Patent Application
20210309230
The present invention relates to a method and a device for determining a state of a road surface on which a vehicle travels, and more specifically, relates to a method for determining the road surface using only data of a time-series waveform of vibration of a running tire.
Conventionally, as a method for determining a state of a road surface by using only data of a time-series waveform of vibration of a running tire, there has been proposed a method in which a state of a road surface is determined by using a kernel function which is calculated from feature amounts in respective time windows calculated from time-series waveforms extracted by multiplying the time-series waveform of the tire vibration by a window function and from reference feature amounts which are feature amounts in the respective time windows obtained in advance for respective road surface states.
The reference feature amounts are obtained by machine learning (SVM) using, as learning data, the feature amount in each time window calculated from the time-series waveform of the tire vibration obtained in advance for each of road surface states (see Patent Document 1, for example).
However, in the conventional method, because a time stretching/contraction requires a significant amount of calculations even for determining two road surfaces of DRY/WET, there have been problems that the calculation takes a long time and the processing becomes very heavy.
The present invention has been made in view of the conventional problems and aims at improving the calculation speed by reducing the amount of calculations while securing the precision in determination of two road surfaces of DRY/WET, by suitably selecting a feature amount to be used for calculation of the kernel function.
The inventors have found, as a result of earnest examinations, that by detecting vibration of a tire generated at the time the tire collides with water on a road surface when the tire is running on the road surface, it is possible to determine whether the road surface is a WET road surface where a water curtain exists in a certain degree that causes generation of the tire vibration or a DRY road surface where the water curtain does not exist, and thus the inventors have reached the present invention.
From
Accordingly, when determining the state of the road surface using a kernel function, it is possible to improve the calculation speed by reducing the amount of calculations while securing the precision in determination of two road surfaces of DRY/WET, if only vibration levels of the frequency band of 500 Hz or greater are used as feature amounts for determination of the road surface, among the feature amounts extracted from the time-series waveform of the tire vibration.
Namely, the present invention relates to a method for determining a state of a road surface being in contact with a running tire, the method including: a step (a) of detecting vibration of the running tire with the use of a vibration detecting means provided inside of the tire; a step (b) of taking out a time-series waveform of the detected tire vibration; a step (c) of extracting a time-series waveform for each time window by multiplying the time-series waveform of the tire vibration by a window function of a predetermined time width; a step (d) of calculating a feature amount from the time-series waveform in each time window; a step (e) of calculating a kernel function from the feature amount in each time window calculated in the step (d) and a reference feature amount selected from feature amounts in the respective time windows calculated from a time series waveform of tire vibration obtained in advance for each road surface state; and a step (f) of determining the state of the road surface based on a value of a discriminant function using the kernel function, in which the feature amount in each time window calculated in the step (d) and the reference feature amount are either one of, or a plurality of, or all of a vibration level of a frequency band of 500 Hz or greater extracted from the time-series waveform in each time window, a time-varying dispersion of the vibration level of the frequency band, and a Cepstrum coefficient of the time-series waveform; and in which the step (f) includes determining whether the state of the road surface is a WET state in which a water curtain that collides with the running tire exists on the road surface, or a DRY state in which the water curtain does not exist.
Further, the present invention also relates to a road surface state determination device for determining a state of a road surface being in contact with a running tire, the device including: a tire vibration detecting means that is disposed on an air chamber side of an inner liner portion of a tire tread portion and that detects vibration of the running tire; a windowing means that windows, with a previously set time width, a time-series waveform of the tire vibration detected by the tire vibration detecting means to extract a time-series waveform of the tire vibration for each time window; a feature amount calculating means that calculates a feature amount having, as a component thereof, a vibration level of a specific frequency in the extracted time-series waveform in each time window, or a feature amount having, as a component thereof, a function of the vibration level of the specific frequency; a storage means that stores a reference feature amount selected from feature amounts in respective time windows calculated from a time-series waveform of tire vibration that has been calculated in advance for each road surface state; a kernel function calculating means that calculates a kernel function from the feature amount in each time window calculated by the feature amount calculating means and the reference feature amount stored in the storage means; and a road surface state determining means that determines the state of the road surface based on a value of a discriminant function using the kernel function, in which the feature amount in each time window calculated by the feature amount calculating means and the reference feature amount stored in the storage means are either one of, or a plurality of, or all of a vibration level of a frequency band of 500 Hz or greater extracted from the time-series waveform in each time window, a time-varying dispersion of the vibration level of the frequency band and a Cepstrum coefficient of the time-series waveform; and in which the road surface state determining means determines whether the state of the road surface is a WET state in which a water curtain that collides with the running tire exists on the road surface, or a DRY state in which the water curtain does not exist.
