JP7419031B2 - Evaluation method of tire slipping behavior - Google Patents

Description

The present invention relates to a method for evaluating tire slipping behavior.

The frictional characteristics of tires are closely related to the sliding behavior of the tread surface. Therefore, in order to clarify the frictional characteristics of a tire, it is necessary to determine the sliding behavior of the tire's contact surface.

The method disclosed in Patent Document 1 is known as a method for determining the sliding speed of the contact surface of a tire. In this method, a camera under the transparent plate takes pictures of the tread surface at regular intervals from when the tire touches the transparent plate until it leaves the ground. Then, from the photographed image, the amount of displacement per unit time of a predetermined measurement point is determined and used as the sliding speed of the measurement point.

Japanese Patent Application Publication No. 2010-78416

The method of Patent Document 1 is a method for determining the sliding speed under one rolling condition. However, it has not been possible to change the rolling state of a tire, such as changing it from its normal rolling state at a constant speed to the state when braking force is applied, and to evaluate the slipping behavior associated with that change. It wasn't.

Therefore, an object of the present invention is to provide a method that can evaluate the slipping behavior accompanying changes in the rolling state of a tire.

An aspect of the present invention includes a step of photographing an image of a ground contact surface in a first state a plurality of times, a step of photographing a plurality of images of a ground contact surface in a second state different from the first state, and a step of photographing a plurality of images of a ground contact surface in a second state different from the first state. a step of setting a feature point in an image taken in the first state, and a step of setting a feature point in an image taken in the first state, and based on a time-series change in the amount of displacement of the feature point that becomes clear from the plurality of images in the first state. The step of determining the time-series change in the sliding speed of the feature point, and the time-series change in the amount of displacement of the feature point that becomes clear from the plurality of images in the second state, a step of determining a time-series change in sliding speed, and a step of temporally corresponding a time-series change in the sliding speed of the feature point in a first state and a time-series change in the sliding speed of the feature point in a second state; and then determining a time-series change in relative sliding speed in a second state with respect to the first state, wherein the first state is a constant speed without applying torque to the tire. and the tire is in a normal driving state in which it is rolled at a slip angle of 0°, and the second state is a state in which the tire is braking, cornering, driving, or braking or driving and cornering. This is a method for evaluating the sliding behavior of.

According to the present invention, it is possible to evaluate the slipping behavior accompanying the change in the rolling state of the tire from the first state to the second state.

FIG. 2 is a diagram of the tire slip behavior evaluation device viewed from the tire axial direction. A diagram showing a tread pattern of a tire to be evaluated. Flowchart of a method for evaluating tire slipping behavior. A diagram illustrating the amount of displacement of feature points. A solid line indicates a block and a feature point photographed at a certain time, and a broken line indicates a block and a feature point photographed at the next time. The figure which shows the time series change of the average sliding speed in the 2nd state of tire T1. (a) shows the average sliding speed of the shoulder block, (b) shows the average sliding speed of the mediate block, and (c) shows the average sliding speed of the center block. An enlarged view of FIG. 5(a). The figure which shows the time series change of the relative sliding speed of tire T1. (a) shows the relative sliding speed of the shoulder block, (b) shows the relative sliding speed of the mediate block, and (c) shows the relative sliding speed of the center block. The figure which shows the time series change of the average sliding speed in the 2nd state of tire T2. (a) shows the average sliding speed of the shoulder block, (b) shows the average sliding speed of the mediate block, and (c) shows the average sliding speed of the center block. The figure which shows the time series change of the relative sliding speed of tire T2. (a) shows the relative sliding speed of the shoulder block, (b) shows the relative sliding speed of the mediate block, and (c) shows the relative sliding speed of the center block. FIG. 3 is a diagram superimposing the time-series changes in the average sliding speeds of tire T1 and tire T2 in the second state. (a) shows the average sliding speed of the shoulder block, (b) shows the average sliding speed of the mediate block, and (c) shows the average sliding speed of the center block. FIG. 3 is a diagram showing time-series changes in relative sliding speeds of tire T1 and tire T2 in an overlapping manner. (a) shows the relative sliding speed of the shoulder block, (b) shows the relative sliding speed of the mediate block, and (c) shows the relative sliding speed of the center block.

First, the evaluation device 10 used to evaluate the slipping behavior of a tire according to the present embodiment will be described. As shown in FIG. 1, this evaluation device 10 includes a test stand 11 for grounding a pneumatic tire (hereinafter referred to as "tire"), a support section 12 for supporting the tire T, and a test surface 17 for grounding the pneumatic tire (hereinafter referred to as "tire"). It has a camera 13 for photographing the ground contact surface of the tire T, and a processing device 14 for processing the image photographed by the camera 13.

