WO2019244380A1 - Procédé de détermination d'état de surface de chaussée et dispositif de détermination d'état de surface de chaussée - Google Patents

Procédé de détermination d'état de surface de chaussée et dispositif de détermination d'état de surface de chaussée Download PDF

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Publication number
WO2019244380A1
WO2019244380A1 PCT/JP2018/047164 JP2018047164W WO2019244380A1 WO 2019244380 A1 WO2019244380 A1 WO 2019244380A1 JP 2018047164 W JP2018047164 W JP 2018047164W WO 2019244380 A1 WO2019244380 A1 WO 2019244380A1
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Prior art keywords
time
road surface
tire
series waveform
kernel function
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English (en)
Japanese (ja)
Inventor
啓太 石井
剛 真砂
嵩人 後藤
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Bridgestone Corp
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Bridgestone Corp
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
    • B60C19/00—Tyre parts or constructions not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/068—Road friction coefficient
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology

Definitions

  • the present invention relates to a method and an apparatus for determining a road surface state using only data of a time-series waveform of tire vibration during running.
  • a time window calculated from a time series waveform extracted by multiplying a time series waveform of a tire vibration by a window function is used.
  • a method has been proposed in which a road surface state is determined using a kernel function calculated from a characteristic amount for each road surface and a reference characteristic amount, which is a characteristic amount for each time window, obtained in advance for each road surface state.
  • the reference feature amount is obtained by machine learning (SVM) using, as learning data, a feature amount for each time window calculated from a time series waveform of tire vibration previously obtained for each road surface condition (for example, see Patent Document 1). ).
  • the present invention has been made in view of the conventional problems, and provides a road surface state determination method and a road surface state determination device that can ensure the road surface state determination accuracy even when the amount of time expansion / contraction calculation is reduced.
  • the purpose is to:
  • the present invention provides a step (a) of detecting a vibration of a tire during running, a step (b) of extracting a time-series waveform of the detected tire vibration, and a step of adding a predetermined time width to the time-series waveform of the tire vibration.
  • the vibration level of a specific frequency band of the time-series waveform for each time window extracted by applying the window function may be used.
  • the vibration level of the specific frequency band may be a frequency spectrum of a time-series waveform for each time window extracted by applying the window function, or a time-series waveform for each time window extracted by applying the window function.
  • the accuracy of determining the time-series waveform road surface state obtained through the filter can be improved.
  • the kernel function is a global alignment kernel function, a dynamic time warping kernel function, or an operation value of the kernel function, it is possible to improve the determination accuracy of the road surface state.
  • the present invention provides a tire vibration detecting means disposed on an air chamber side of an inner liner part of a tire tread part for detecting vibration of a running tire, and the tire vibration detected by the tire vibration detecting means.
  • Windowing means for windowing the time-series waveform of the predetermined time width to extract a time-series waveform of tire vibration for each time window, and a vibration level of a specific frequency in the extracted time-series waveform for each time window.
  • a feature amount calculating means for calculating a feature amount having a component of the vibration level as a component, and a time window calculated from a time series waveform of tire vibration for each road surface condition calculated in advance.
  • Storage means for storing a reference feature quantity selected from the feature quantities of the above and a Lagrange undetermined multiplier corresponding to the reference feature quantity; a feature quantity for each time window calculated by the feature quantity calculation means; A kernel function calculating unit that calculates a kernel function from the stored reference feature amount; and a road surface state determining unit that determines a road surface state based on a value of an identification function using the kernel function.
  • the storage means changes the number of digits n 0 after the decimal point of the calculated reference feature amount to a number of digits n smaller than n 0 and stores the changed value.
  • Numerical accuracy reducing means for changing the number of digits n 0 of the feature amount for each time window calculated by the amount calculating means to n smaller than n 0 is provided, and the kernel function calculating means changes the number of digits to n.
  • a kernel function is calculated from the feature amount for each time window and the reference feature amount.
  • FIG. 4 is a diagram illustrating a method for calculating a GA kernel. 4 is a flowchart illustrating a road surface state determination method according to the present invention.
  • FIG. 7 is a diagram showing a numerical example of a feature matrix stored in a storage unit. It is the figure which compared the discrimination accuracy of the road surface state by the difference of numerical accuracy.
  • FIG. 1 is a diagram illustrating a configuration of a road surface state determination device 10 according to the present embodiment.
  • the road surface condition determination device 10 includes an acceleration sensor 11 as a tire vibration detection unit, a vibration waveform extraction unit 12, a windowing unit 13, a feature vector calculation unit 14, a numerical accuracy reduction unit 15, a storage unit 16,
  • the vehicle includes a kernel function calculating unit 17 and a road surface state determining unit 18 and performs two road surface determinations as to whether the road surface on which the tire 20 is traveling is a DRY road surface or a WET road surface.