The summary of the invention does not enumerate all the features required for the present invention, and sub-combinations of these features may also become the invention.
Although the present invention is herein described in detail through an exemplary embodiment, however, the exemplary embodiment described below does not limit the inventions set forth in the claims, and not all of the combinations of the features described in the exemplary embodiment are necessarily required for the solving means of the invention.
The road surface state determination device 10 includes an acceleration sensor 11 as a tire vibration detecting means, a vibration waveform extracting means 12, a windowing means 13, a feature vector calculating means 14, a storage means 15, a kernel function calculating means 16, and a road surface state determining means 17, and determines whether the road surface on which a tire is running is a Wet state in which a water curtain that collides with the running tire exists on the road surface, or a DRY state in which the water curtain does not exist.
The vibration waveform extracting means 12 to the road surface state determining means 17 are each configured, for example, by computer software and a memory such as a RAM.
The acceleration sensor 11 is, as illustrated in
The vibration waveform extracting means 12 extracts, for each one rotation of the tire, a time-series waveform of the tire vibration, from the signal of the tire vibration detected by the acceleration sensor 11.
As illustrated in
The windowing means 13, as illustrated in
Incidentally, as described above, since the time-series waveforms of the outside-road-surface regions do not include the information on the road surface, in the present exemplary embodiment, only the time-series waveform of the road surface region is sent to the feature vector calculating means 14 so as to accelerate the speed of calculation of the kernel function.
Meantime, the outside-road-surface region may be defined in such a manner that, for example, a background level is set for the time-series waveform of the tire vibration, and a region having a vibration level lower than the background level is determined to be the outside-road-surface region.
The feature vector calculating means 14, as illustrated in
In the present exemplary embodiment, as the feature vector Xi to be calculated, vibration levels (power values of filter filtration waves) aik (k=1-3) of specific frequency bands obtained by allowing the time-series waveform of the tire vibration to pass through respective band path filters of 2-3 kHz, 3-4 kHz and 4-5 kHz, were used.
The feature vector is Xi=(ai1, ai2, ai3), and the number of the feature vectors Xi is N.
In
Here, the “WET road surface” refers to a road surface where the water curtain that collides with the running tire exists and the “DRY road surface” refers to a road surface where the water curtain does not exist.
The storage means 15 stores reference feature vectors YASV (yjk), which have been obtained in advance and which are reference feature amounts for separating the DRY road surface and the WET road surface by a discriminant function f (x) representing a separate hyperplane, and a Lagrange multiplier λA for weighting the reference feature vectors YASV.
The reference feature amounts (YASV (yjk) and λA) are obtained by learning using, as input data, road surface feature vectors YA (yjk), which are each a feature vector in each time windows, calculated from time-series waveforms of the tire vibration obtained by running, on the DRY road surface and the WET road surface at various speeds, a test vehicle loaded with a tire having the acceleration sensor attached thereto.
The tire size to be used in the learning may be one type or may be plural types.
The suffix A of the reference feature vector YASV (yjk) represents DRY or WET.
The suffix j (j=1−M) represents a window number of the time-series waveform extracted for each time window, and the suffix k represents a vector component (k=1-3). Namely, yjk=(aj1, aj2, aj3). Further, SV is an abbreviation for the support vector.
Incidentally, in a case where the global alignment kernel function is used, as in the present exemplary embodiment, the reference feature vectors YASV (yjk) become matrixes of the number of dimensions (here, 3×M (M: the number of windows) of the vector yi.
Hereinafter, the road surface feature vector YA (yjk) and the reference feature vector YASV (yjk) are expressed as YA and YASV, respectively.
The method for calculating the road surface feature vector YA is such that, as similar to the above-described feature vector Xj, for a reference feature vector YD of the DRY road surface, for example, the time-series waveform of the tire vibration when running on the DRY road surface is windowed by the time width T to extract a time-series waveform of the tire vibration for each time window, and the DRY road surface feature vector YD is calculated for each of the extracted time-series waveforms in each time window. Similarly, a WET road surface feature vector YW is calculated from each of the time-series waveforms in each time window when running on the WET road surface.
