Tire remaining life prediction system, tire remaining life prediction method, program

Abstract

To provide a tire remaining life prediction system, a tire remaining life prediction method, and a program capable of predicting the remaining life of a tire experiencing uneven wear. [Solution] The tire determination system 100 includes a depth camera 4 that takes images of a tire mounted on a vehicle 5, a determination processing unit that determines whether or not uneven wear is occurring in the tire based on the images taken by the depth camera 4, a first acquisition processing unit that acquires transition information showing the transition of the remaining tread depth of the tire, and a prediction processing unit that, when the determination processing unit determines that uneven wear is occurring, predicts the remaining lifespan of the tire based on the degree of uneven wear determined based on the images and the transition information acquired by the first acquisition processing unit.

Description

Technical Field

[0001] The present disclosure relates to a tire remaining life prediction system, a tire remaining life prediction method, and a program.

Background Art

[0002] There is known a system that determines the progress of uneven wear in a tire based on a captured image of the tire mounted on a vehicle (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Uneven wear of a tire can be eliminated by rotating the tire in a vehicle on which the tire is mounted. Here, if the remaining life of a tire with uneven wear can be notified to the user of the vehicle, it is possible to prompt the user to rotate the tire. However, conventionally, there has been no configuration for predicting the remaining life of a tire with uneven wear. Therefore, it has not been possible to notify the user of the vehicle of the remaining life of a tire with uneven wear.

[0005] An object of the present disclosure is to provide a tire remaining life prediction system, a tire remaining life prediction method, and a program capable of predicting the remaining life of a tire with uneven wear.

Means for Solving the Problems

[0006] A tire remaining life prediction system according to one aspect of the present disclosure comprises a camera, a determination processing unit, a first acquisition processing unit, and a prediction processing unit. The camera captures images of a tire mounted on a vehicle. The determination processing unit determines whether or not uneven wear is occurring on the tire based on the captured images taken by the camera. The first acquisition processing unit acquires transition information showing the progression of the remaining tread depth of the tire. If the determination processing unit determines that uneven wear is occurring, the prediction processing unit predicts the remaining life of the tire based on the degree of uneven wear determined based on the captured images and the transition information acquired by the first acquisition processing unit.

[0007] This tire remaining life prediction system can predict the remaining life of tires experiencing uneven wear. [Effects of the Invention]

[0008] According to this disclosure, it is possible to predict the remaining lifespan of a tire experiencing uneven wear. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 shows the configuration of a tire determination system according to an embodiment of this disclosure. [Figure 2] Figure 2 shows the configuration of a portable terminal for a tire detection system according to an embodiment of this disclosure. [Figure 3] Figure 3 shows the configuration of the server for the tire determination system according to the present disclosure. [Figure 4] Figure 4 is a flowchart showing an example of the tire determination process performed by the tire determination system according to the embodiment of this disclosure. [Figure 5] Figure 5 shows an example of the first inspection result screen displayed in the tire inspection system according to the present disclosure. [Figure 6] Figure 6 shows an example of how tire tread depth changes over time. [Figure 7] Figure 7 shows an example of how tire tread depth changes over time. [Figure 8] FIG. 8 is a diagram showing an example of the transition of the remaining groove of the tire. [Figure 9] FIG. 9 is a diagram showing an example of the transition of the remaining groove of the tire. [Figure 10] FIG. 10 is a diagram showing an example of the transition of the remaining groove of the tire. [Figure 11] FIG. 11 is a diagram showing an example of the transition of the remaining groove of the tire. [Figure 12] FIG. 12 is a diagram showing the configuration of the server of the tire determination system according to another embodiment of the present disclosure. [Figure 13] FIG. 13 is a diagram showing an example of the second inspection result screen displayed on the tire determination system according to another embodiment of the present disclosure.

Mode for Carrying Out the Invention

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. Note that the following embodiments are an example of embodying the present disclosure and do not limit the technical scope of the present disclosure.

[0011] [Configuration of Tire Determination System 100] First, the configuration of the tire determination system 100 according to the embodiment of the present disclosure will be described with reference to FIG. 1.

[0012] The tire determination system 100 determines the wear condition of the tires of the vehicle 5 (see FIG. 1).

[0013] Specifically, the vehicle 5 is a truck used for the transportation business.

[0014] Also, the tire is a pneumatic tire.

[0015] Note that the vehicle 5 is not limited to a truck, and any moving body that travels using the tire may be used. For example, the vehicle 5 may be a passenger car, a bus, a motorcycle, a three-wheeled passenger vehicle, a motorized bicycle, or a bicycle. Further, the vehicle 5 may be a freight vehicle such as a trailer or a cart.

[0016] Further, the tire is not limited to a pneumatic tire, and may be a non-pneumatic tire such as a so-called solid tire. Further, the tire may be a resin tire using a thermoplastic elastomer.

[0017] As shown in FIG. 1, the tire determination system 100 includes a server 1, a mobile terminal 2, and a wear detection device 3.

[0018] In the tire determination system 100, the server 1 and the mobile terminal 2 are communicably connected to each other by a communication network such as the Internet or a LAN (Local Area Network).

[0019] Further, in the tire determination system 100, the wear detection device 3 can perform wireless communication with the mobile terminal 2 according to a wireless communication standard such as Bluetooth (registered trademark) or IEEE802.11ac wireless LAN (Wi-Fi). For example, between the wear detection device 3 and the mobile terminal 2, pairing, which is an authentication procedure for communication partners in Bluetooth communication, is performed in advance. For example, the wear detection device 3 performs wireless communication by Bluetooth with the mobile terminal 2 existing within a range of about 10 m at a radio wave intensity (2.5 mW) called Class 2.

[0020] The wear detection device 3 detects the wear condition of the tire. The wear detection device 3 is carried to the work site of the tire inspection work by the operator who performs the inspection work of the tire. The tire inspection work is carried out at the business office of a transportation company that conducts transportation business using the vehicle 5.

[0021] As shown in Figure 1, the wear detection device 3 includes a depth camera 4. The wear detection device 3 also includes a communication unit that performs wireless communication with the mobile terminal 2 via Bluetooth or Wi-Fi. Furthermore, the wear detection device 3 includes a control unit that comprehensively controls the device itself.

[0022] The depth camera 4 is capable of detecting the distance to a subject. The depth camera 4 is also called a three-dimensional camera. For example, the depth camera 4 images a subject using a stereo camera system. The depth camera 4 is used to image the tires mounted on the vehicle 5. By imaging the tread portion of the tire with the depth camera 4, it is possible to measure the depth (remaining tread) of the shallowest groove among the multiple grooves contained in the tread portion. The depth camera 4 may also image the subject using a system other than the stereo camera system, such as the ToF (Time of Flight) system.

[0023] For example, when the wear detection device 3 receives an imaging instruction from the mobile terminal 2, it uses the depth camera 4 to image the subject (the tire). The wear detection device 3 then transmits the image data showing the imaged subject (the tire) to the mobile terminal 2.

[0024] The wear detection device 3 may also include an operating unit used for inputting the imaging instruction. In this case, the wear detection device 3 may perform imaging processing using the depth camera 4 and transmission processing of the captured image data in response to user operations on the operating unit.

[0025] [Configuration of Mobile Device 2] Next, we will explain the configuration of mobile terminal 2 with reference to Figure 2.

[0026] Mobile terminal 2 is a portable terminal device that functions as the user interface for the tire detection system 100.

[0027] Specifically, mobile device 2 is a smartphone owned by the worker.

[0028] Furthermore, the mobile terminal 2 is not limited to a smartphone, but may also be a tablet or a laptop computer. Also, the mobile terminal 2 is not limited to a general-purpose terminal device, but may also be a dedicated terminal device specifically designed for the user interface functions of the tire judgment system 100.

[0029] As shown in Figure 2, the mobile terminal 2 comprises a control unit 11, an operation display unit 12, a communication unit 13, a storage unit 14, and a camera 15.

