Tire wear prediction system, tire wear prediction program, tire wear prediction method and data structure

Abstract

To provide a tire wear prediction system and a tire wear prediction program which can accurately predict a wear state of a tire mounted on a vehicle when tire rotation is performed or the tire is exchanged with other tire.SOLUTION: A tire wear prediction system (100) includes: a wear prediction part (110) for predicting a wear state of a tire mounted at a predetermined wheel position of a vehicle on the basis of a travel state of the vehicle; a change history acquisition part (120) for acquiring a change history including rotation of a wheel position on which the tire is mounted and an exchange content to another tire; and a wear state correction part (140) for correcting a reference of the wear state of the tire predicted by the wear prediction part (110) on the basis of the change history. The wear prediction part (110) predicts the wear state of the tire, on the basis of the reference of the wear state corrected by the wear state correction part (140).SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a tire wear prediction system and a tire wear prediction program for predicting the wear state of tires mounted on a vehicle. [Background technology]

[0002] 2. Description of the Related Art Conventionally, methods for predicting the wear state of tires mounted on vehicles such as automobiles are known.

[0003] For example, a method has been established for predicting the wear state (wear life) of a tire based on the wear energy during free rolling, when a toe angle is applied, when a lateral force is applied, when a driving force is applied, and when a braking force is applied, and the amount of wear per specified traveling distance (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 3277156 Summary of the Invention

[0005] According to Patent Document 1, it is possible to predict the wear state of tires mounted on a vehicle, that is, the amount of tire wear, with a certain degree of accuracy. However, in the actual vehicle usage environment, the following problems arise.

[0006] Specifically, it is common for the wheel positions of tires mounted on a vehicle to be rotated (tire rotation) during use in order to prevent uneven wear and ensure a relatively uniform wear.

[0007] The above-mentioned method for predicting the wear condition of a tire predicts the wear condition of a new tire mounted on a specified wheel position, so when tire rotation is performed in this manner, it becomes difficult to accurately predict the amount of wear of the tire.

[0008] Furthermore, particularly in the case of heavy-duty tires fitted to trucks, buses and the like, they are often replaced with other tires in stock during tire rotation, specifically new or used tires, or tires of a different brand or type (such as studless tires), making it even more difficult to predict the amount of wear of the heavy-duty tires.

[0009] Therefore, the present invention has been made in consideration of the above circumstances, and aims to provide a tire wear prediction system, a tire wear prediction program, a tire wear prediction method, and a data structure that can accurately predict the wear state of tires mounted on a vehicle even if the tires are rotated or replaced with other tires.

[0010] One aspect of the present invention is a tire wear prediction system (tire wear prediction system 100) that includes a wear prediction unit (wear prediction unit 110) that predicts the wear state of a tire attached to a specific wheel position of a vehicle (bus 10) based on the driving state of the vehicle, a change history acquisition unit (change history acquisition unit 120) that acquires a change history including the rotation of the wheel position at which the tire is attached or the replacement with another tire, and a wear condition correction unit (wear condition correction unit 140) that corrects the standard for the wear state of the tire predicted by the wear prediction unit based on the change history, and the wear prediction unit predicts the wear state of the tire based on the standard for the wear state corrected by the wear condition correction unit.

[0011] One aspect of the present invention is a tire wear prediction program that causes a computer to execute a wear prediction process that predicts the wear state of a tire mounted at a specific wheel position based on the driving condition of a vehicle, a change history acquisition process that acquires a change history including the rotation of the wheel position at which the tire is mounted or the replacement with another tire, and a wear state correction process that corrects the criteria for the tire wear state predicted by the process based on the change history, and the wear prediction process predicts the tire wear state based on the criteria for the previous wear state corrected by the wear state correction process.

[0012] One aspect of the present invention is a tire wear prediction method including a wear prediction step of predicting the wear state of a tire mounted at a specific wheel position of a vehicle based on the driving condition of the vehicle, a change history acquisition step of acquiring a change history including the rotation of the wheel position at which the tire is mounted or the replacement with another tire, and a wear state correction step of correcting the criteria for the wear state of the tire predicted by the wear prediction step based on the change history, wherein the wear prediction step predicts the wear state of the tire based on the criteria for the wear state corrected by the wear state correction step.