  • Each unit from the vibration waveform extracting unit 12 to the road surface state determining unit 18 is composed of, for example, computer software and a memory such as a RAM. As shown in FIG.
  • the acceleration sensor 11 is disposed integrally at a substantially central portion of the inner liner portion 21 of the tire 20 on the tire air chamber 22 side, and detects vibration of the tire 20 due to input from a road surface.
  • the tire vibration signal output from the acceleration sensor 11 is, for example, amplified by an amplifier, converted into a digital signal, and sent to the vibration waveform extracting means 12.
  • the vibration waveform extracting means 12 extracts a time series waveform of the tire vibration for each rotation of the tire from the signal of the tire vibration detected by the acceleration sensor 11.
  • FIG. 3 is a diagram showing an example of a time series waveform of the tire vibration.
  • the time series waveform of the tire vibration has large peaks near the stepping position and the kicking position, and the land portion of the tire 20 is in contact with the ground.
  • the kick-out region R k after the land portion of the tire 20 is separated from the road surface, and the ground contact region R s in which the land portion of the tire 20 is in contact with the road surface, Different vibrations appear depending on the state.
  • the area before the stepping-in area Rf and the area after the kicking-out area Rk are hardly affected by the road surface, so that the vibration level is small and the road surface information is low. Not included.
  • Up area R k after kicking from depression before area R f hereinafter referred to as the road surface area.
  • the windowing means 13 windows the extracted time-series waveform with a predetermined time width (also referred to as a time window width) ⁇ T, and generates a time-series waveform of tire vibration for each time window. It is extracted and sent to the feature vector calculation means 14.
  • T s in the figure, a time width of the road area.
  • the time-series waveform of the road surface area does not include the information of the road surface, in order to increase the calculation speed of the kernel function, in this example, only the time-series waveform of the road surface area is used as the feature vector calculation means. I send it to 14.
  • a background level may be set for a time-series waveform of tire vibration, and an area having a vibration level smaller than the background level may be set as the off-road area.
  • the time series waveforms of the tire vibration are used as the feature vectors X i to be calculated, and the band-pass filters of 0-0.5 kHz, 0.5-1 kHz, 1-2 kHz, 2-3 kHz, 3-4 kHz, and 4-5 kHz, respectively.
  • the vibration level (power value of the filtered wave) a ik (k 1 to 6) of the specific frequency band obtained by passing through each of them was used.
  • FIG. 5 is a schematic diagram showing the input space of feature vectors X i, each axis represents the vibration level a ik of a specific frequency band, which is a feature quantity, each point representing a feature vector X i.
  • the actual input space is a seven-dimensional space when combined with the time axis because the number of specific frequency bands is three. However, the figure is expressed in two dimensions (the horizontal axis is a 1 and the vertical axis is a 2 ).
  • a set of feature vectors X i when the group C travels the DRY road when group C be the set of i 'is the feature vector X of when traveling the WET road', and the group C If the tire can be distinguished from the group C ′, it can be determined whether the road on which the tires are traveling is a DRY road surface or a WET road surface.
  • the numerical precision reducing unit 15 is a unit that changes the number of digits after the decimal point of the input data x from the current n 0 to n smaller than n 0 .
  • the number of digits n 0 below the decimal point of the feature vector X i calculated by the feature vector calculation means 14 is changed to a number n of digits smaller than n 0 by the numerical precision reduction means 15.
  • the storage unit 16 stores a DW identification model for identifying a DRY road surface and a WET road surface, which is obtained in advance.
  • the DW identification model includes a reference feature vector Y AK (y jk ), which is a reference feature amount for separating a DRY road surface and a WET road surface by an identification function f (x) representing a separation hyperplane, and a reference feature vector Y AK ( y jk ) and a Lagrange multiplier ⁇ A.
  • the reference feature vectors Y AK (y jk ) and ⁇ A are the values of tire vibration obtained by running a test vehicle equipped with a tire to which the acceleration sensor 11 is mounted at various speeds on a DRY road surface and a WET road surface.
  • the subscript A of the reference feature vector Y AK (y jk ) indicates DRY or WET.
  • SV is an abbreviation for support vector.
  • the reference feature vector Y AK (y jk ) is the number of dimensions of the vector y i (here, 6 ⁇ M (M; number of windows)). Of the matrix.
  • the method of calculating the road surface feature vector Y A is the same as the feature vector X j described above, for example, if the reference feature vectors Y D of DRY road, the time-series waveform of tire vibrations when traveling along DRY road in time width ⁇ T and windowing, to extract the time-series waveform of tire vibrations per time window, calculates DRY road feature vector Y D for each of the time-series waveform of each time window is extracted. Similarly, the WET road surface feature vector Y W is calculated from a time-series waveform for each time window when traveling on a WET road surface.