[0030] The control unit 11 comprehensively controls the mobile terminal 2. As shown in Figure 2, the control unit 11 comprises a CPU 21, ROM 22, and RAM 23. The CPU 21 is a processor that performs various arithmetic operations. The ROM 22 is a non-volatile memory device in which information such as control programs for causing the CPU 21 to perform various operations is pre-stored. The RAM 23 is a volatile or non-volatile memory device used as temporary storage memory (work area) for the various operations performed by the CPU 21. The CPU 21 executes the various control programs pre-stored in the ROM 22. In this way, the CPU 21 comprehensively controls the mobile terminal 2.

[0031] The operation display unit 12 is the user interface of the mobile terminal 2. The operation display unit 12 comprises a display unit and an operation unit. The display unit displays various information in response to control instructions from the control unit 11. For example, the display unit is a flat panel display such as a liquid crystal display. The operation unit inputs various information to the control unit 11 in response to user operations. For example, the operation unit includes operation keys and a touch panel.

[0032] The communication unit 13 is a communication interface capable of performing wireless data communication with external devices. Specifically, the communication unit 13 performs data communication with the server 1 via the communication network. The communication unit 13 also performs wireless communication with the wear detection device 3 using Bluetooth.

[0033] The memory unit 14 is a non-volatile memory device. For example, the memory unit 14 is a non-volatile memory such as flash memory.

[0034] The memory unit 14 has worker information about the worker pre-stored. For example, the worker information includes information indicating the name of the company to which the worker belongs, information indicating the name of the sales office to which the worker belongs, and information indicating the worker's name. The worker information also includes information indicating the number of sales offices of the transportation company visited by the worker during a predetermined aggregation period, and information indicating the number of vehicles 5 inspected by the worker during the aggregation period.

[0035] Camera 15 is a two-dimensional camera that captures images of the subject.

[0036] [Server 1 Configuration] Next, we will explain the configuration of Server 1, referring to Figure 3.

[0037] Server 1 is an information processing device that implements the main functions of the tire detection system 100.

[0038] As shown in Figure 3, Server 1 comprises a control unit 31, an operation display unit 32, a communication unit 33, and a storage unit 34.

[0039] The control unit 31 comprehensively controls the server 1. As shown in Figure 3, the control unit 31 comprises a CPU 41, a ROM 42, and a RAM 43. The CPU 41 is a processor that performs various arithmetic operations. The ROM 42 is a non-volatile memory device in which information such as control programs for instructing the CPU 41 to perform various operations is pre-stored. The RAM 43 is a volatile or non-volatile memory device used as temporary storage memory (work area) for the various operations performed by the CPU 41. The CPU 41 executes the various control programs pre-stored in the ROM 42. In this way, the CPU 41 comprehensively controls the server 1.

[0040] The operation display unit 32 is the user interface of the server 1. The operation display unit 32 comprises a display unit and an operation unit. The display unit displays various information in response to control instructions from the control unit 31. For example, the display unit is a flat panel display such as a liquid crystal display. The operation unit inputs various information to the control unit 31 in response to user operations. For example, the operation unit includes a keyboard, mouse, and touch panel.

[0041] The communication unit 33 is a communication interface capable of performing wireless data communication with an external device. Specifically, the communication unit 33 performs data communication with the mobile terminal 2 via the communication network.

[0042] The memory unit 34 is a non-volatile storage device. For example, the memory unit 34 is a storage device such as a non-volatile memory like flash memory, an SSD (solid state drive), or an HDD (hard disk drive).

[0043] As shown in Figure 3, the server 1 stores the database 61.

[0044] The database 61 contains pre-registered vehicle data for vehicle 5. For example, the vehicle data consists of vehicle ID, registration number information, vehicle name information, vehicle dimensions information, axle configuration information, double tire information, spare tire information, operator information, and business office information.

[0045] The aforementioned vehicle ID is identification information for vehicle 5 assigned by server 1. The aforementioned registration number information is information indicating the vehicle registration number displayed on the license plate of vehicle 5. The aforementioned vehicle name information is information indicating the product name of vehicle 5. The aforementioned vehicle dimension information is information indicating the dimensions of vehicle 5.

[0046] The axle configuration information is information indicating the configuration of the axles of the vehicle 5. For example, the axle configuration includes a first axle configuration consisting of a front axle and a rear axle. The axle configuration also includes a second axle configuration consisting of a first front axle, a second front axle located behind the first front axle, and a rear axle. The axle configuration also includes a third axle configuration consisting of a front axle, a first rear axle, and a second rear axle located behind the first rear axle. The axle configuration also includes a fourth axle configuration consisting of a first front axle, a second front axle, a first rear axle, and a second rear axle.

[0047] The double tire information indicates whether or not the rear wheels of vehicle 5 are double tires. The spare tire information indicates whether or not vehicle 5 has a spare tire. The business operator information indicates the name of the transportation business operator. The office information indicates the name of the office of the transportation business operator where vehicle 5 is parked.

[0048] When a user of the tire determination system 100 inputs information from among the multiple pieces of information constituting the vehicle data, excluding the vehicle ID, Server 1 generates the vehicle ID corresponding to the input information. Then, Server 1 registers the generated vehicle ID and the information input by the user as new vehicle data in the database 61.

[0049] Furthermore, the database 61 has tire data related to the tire pre-registered. For example, the tire data consists of a tire ID, the vehicle ID of the vehicle 5 on which the tire is mounted, tire name information, tire dimensions information, tread pattern information, usage information, retreading information, and mounting position information.

[0050] The tire ID is identification information of the tire assigned by server 1. The tire name information is information indicating the product name of the tire. The tire dimension information is information indicating the dimensions of the tire. The tread pattern information is information used to identify the tread pattern of the tire. The usage information indicates whether the tire is a summer tire, a winter tire, or an all-season tire. The retreading information indicates whether the tire is a new tire or a retreaded tire.

[0051] The mounting position information indicates which of the multiple tire mounting positions on the vehicle 5 the tire is mounted at. The multiple tire mounting positions on the vehicle 5 are determined by a combination of the axle configuration on the vehicle 5, whether or not the rear wheels are double tires, and whether or not there is a spare tire.

[0052] For example, suppose there is a vehicle 5 in which the axle configuration is the first axle configuration, the rear wheels are double tires, and the vehicle is equipped with a spare tire. The multiple tire mounting positions in this vehicle 5 are: (1) the left side of the front axle, (2) the right side of the front axle, (3) the left outer position of the rear axle, (4) the left inner position of the rear axle, (5) the right inner position of the rear axle, (6) the right outer position of the rear axle, and (7) the spare tire mounting position.

[0053] Furthermore, suppose there is a vehicle 5 in which the axle configuration is the fourth axle configuration, the rear wheels are double tires, and the vehicle is equipped with a spare tire. The multiple tire mounting positions in this vehicle 5 are: (1) left side of the first front axle, (2) right side of the first front axle, (3) left side of the second front axle, (4) right side of the second front axle, (5) left outer side of the first rear axle, (6) left inner side of the first rear axle, (7) right inner side of the first rear axle, (8) right outer side of the first rear axle, (9) left outer side of the second rear axle, (10) left inner side of the second rear axle, (11) right inner side of the second rear axle, (12) right outer side of the second rear axle, and (13) spare tire mounting position.

[0054] When a user of the tire determination system 100 inputs information from among the multiple pieces of information constituting the tire data, excluding the tire ID, the server 1 generates the tire ID corresponding to the input information. The server 1 then registers the generated tire ID and the information input by the user as new tire data in the database 61.