A hole 15 is formed in a part of the test stand 11. The upper opening of the hole 15 is closed by a transparent plate 16 made of glass or acrylic. The upper surface of the transparent plate 16 forms the same plane as the upper surface of the test stand 11. This plane is the test surface 17 on which the tire T is in contact with the ground. The test surface 17 may have irregularities similar to those of an actual road surface.

The camera 13 is placed inside the hole 15 so as to face upward. Therefore, when the tire T is in contact with the upper surface of the transparent plate 16, the camera 13 can photograph the ground contact surface of the tire T from below the transparent plate 16. The processing device 14 described above is connected to this camera 13. An input device 18 and a display device 19 are connected to the processing device 14 . The processing device 14 is a computer, the input device 18 is a keyboard or mouse, and the display device 19 is a display.

The support portion 12 has a known configuration for applying a load, driving force, braking force, slip angle, camber angle, etc. to the tire T. The support portion 12 controls the tire T in various states.

FIG. 2 is a simplified view of the tread portion of the tire T to be evaluated in this embodiment. As shown in FIG. 2, the tread portion of the tire T includes a plurality of center blocks 21 as land portions arranged in the tire circumferential direction at the center in the tire width direction, and a plurality of center blocks 21 arranged in the tire circumferential direction on both sides in the tire width direction. A plurality of shoulder blocks 23 and a plurality of mediate blocks 22 arranged in the tire circumferential direction are formed between the center block 21 and the shoulder blocks 23.

Next, a method for evaluating the slipping behavior of the tire T of this embodiment will be explained.

As shown in FIG. 3, the evaluation method of the present embodiment includes a step (S1) of adding colored points to the tread surface of the tire T, and a step (S2) of taking images of the tread surface in a first state multiple times. , a step of taking images of the ground contact surface in the second state multiple times (S3), a step of determining time-series changes in the sliding speed of the feature points in the first state (S4), and a step of obtaining images of the feature points in the second state. The step includes a step (S5) of determining a time-series change in the sliding speed, and a step (S6) of determining a relative time-series change in the sliding speed in the second state with respect to the first state.

Here, the first state is a state that serves as a standard for evaluation, and the second state is a state that is different from the standard. In this embodiment, the first state is a state during normal rolling, and the second state is a state when a braking force is applied to the first state. Here, the term "normal rolling" refers to when the tire T is rolling at a constant speed without applying an axial torque to the tire T, but with a predetermined load applied thereto. Further, the slip angle of the tire T during normal rolling is 0°. Note that the feature points will be described later.

Next, each step will be explained in detail.

First, in step S1, a plurality of colored points M are applied to the entire tread portion of the tire T, as shown in FIG. The colored point M is a point colored in a different color from the ground contact surface. In this embodiment, as a method of applying a plurality of colored points M, a method of applying by spraying is adopted. However, the colored points M may be added by other methods such as an operator manually adding the colored points M. A plurality of coloring points M are given to one block.

As shown in FIG. 2, grooves 20 are formed between the blocks 21, 22, 23 of the tread portion of the tire T. As a result of applying the colored dots M by spraying, as shown in FIG. ) is also given.

The distribution of the given colored points M is random (that is, there is no regularity). Furthermore, in FIG. 2, all the colored points M are shown as points having the same shape, but in reality, the shapes of the colored points M vary. Therefore, the distribution and shape of the colored points M differ depending on the location of the ground contact surface, and conversely, the location of the ground contact surface can be identified from the distribution and shape of the colored points M.

These colored points M are given to the image of the tire T to set feature points, as will be described later. A feature point is a feature point that is a target of tracking in tracking processing, which will be described later. The feature points are set using a predetermined algorithm, but since the image of the surface of the tire T without the colored points M has low contrast, it is difficult to set the feature points as is. Therefore, by providing the colored points M in this manner, the image of the surface of the tire T has a high contrast, thereby making it easier to set the characteristic points.

In the next step S2, the tire T normally rolls on the test surface 17. Then, while the tire T is rolling on the transparent plate 16, the camera 13 continuously captures a plurality of images of a predetermined range (hereinafter, this predetermined range will be referred to as the "photographing range") of the ground contact surface of the tire T. Take photos twice. An example of the photographing range 24 is shown in FIG. 2 by a two-dot chain line. As shown in FIG. 2, the imaging range 24 includes a plurality of center blocks 21, mediate blocks 22, and shoulder blocks 23, respectively. Photographing is performed at least from immediately before the photographing range 24 begins to touch the transparent plate 16 until it completely leaves the transparent plate 16. The time interval T between imaging in this step S2 and the next step S3 (hereinafter referred to as "imaging interval") is not limited, but for example, 1/24 to 1/120 seconds (1/24 second to 1/120 seconds).

