshold value. For the reasons described above, according to one example of the present embodiment, using the measurement values ​​of the feature quantities of the front and rear wheels as judgment indices can be expected to improve the accuracy of detecting road damage.

[0040] In addition, in one example, in addition to the first judgment condition, a sixth judgment condition may be further used to detect road surface damage. Accordingly, determining the presence or absence of road surface damage in the target area during the target period may be configured to determine the presence or absence of road surface damage in the target area during the target period based on the difference between the maximum and minimum values ​​among the calculated overall maximum values, the result of comparing the measurement value of the drive wheel with a third threshold, and the result of comparing the measurement value of the driven wheel with a fourth threshold. For the reasons described above, according to one example of the present embodiment, by using the measurement values ​​of the feature quantities of the drive wheels and the driven wheels as judgment indices, it is expected that the accuracy of detecting road surface damage can be improved.

[0041] [2 Configuration Examples] 4 schematically illustrates an example of the 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.

[0042] 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. 5 described below) relating to the detection of road surface damage. The program 81 includes a series of instructions for the information processing.

[0043] 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.).

[0044] 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.

[0045] 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.

[0046] 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.

[0047] [3 Example of operation] FIG. 5 shows an example of a processing procedure for determining the presence or absence of road surface damage 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 by means of 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.

[0048] (Step S101) In step S101, the control unit 11 detects a plurality of vehicles M in a target area during a target period. The control unit 11 acquires wheel speed data WD collected from the target area. The target period and target area may be specified as appropriate. In one example, the wheel speed data WD may include time series data of wheel speed fluctuations during the time when the vehicle M passes through the target area during the target period. The wheel speed data WD may be acquired for each wheel of each vehicle M. After acquiring the wheel speed data WD, the control unit 11 proceeds to step S102.

[0049] (Steps S102 and S103) In step S102, the control unit 11 calculates a feature value IWF of wheel speed fluctuations for each vehicle from the acquired wheel speed data WD. The feature value IWF may be selected as appropriate. For example, the feature value IWF may be the maximum value, average value, etc. of the wheel speed data WD.

[0050] In step S103, the control unit 11 calculates a feature quantity WWF of wheel speed fluctuations for the entire vehicle for each wheel from the calculated feature quantity IWF. For example, the feature quantity WWF may be a maximum value, an average value, a median value, etc. of the feature quantity IWF. In one example, the control unit 11 may calculate a feature quantity for at least one of the above-mentioned first to sixth judgment conditions. After calculating the feature quantity WWF, the control unit 11 proceeds to the next step S104.

[0051] (Step S104) In step S104, the control unit 11 determines whether or not there is road damage according to the calculated feature quantity WWF. In one example, the control unit 11 may determine whether or not there is road damage according to at least one of the first to sixth determination conditions. For example, the control unit 11 may determine whether or not there is road damage according to the first determination condition. Furthermore, for example, the control unit 11 may determine whether or not there is road damage according to the first and third determination conditions. Furthermore, for example, the control unit 11 may determine whether or not there is road damage according to the first and fourth determination conditions. Furthermore, for example, the control unit 11 may determine whether or not there is road damage according to the first and fifth determination conditions. Furthermore, for example, the control unit 11 may determine whether or not there is road damage according to the first and sixth determination conditions. After determining whether or not there is road damage, the control unit 11 proceeds to step S105.

[0052] (Step S105) In step S105, the control unit 11 outputs the determination result of whether or not road surface damage exists. The content of the information to be output and the output destination may be selected appropriately depending on the embodiment. In one example, the control unit 11 may output the determination result of whether or not road surface damage exists as is. In another example, the control unit 11 may perform any information processing depending on the determination result. The control unit 11 may output the result of the information processing as information related to the determination result. The output of the result of the information processing may include, for example, outputting a specific message depending on the determination result, or controlling the operation of the controlled device depending on the determination result. In one example, the control unit 11 may notify a database that stores map information of the location where road surface damage was detected, thereby updating the information stored in the database. In another example, when road surface damage is detected, the control unit 11 may notify an administrator of the location where the road surface damage was detected, thereby prompting the administrator to repair the road surface damage. In yet another example, the controlled device may be an autonomous vehicle configured to repair road surfaces. When road damage is detected, the control unit 11 may output a command to the autonomous vehicle to move to the location of the detected road damage and repair the road damage. The output destination may be, for example, RAM, the storage unit 12, the output device 15, another computer, a controlled device, etc.