[0013] A data structure used in a tire wear prediction system that predicts the wear state of a tire mounted at a specific wheel position of a vehicle based on the driving condition of the vehicle, the data structure including a tire ID which is unique identification information assigned to the tire, the type of the tire, the size of the tire, the brand of the tire, a remaining groove amount indicating the remaining groove depth of a groove portion formed in the tread of the tire, and wear characteristics indicating the characteristics of the tire used to predict the wear state of the tire, the tire wear prediction system being used in a process of revising the criteria for the wear state of the tire predicted by the wear prediction process based on a change history including the rotation of the wheel position on which the tire is mounted or the replacement with another tire. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a functional block diagram of a tire wear prediction system 100. As shown in FIG. [Diagram 2] FIG. 2 is a schematic perspective view of a bus 10 equipped with a tire 30 that is the subject of the tire wear prediction system 100. As shown in FIG. [Diagram 3] FIG. 3 is an explanatory diagram of the axle configuration of the bus 10. [Figure 4] FIG. 4 is a diagram showing an operation flow for predicting the wear state of the tire 30 by the tire wear prediction system 100. As shown in FIG. [Diagram 5]FIG. 5 is a diagram showing an example of the tire database 130. As shown in FIG. [Figure 6] FIG. 6 is a diagram showing an example of how the wear state of the tire 30 is displayed by the wear state display unit 150. As shown in FIG. [Figure 7] FIG. 7 is a diagram conceptually showing prediction of the wear state of the tire 30 by the tire wear prediction system 100. As shown in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment will be described with reference to the drawings. Note that the same or similar reference numerals are used to designate the same functions or configurations, and the description thereof will be omitted as appropriate.

[0016] (1) Overall configuration of tire wear prediction system 1 is a functional block diagram of a tire wear prediction system 100. The tire wear prediction system 100 predicts the wear state of a tire mounted on a predetermined wheel position of a vehicle. Specifically, the tire wear prediction system 100 predicts the amount of wear from a brand new tire based on a known prediction method, and also accurately predicts the amount of wear of the tire even if the tire has been rotated and replaced with another tire.

[0017] Known prediction methods include the above-mentioned Japanese Patent Publication No. 3277156, as well as Japanese Patent Nos. 3320653 and 3277155. However, the prediction method is not limited to these methods, and any method can be used as long as it can predict the amount of wear of a tire mounted at a specified wheel position based on the vehicle's running conditions (running speed, acceleration / deceleration, lateral G, steering angle, load, tire internal pressure, etc.).

[0018] Fig. 2 is a schematic perspective view of a bus 10 equipped with a tire 30 that is a target of the tire wear prediction system 100. As shown in Fig. 2, the bus 10 is a vehicle equipped with two axles, a front axle 20F and a rear axle 20R. A plurality of tires 30 are fitted to the front axle 20F and the rear axle 20R.

[0019] The front axle 20F is a steering axle, and the tires 30 mounted on the front axle 20F are called steering wheels. The rear axle 20R is a driving axle, and the tires 30 mounted on the rear axle 20R are called driving wheels.

[0020] The bus 10 is a type of vehicle that is subject to management by the tire wear prediction system 100, and is a large vehicle (heavy-duty vehicle) capable of transporting a large number of passengers. The tires 30 mounted on the bus 10 are heavy-duty tires.

[0021] The bus 10 is equipped with various sensors that detect the values ​​of parameters (described later) that indicate the traveling state of the bus 10. Specifically, the bus 10 is equipped with sensors that detect the traveling speed, acceleration, deceleration, lateral G, steering angle, load, tire pressure, etc. Note that these sensors may be shared with the equipment of the bus 10 (including a tire pressure monitoring system (TPMS)), or may be equipped for predicting the wear state of the tires 30.

[0022] Fig. 3 is an explanatory diagram of the axle configuration of the bus 10. As shown in Fig. 3, a tire 30 identified as Tire 1 is mounted at wheel position #1 (POS.1, position of the left front wheel ((1) in the figure)). A tire 30 identified as Tire 2 is mounted at wheel position #2 (POS.2, position of the right front wheel ((2) in the figure)).

[0023] Similarly, tires 30 identified as Tires 3 to 6 are mounted on POS. 3 to 6 (left outer rear wheel, left inner rear wheel, right inner rear wheel, right outer rear wheel, ((3) to (6) in the drawing)).