Each photographed image shows a tread pattern and a colored point M. Each photographed image is taken into the processing device 14.

In the next step S3, the tire T runs on the test surface 17 again. At this time, so that the same photographing range 24 that was photographed in the previous step S2 is photographed in this step S3, the place where it rolled on the transparent plate 16 in the previous step S2 is also on the transparent plate 16 in this step S3. Roll on top of 16.

However, in this step S3, braking force is applied to the tire T while the tire T is passing over the transparent plate 16 or slightly before that. Then, while the tire T is rolling on the transparent plate 16 and under the influence of braking, the camera 13 continuously photographs images of the photographing range 24 of the tire T a plurality of times. As in the previous step S2, imaging is performed at least from immediately before the imaging range 24 begins to touch the transparent plate 16 until it completely leaves the transparent plate 16. Each photographed image is then taken into the processing device 14.

While the tread surface of the tire T is in contact with the ground, the surface of the tire T is slipping. Therefore, in the next step S4, the processing device 14 calculates the sliding speed at each point on the surface of the tire T in the first state.

In order to obtain the sliding speed, the processing device 14 first sets a plurality of feature points on the image photographed in step S2. The feature points are set using a predetermined algorithm based on the colored points M described above. As the predetermined algorithm, one of various algorithms such as FAST (Features from Accelerated Segment Test) algorithm and SIFT (Scale Invariant Feature Transform) algorithm is used. Here, when using the FAST algorithm for detecting corners in an image, if one colored point M is large, a plurality of feature points can be set for one colored point M. Furthermore, depending on the algorithm, it is also possible to set one feature point for one colored point M. It can be said that each of the set feature points is a point that is restrained by the surface of the tire T and moves together with the surface of the tire T. These feature points become targets for tracking in tracking processing, which will be described later.

Next, the processing device 14 determines a time-series change in the amount of displacement of each feature point based on the plurality of images taken in step S2. Here, the amount of displacement of a feature point is the distance that a feature point has moved from when one image is captured to when the next image is captured.

In order to explain the amount of displacement of a feature point, FIG. ). In this figure, the solid line represents an image taken when one image was taken, and the broken line represents an image taken when the next one image was taken. As shown in this figure, while the tread is in contact with the ground, the center block 21 is slipping, and the feature points are moving due to the slip between the two shootings. The arrow in the figure represents the displacement vector of one feature point. The displacement amount of the feature point is the length of this arrow. Note that since the camera 13 is fixed, the coordinates of the feature point in the image correspond to the coordinates of the feature point on the transparent plate 16. Therefore, the actual amount of displacement of the feature point on the transparent plate 16 can be determined from the amount of change in the coordinates of the feature point in the image.

The above is the amount of displacement of feature points, but the time-series change in the amount of displacement of feature points refers to the change in the amount of displacement of feature points over time, and can also be referred to as the time dependence of the amount of displacement of feature points. can.

Time-series changes in the amount of displacement of each feature point can be determined by known tracking processing. In the tracking process, each feature point is tracked. For example, the KLT (Kanade-Lucas-Tomasi) method is used for tracking processing. By tracking a plurality of feature points, time-series changes in the amount of displacement of those feature points are determined.

Next, the processing device 14 determines the sliding speed of each feature point at each time point based on the time-series change in the displacement amount of the feature point, and determines the time-series change in the sliding speed of each feature point. The time-series change in the sliding speed of a feature point refers to a change in the sliding speed of a feature point over time, and can also be referred to as the time dependence of the sliding speed of a feature point. In addition, a time point is any point in time between one shooting and the next shooting, for example, the middle point between two consecutive shootings, or the previous one of two consecutive shootings. Or, it refers to the later point in time when the photo was taken.

The sliding speed of the feature point at each point in time can be determined by dividing the amount of displacement of the feature point between two consecutively shot images by the shooting interval T. For example, the sliding speed at any point in time between the time of taking the first image and the time of taking the second image is the amount of displacement of the feature point from the time of taking the first image to the time of taking the second image. It can be determined by dividing by the photographing interval T (the time from when the first image is photographed to when the second image is photographed).