The reference feature vectors YASV are feature vectors selected as support vectors by the support vector machine (SVM) using, as learning data, the DRY road surface feature vector YD and the WET road surface feature vector YW.
Incidentally, all of the reference feature vectors YASV are not necessarily stored in the storage means 15, in general, it is sufficient to store only the support vectors YASV whose Lagrange multiplier a has a predetermined value λmin (for example, λmin=0.05) or greater, as the reference feature vectors YASV.
Here, it is vital that the time width T is the same value as the time width T for obtaining the feature vector Xj. When the time width T is constant, the number M of the time-series waveforms in the time windows differs depending a tire type and a vehicle speed. That is, the number M of the time-series waveforms in the time windows of the road surface feature vector YA do not necessarily coincide with the number N of the time-series waveforms in the time windows of the feature vector Xj. For example, in a case where a vehicle speed when obtaining the feature vector Xj is slower than a vehicle speed when obtaining the road surface feature vector YA, M>N holds, and when faster, M<N holds.
As described above, the DRY road surface feature vector YD and the WET road surface feature vector YW are both matrixes, however, for explaining the manner of obtaining discrimination boundaries of groups, the DRY road surface feature vector YD and the WET road surface feature vector YW are shown in two-dimensional vectors, respectively.
Generally, linear separation of the discrimination boundaries of the groups is impossible. Therefore, the linear separation is performed, with the use of the kernel method, by mapping the road surface feature vectors YD and YW into a high-dimensional feature space by non-linear map φ, so as to perform non-linear classification for the road surface feature vectors YD and YW in the original input space.
To distinguish the DRY road surface from the WET road surface, a margin is given to a discriminant function f (x), which is the separation hyperplane that separates the DRY road surface feature vector YD from the WET road surface feature vector YW, whereby the DRY road surface and the WET road surface are precisely distinguished.
The margin refers to a distance to a sample that is nearest from the separation hyperplane, and the separation hyperplane, which is the discrimination boundary, is f (x)=0. Further, all the DRY load surface feature vectors YDj exist in the region of f (x)=≥+1, and all the WET road surface feature vectors YWj exist in the region of f (x)≤−1.
Next, by using aggregation of data X=(x1, x2, . . . xn) and belonging classes z={1, −1}, an optimum discriminant function f (x)=wT φ(x)−b for discriminating data is obtained. Here, w is a vector representing a weighting factor and b is a constant.
The data are the DRY road surface feature vector YDj and the WET road surface feature vector YWj. As to the belonging class, z=1 represents data of the DRY road surface indicated by χ1, and z=−1 represents data of the WET road surface indicated by χ2 in
The discriminant function f (x)=wT φ(x)−b is optimized, by using the Lagrange undetermined multiplier method, for example. The optimization problem is replaced with the following equations (1) and (2).
Here, α and β are indices of plural learning data. Also, λ is the Lagrange multiplier, and the road surface feature vector YAj whose λ=0 is vector data that is not involved in (not the support data) the discriminant function f (x).
At this time, by replacing an inner product φT(xα)φ(xβ) with the kernel function K (xα, xβ), the discriminant function f (x)=wTφ(x)−b can be non-linearized.
The φT(xα)φ(xβ) is the inner product obtained after mapping the xα and the xβ by the map p into a high-dimensional space.
The Lagrange multiplier λ can be obtained by using, in the equation (2), an optimization algorithm such as the steepest descent method, the sequential minimal optimization (SMO) and the like. In this manner, by replacing to the kernel function K (xα, xβ) without directly obtaining the inner product φT(xα)φ(xβ), it becomes unnecessary to directly obtain an inner product of high dimensions. Accordingly, the calculation time can be reduced remarkably.
In the present exemplary embodiment, as the kernel function K (xα, xβ), a global alignment kernel function (GA kernel) is used.
The GA kernel K (xα, xβ) is, as shown in
Here, ∥xαi, xβj∥ is a distance (norm) between the feature vectors and σ is a constant.
The kernel function calculating means 16 calculates DRY GA kernels KD (X, YDSV) and WET GA kernels KW (X, YWSV), from the feature vector Xi calculated by the feature vector calculating means 14 and the reference feature vector YDSV of the DRY road surface and the reference feature vector YWSV of the WET road surface that are stored in the storage means 15.