[0055] Furthermore, the database 61 registers tire inspection data related to the tire inspection work. For example, the tire inspection data consists of a tire inspection ID, the tire ID of the tire being inspected, inspection date information, inspection location information, inspector information, mileage information, air pressure information, tread depth information, image data captured by the depth camera 4, and judgment result information. The tire inspection ID is identification information for the tire inspection work assigned by the server 1. The inspection date information indicates the date on which the tire inspection work was performed. The inspection location information indicates the location on which the tire inspection work was performed. The inspector information indicates the name of the worker performing the tire inspection work. The mileage information indicates the mileage of the vehicle 5 equipped with the tire being inspected at the time of the tire inspection work. The air pressure information indicates the detection results of the tire's air pressure and air temperature. The tread depth information indicates the measurement results of the tire's tread depth. The judgment result information indicates the judgment result based on the data obtained from the tire inspection work. Specifically, the judgment result information includes information indicating whether or not the tire is unevenly worn. Furthermore, the judgment result information includes information indicating whether or not tire rotation is necessary. Furthermore, the judgment result information includes information indicating whether or not the tire needs to be replaced.

[0056] In the tire detection system 100, the mileage information, air pressure information, tread depth information, captured image data, and detection result information are acquired by executing the tire detection process (see Figure 4) described later. When the tire detection process described later is executed, the server 1 generates tire inspection data including the information acquired by the process. The server 1 then registers the generated tire inspection data in the database 61.

[0057] Incidentally, a system is known that determines the degree of uneven wear on a tire based on an image of the tire mounted on the vehicle 5.

[0058] The uneven wear of the aforementioned tire can be resolved by rotating the tire on the vehicle 5 to which the tire is mounted. If the remaining lifespan of the tire experiencing uneven wear could be notified to the user of vehicle 5, it would be possible to encourage the user to rotate the tire. However, conventionally, there has been no configuration to predict the remaining lifespan of the tire experiencing uneven wear. Therefore, it has not been possible to notify the user of vehicle 5 of the remaining lifespan of the tire experiencing uneven wear.

[0059] In contrast, the tire determination system 100 according to the embodiment of this disclosure can predict the remaining lifespan of the tire that is experiencing uneven wear, as described below.

[0060] Specifically, the storage unit 34 of server 1 pre-stores a tire determination program that causes the control unit 31 to function as the first identification processing unit 51, first acquisition processing unit 52, second identification processing unit 53, imaging processing unit 54, determination processing unit 55, second acquisition processing unit 56, first prediction processing unit 57, second prediction processing unit 58, and first display processing unit 59, as shown in Figure 3. The CPU 41 of the control unit 31 functions as each of the above-mentioned processing units by executing the tire determination program.

[0061] Furthermore, some or all of the processing units included in the control unit 31 may be composed of electronic circuits. Also, the tire determination program may be a program that causes multiple processors to function as the processing units shown in Figure 3.

[0062] The first identification processing unit 51 identifies the vehicle 5 to be inspected in response to the operator's operation on the mobile terminal 2.

[0063] Specifically, the first identification processing unit 51 displays a vehicle registration number input screen on the operation display unit 12 of the mobile terminal 2, which is used to input the vehicle registration number of the vehicle 5 to be inspected. The first identification processing unit 51 then identifies the vehicle 5 that corresponds to the vehicle registration number entered on the vehicle registration number input screen from among the vehicles 5 registered in the database 61 as the vehicle 5 to be inspected.

[0064] The first identification processing unit 51 may also identify the vehicle 5 to be inspected based on the image of the vehicle's license plate taken by the camera 15 of the mobile terminal 2.

[0065] The first data acquisition processing unit 52 acquires the mileage of the vehicle 5 to be inspected, as well as the air pressure and air temperature of each of the tires of the vehicle 5.

[0066] For example, each of the tires of the vehicle 5 is equipped with a tire pressure detection device for detecting the air pressure of the tire. The tire pressure detection device is equipped with a sensor for periodically detecting the temperature and air pressure of the tire. The tire pressure detection device is also equipped with a communication unit for performing wireless communication with an external device. Furthermore, the tire pressure detection device is equipped with a control unit for comprehensively controlling the device itself.

[0067] Furthermore, the vehicle 5 is equipped with an on-board device for managing various information related to the vehicle 5. The on-board device includes a communication unit that performs wireless communication with the server 1 and external devices including the tire pressure detection device. The on-board device also includes a storage unit that stores information indicating the cumulative mileage measured in the vehicle 5, and information indicating the detection results of the tire pressure and air temperature transmitted from the tire pressure detection device. The on-board device also includes a control unit that comprehensively controls itself.

[0068] The first acquisition processing unit 52 performs wireless communication with the on-board device of the vehicle 5 to be inspected and acquires information indicating the cumulative mileage of the vehicle 5 stored in the storage unit of the on-board device, as well as information indicating the detection results of the air pressure and air temperature of each of the tires.

[0069] The first acquisition processing unit 52 may also display a mileage input screen on the operation display unit 12 of the mobile terminal 2, which is used to input the mileage of the vehicle 5 to be inspected, as well as the air pressure and air temperature of each of the tires of the vehicle 5. In this case, the first acquisition processing unit 52 only needs to acquire the information entered on the mileage input screen as the mileage of the vehicle 5 to be inspected, as well as the air pressure and air temperature of each of the tires of the vehicle 5.

[0070] The second identification processing unit 53 identifies the tire to be inspected in response to the operator's operation on the mobile terminal 2.

[0071] Specifically, the second identification processing unit 53 displays a mounting position selection screen on the operation display unit 12 of the mobile terminal 2, which is used to select one of the multiple tire mounting positions on the vehicle 5 to be inspected. The second identification processing unit 53 then identifies the tire to be inspected from among the tires registered in the database 61 that corresponds to the combination of the vehicle 5 to be inspected and the tire mounting position selected on the mounting position selection screen.

[0072] The imaging processing unit 54 uses the depth camera 4 to image the tire to be inspected.

[0073] Specifically, the imaging processing unit 54 displays an imaging operation reception screen used for imaging the tire to be inspected on the operation display unit 12 of the mobile terminal 2. When the imaging operation is received on the imaging operation reception screen, the imaging processing unit 54 sends the imaging instruction to the mobile terminal 2. The imaging processing unit 54 also acquires the image data transmitted from the wear detection device 3 to the mobile terminal 2 in response to the transmission of the imaging instruction as the image data corresponding to the tire to be inspected.

[0074] The determination processing unit 55 determines whether or not uneven wear has occurred in the tire being inspected, based on the image data captured by the depth camera 4. The determination processing unit 55 also determines the degree of uneven wear in the tire being inspected, based on the image data captured by the depth camera 4.

[0075] The tire inspection system 100 determines whether or not uneven wear, also known as "unilateral wear," occurs, where wear progresses more rapidly on one end of the tire in the axle direction than on the other. For example, the tire inspection system 100 determines that uneven wear (unilateral wear) has occurred if the difference in the amount of wear between the two ends of the tire in the axle direction exceeds a predetermined threshold.

[0076] Furthermore, the tire judgment system 100 evaluates the degree of uneven wear in a tire in five stages, from the first stage (lowest) to the fifth stage (highest). The degree of uneven wear increases as the difference in wear between the axle-axis ends of the tire increases. Note that the degree of uneven wear can be evaluated in any number of stages.

[0077] Specifically, the determination processing unit 55 uses a determination model 63 (see Figure 3) learned based on the first training data 62 (see Figure 3) to determine whether or not uneven wear has occurred in the tire being inspected and the degree to which the uneven wear has progressed.

[0078] As shown in Figure 3, the first training data 62 and the judgment model 63 are stored in the storage unit 34.

[0079] The judgment model 63 is a trained model that has learned the relationship between the captured image data of the tire and the presence or absence of uneven wear and the degree of uneven wear in the tire. In response to the input of the captured image data (explanatory variable) of the tire to be inspected, the judgment model 63 outputs information (dependent variable) indicating the presence or absence of uneven wear and the degree of uneven wear in the tire.