That is, if the distance (that is, the amount of displacement) that the feature point moved during the imaging interval T is L1, the sliding speed V1 of the feature point in the first state is

It is.

The processing device 14 thus calculates the sliding speed V1 for each feature point for which the time-series change in displacement amount has been calculated. Furthermore, the processing device 14 determines the sliding speed V1 at each time point in the time period including the period from just before the imaging range 24 starts to touch the transparent plate 16 until it completely leaves the transparent plate 16. Thereby, time-series changes in the sliding speed V1 of each feature point are determined.

In the next step S5, in the same manner as in the previous step S4, the processing device 14 sets feature points in the image taken in the second state, and determines the sliding speed of the feature points in the second state. Find the time series change of. Similarly to the above, if the distance (that is, the amount of displacement) that the feature point moved during the imaging interval T is L2, then the sliding speed in the second state is V2.

である。

It is.

The processing device 14 thus calculates the sliding speed V2 for each feature point for which the time-series change in displacement amount has been calculated. Furthermore, the processing device 14 determines the sliding speed V2 at each time point in the time period including the period from just before the imaging range 24 starts to touch the transparent plate 16 until it completely leaves the transparent plate 16. As a result, time-series changes in the sliding speed V2 of each feature point are determined.

Note that by calculating the sliding speeds V1 and V2 of each feature point at each time point, the processing device 14 can display the distribution of the sliding speeds V1 and V2 within a predetermined range at each time point.

In this embodiment, in the steps up to this point, it is assumed that the sliding speeds V1 and V2 have been determined for each feature point in the range including at least one shoulder block 23, one mediate block 22, and one center block 21. . In the following, attention will be paid to only one shoulder block 23, one mediate block 22, and one center block 21. Then, the sliding behavior of each of these blocks 21, 22, and 23 will be examined using only the time-series changes in the sliding speeds V1 and V2 of the feature points of these blocks 21, 22, and 23.

Here, FIG. 5 shows an example of a time-series change in the sliding speed V2 in the second state determined by the above method. FIG. 5 shows a time-series change in the average sliding speed V2avg in the second state for the tire T1 having the shoulder block 23, mediate block 22, and center block 21 as shown in FIG. In FIG. 5, (a) shows the time-series changes in the average sliding speed V2avg of the shoulder block 23, (b) the mediate block 22, and (c) the center block 21, respectively. Here, the average sliding speed V2avg is the average value of the sliding speeds V2 of a plurality of feature points within one block. Therefore, (a) to (c) of FIG. 5 are graphs in which the horizontal axis is time and the vertical axis is the average value of the sliding speed V2 of a plurality of feature points at each time point.

To explain how to view FIG. 5, FIG. 5(a) is enlarged and shown in FIG. In FIG. 6, the range indicated by A is the time period before the shoulder block 23 touches the ground. Since the shoulder block 23 is not in contact with the ground during this time period, the absolute value of the average sliding speed V2avg is calculated to be large.

Further, the position indicated by B is when the shoulder block 23 touches the ground, and the average sliding speed V2avg is slightly larger. Furthermore, the ranges indicated by C and D are the time periods during which the shoulder block 23 is in contact with the ground. The range shown by C is a time period when the feature point is in the sticky region, and the average sliding speed V2avg is 0 or almost 0. The range indicated by D is a time period when the feature point is in the slip region, and the absolute value of the average slip speed V2avg is larger than the range indicated by C. Further, the position indicated by E is when the shoulder block 23 leaves the transparent plate 16. In this way, the characteristic point is in the sticky region for a while after touching the ground and hardly slips, but then it enters the slipping region and slides significantly before leaving the transparent plate 16.

Further, the range indicated by F is the time period after the shoulder block 23 is separated from the transparent plate 16. Since the shoulder block 23 is not in contact with the ground during this time period, the absolute value of the average sliding speed V2avg is calculated to be large.

5(b) and 5(c) can be viewed in the same way as FIG. 6.

In this way, the sliding behavior of each block 21, 22, and 23 in the second state can be seen from the time-series changes in the average sliding speed V2avg. Similarly, the sliding behavior of each block 21, 22, and 23 in the first state is determined from the time-series change in the average sliding speed V1avg (that is, the average value of the sliding speed V1 of a plurality of feature points in one block). You can also do that.

Now, in the next step S6, for each block 21, 22, 23, the time-series change in relative average sliding speed in the second state when the average sliding speed V1avg in the first state is taken as a reference is processed. The device 14 determines.

To this end, the processing device 14 temporally correlates the time-series change in the average sliding speed V1avg in the first state with the time-series change in the average sliding speed V2avg in the second state. The methods are various and not limited.