[0053] [Features] In one example of this embodiment, in step S103, the overall maximum value for each wheel may be calculated. In step S104, the presence or absence of road surface damage in the target area for the target period may be determined based on the difference between the maximum and minimum values ​​of the overall maximum values ​​for each wheel. This is expected to improve the accuracy of detecting road surface damage for the reasons described above.

[0054] [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.

[0055] 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.

[0056] 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.

[0057] Furthermore, each of the above feature quantities is basically defined so that its measurement value increases as road surface damage increases. However, the definition of the feature quantity is not limited to this example. In another example, the feature quantity may be defined so that its measurement value decreases as road surface damage increases. In this case, each of the above judgment conditions may be defined in an inverse manner. For example, when the first judgment condition is adopted, the presence or absence of road surface damage may be determined depending on whether the reciprocal of the difference between the maximum value and the minimum value among the overall maximum values ​​for each wheel is smaller than a threshold value. When the fifth judgment condition is adopted, the second threshold value may be set smaller than the first threshold value. When the sixth judgment condition is adopted, the fourth threshold value may be set smaller than the third threshold value.

[0058] Furthermore, in the above embodiment, a four-wheel 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, or five or more. The front wheels, rear wheels, left wheel, right wheel, drive wheels, and driven wheels may each be determined appropriately depending on the wheel configuration of the vehicle M, and accordingly, the above first to sixth determination conditions may be employed to determine the presence or absence of road surface damage even in cases other than four-wheel vehicles. [Explanation of symbols]

[0059] 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 Obtaining a maximum value of wheel speed fluctuation for each wheel of one or more vehicles passing through the target area during the target period; extracting, for each wheel, a maximum value of the wheel speed fluctuation across all vehicles as an overall maximum value from the acquired maximum values ​​of the wheel speed fluctuation for each of the one or more vehicles; calculating a difference between a maximum value and a minimum value among the extracted overall maximum values ​​for each wheel; Determining whether or not there is road surface damage in the target area during the target period according to the difference between the calculated maximum value and the calculated minimum value; and outputting the determination result of the presence or absence of road surface damage; To execute Information processing device.

2. the control unit is further configured to acquire, during the target period, a measurement value of a feature amount related to road surface damage of at least one of a front and rear left wheel of the one or more vehicles passing through the target area; determining whether or not there is road surface damage in the target area during the target period comprises determining whether or not there is road surface damage in the target area during the target period based on the difference between the calculated maximum value and the minimum value and the acquired measurement value of the feature amount of the left wheel. The information processing device according to claim 1 .

3. the control unit is further configured to acquire, during the target period, a measurement value of a feature amount related to road surface damage of at least one of a front and rear right wheel of the one or more vehicles passing through the target area; determining whether or not there is road surface damage in the target area during the target period comprises determining whether or not there is road surface damage in the target area during the target period based on the difference between the calculated maximum value and the minimum value and the acquired measurement value of the feature amount of the right wheel. The information processing device according to claim 1 .

4. The control unit acquiring, during the target period, measurement values ​​of feature quantities related to road surface damage for at least one of the front and rear wheels of the one or more vehicles passing through the target area; comparing the acquired measurement value of the feature amount of the front wheel with a first threshold value; and comparing the acquired measurement value of the rear wheel feature amount with a second threshold value; and further configured to perform The second threshold is set to be larger than the first threshold, Determining whether or not there is road surface damage in the target area during the target period comprises determining whether or not there is road surface damage in the target area during the target period based on a difference between the calculated maximum value and minimum value, a result of comparing the measurement value of the front wheels with a first threshold value, and a result of comparing the measurement value of the rear wheels with a second threshold value. The information processing device according to claim 1 .

5. The control unit acquiring, during the target period, measurement values ​​of feature quantities related to road surface damage for at least one of the left and right driving wheels and the driven wheels of the one or more vehicles passing through the target area; comparing the acquired measurement value of the characteristic amount of the drive wheel with a third threshold value; and comparing the acquired measurement value of the characteristic amount of the driven wheel with a fourth threshold value; and further configured to perform the fourth threshold is set to be greater than the third threshold, Determining whether or not there is road surface damage in the target area during the target period comprises determining whether or not there is road surface damage in the target area during the target period based on the difference between the calculated maximum value and minimum value, a result of comparing the measurement value of the drive wheel with a third threshold value, and a result of comparing the measurement value of the driven wheel with a fourth threshold value. The information processing device according to claim 1 .

Images