[0080] Specifically, the judgment model 63 is a convolutional neural network. However, the judgment model 63 is not limited to a convolutional neural network; any trained model capable of outputting information indicating whether or not uneven wear has occurred and the degree of uneven wear in the tire, in response to the input of the captured image data of the tire to be inspected, is acceptable.

[0081] The judgment model 63 is generated by machine learning performed by the control unit 31 based on the first training data 62 stored in the memory unit 34 and a predetermined algorithm. The judgment model 63 may also be generated or learned by the control unit of an external information processing device. In this case, the judgment model 63 simply needs to be transferred from the external information processing device and stored in the memory unit 34.

[0082] The first training data 62 is a dataset used for generating and training the decision model 63.

[0083] The first training data 62 includes a combination of the image data of the tire and information (ground truth data) indicating whether or not uneven wear occurs and the degree of uneven wear progression.

[0084] Specifically, the first training data 62 includes a combination of the image data of the tire in which uneven wear is not occurring and information indicating that uneven wear is not occurring (ground truth data).

[0085] Furthermore, the first training data 62 includes a combination of the captured image data of the tire where the degree of uneven wear is at the nth stage (1 ≤ n ≤ 5) and information indicating that the degree of uneven wear is at the nth stage (ground truth data).

[0086] The judgment processing unit 55 inputs the captured image data acquired by the imaging processing unit 54 to the judgment model 63. As a result, the judgment model 63 outputs information indicating whether or not uneven wear has occurred in the tire to be inspected and the degree of uneven wear progression. The judgment processing unit 55 acquires the information output from the judgment model 63 as the judgment result.

[0087] The second data acquisition processing unit 56 acquires trend information showing the change in the remaining tread depth of the tire being inspected. The second data acquisition processing unit 56 is an example of the first data acquisition processing unit of this disclosure.

[0088] In the tire inspection system 100, the trend information is a relational expression that shows the relationship between the remaining tread depth of the tire being inspected and the distance traveled by the vehicle 5 that has the tire installed since the tire was installed.

[0089] Specifically, the second acquisition processing unit 56 acquires a relational expression (the aforementioned transitional information) that shows the relationship between the mileage traveled by the vehicle 5 to be inspected since the tire was installed and the remaining tread depth of the tire, from the start of use of the tire to the present. This relational expression can be acquired based on the current mileage of the vehicle 5, the current remaining tread depth of the tire, and past tire inspection data of the tire stored in the database 61. The current mileage of the vehicle 5 is acquired by the first acquisition processing unit 52. The current remaining tread depth of the tire is acquired based on the imaging results of the tire by the imaging processing unit 54.

[0090] The first prediction processing unit 57 predicts the remaining lifespan of the tire based on the degree of uneven wear determined based on the captured image data and the transition information acquired by the second acquisition processing unit 56, when the determination processing unit 55 determines that uneven wear is occurring. The first prediction processing unit 57 is an example of the prediction processing unit of this disclosure.

[0091] Specifically, the first prediction processing unit 57 predicts the remaining lifespan of the tire using a first prediction model 65 (see Figure 3) (an example of a learning model in this disclosure) that has been learned based on second training data 64 (see Figure 3), which includes transition information showing the progression of the remaining tread depth of the tire until the degree of uneven wear reaches one of the stages, information indicating that stage, and transition information showing the progression of the remaining tread depth of the tire after the degree of uneven wear has reached that stage.

[0092] In the tire determination system 100, the remaining lifespan of the tire is the remaining mileage the tire will travel until its lifespan is reached.

[0093] As shown in Figure 3, the second training data 64 and the first prediction model 65 are stored in the storage unit 34.

[0094] The first prediction model 65 is a trained model that has learned the relationship between the transition information showing the progression of the tire's remaining tread depth until the degree of uneven wear reaches one of the stages, and the information indicating that stage, and the transition information showing the progression of the tire's remaining tread depth after the degree of uneven wear has reached that stage. The first prediction model 65 outputs the transition information showing the progression of the tire's remaining tread depth after the degree of uneven wear has reached that stage (dependent variable) in response to the input of the transition information showing the progression of the tire's remaining tread depth until the degree of uneven wear reaches one of the stages, and the information indicating that stage (explanatory variables).

[0095] Specifically, the first prediction model 65 is a neural network. However, the first prediction model 65 is not limited to a neural network; any trained model capable of outputting transition information showing the progression of the tire's remaining tread depth until the degree of uneven wear reaches a certain stage, and transition information showing the progression of the tire's remaining tread depth after the degree of uneven wear reaches a certain stage, in response to input of information indicating that stage.

[0096] The first prediction model 65 is generated by machine learning performed by the control unit 31 based on the second training data 64 stored in the memory unit 34 and a predetermined algorithm. The first prediction model 65 may also be generated or learned by the control unit of an external information processing device. In this case, the first prediction model 65 can be transferred from the external information processing device and stored in the memory unit 34.

[0097] The second training data 64 is the dataset used to generate and train the first prediction model 65.

[0098] The second training data 64 includes transition information showing the progression of the remaining tread depth of the tire until the degree of uneven wear reaches one of the stages, and a combination of information indicating that stage and transition information (correct answer data) showing the progression of the remaining tread depth of the tire after the degree of uneven wear has reached that stage.

[0099] Here, with reference to Figure 6, we will specifically explain the data included in the second training data 64. Figure 6 shows an example of the change in remaining tread depth from the start of use for the tire in question, which experienced uneven wear during use and was used until its lifespan expired without rotation. In Figure 6, the threshold for determining when the tire has reached the end of its lifespan is set to a remaining tread depth of 3 mm (millimeters). Also, "tn(1≦n≦5)" in Figure 6 indicates the timing when the progression of uneven wear has reached the nth stage.

[0100] For example, the second training data 64 includes a combination of the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t1 as shown in Figure 6, and information indicating the first stage, and the transition information (correct answer data) showing the change in the remaining tread depth of the tire from timing t1 as shown in Figure 6 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0101] Furthermore, the second training data 64 includes a combination of the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t2 as shown in Figure 6, and information indicating the second stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t2 as shown in Figure 6 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0102] Furthermore, the second training data 64 includes a combination of the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t3 as shown in Figure 6, and the information indicating the third stage, and the transition information (correct answer data) showing the change in the remaining tread depth of the tire from timing t3 as shown in Figure 6 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0103] Furthermore, the second training data 64 includes a combination of the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t4 as shown in Figure 6, and the information indicating the fourth stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t4 as shown in Figure 6 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0104] Furthermore, the second training data 64 includes the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t5 as shown in Figure 6, and the information indicating the fifth stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t5 as shown in Figure 6 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0105] The first prediction processing unit 57 inputs the determination result of the degree of uneven wear by the judgment processing unit 55 and the transition information acquired by the second acquisition processing unit 56 to the first prediction model 65. As a result, the first prediction model 65 outputs the transition information (prediction result of the transition of remaining tread) that shows the transition of the remaining tread of the tire from the present. Based on the transition information output from the first prediction model 65, the first prediction processing unit 57 calculates the remaining mileage of the tire until its lifespan is reached (the remaining lifespan of the tire).

[0106] When the determination processing unit 55 determines that uneven wear is occurring, the second prediction processing unit 58 predicts the remaining lifespan of the tire when the tire is rotated, based on the degree of uneven wear determined based on the captured image data and the transition information acquired by the second acquisition processing unit 56.

[0107] Specifically, the second prediction processing unit 58 uses a second prediction model 67 (see Figure 3), which has been trained based on third training data 66 (see Figure 3), which includes the transition information showing the transition of the remaining tread depth of the tire until the tire rotation is performed, information showing the degree of uneven wear of the tire at the time the tire rotation is performed, and the transition information showing the transition of the remaining tread depth of the tire after the tire rotation has been performed, to predict the remaining lifespan of the tire when the tire rotation is performed.

[0108] As shown in Figure 3, the third training data 66 and the second prediction model 67 are stored in the storage unit 34.