For example, the processing device 14 identifies the moment when a specific feature point in the shoulder block 23 touches the ground in the first state based on the time-series change in the sliding speed V1. Furthermore, the processing device 14 identifies the moment when the same specific feature point touches the ground in the second state based on the time-series change in the sliding speed V2. Then, by matching these moments, the processing device 14 temporally analyzes the entire data on the time-series changes in the sliding speed V1 and the entire data on the time-series changes in the sliding speed V2 regarding the shoulder block 23. Make it correspond. Thereby, the time-series change in the average sliding speed V1avg and the time-series change in the average sliding speed V2avg of the shoulder block 23 can be made to correspond temporally. In the same way, the processing device 14 temporally corresponds the time-series changes in the average sliding speed V1avg and the time-series changes in the average sliding speed V2avg for each of the mediate block 22 and the center block 21. let

In addition, as a method of temporally correlating the time-series changes in the average sliding speed V1avg and the time-series changes in the average sliding speed V2avg, there is a method that uses the measurement results of the rotation angle of the tire T, and a method that uses the measurement results of the rotation angle of the tire T. There is a method of matching the shapes of the ground contact surfaces in two states.

After making the correspondence in this way, the processing device 14 calculates the relative sliding speed in the second state for each block 21, 22, and 23 based on the average sliding speed V1avg in the first state. . That is, the processing device 14 processes the blocks 21, 22, and 23 at each point in time.

を計算する。このようにして求まるＶｓを「相対滑り速度」と言うこととする。

Calculate. Vs determined in this way will be referred to as a "relative sliding speed."

The time-series change in the sliding speed V2 of the tire T1 was previously shown in FIG. 5, and FIG. 7 shows an example of the time-series change in the relative sliding speed Vs for the same tire T1. This FIG. 7 is a diagram of the relative sliding speed Vs obtained from the time-series change data of the average slip speed V2avg used to create FIG. 5 and the time-series change data of the average slip speed V1avg obtained separately. It is.

Since V1avg is the average sliding speed in the first state (normal rolling state) and V2avg is the average sliding speed in the second state (braking state), Figures 7(a) to (c) indicate that braking force is applied to tire T1. The figure shows the sliding behavior of each block 21, 22, and 23. Here, from a comparison between FIG. 5 and FIG. 7, the time period (ground contact time period) in which the blocks 21, 22, and 23 were in contact with the ground in FIG. 7 can be seen. Then, based on the integrated value of the relative slipping speed Vs during the contact time period, the maximum value of the relative slipping speed Vs during the contact time period, the length of the time period in which the relative slipping speed Vs shows a predetermined value, etc., the tire T1 is determined. The influence of the applied braking force on the sliding behavior of each block 21, 22, 23 can be quantitatively evaluated. For example, the amount of slippage resulting from the application of braking force to the tire T1 can be quantitatively determined from the integrated value of the relative slippage speed Vs during the ground contact time period. Furthermore, by comparing the sliding behavior of each block 21, 22, 23, it is also possible to quantitatively evaluate the balance of sliding between the blocks 21, 22, 23.

Such evaluation results can be used to improve the tread pattern of the tire T1. For example, if the evaluation results show that the balance of sliding between the blocks 21, 22, and 23 is poor, the designer changes the design of the tread pattern to improve the balance of sliding.

In this way, it is possible not only to evaluate the slipping behavior of one tire T1, but also to evaluate the slipping behavior of a plurality of tires. Here, an example will be described in which the difference in slipping behavior between two tires with different tread patterns is evaluated.

FIGS. 8 and 9 show examples of time-series changes in the average sliding speed V2avg and time-series changes in the relative sliding speed Vs of the tire T2, which is different from the tire T1. The tire T2 has a shoulder block 23, a mediate block 22, and a center block 21 like the tire T1, but the shapes of the blocks 21, 22, and 23 are slightly different from the tire T1.

In FIG. 10, a time-series change in the average sliding speed V2avg of the tire T1 in the second state and a time-series change in the average sliding speed V2avg of the tire T2 in the second state are shown superimposed. Further, in FIG. 11, the time-series change in the relative sliding speed Vs of the tire T1 and the time-series change in the relative sliding speed Vs of the tire T2 are shown superimposed.

Looking at FIG. 10, it can be seen that there is no significant difference in the time-series changes in the average sliding speed V2avg in the second state between the tire T1 and the tire T2. However, looking at FIG. 11, it can be seen that there is a large difference in the relative sliding speed Vs between the tire T1 and the tire T2. In other words, the time-series changes in the average sliding speed V2avg during braking are almost the same for tires T1 and T2, but when looking at the relative sliding speed Vs during braking with respect to the normal rolling time, It can be seen that there is a big difference between tire T1 and tire T2.