[0024] As shown in FIG. 1, the tire wear prediction system 100 includes a wear prediction unit 110, a change history acquisition unit 120, a tire database 130, a wear state correction unit 140, and a wear state display unit 150.

[0025] These functional blocks are realized by executing a computer program (software) on hardware such as a server computer. Specifically, the tire wear prediction system 100 includes, as hardware elements, a processor 101, a memory 103, an input device 105, a display 107, and an external IF 109.

[0026] The wear prediction unit 110 predicts the wear state of the tire 30. Specifically, the wear prediction unit 110 predicts the wear state of a new tire 30 using the known prediction method described above.

[0027] More specifically, the wear prediction unit 110 predicts the wear state of the tires 30 mounted at predetermined wheel positions (POS. 1 to 6 in FIG. 3) of the bus 10 based on the running state of the bus 10 (vehicle).

[0028] The running state of the bus 10 is a state of the bus 10 that is determined mainly based on the following parameters. Specifically, these include running speed, running distance, acceleration, deceleration, lateral G, driving force, braking force, lateral force, steering angle, yaw rate, roll rate, pitch rate, and load (cargo weight). The wear prediction unit 110 predicts the wear state of the tires 30 using some or all of these parameters.

[0029] The wear prediction unit 110 acquires, via a communication network, parameters indicating the running state of the bus 10. Specifically, the wear prediction unit 110 acquires measurement data from various sensors equipped on the bus 10 via a wireless communication network or a combination of a wireless communication network and a wired communication network.

[0030] The wear prediction unit 110 predicts the wear state of the tires 30 mounted at each wheel position using the acquired measurement data, but the prediction does not necessarily have to be performed in real time. In other words, the prediction of the wear state of the tires 30 may be performed as a batch process at predetermined intervals (for example, on a daily or weekly basis).

[0031] The wear state of the tire 30 is simply the amount of wear of the tire 30. In addition, based on the predicted wear state of the tire 30, it may be indicated as the depth of the grooves formed in the tread of the tire 30 (remaining groove depth).

[0032] Furthermore, the wear prediction unit 110 predicts the wear state of the tire 30 based on the wear state criterion corrected by the wear state correction unit 140. Specifically, the wear prediction unit 110 obtains from the wear state correction unit 140 information on whether or not the tire has been rotated or replaced with another tire.

[0033] Tire rotation refers to changing the wheel position of the tires 30 mounted on the bus 10 to another wheel position (rotating) among the multiple tires 30 mounted on the bus 10.

[0034] The term "changing to other tires" refers to changing to tires (new or used) that have not been installed on the bus 10. In this case, changing to a different tire brand (product name, etc.) or type (such as summer tires to studless tires (winter tires)) is also included.

[0035] When the wear prediction unit 110 acquires the information from the wear condition correction unit 140, i.e., when it acquires the corrected wear condition standard, it refers to the tire database 130 as necessary and changes the method of predicting the wear condition of the tire 30 and the values ​​of the parameters used to predict the wear condition.

[0036] In addition, when tire 30 wears to a certain extent, the remaining groove depth of the tread decreases, thereby increasing the block rigidity of the tread. Therefore, after tire 30 wears to a certain extent, wear prediction unit 110 can switch to a prediction method that slows down the progression of the wear condition (referred to as "tread rigidity correction").

[0037] The change history acquisition unit 120 acquires the change history of the tires 30 mounted on the bus 10. Specifically, the change history acquisition unit 120 acquires the change history including the rotation of the wheel positions at which the tires 30 are mounted (tire rotation) or the details of replacement with other tires.

[0038] The change history may be obtained automatically using the ID of a TPMS measurement unit provided on the tire 30 (specifically, the tire 30 mounted on a rim wheel), or may be obtained by a user manually or semi-manually inputting the ID (such as using a handheld module that reads the ID of the tire 30).

[0039] The tire database 130 is made up of items related to tires 30 that may be mounted on the bus 10 .

[0040] Fig. 5 shows an example of the tire database 130. As shown in Fig. 5, the tire database 130 is made up of items such as "Tire ID", "Tire type", "Size", "Brand", "Remaining tread depth", and "Wear characteristics".