[0109] The second prediction model 67 is a trained model that has learned the relationship between the transition information showing the change in the remaining tread depth of the tire until the tire rotation is performed, the information showing the degree of uneven wear of the tire at the time the tire rotation is performed, and the transition information showing the change in the remaining tread depth of the tire after the tire rotation is performed. The second prediction model 67 outputs the transition information showing the change in the remaining tread depth of the tire after the tire rotation is performed (dependent variable) in response to the input of the transition information showing the change in the remaining tread depth of the tire until the tire rotation is performed and the information showing the degree of uneven wear of the tire at the time the tire rotation is performed (explanatory variables).

[0110] Specifically, the second prediction model 67 is a neural network. However, the second prediction model 67 is not limited to a neural network; it can be any trained model that, in response to input of transition information showing the change in the remaining tread depth of the tire until the tire rotation is performed, and information showing the degree of uneven wear of the tire at the time the tire rotation is performed, outputs transition information showing the change in the remaining tread depth of the tire after the tire rotation has been performed.

[0111] The second prediction model 67 is generated by machine learning performed by the control unit 31 based on the third training data 66 stored in the memory unit 34 and a predetermined algorithm. The second prediction model 67 may also be generated or learned by the control unit of an external information processing device. In this case, the second prediction model 67 can be transferred from the external information processing device and stored in the memory unit 34.

[0112] The third training data 66 is the dataset used to generate and train the second prediction model 67.

[0113] The third training data 66 includes a combination of the transition information showing the change in the remaining tread depth of the tire until the tire rotation is performed, and information showing the degree of uneven wear of the tire at the time the tire rotation is performed, and the transition information (correct answer data) showing the change in the remaining tread depth of the tire after the tire rotation has been performed.

[0114] Here, with reference to Figures 7 to 11, the data included in the third training data 66 will be explained in detail. Figure 7 shows an example of the change in remaining tread depth from the start of use for a tire that was rotated at timing t1 when the degree of uneven wear reached the first stage and was then used until the end of its lifespan. Figure 8 shows an example of the change in remaining tread depth from the start of use for a tire that was rotated at timing t2 when the degree of uneven wear reached the second stage and was then used until the end of its lifespan. Figure 9 shows an example of the change in remaining tread depth from the start of use for a tire that was rotated at timing t3 when the degree of uneven wear reached the third stage and was then used until the end of its lifespan. Figure 10 shows an example of the change in remaining tread depth from the start of use for a tire that was rotated at timing t4 when the degree of uneven wear reached the fourth stage and was then used until the end of its lifespan. Figure 11 also shows an example of the change in remaining tread depth from the start of use for a tire that was rotated at timing t5 when the degree of uneven wear reached the fifth stage, and was then used until the end of its lifespan. In Figures 7 to 11, the predicted change in remaining tread depth for the tire when rotation is not performed is shown by a dotted line.

[0115] For example, the third training data 66 includes the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t1 as shown in Figure 7, and the information indicating the first stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t1 as shown in Figure 7 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0116] Furthermore, the third training data 66 includes a combination of the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t2 as shown in Figure 8, and the information indicating the second stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t2 as shown in Figure 8 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0117] Furthermore, the third training data 66 includes the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t3 as shown in Figure 9, and the information indicating the third stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t3 as shown in Figure 9 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0118] Furthermore, the third training data 66 includes the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t4 as shown in Figure 10, and the information indicating the fourth stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t4 as shown in Figure 10 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0119] Furthermore, the third training data 66 includes the transition information showing the change in the remaining tread depth of the tire from the start of use of the tire to timing t5 as shown in Figure 11, and the information indicating the fifth stage, and the transition information (correct data) showing the change in the remaining tread depth of the tire from timing t5 as shown in Figure 11 until the remaining tread depth of the tire becomes less than 3 mm (millimeters).

[0120] The second prediction processing unit 58 inputs the determination result of the degree of uneven wear by the judgment processing unit 55 and the transition information acquired by the second acquisition processing unit 56 to the second prediction model 67. As a result, the second prediction model 67 outputs the transition information (prediction result of the transition of remaining tread) that shows the transition of the remaining tread of the tire from the present when rotation is performed. Based on the transition information output from the second prediction model 67, the second prediction processing unit 58 calculates the remaining mileage of the tire until its lifespan is reached (the remaining lifespan of the tire) when rotation is performed.

[0121] The first display processing unit 59 displays the prediction result from the first prediction processing unit 57 on the operation display unit 12 of the mobile terminal 2 (an example of a display unit in this disclosure).

[0122] Specifically, the first display processing unit 59 displays the prediction result from the first prediction processing unit 57 and increase amount information indicating the amount of increase in the remaining lifespan of the tire due to the tire rotation on the operation display unit 12 of the mobile terminal 2.

[0123] Figure 5 shows an example of the first inspection result screen SC10 displayed on the operation display unit 12 of the mobile terminal 2 by the first display processing unit 59.

[0124] As shown in Figure 5, the first inspection result screen SC10 includes an inspection result display field IM11 and an operation key IM12.

[0125] The inspection result display area IM11 displays the inspection results for the tire being inspected. Specifically, as shown in Figure 5, the inspection result display area IM11 includes an image IM21 showing the name of the user (the transport operator) of the vehicle 5 being inspected. The inspection result display area IM11 also includes an image IM22 showing the name of the vehicle 5 being inspected. The inspection result display area IM11 also includes an image IM23 showing the name of the tire being inspected. The inspection result display area IM11 also includes an image IM24 showing the mounting position of the tire being inspected on the vehicle 5 being inspected. The inspection result display area IM11 also includes an image IM25 showing the air pressure and air temperature of the tire being inspected, acquired by the first acquisition processing unit 52. The inspection result display area IM11 also includes an image IM26 showing the mileage of the vehicle 5 being inspected, acquired by the first acquisition processing unit 52. The inspection result display area IM11 also includes an image IM27 showing the remaining tread depth of the tire being inspected, measured using the depth camera 4. Furthermore, the inspection result display area IM11 includes an image IM28 showing the judgment result of the judgment processing unit 55. Also, the inspection result display area IM11 includes an image IM29 showing the prediction result by the first prediction processing unit 57 and the prediction result by the second prediction processing unit 58. Image IM29 is an image showing the increase amount information. Also, the inspection result display area IM11 includes an image IM30 showing the judgment result of whether or not the tire rotation is necessary. For example, in the tire judgment system 100, if the degree of uneven wear of the tire is at or above the first stage, it is determined that the tire rotation is necessary. Also, the inspection result display area IM11 includes an image IM31 showing the judgment result of whether or not the tire replacement is necessary. For example, in the tire judgment system 100, if the remaining tread depth of the tire is 3 mm (millimeters) or less, it is determined that the tire replacement is necessary.

[0126] The operation key IM12 is used to input the command to end the display of the first inspection result screen SC10.

[0127] Furthermore, the tire determination system 100 may determine that tire rotation is necessary if the degree of uneven wear on the tire is at or above the nth stage (2 ≤ n ≤ 5). In this configuration, if the degree of uneven wear on the tire is less than the nth stage, the control unit 31 may display the remaining driving distance until the degree of uneven wear reaches the nth stage on the operation display unit 12 of the mobile terminal 2. In other words, the control unit 31 may predict the timing for performing the tire rotation.

[0128] For example, if the determination processing unit 55 determines that uneven wear is occurring and the degree of uneven wear of the tire is less than the nth stage, the control unit 31 predicts the timing for performing tire rotation based on the degree of uneven wear determined based on the captured image data and the transition information acquired by the second acquisition processing unit 56.

[0129] For example, the control unit 31 calculates a predicted value of the remaining tread depth of the tire at a specific point in time beyond the present (hereinafter referred to as the "first predicted value") based on the transition information that shows the transition of the remaining tread depth of the tire from the present in the case where rotation is not performed, output from the first prediction model 65. The first predicted value is a predicted value of the groove depth of the part of the tire tread where uneven wear is occurring.