Specifically, in the shoulder block 23 and the center block 21, the value of the relative sliding speed Vs of the tire T2 starts to increase toward the peak from an earlier point in time. From this, it can be seen that slippage as an effect of applying braking force occurs earlier in the tire T2 at the shoulder block 23 and center block 21. The speed can be quantitatively determined from data on time-series changes in the relative sliding speed Vs.

If the slipping behavior of the two tires T1 and T2 can be evaluated in this way, it is possible to improve the tread pattern, etc. For example, it can be seen from the above example that slipping occurs too quickly in the shoulder block 23 and center block 21 of the tire T2, so as a countermeasure, the designer can change the design of the tread pattern of the tire T2.

Next, the effects of this embodiment will be explained.

As mentioned above, in this embodiment, not only the time-series changes in the sliding speeds V1 and V2 of the feature points in the first state and the second state are determined, but also in the second state when the first state is taken as a reference. Find the time-series changes in the relative sliding speed Vs. Thereby, it is possible to evaluate the sliding behavior accompanying the change in rolling state from the first state to the second state. As in the above embodiment, if the first state is the state during normal rolling and the second state is the state when braking force is applied to the first state, the effect of applying the braking force is It is possible to obtain a time-series change in the relative sliding speed Vs of the vehicle, and it is possible to evaluate the sliding behavior as an effect of applying the braking force. The evaluation results of the sliding behavior can be used to improve the tread pattern, etc.

Furthermore, by determining the time-series changes in the relative sliding speed Vs of each block 21, 22, 23, it is possible to evaluate the sliding behavior of each block 21, 22, 23, as well as , 23 can also be compared.

The above embodiments are illustrative, and the scope of the invention is not limited to the above embodiments. Various changes can be made to the above embodiments without departing from the spirit of the invention. A plurality of modification examples will be described below, but any one of the plurality of modification examples may be applied to the above embodiment, or any two or more of the plurality of modification examples may be combined. May be applied.

(Change example 1)
It is possible to select various states as the first state and the second state. For example, the first state can be a normal rolling state, and the second state can be a cornering state. Further, the first state may be a state during normal rolling, and the second state may be a state during acceleration.

(Change example 2)
In the embodiment described above, the image of the ground contact surface is taken with one camera 13, but the image may be taken with a plurality of cameras. You may also shoot a video.

(Change example 3)
In the above embodiment, the sliding velocities V1 and V2 are calculated for all the feature points in the photographing range 24, but before calculating the sliding velocities V1 and V2, the operator selects only some of the feature points. The processing device 14 may calculate the sliding velocities V1 and V2 for the calculated feature points, and perform the subsequent steps.

(Change example 4)
As in Modification Example 7, which will be described later, the slipping behavior may be evaluated over a wide range spanning a plurality of blocks 21, 22, and 23. However, in the embodiment described above, the colored points M are also provided at the bottom of the groove. Therefore, when feature points are set based on all the colored points M within the photographing range 24 as in the above embodiment and the sliding speeds V1 and V2 are calculated for those feature points, feature points are also set at the groove bottom. , the sliding velocities V1 and V2 are also calculated for the feature points on the groove bottom. Then, the data of the feature points at the bottom of the groove will be included in the final evaluation result, affecting the evaluation result.

In order to prevent this, the processing device 14 sets a plurality of feature points based on all the colored points M in a wide range including the groove bottom, and calculates the sliding speeds V1 and V2 for each of the feature points. It is preferable to remove data on the sliding velocities V1 and V2 regarding the feature points on the groove bottom.

The calculated sliding speeds of the feature points on the groove bottom and the feature points on the blocks 21, 22, and 23 are significantly different. Since the feature points on the blocks 21, 22, and 23 are grounded and restrained by the test surface 17, even if slippage occurs at these feature points, the amount of displacement is small and the sliding speed is small. On the other hand, the feature points on the groove bottom are not in contact with the ground and are not restrained by the test surface 17, and are further away from the transparent plate 16, so the amount of displacement is large and the calculated sliding speed is large.

Therefore, the processing device 14 compares the preset sliding speed threshold Vth with the sliding speeds V1 and V2 of each feature point found in steps S4 and S5, and determines that the sliding speeds V1 and V2 are lower than the threshold Vth. Remove large feature point data. As a result, the data on the sliding velocities V1 and V2 regarding the feature points on the groove bottom are removed.