[0041] "Tire ID" is unique identification information given to the tire 30. "Tire type" is a type according to the performance of the tire 30 (summer, all-season, and studless in FIG. 5). The type may be based on the value of rolling resistance or the rigidity of the tread rubber.

[0042] "Size" is the size of the tire 30 (tread width, wheel diameter), and "brand" corresponds to the product name (brand name) of the tire 30. "Remaining tread depth" is the remaining depth of the grooves formed in the tread of the tire 30. For new tires, NA (Not Applicable) is set.

[0043] "Wear characteristics" are tire characteristics (such as the material and stiffness of the tread rubber) used to predict the wear state of the tire. In addition, the wear characteristics differ depending on the axle (front axle 20F (steering axle) or rear axle 20R (drive axle)) on which the tire 30 is mounted.

[0044] The above-mentioned tread stiffness correction is also performed using the “wear characteristics.” For example, “A′” and “C′” in Fig. 5 show the wear characteristics after the wear of the tire has progressed to a certain degree.

[0045] The wear state correction unit 140 corrects the standard for the wear state of the tire 30 predicted by the wear prediction unit 110 based on the change history of the tire 30 acquired by the change history acquisition unit 120.

[0046] Specifically, when the change history of the tire 30 is a rotation of the wheel position (POS.1 to 6 in FIG. 3), the wear condition correction unit 140 corrects the wear condition standard based on the wheel position after the rotation. For example, when the tire 30 moves from the front axle 20F (POS.1) to the rear axle 20R (POS.3), the wear characteristics are changed from those for steering wheels to those for driving wheels. This corrects the wear condition standard of the tire 30. Note that in the case of tire rotation, the wear condition correction unit 140 may automatically change the wear characteristics using parameters indicating the running state of the bus 10.

[0047] Furthermore, when the change history of the tire 30 is a change to another tire, the wear condition correction unit 140 corrects the wear condition criterion based on at least one of the brand, type, and whether or not the other tire is new.

[0048] For example, when the tire 30 mounted on POS.1 is changed from a summer tire of "brand ABC" (see tire ID=1 in FIG. 5) to a summer tire of the same "brand ABC" (see tire ID=31), the wear condition correction unit 140 changes the wear characteristic used to predict the wear condition of the tire 30 from "A" to "A'". As described above, "A'" indicates the wear characteristic after the wear of the tire has progressed to a certain extent (the tread stiffness has been corrected). This causes the standard of the wear condition of the tire 30 to be corrected.

[0049] The wear state display unit 150 displays the wear state of the tires 30 predicted by the wear prediction unit 110. Specifically, the wear state display unit 150 displays the wear state of the tires 30 for each wheel position.

[0050] FIG. 6 shows an example of the wear state of the tire 30 displayed by the wear state display unit 150. The (1) to (6) on the horizontal axis of the graph shown in FIG. 6 correspond to the wheel positions shown in FIG. 3. The vertical axis of the graph indicates the amount of wear (unit: mm) of the tire 30 mounted at each wheel position. The "use limit" of the amount of wear corresponds to the state where the tread of the tire 30 has worn down to the limit at which it can be legally used. Generally, this is the state where the tread wear indicator is exposed on the surface of the tread.

[0051] "Ideal" in Fig. 6 is the amount of wear that would occur if the bus were to travel the relevant distance at the relevant speed under ideal conditions where no unnecessary wear would occur. In other words, the ideal condition is an optimal condition in which the bus 10 is driven in a way that does not place unnecessary strain on the tires. Note that the shape of the road on which the bus 10 actually traveled (road type, road curvature, etc.) may be taken into consideration when predicting the amount of wear that corresponds to "ideal."

[0052] The "additional wear on driving wheels" is the amount of wear caused by the driving wheels that is added to the "ideal" amount of wear. The "additional wear on steering wheels" is the amount of wear caused by the steering wheels that is added to the "ideal" amount of wear. In other words, the "additional wear on driving wheels" refers to the amount of wear increased by the driver's unnecessary accelerator operation, etc., and the "additional wear on steering wheels" refers to the amount of wear increased by the driver's sudden steering operation, etc.

[0053] The "additional load" is the amount of wear caused by a load added to the "ideal" amount of wear, specifically, the amount of wear caused by exceeding the standard load (such as the number of passengers) of the bus 10.