[0130] Furthermore, the control unit 31 acquires a predicted value (hereinafter referred to as the "second predicted value") of the groove depth of the part of the tire tread where uneven wear has not occurred at the specified time. For example, the control unit 31 can acquire as the second predicted value a predicted value of the remaining groove of the tire at the specified time when the tire rotation is performed, calculated based on the transition information that shows the transition of the remaining groove of the tire from the present when the rotation is performed, output from the second prediction model 67.

[0131] The control unit 31 can determine that the specific time point at which the difference between the first predicted value and the second predicted value is evaluated to have reached the nth stage is the timing for performing the tire rotation.

[0132] [Tire detection process] The tire remaining life prediction method of this disclosure will be described below with reference to Figure 4, along with an example of the procedure for tire determination processing performed by the control unit 31 of server 1. Here, steps S11, S12, etc. represent the processing procedure (step) numbers performed by the control unit 31.

[0133] The tire detection process is executed when a predetermined operation is performed on the operation display unit 12 of the mobile terminal 2.

[0134] <Step S11> First, in step S11, the control unit 31 identifies the vehicle 5 to be inspected. The process in step S11 is performed by the first identification processing unit 51 of the control unit 31.

[0135] Specifically, the control unit 31 displays the vehicle registration number input screen on the operation display unit 12 of the mobile terminal 2. The control unit 31 then identifies the vehicle 5 that corresponds to the vehicle registration number entered on the vehicle registration number input screen from among the vehicles 5 registered in the database 61 as the vehicle 5 to be inspected.

[0136] <Step S12> In step S12, the control unit 31 acquires the mileage of the vehicle 5 to be inspected, as well as the air pressure and air temperature of each of the tires of the vehicle 5. The processing in step S12 is performed by the first acquisition processing unit 52 of the control unit 31.

[0137] Specifically, the control unit 31 performs wireless communication with the on-board device of the vehicle 5 to be inspected and acquires information indicating the cumulative mileage of the vehicle 5 stored in the storage unit of the on-board device, as well as information indicating the detection results of the air pressure and air temperature of each of the tires.

[0138] <Step S13> In step S13, the control unit 31 identifies the tire to be inspected. The process in step S13 is performed by the second identification processing unit 53 of the control unit 31.

[0139] Specifically, the control unit 31 displays the mounting position selection screen on the operation display unit 12 of the mobile terminal 2. The control unit 31 then identifies the tire to be inspected from among the tires registered in the database 61 that corresponds to the combination of the vehicle 5 to be inspected and the tire mounting position selected on the mounting position selection screen.

[0140] <Step S14> In step S14, the control unit 31 uses the depth camera 4 to image the tire to be inspected. The processing in step S14 is performed by the image processing unit 54 of the control unit 31.

[0141] Specifically, the control unit 31 displays the imaging operation reception screen on the operation display unit 12 of the mobile terminal 2. When the imaging operation is received on the imaging operation reception screen, the control unit 31 sends the imaging instruction to the mobile terminal 2. The control unit 31 also acquires the image data transmitted from the wear detection device 3 to the mobile terminal 2 in response to the transmission of the imaging instruction as the image data corresponding to the tire to be inspected.

[0142] <Step S15> In step S15, the control unit 31 performs a determination process. This determination process determines whether or not uneven wear has occurred in the tire to be inspected, and the degree of progression of the uneven wear, based on the image data acquired in step S14. The process in step S15 is an example of the determination steps of this disclosure and is performed by the determination processing unit 55 of the control unit 31.

[0143] Specifically, the control unit 31 inputs the captured image data obtained by the processing in step S14 to the judgment model 63. As a result, the judgment model 63 outputs information indicating whether or not uneven wear has occurred in the tire being inspected and the degree of uneven wear progression. The control unit 31 acquires the information output from the judgment model 63 as the judgment result.

[0144] <Step S16> In step S16, the control unit 31 switches the subsequent processing based on the result of the processing in step S15.

[0145] Specifically, if the control unit 31 determines that uneven wear has occurred in the tire being inspected (Yes side of S16), it proceeds to step S17. If the control unit 31 determines that uneven wear has not occurred in the tire being inspected (No side of S16), it proceeds to step S21.

[0146] <Step S17> In step S17, the control unit 31 acquires the transition information showing the transition of the remaining tread depth of the tire to be inspected. The processing in step S17 is an example of the acquisition step of this disclosure and is performed by the second acquisition processing unit 56 of the control unit 31.

[0147] Specifically, the control unit 31 acquires a relational expression (the aforementioned transition information) that shows the relationship between the mileage traveled by the vehicle 5 to be inspected since the tire was installed and the remaining tread depth of the tire, from the time the tire was put into use until the present.

[0148] <Step S18> In step S18, the control unit 31 performs a first prediction process. The first prediction process predicts the remaining lifespan of the tire based on the degree of uneven wear determined by the process in step S15 and the transition information obtained by the process in step S17. The process in step S18 is an example of the prediction steps of this disclosure and is performed by the first prediction processing unit 57 of the control unit 31.

[0149] Specifically, the control unit 31 inputs the determination result of the degree of uneven wear obtained by the process in step S15 and the transition information obtained by the process in step S17 to the first prediction model 65. As a result, the first prediction model 65 outputs the transition information (prediction result of the transition of remaining tread) that shows the transition of the remaining tread of the tire from the present. Based on the transition information output from the first prediction model 65, the control unit 31 calculates the remaining mileage of the tire until its lifespan is reached (the remaining lifespan of the tire).

[0150] <Step S19> In step S19, the control unit 31 performs a second prediction process. The second prediction process predicts the remaining lifespan of the tire when the tire is rotated, based on the degree of uneven wear determined by the process in step S15 and the transition information obtained by the process in step S17. The process in step S19 is performed by the second prediction processing unit 58 of the control unit 31.

[0151] Specifically, the control unit 31 inputs the determination result of the degree of uneven wear progression by the process in step S15 and the transition information obtained by the process in step S17 to the second prediction model 67. As a result, the second prediction model 67 outputs the transition information (prediction result of the transition of remaining tread) that shows the transition of the remaining tread of the tire from the present when rotation is performed. Based on the transition information output from the second prediction model 67, the control unit 31 calculates the remaining mileage of the tire until its lifespan is reached (the remaining lifespan of the tire) when rotation is performed.

[0152] <Step S20> In step S20, the control unit 31 displays the first inspection result screen SC10 (see Figure 5) on the operation display unit 12 of the mobile terminal 2. The processing in step S20 is performed by the first display processing unit 59 of the control unit 31.

[0153] <Step S21> In step S21, the control unit 31 displays the first inspection result screen SC10 (see Figure 5) on the operation display unit 12 of the mobile terminal 2. The processing in step S21 is performed by the first display processing unit 59 of the control unit 31.

[0154] Furthermore, the first inspection result screen SC10 displayed in step S21 will show "none" as indicating uneven wear of the tire. In addition, the first inspection result screen SC10 displayed in step S21 will show a predicted value for the remaining mileage of the tire until it reaches the end of its lifespan, assuming no uneven wear occurs. The predicted value for the remaining mileage of the tire until it reaches the end of its lifespan, assuming no uneven wear occurs, can be obtained by conventionally known methods.

[0155] <Step S22> In step S22, the control unit 31 determines whether or not the inspection of the tires mounted on the vehicle 5 to be inspected has been completed.

[0156] Specifically, the control unit 31 determines that the tire inspection work is complete when all of the tire mounting positions have been selected on the mounting position selection screen.

[0157] At this point, if the control unit 31 determines that the tire inspection work is complete (Yes side of S22), it terminates the tire determination process. If the tire inspection work is not complete (No side of S22), the control unit 31 proceeds to step S13.