The DRY GA kernel KD (X, YDSV) is a function formed of a total sum or a total product of local kernels κij (Xi, YDSVj), which are obtained when the feature vector xα in the above equations (3) and (4) is replaced with the feature vector Xi calculated by the feature vector calculation means 14 and the feature vector xβ in the above equations (3) and (4) is replaced with the reference feature vector YDSVj of the DRY road surface, and the WET GA kernel KW (X, YWSV) is a function formed of a total sum or a total product of local kernels κij (Xi, YWSVj) when the feature vector xβ is replaced with the reference feature vector YWSVj of the WET road surface. By using these GA kernels KD (X, YDSV) and KW (X, YWSV), time-series waveforms with different time lengths can directly be compared.
Incidentally, as described above, even in a case where the number n of the time-series waveforms in the time windows when obtaining the feature vector Xi and the number m of the time-series waveforms in the time windows when obtaining the road surface feature vector YAj are different, a degree of similarity between the feature vector Xi and the reference feature vector YASVj can be obtained.
The road surface determining means 17 determines the road surface state based on a value of the discriminant function fDW (x) using the kernel function KD (X, Y) and the kernel function KW (X, Y) shown in the following equation (5).
Here, NDSV is the number of the reference feature vectors YDSVj of the DRY road surface and NWSV is the number of the reference feature vectors YWSVj of the WET road surface.
In the present exemplary embodiment, the discriminant function fDW is calculated and if fDW>0, it is determined that the road surface is the DRY road surface, and if fDW<0, it is determined that the road surface is the WET road surface.
Next, an explanation is given, by referring to the flowchart of
First of all, a tire vibration, which is generated due to an input from a road surface R on which the tire 20 is running, is detected by the acceleration sensor 11 (Step 10), a time-series waveform of the tire vibration is extracted from a signal of the detected tire vibration (Step 11).
Then, the extracted time-series waveform of the tire vibration is windowed with a previously set time width T, and a time-series waveform of the tire vibration in each time window is obtained. Here, the number of the time-series waveforms of the tire vibration in the respective time windows is set to m (Step 12).
Next, the feature vectors Xi=(xi1, xi2, xi3) are calculated for each of the extracted time-series waveforms in each time window (Step 13). In the present exemplary embodiment, the time width T is set to 3 msec. Further, the number of the feature vectors Xi is six (6).
Each component xi1-xi3 (i=1-6) of the feature vector Xi is, as described above, the power value of the filter filtration wave of the time-series waveform of the tire vibration.
Next, from the calculated feature vectors Xi and the reference feature vectors YASVj of the DRY road surface and the WET road surface stored in the storage means 15, the local kernels κij (Xi, YASVj) are calculated and thereafter a total sum of the local kernels κij (Xi, YASV) is obtained, and each of the GA kernel functions KA (X, YASV) is calculated (Step 14).
The kernel function KD (X, YDSV) in which λ=D is the GA kernel function of the DRY road surface and the kernel function KW (X, YWSV) in which A=W is the GA kernel function of the WET road surface.
Then, the discriminant function fDW (x) using the GA kernel function KD of the DRY road surface and the GA kernel function KW of the WET road surface is calculated (Step 15), and if fDW>0, it is determined that the road surface is the DRY road surface, and if fDW<0, it is determined that the road surface is the WET road surface (Step 16).
In this manner, in the present exemplary embodiment, it is so arranged that the time-series waveform of the tire vibration detected by the acceleration sensor 11 is windowed by the windowing means 13, the time-series waveform of the tire vibration in each time window is extracted to calculate the feature vector Xi. Thereafter, the kernel functions KA (X, YASVj) of the feature vector Xi and the reference feature vector YASVj of each road surface are obtained, and from a value of the discriminant function fDW (X) using these kernel functions KA (X, YASVj), a road surface state of the road surface on which the tire 20 is running is determined. With this arrangement, the road surface state can be determined without detecting a peak position and without measuring a tire speed.
Furthermore, because the feature amount to be used for calculation of the kernel function is limited only to a high-frequency component, the amount of calculation can be reduced, and because a feature amount having a large difference between the DRY road surface and the WET road surface is used as the feature amount, the precision in determination of two road surfaces of DRY/WET can also be improved.