By removing the data on the feature points at the groove bottom in this manner, it is possible to prevent the data on the feature points at the groove bottom from entering the final evaluation result and affecting the evaluation result. Here, by comparing the threshold value Vth with the sliding speeds V1 and V2 of each feature point and removing the data of the feature points whose sliding speeds V1 and V2 are larger than the threshold value Vth, the data of the feature points on the groove bottom can be easily obtained. and can be removed reliably.

(Change example 5)
The method of removing the data of the feature points of the groove bottom is not limited to the method of the above modification example using the threshold value Vth of the sliding speed.

For example, when the ground pressure of the tire T is measured, a large ground pressure occurs at the locations of the blocks 21, 22, and 23, and no ground pressure occurs at the groove 20 location. Therefore, the processing device 14 correlates the distribution of the ground contact pressure of the tire T with the image taken by the camera 13, removes the data of the feature points at locations where the ground pressure is 0 or a predetermined small value, and thereby The data of the bottom feature points may be removed.

(Change example 6)
In the step (S1) of applying colored points M to the ground contact surface of the tire T, the colored points M may be applied only to the surfaces of the blocks 21, 22, and 23, and the colored points M may not be applied to the groove bottoms. In that case, there is no need to remove the data of the feature points at the bottom of the groove.

(Change example 7)
In the above embodiment, the time-series changes in the relative sliding speed Vs for each block are determined and the sliding behavior is compared for each block, but the evaluation unit is not limited to this.

For example, one block is divided into two regions, the stepping side and the kicking side during rolling, and the time-series changes in the relative sliding speed Vs are determined for each of the two regions using the method of the above embodiment, and the two regions are You can also compare the sliding behavior of In this case, it is necessary to assign one or more colored points M to each of the two regions on the stepping side and the kicking side, and to set feature points in each region based on those colored points M. .

In addition, two regions (regions including a plurality of blocks) on the stepping side and the kicking side during rolling are extracted from the entire wide ground contact surface including a plurality of blocks, and the above embodiment is applied to each of the two regions. The time-series changes in the relative sliding speed Vs may be determined using the method described above, and the sliding behavior in the two regions may be compared. In this case, one or more colored points M are assigned to each of the two areas on the stepping side and the kicking side of the entire ground contact surface, and feature points are set in each area based on those colored points M. It is necessary to do so.

In addition, a shoulder rib consisting of a plurality of shoulder blocks 23 arranged in the tire circumferential direction, a mediate rib consisting of a plurality of mediate blocks 22 arranged in the tire circumferential direction, and a plurality of center blocks 21 arranged in the tire circumferential direction are arranged in the ground contact surface. Divide into a center rib consisting of. Then, for each rib, the time-series change in the relative sliding speed Vs may be obtained using the method of the above embodiment, and the sliding behavior of the three ribs may be compared. In this case, it is necessary to assign one or more colored points M to each of the three ribs, and to set feature points for each rib based on the colored points M.

Here, when the evaluation unit is a region or rib including a plurality of blocks 21, 22, 23, the groove 20 exists in the region or rib. However, if the processing device 14 removes the data of the feature points on the groove bottom using the method of the above modification, the sliding behavior of only the surfaces of the blocks 21, 22, and 23 can be controlled without being influenced by the data on the feature points on the groove bottom. can be evaluated.

(Change example 8)
The sliding behavior of the tire may be evaluated by converting the time-series change in the relative sliding speed Vs into the positional dependence of the relative sliding speed Vs on the contact surface. Alternatively, the time-series changes in the sliding speeds V1 and V2 are converted into the position dependence of the sliding speeds V1 and V2 on the contact surface, and the relative sliding speed is calculated based on the position dependence of the sliding speeds V1 and V2 on the contact surface. The position dependence of Vs on the ground contact surface may also be determined.

These conversions can be performed by multiplying the time of the chronological change by the rolling speed of the tire T.

For example, if the time of the data in FIG. 6 is multiplied by the rolling speed of the tire T, the sliding speed V2 at time 0 of the data in FIG. ) is converted into a sliding speed V2 at each position (each distance from the reference position). As a result, the horizontal axis in FIG. 6 is changed from time to position on the ground contact surface (distance from the reference position).