[0158] Thus, when the tire determination system 100 determines that uneven wear is occurring, it includes a first prediction processing unit 57 that predicts the remaining lifespan of the tire based on the degree of uneven wear determined based on the captured image data and the transition information acquired by the second acquisition processing unit 56. This makes it possible to predict the remaining lifespan of the tire experiencing uneven wear.

[0159] The tire judgment system 100 may also determine whether or not uneven wear of the tire has occurred, and the degree to which the uneven wear has progressed, which is a different type of uneven wear than "uneven wear".

[0160] Furthermore, the tire determination system 100 may include, for each type of tire to be inspected, a first training data 62, a determination model 63, a second training data 64, a first prediction model 65, a third training data 66, and a second prediction model 67, as shown in Figure 3. The type of tire can be identified by one or more combinations of the tire dimension information, tread pattern information, usage information, and retreading information included in the tire data. This makes it possible to improve the accuracy of predicting the remaining lifespan of the tire experiencing uneven wear.

[0161] Furthermore, the tire inspection system 100 does not necessarily have to include the wear detection device 3. In this case, the imaging processing unit 54 can use the camera 15 of the mobile terminal 2 to image the tire to be inspected. The determination processing unit 55 can then determine whether or not uneven wear has occurred in the tire to be inspected based on the image data captured by the camera 15. The current remaining tread depth of the tire can be obtained based on the image capture results of the tire by the imaging processing unit 54.

[0162] Furthermore, some or all of the processing units included in the control unit 31 of server 1 may be provided in the mobile terminal 2.

[0163] [Other embodiments] The control unit 31 of server 1 may also include the third acquisition processing unit 71, the second display processing unit 72, the first correction processing unit 73, and the second correction processing unit 74 shown in Figure 12.

[0164] The third acquisition processing unit 71 acquires three-dimensional point cloud data indicating the shape of the tire based on the captured image data captured by the depth camera 4 (see Figure 1). The third acquisition processing unit 71 is an example of the second acquisition processing unit of this disclosure.

[0165] Specifically, the third acquisition processing unit 71 generates the three-dimensional point cloud data based on a plurality of the captured image data that are periodically captured by the depth camera 4 during one rotation of the tire.

[0166] The three-dimensional point cloud data generation process may be performed by an external information processing device. In this case, the third acquisition processing unit 71 can cause the external information processing device to perform the three-dimensional point cloud data generation process and acquire the three-dimensional point cloud data generated by the generation process.

[0167] The second display processing unit 72 displays a three-dimensional tire image IM51 (see Figure 13) based on the three-dimensional point cloud data acquired by the third acquisition processing unit 71 on the operation display unit 12 of the mobile terminal 2.

[0168] For example, when a predetermined display operation is received on the first inspection result screen SC10 (see Figure 5), the second display processing unit 72 displays the second inspection result screen SC20 shown in Figure 13 on the operation display unit 12 of the mobile terminal 2.

[0169] As shown in Figure 13, the second inspection results screen SC20 includes a tire image display field IM41, a specified operation reception field IM42, and an operation key IM43.

[0170] The tire image display area IM41 displays the tire image IM51 (see Figure 13). The tire image display area IM41 also accepts rotation operations to rotate the tire image IM51. For example, this rotation operation is a swipe operation. The tire image display area IM41 also accepts zoom operations to enlarge or reduce the tire image IM51. For example, this zoom operation is a pinch-out operation and a pinch-in operation.

[0171] The second display processing unit 72 rotates the tire image IM51 in response to the rotation operation in the tire image display field IM41. Specifically, the second display processing unit 72 rotates the tire image IM51 about an axis perpendicular to the direction of the rotation operation in response to the rotation operation (swipe operation) in the tire image display field IM41.

[0172] Furthermore, the second display processing unit 72 enlarges or reduces the tire image IM51 in response to the enlargement or reduction operation in the tire image display field IM41.

[0173] The designated operation reception area IM42 includes the first slide button IM61 and the second slide button IM62 shown in Figure 13. The first slide button IM61 is used to select either "with rotation" or "without rotation". The second slide button IM62 is used to specify any point in time from the present until the end of the tire's lifespan.

[0174] The operation key IM43 is used to input the command to terminate the display of the second inspection results screen SC20.

[0175] The first correction processing unit 73 corrects the tire image IM51 displayed by the second display processing unit 72 in response to a first designation operation that specifies a point in time later than the time of imaging in the first period from the time of imaging of the image data until the predicted lifespan of the tire is reached when the tire is not rotated.

[0176] Specifically, the first designated operation is the operation of sliding the second slide button IM62 while the first slide button IM61 is selected with "No Rotation".

[0177] When the first specified operation is received, the first correction processing unit 73 calculates a predicted value of the remaining tread depth of the tire at the time specified by the first specified operation (hereinafter referred to as the "third predicted value") based on the transition information that shows the transition of the remaining tread depth of the tire from the present in the case where rotation is not performed, output from the first prediction model 65. The third predicted value is a predicted value of the groove depth of the part of the tread portion of the tire where uneven wear is occurring.

[0178] Furthermore, when the first specified operation is received, the first correction processing unit 73 obtains a predicted value (hereinafter referred to as the "fourth predicted value") of the groove depth of the part of the tire tread that has not experienced uneven wear at the time specified by the first specified operation. For example, the first correction processing unit 73 can obtain as the fourth predicted value a predicted value of the remaining groove of the tire at the time specified by the first specified operation when the tire rotation is performed, calculated based on the transition information that shows the transition of the remaining groove of the tire from the present when the rotation is performed, output from the second prediction model 67.

[0179] Furthermore, the first correction processing unit 73 corrects the three-dimensional point cloud data based on the third predicted value and the fourth predicted value. Specifically, the first correction processing unit 73 corrects the three-dimensional point cloud data so that the groove depth in the part of the tire tread where uneven wear occurs becomes the third predicted value, and the groove depth in the part of the tread where uneven wear does not occur becomes the fourth predicted value.

[0180] The first correction processing unit 73 then displays the corrected tire image IM51, based on the corrected three-dimensional point cloud data, in the tire image display field IM41.

[0181] The second correction processing unit 74 corrects the tire image IM51 displayed by the second display processing unit 72 in response to a second designation operation that specifies a point in time later than the time of imaging in the second period from the time of imaging of the image data until the predicted lifespan of the tire is reached when the tire is rotated.

[0182] Specifically, the second specified operation is the operation of sliding the second slide button IM62 while the first slide button IM61 is used to select "Rotation enabled".

[0183] When the second specified operation is received, the second correction processing unit 74 calculates a predicted value of the remaining tread depth of the tire at the time specified by the second specified operation (hereinafter referred to as the "fifth predicted value") based on the transition information that shows the transition of the remaining tread depth of the tire from the present when a rotation is performed, output from the second prediction model 67. The fifth predicted value is a predicted value of the depth of each groove included in the tread portion of the tire.

[0184] Furthermore, the second correction processing unit 74 corrects the three-dimensional point cloud data based on the fifth predicted value. Specifically, the second correction processing unit 74 corrects the three-dimensional point cloud data so that the depth of each groove included in the tread portion of the tire becomes the fifth predicted value.

[0185] The second correction processing unit 74 then displays the corrected tire image IM51, based on the corrected three-dimensional point cloud data, in the tire image display field IM41.

[0186] The embodiments of this disclosure described above include the following disclosures (1) to (8).

[0187] Disclosure item (1) is a tire remaining life prediction system comprising: a camera for imaging a tire mounted on a vehicle; a determination processing unit for determining whether or not uneven wear is occurring in the tire based on the image captured by the camera; a first acquisition processing unit for acquiring transition information showing the transition of the remaining tread depth of the tire; and a prediction processing unit for predicting the remaining life of the tire based on the degree of uneven wear determined based on the image and the transition information acquired by the first acquisition processing unit, when the determination processing unit determines that uneven wear is occurring.