Table 1 is a table showing comparison between a data storage capacity when frequency bands of the feature vector Xi and the road surface feature vector YAj, which are the feature amounts, are high frequency bands (2-5 kHz) and a data storage capacity when the frequency bands are all of the frequency bands (0-5 kHz).
As apparent from the table, by setting the frequency bands of the feature amounts to be extracted to the high frequency band (2-5 kHz), data storage capacity was reduced to 44% of the data storage capacity when all of the frequency bands (0-5 kHz) are used.
With this, it has been confirmed that when the feature amount to be used for calculation of the kernel function is limited only to a high-frequency component, the amount of calculation can be reduced, and also the precision in determination of two road surfaces of DRY/WET can be improved.
Furthermore, because determination of the road surface can be performed regardless of a ground contact length, a robustnesss can be improved.
As a concrete example of robustnesss improvement, a result of search of degradation of precision in determination of two road surfaces of DRY/WET depending on the attachment position of the acceleration sensor 11 is shown by the graph in
In the graph, the horizontal axis represents sensor positions and A is an amount (mm) of deviation from a tire width direction center (center), and the vertical axis represents a road surface determination precision (%).
As apparent from the graph, it is noticed that in the present invention, when the frequency band of the feature amount is in the high frequency band, degradation of the determination precision due to deviation of the sensor position can be more effectively controlled, compared to a case where the frequency band of the feature amount is in all the frequency bands (0-5 kHz).
In the present exemplary embodiment, the frequency bands of the feature vectors Xi were 2-5 kHz and, the feature vectors Xi to be calculated were set to vibration levels (the power value of the filter filtration wave) aik (k=1-3) of the specific frequency bands obtained by allowing the time-series waveforms of the tire vibration to pass through respective band filters of 2-3 kHz, 3-4 kHz and 4-5 kHz. However, the frequency bands of the feature vectors Xi to be extracted are not limited to these bands, the frequency bands for extracting the feature vectors Xi may be changed such that, for example, the frequency bands are set to 0.5-5 kHz and the feature vectors Xi are of the power values of five filter filtration waves of 0.5-1 kHz, 1-2 kHz, 2-3 kHz, 3-4 kHz and 4-5 kHz. In short, it is sufficient to set the frequency bands for extracting the feature vectors Xi to 500 Hz or greater which is suitable for determining whether the road surface on which the tire is running is the DRY road surface or the WET road surface.
In the present exemplary embodiment, the tire vibration detecting means is the acceleration sensor 11, however, other vibration detecting means such as a pressure sensor may be used. Further, with respect to the mounting position of the acceleration sensor 11, one acceleration sensor 11 may be disposed at each of positions separated by a predetermined distance in the width direction from the center of the tire width direction, or may be disposed at other position such as inside of a block. Furthermore, the number of the acceleration sensor 11 is not limited to one, but may be disposed at plural positions in the tire circumferential direction.
In the present exemplary embodiment, the feature vector Xi is set to the power value xik of the filter filtration wave, however, time-varying variance (log [xik(t)2+xik(t−1)2] of the power value xik of the filter filtration wave may be used. Or, the feature vector Xi may be a Fourier coefficient that is a vibration level of the specific frequency band when the time-series waveform of the tire vibration is subjected to Fourier conversion, or may be a Cepstrum coefficient. The Cepstrum coefficient can be obtained by assuming the Fourier-converted waveform to be the spectrum waveform and again Fourier converting the Fourier-converted waveform, or assuming an AR spectrum to be the waveform and further obtaining an AR coefficient (LPC Cepstrum). This enables to characterize the shape of the spectrum without being affected by the absolute level, and therefore, the determination precision is improved compared to a case where the frequency spectrum obtained by Fourier conversion is used.
In the present exemplary embodiment, the feature vector Xi is obtained by extracting the time-series waveform of the road surface region from the time-series waveform of the tire vibration, however, as illustrated in
In the present exemplary embodiment, determination of two road surfaces of DRY/WET is performed, however, the present invention may be applied, not only to determination of the two road surfaces, but also to determination of four road surfaces of DRY/WET/ICE/SNOW and so on.
In the present exemplary embodiment, the GA kernel is used as the kernel function, however, a dynamic time warping kernel function (DTW kernel) may be used. Or, the GA kernel and a DTW kernel arithmetic value may be used.