(Change example 9)
In the embodiment described above, the processing device 14 detects the time-series change in the average sliding speed V1avg of the plurality of feature points in the first state and the time-series change in the average sliding speed V2avg of the plurality of feature points in the second state. After the determination, based on the time-series changes in these average sliding speeds V1avg and V2avg, the time-series changes in the relative sliding speed Vs for each block 21, 22, and 23 were determined. However, there is also a method of determining time-series changes in the relative sliding speed Vs without determining the average sliding speeds V1avg and V2avg.

For example, the processing device 14 first associates each feature point in the image taken in the first state with each feature point in the image taken in the second state. Here, as mentioned above, the photographing range 24 is the same in the first state and the second state, and the same colored point M is photographed in the first state and the second state, so the image taken in the first state is It is possible to create a 1:1 correspondence between each feature point and each feature point in the image photographed in the second state. Moreover, since the distribution of the colored points M is random as described above, the distribution of the feature points is also random, and if there is a mistake in the correspondence, it will be clearly visible. This correspondence can be performed using a known pattern matching method.

Further, the processing device 14 temporally associates data on a time-series change in the sliding speed V1 in the first state with data on a time-series change in the sliding speed V2 in the second state. The method is the same as the method of associating the time-series changes in the average sliding speeds V1avg and V2avg in the above embodiment.

After making the correspondence in this way, the processing device 14 calculates the relative sliding speed in the second state for each feature point, based on the sliding speed V1 in the first state. That is, the processing device 14 performs the processing at each point in time for each feature point.

を計算する。このようにして相対滑り速度Ｖｓが求まる。相対滑り速度Ｖｓは、対応付けできた全ての特徴点について、かつ対応付けできた全ての時点について計算される。その計算結果から、特徴点毎の相対滑り速度Ｖｓの時系列変化が求まる。

Calculate. In this way, the relative sliding speed Vs is determined. The relative sliding speed Vs is calculated for all the feature points that can be matched and for all the points that can be matched. From the calculation results, time-series changes in relative slip velocity Vs for each feature point are determined.

Note that the above calculation makes it possible to display the distribution of relative sliding speed Vs within a predetermined range at each point in time.

After determining the time-series change in the relative sliding speed Vs for each feature point, the processing device 14 calculates the time-series change in the average value of the relative sliding speed Vs of the plurality of feature points in each block 21, 22, and 23. All you have to do is ask for it.

M... Coloring point, T... Tire, 10... Evaluation device, 11... Test stand, 12... Support part, 13... Camera, 14... Processing device, 15... Hole, 16... Transparent plate, 17... Test surface, 18... Input Device, 19...Display device, 20...Groove, 21...Center block, 22...Mediate block, 23...Shoulder block, 24...Photography range

Claims (6)

a step of taking images of the ground contact surface in the first state multiple times;
a step of capturing images of the ground contact surface in a second state different from the first state a plurality of times;
Setting feature points in images taken in the first state and the second state;
determining a time-series change in the sliding speed of the feature point in the first state based on a time-series change in the displacement amount of the feature point that becomes clear from the plurality of images in the first state;
determining a time-series change in the sliding speed of the feature point in the second state based on a time-series change in the displacement amount of the feature point that becomes clear from the plurality of images in the second state;
The time-series change in the sliding speed of the feature point in the first state and the time-series change in the sliding speed of the feature point in the second state are made to correspond temporally , and the first state is used as a reference. a step of determining a time-series change in relative sliding speed in a second state when
including;
The first state is a normal driving state in which the tires are rolled at a constant speed and a slip angle of 0° without applying torque,
The second state is a state during braking, cornering, driving, or braking or driving and cornering.
Method for evaluating tire slipping behavior. In the process of setting feature points in the captured image, feature points are also set at the bottom of the tire groove.
The method for evaluating the slipping behavior of a tire according to claim 1, wherein after the step of photographing images of the ground contact surface in the first state and the second state, data on the feature point set on the groove bottom is removed. In the process of setting feature points in the captured image, feature points are set in multiple blocks,
The method for evaluating tire slipping behavior according to claim 1 or 2, wherein the time-series change in the relative slipping speed is determined for each block. In the process of setting feature points in the photographed image, feature points are set in the stepping area and kicking area during rolling,
The method for evaluating the slipping behavior of a tire according to claim 1 or 2, wherein the time-series change in the relative sliding speed is determined for each of the stepping region and the kicking region. In the process of setting feature points in the captured image, feature points are set on multiple ribs,
The method for evaluating tire slipping behavior according to claim 1 or 2, wherein the time-series change in the relative slipping speed is determined for each rib. The method for evaluating the sliding behavior of a tire according to any one of claims 1 to 5, wherein a time-series change in sliding speed is converted into a positional dependence of the sliding speed on a contact surface.

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