[0188] This system can predict the remaining lifespan of tires experiencing uneven wear.

[0189] Disclosure item (2) is the tire remaining life prediction system described in disclosure item (1), wherein the prediction processing unit predicts the remaining life of the tire using a learning model learned based on training data including transition information showing the transition of the remaining tread until the degree of uneven wear reaches any stage, information indicating that stage, and transition information showing the transition of the remaining tread after the degree of uneven wear has reached that stage.

[0190] This system makes it possible to improve the accuracy of predicting the remaining lifespan of tires experiencing uneven wear.

[0191] Disclosure item (3) is the tire remaining life prediction system described in disclosure item (1) or (2), which includes a first display processing unit that displays the prediction results from the prediction processing unit on a predetermined display unit.

[0192] This system makes it possible to notify the vehicle user of the prediction results from the prediction processing unit. Therefore, it is possible to encourage the vehicle user to perform the tire rotation.

[0193] Disclosure item (4) is the tire remaining life prediction system described in disclosure item (3), wherein the first display processing unit causes the display unit to display the prediction result from the prediction processing unit and increase amount information indicating the amount of increase in the remaining life of the tire due to the implementation of tire rotation.

[0194] This system makes it possible to more strongly encourage the vehicle user to rotate the tires.

[0195] Disclosure item (5) is a tire remaining life prediction system according to disclosure item (3) or (4), wherein the camera is a depth camera, and the tire remaining life prediction system comprises a second acquisition processing unit that acquires three-dimensional point cloud data indicating the shape of the tire based on the image captured by the depth camera, and a second display processing unit that causes the display unit to display a tire image based on the three-dimensional point cloud data acquired by the second acquisition processing unit.

[0196] This system makes it possible to check the condition of the tires in detail on the screen displayed on the display unit.

[0197] Disclosure item (6) is a tire remaining life prediction system according to disclosure item (5), comprising: a first correction processing unit that corrects the tire image displayed by the second display processing unit in response to a first designation operation that designates a time later than the time of imaging in a first period from the time of imaging of the image until the predicted life of the tire when the tire rotation is not performed; and a second correction processing unit that corrects the tire image displayed by the second display processing unit in response to a second designation operation that designates a time later than the time of imaging in a second period from the time of imaging of the image until the predicted life of the tire when the tire rotation is performed.

[0198] This system makes it possible to visualize the predicted shape of the tire in the future.

[0199] Disclosure item (7) is a tire remaining life prediction method performed by one or more processors of a tire remaining life prediction system equipped with a camera for imaging a tire mounted on a vehicle, the method comprising: a determination step of determining whether or not uneven wear is occurring in the tire based on an image captured by the camera; an acquisition step of acquiring transition information showing the transition of the remaining tread of the tire; and, if the determination step determines that uneven wear is occurring, a prediction step of predicting the remaining life of the tire based on the degree of uneven wear determined based on the image and the transition information acquired by the acquisition step.

[0200] This method, like the system described in disclosure (1), makes it possible to predict the remaining lifespan of a tire experiencing uneven wear.

[0201] Disclosure item (8) is a program for causing one or more processors of a tire remaining life prediction system equipped with a camera for imaging tires mounted on a vehicle to execute a determination step of determining whether or not uneven wear is occurring in the tire based on the image captured by the camera; an acquisition step of acquiring transition information showing the transition of the remaining tread of the tire; and a prediction step of predicting the remaining life of the tire based on the degree of uneven wear determined based on the image and the transition information acquired by the acquisition step, if the determination step determines that uneven wear is occurring.

[0202] According to this program, similar to the system described in disclosure (1), it is possible to predict the remaining lifespan of tires experiencing uneven wear.

[0203] Furthermore, this disclosure may also be a computer-readable recording medium on which the program described in disclosure item (8) is recorded non-temporarily. [Explanation of Symbols]

[0204] 1 server 2 Mobile devices 3. Wear detection device 4. Depth Camera 5 vehicles 11 Control Unit 12 Operation display section 13 Communications Department 14 Storage section 15 Cameras 31 Control Unit 32 Operation display section 33 Communications Department 34 Storage section 51 First Specific Processing Unit 52 First Acquisition Processing Unit 53 Second Specific Processing Unit 54 Imaging Processing Unit 55. Determination Processing Unit 56 Second Acquisition Processing Unit 57 First Prediction Processing Unit 58 Second Prediction Processing Unit 59 First display processing unit 61 Databases 62. First training data 63 Decision Models 64. Second training data 65 First Prediction Model 66 Third Teacher Data 67. Second Prediction Model 71 Third Acquisition Processing Unit 72 Second Display Processing Unit 73 First Correction Processing Unit 74 Second Correction Processing Unit 100 Tire Judgment System

Claims

1. A camera that takes images of the tires mounted on the vehicle, A determination processing unit that determines whether or not uneven wear is occurring in the tire based on the image captured by the camera, A first acquisition processing unit that acquires change information showing the change in the remaining tread depth of the tire, If the determination processing unit determines that uneven wear is occurring, a prediction processing unit predicts the remaining lifespan of the tire based on the degree of uneven wear determined based on the captured image and the transition information acquired by the first acquisition processing unit. A tire remaining life prediction system equipped with this feature.

2. The prediction processing unit predicts the remaining lifespan of the tire using a learning model that has been trained based on training data including transition information showing the progression of the remaining tread depth until the degree of uneven wear reaches any stage, information indicating that stage, and transition information showing the progression of the remaining tread depth after the degree of uneven wear has reached that stage. The tire remaining life prediction system according to claim 1.

3. The system includes a first display processing unit that displays the prediction results from the prediction processing unit on a predetermined display unit. The tire remaining life prediction system according to claim 1 or 2.

4. The first display processing unit causes the display unit to display the prediction result from the prediction processing unit and the increase amount information indicating the amount of increase in the remaining life of the tire due to the tire rotation. The tire remaining life prediction system according to claim 3.

5. The aforementioned camera is a depth camera, The aforementioned tire remaining life prediction system is: A second acquisition processing unit acquires three-dimensional point cloud data showing the shape of the tire based on the image captured by the depth camera, A second display processing unit that causes the tire image based on the three-dimensional point cloud data acquired by the second acquisition processing unit to be displayed on the display unit, The tire remaining life prediction system according to claim 4, comprising:

6. A first correction processing unit corrects the tire image displayed by the second display processing unit in response to a first designation operation that specifies a point in time later than the time of the image capture in a first period from the time of the image capture until the predicted lifespan of the tire is reached when the tire is not rotated. A second correction processing unit corrects the tire image displayed by the second display processing unit in response to a second designation operation that specifies a point in time later than the time of the image capture in the second period from the time of the image capture until the predicted lifespan of the tire is reached when the tire is rotated, The tire remaining life prediction system according to claim 5, comprising:

7. A tire remaining life prediction method performed by one or more processors of a tire remaining life prediction system equipped with a camera for imaging tires mounted on a vehicle, A determination step of determining whether or not uneven wear is occurring in the tire based on the image captured by the camera, An acquisition step to acquire information showing the change in the remaining tread depth of the tire, If the determination step determines that uneven wear is occurring, a prediction step is made to predict the remaining lifespan of the tire based on the degree of uneven wear determined based on the captured image and the transition information obtained in the acquisition step. A method for predicting the remaining lifespan of tires, including the tire lifespan itself.

8. One or more processors in a tire remaining life prediction system equipped with a camera that images the tires mounted on a vehicle, A determination step of determining whether or not uneven wear is occurring in the tire based on the image captured by the camera, An acquisition step to acquire information showing the change in the remaining tread depth of the tire, If the determination step determines that uneven wear is occurring, a prediction step is made to predict the remaining lifespan of the tire based on the degree of uneven wear determined based on the captured image and the transition information obtained in the acquisition step. A program to execute.

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