[0054] In this way, the wear state display unit 150 can display the wear state of the tire 30 according to the cause of wear.

[0055] (2) Operation of tire wear prediction system Next, a description will be given of the operation of the tire wear prediction system 100. Specifically, a description will be given of the operation of predicting the wear state of the tire 30 by the tire wear prediction system 100.

[0056] 4 shows an operation flow for predicting the wear state of the tire 30 by the tire wear prediction system 100. As described above, in this embodiment, the tire 30 mounted on the bus 10 is rotated or replaced with another tire during use.

[0057] 4, the tire wear prediction system 100 predicts the amount of wear of the tires 30 at each wheel position of the bus 10 (S10). Specifically, the tire wear prediction system 100 predicts the wear state of the tires 30 using parameters indicating the running state of the bus 10, as described above.

[0058] Next, the tire wear prediction system 100 checks whether there is a change history of the tire 30 (S20). Specifically, the tire wear prediction system 100 checks whether the tire has been rotated and replaced with another tire.

[0059] When there is a change history of the tire 30, the tire wear prediction system 100 determines whether the change history is a tire rotation or not (S30).

[0060] In the case of tire rotation, the tire wear prediction system 100 resets the wheel position after rotation and the wear amount of the tire 30 up to that rotation (S40). In addition, the tire wear prediction system 100 changes the wear characteristics of the tire 30 (related to the steering axle or drive axle, see the explanation for FIG. 5) as necessary based on the wheel position after rotation.

[0061] If the tires are not being rotated, that is, if the tires are being replaced with other tires, the tire wear prediction system 100 determines whether the tires are being replaced with new tires (S50).

[0062] In the case of replacement with new tires, the tire wear prediction system 100 resets the amount of wear of the tire 30 at the wheel position (S60).

[0063] On the other hand, when replacing tires with used tires, that is, tires that have been worn down to a certain degree or more, the tire wear prediction system 100 acquires the amount of wear of the replaced tires (S70). Here, the replaced tires refer to both the tires 30 that were mounted on the bus 10 before the replacement and the used tires that are newly mounted on the bus 10. This makes it possible to display the amount of wear of the removed tires 30 and accurately predict the wear state of the used tires.

[0064] The tire wear prediction system 100 determines whether the replaced tire is the same brand and size as the original tire 30 (S80).

[0065] If the replaced tire is not the same brand and size as the replaced tire 30, that is, if any of the type, brand, or size is different, the tire wear prediction system 100 changes the wear characteristics to those of the replaced tire 30 (see the explanation related to FIG. 5) (S90). Specifically, the tire wear prediction system 100 changes the wear characteristics to those associated with the brand and size of the replaced tire 30.

[0066] 7 conceptually illustrates prediction of the wear state of the tire 30 by the tire wear prediction system 100. As shown in Fig. 7, the tire wear prediction system 100 starts predicting the wear state of the tire 30. Here, it is assumed that a brand new tire 30 is mounted on the bus 10.

[0067] The tire wear prediction system 100 predicts the wear state of the tire 30 based on the driving conditions (driving speed, acceleration, deceleration, lateral G, steering angle, load, tire internal pressure, etc.) of the bus 10. As a result, it is predicted that the remaining tread amount (remaining tread depth) of the tire 30 will decrease along the straight line α1.

[0068] Thereafter, tire rotation is performed on the tires 30. As described above, the tire wear prediction system 100 changes the wear characteristics used to predict the wear state of the tires 30 based on the wheel position on which the tires 30 are mounted, the tire type, size, brand (including whether the tire is new or used), and the remaining tread depth (in the case of used tires).

[0069] As a result, after tire rotation, it is predicted that the remaining groove depth will decrease along the straight line α2.

[0070] Furthermore, the tire wear prediction system 100 can display the wear state of the tire 30 as shown in FIG. 6 at any timing among the above-mentioned steps S10 to S90.

[0071] (3) Actions and Effects According to the above-described embodiment, the following advantageous effects can be obtained. Specifically, the tire wear prediction system 100 modifies the standard (wear characteristics) of the predicted wear state of the tire 30 based on the acquired change history of the tire 30 (tire rotation or replacement with another tire). The tire wear prediction system 100 predicts the wear state of the tire 30 based on the modified standard of the wear state.