Although the present invention has been described using the exemplary embodiment, the present invention can also be described as follows. That is, the present invention provides a method for determining a state of a road surface being in contact with a running tire, the method including: a step (a) of detecting vibration of the running tire with the use of a vibration detecting means provided inside of the tire; a step (b) of taking out a time-series waveform of the detected tire vibration; a step (c) of extracting a time-series waveform for each time window by multiplying the time-series waveform of the tire vibration by a window function of a predetermined time width; a step (d) of calculating a feature amount from the time-series waveform in each time window; a step (e) of calculating a kernel function from the feature amount in each time window calculated in the step (d) and a reference feature amount selected from feature amounts in the respective time windows calculated from a time series waveform of tire vibration obtained in advance for each road surface state; and a step (f) of determining the state of the road surface based on a value of a discriminant function using the kernel function, in which the feature amount in each time window calculated in the step (d) and the reference feature amount are either one of, or a plurality of, or all of a vibration level of a frequency band of 500 Hz or greater extracted from the time-series waveform in each time window, a time-varying dispersion of the vibration level of the frequency band, and a Cepstrum coefficient of the time-series waveform; and in which the step (f) includes determining whether the state of the road surface is a WET state in which a water curtain that collides with the running tire exists on the road surface, or a DRY state in which the water curtain does not exist.
More specifically, the “reference feature amount” is obtained by machine learning (SVM) using, as learning data, the feature amount in each time window calculated from the time-series waveform of tire vibration obtained in advance for each of road surface states.
In this way, as the feature amount used in calculation of the kernel function, since only a vibration level of a high-frequency component of 500 Hz or greater extracted from the time-series waveform in each time window is used, the amount of calculation can be reduced and the calculation speed can be improved. Further, since a feature amount having a large difference between the DRY road surface and the WET road surface is used as the feature amount, it is also possible to improve the precision in determination of two road surfaces of DRY/WET.
Furthermore, since as the kernel function, the global alignment kernel function, or the dynamic time warping kernel function, or the arithmetic value of the kernel function is used, the precision in determination of the road surface state was improved.
Further, because the windowing means windows the time-series waveform of the pre-step-in including the step-in point, in which the vibration level of the frequency band of 500 Hz or greater becomes higher in particular, to extract the time-series waveform in each time window, the amount of calculation was further improved while the precision in determination of two road surfaces of DRY/WET was sufficiently secured.
Further, the present invention provides a road surface state determination device for determining a state of a road surface being in contact with a running tire, the device including: a tire vibration detecting means that is disposed on an air chamber side of an inner liner portion of a tire tread portion and that detects vibration of the running tire; a windowing means that windows, with a previously set time width, a time-series waveform of the tire vibration detected by the tire vibration detecting means to extract a time-series waveform of the tire vibration for each time window; a feature amount calculating means that calculates a feature amount having, as a component thereof, a vibration level of a specific frequency in the extracted time-series waveform in each time window, or a feature amount having, as a component thereof, a function of the vibration level of the specific frequency; a storage means that stores a reference feature amount selected from feature amounts in respective time windows calculated from a time-series waveform of tire vibration that has been calculated in advance for each road surface state; a kernel function calculating means that calculates a kernel function from the feature amount in each time window calculated by the feature amount calculating means and the reference feature amount stored in the storage means; and a road surface state determining means that determines the state of the road surface based on a value of a discriminant function using the kernel function, in which the feature amount in each time window calculated by the feature amount calculating means and the reference feature amount stored in the storage means are either one of, or a plurality of, or all of a vibration level of a frequency band of 500 Hz or greater extracted from the time-series waveform in each time window, a time-varying dispersion of the vibration level of the frequency band and a Cepstrum coefficient of the time-series waveform; and in which the road surface state determining means determines whether the state of the road surface is a WET state in which a water curtain that collides with the running tire exists on the road surface, or a DRY state in which the water curtain does not exist.
With such a configuration mentioned above, it is possible to realize a road surface state determination device that is capable of reducing the amount of calculations and that is capable of determining two road surfaces of DRY/WET with a high precision.
10: Road surface state determination device, 11: Acceleration sensor, 12: Vibration waveform detecting means, 13: Windowing means, 14: Feature vector calculating means, 15: Storage means, 16: Kernel function calculating means, 17: Road surface state determining means, 20: Tire, 21: Inner liner portion, and 22: Tire air chamber.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2017-119103 | Jun 2017 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2018/017083 | 4/26/2018 | WO | 00 |