[0072] Therefore, even if the tires 30 mounted on the bus 10 are rotated or replaced with other tires, the wear state of the tires 30 can be accurately predicted using known prediction methods.

[0073] In particular, commercial vehicles such as trucks and buses frequently rotate tires and change tires to other tires, and also often change tires to new or used tires, or tires of a different brand or type (such as studless tires). The tire wear prediction system 100 supports both tire rotation and tire change to other tires, and therefore can accurately predict the wear state of the tire 30 even in such cases.

[0074] In this embodiment, the tire wear prediction system 100 can display the wear state of the tire 30 according to the cause of wear (see FIG. 6). This can encourage the driver of the bus 10 and the operator of the bus 10 (such as a bus company) to improve the driving method and operation method of the bus 10. This can extend the wear life of the tire 30.

[0075] (4) Other embodiments The present invention has been described above with reference to the embodiments. However, the present invention is not limited to these embodiments, and it will be obvious to those skilled in the art that various modifications and improvements are possible.

[0076] For example, in the above embodiment, a heavy-duty tire mounted on the bus 10 has been described as an example, but the tire may be mounted on a large vehicle other than a bus, such as a truck, or a passenger car (particularly for commercial use).

[0077] In the above-described embodiment, the tire wear prediction system 100 supports tire rotation and replacement with other tires, but it may support only one of them depending on the type of vehicle in question.

[0078] In the above-described embodiment, the tire wear prediction system 100 displays the wear state of the tire 30 according to the wear cause, but such display is not essential. Also, among the multiple wear causes, only a part of the wear causes may be displayed.

[0079] As described above, the embodiment of the present invention has been described, but the description and drawings forming a part of this disclosure should not be understood as limiting this invention. From this disclosure, various alternative embodiments, examples and operating techniques will become apparent to those skilled in the art. [Explanation of symbols]

[0080] 10 Bus 20F Front wheel axle 20R rear axle 30 Tires 100 Tire wear prediction system 101 Processor 103 Memory 105 Input Devices 107 Display 109 External IF 110 Wear Prediction Section 120 Change History Acquisition Section 130 Tire Database 140 Wear correction part 150 Wear status indicator

Claims

1. a wear prediction unit that predicts a wear state of a tire mounted at a predetermined wheel position of the vehicle based on a running state of the vehicle; a change history acquisition unit that acquires a change history including a rotation of a wheel position where the tire is mounted or a change to another tire; a wear state correction unit that corrects a standard of the tire wear state predicted by the wear prediction unit based on the change history; Equipped with The wear prediction unit is predicting a wear state of the tire based on the wear state criterion corrected by the wear state correction unit; A tire wear prediction system that switches to a prediction method that slows down the progression of the wear state of the tire after the tire has worn down to a certain extent.

2. The tire wear prediction system according to claim 1 , wherein the wear state correction unit corrects the wear state criterion based on the wheel position after the rotation when the change history is a rotation of the wheel position.

3. The tire wear prediction system according to claim 1 or 2, wherein the wear state correction unit corrects the wear state criteria based on at least one of the brand, type, and whether or not the other tire is new when the change history is a replacement with another tire.

4. A wear prediction process for predicting a wear state of a tire mounted at a predetermined wheel position based on a running state of a vehicle; A change history acquisition process for acquiring a change history including a rotation of the wheel position where the tire is mounted or a change to another tire; a wear state correction process for correcting a standard of the tire wear state predicted by the wear prediction process based on the change history; Run the following on your computer: The wear prediction process predicts the wear state of the tire based on the criteria for the previous wear state corrected by the wear state correction process, and after the tire wear has progressed to a certain extent, switches to a prediction method that slows down the progression of the wear state.

5. a wear prediction step of predicting a wear state of a tire mounted at a predetermined wheel position of the vehicle based on a running state of the vehicle; a change history acquisition step of acquiring a change history including a rotation of a wheel position where the tire is mounted or a change to another tire; a wear state correction step of correcting a standard of the tire wear state predicted by the wear prediction step based on the change history; Including, In the wear prediction step, the wear state of the tire is predicted based on the wear state standard corrected in the wear state correction step, and after the tire wear has progressed to a certain extent, the prediction method switches to a prediction method that slows down the progression of the wear state.

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