  • the number M of time-series waveforms in the time window differs depending on the tire type and the vehicle speed. That is, the number M of time-series waveforms in the time window of the road surface feature vector Y AK does not always match the number N of time-series waveforms in the time window of the feature vector X j .
  • the reference feature vector Y AK obtained above is stored in the storage unit 16 after the number of digits n 0 below the decimal point is changed to a number of digits n smaller than n 0 .
  • a unit similar to the numerical accuracy reducing unit 15 described above may be used.
  • Figure 6 is a conceptual diagram showing a DRY road feature vector Y D and WET road feature vector Y W in the input space, black circles in the figure is DRY road, open circles are WET road.
  • a DRY road feature vectors Y D also WET road feature vector Y W also matrices, for explaining how to determine the decision boundary of the group
  • DRY road feature vectors Y D and WET The road surface feature vector Y W is shown as a two-dimensional vector. Group identification boundaries generally do not allow linear separation.
  • the road surface feature vectors Y V and Y W are mapped to a high-dimensional feature space by a non-linear mapping ⁇ to perform linear separation, so that the road surface feature vectors Y D and Y W are obtained in the original input space.
  • Non-linear classification is performed.
  • a margin is provided for an identification function f (x) that is a separating hyperplane that separates the DRY road surface feature vector Y Dj and the WET road surface feature vector Y Wj .
  • the DRY road surface and the WET road surface can be accurately distinguished.
  • the DRY road surface feature vectors Y Dj are all in the region of f (x) ⁇ + 1, and the WET road surface feature vectors Y Wj are all in the region of f (x) ⁇ ⁇ 1.
  • the optimal identification function f (x) w for identifying the data.
  • T ⁇ (x) -b w
  • w is a vector representing a weight coefficient
  • b is a constant.
  • the optimization problem is replaced by the following equations (1) and (2).
  • ⁇ and ⁇ are indexes of a plurality of learning data.
  • ⁇ is a Lagrange multiplier
  • ⁇ T (x ⁇ ) ⁇ (x ⁇ ) is an inner product after x ⁇ and x ⁇ are mapped to a high-dimensional space by a mapping ⁇ .
  • the Lagrange multiplier ⁇ can be obtained by using an optimization algorithm such as the steepest descent method or SMO (Sequential Minimal Optimization) for the above equation (2).
  • the GA kernel K (x ⁇ , x ⁇ ) is a local kernel ⁇ ij (indicating the similarity between the feature vector x ⁇ and the feature vector x ⁇ ) x ⁇ i , x ⁇ j ) can be directly compared with time series waveforms having different time lengths using a function composed of the sum or the sum of the products.
  • the local kernel ⁇ ij (x ⁇ i , x ⁇ j ) is obtained for each window of the time interval T.
  • the kernel function calculating unit 17 calculates the feature vector X i calculated by the feature vector calculating unit 14, the number of digits of which has been changed by the numerical accuracy reducing unit 15, and the reference of the DRY road surface stored in the storage unit 16.
  • a DRYGA kernel K D (X, Y DK ) and a WETGA kernel K W (X, Y WK ) are calculated from the feature vector Y DK and the reference feature vector Y WK of the WET road surface.
  • GA kernels K D (X, Y DK ) and K W (X, Y WK ) time series waveforms having different time lengths can be directly compared.
  • the calculation time can be shortened.
  • the value of the discriminant function f DW (x) using the kernel function K D (X, Y DK ) and the kernel function K W (X, Y WD ) shown in the following equation (5) The road surface condition is determined based on the above.
  • N WK is the number of reference feature vectors Y Wkj the WET road.
  • the identification function f DW is calculated, and if f DW > 0, the road surface is determined to be a DRY road surface, and if f DW ⁇ 0, the road surface is determined to be a WET road surface.
  • a tire vibration generated by an input from a road surface on which the tire 20 is traveling is detected by the acceleration sensor 11 (step S10), and a time-series waveform of the tire vibration is extracted from the detected tire vibration signal (step S11). ). Then, the extracted time series waveform of the tire vibration is windowed with a predetermined time width ⁇ T to obtain a time series waveform of the tire vibration for each time window.
  • the number of time-series waveforms of tire vibration for each time window is set to m (step S12).
  • a feature vector X i (x i1 , x i2 , x i3 , x i4 , x i5 , x i6 ) is calculated for each of the extracted time-series waveforms in each time window (step S13).
  • the time width T is 3 msec.
  • the number of feature vectors X i is six.
  • step S14 After changing the number of digits n 0 after the decimal point of the calculated feature vector X i to n smaller than n 0 (step S14), the reference of the DRY road surface and the WET road surface stored in the storage means 15 is stored.
  • a reference feature vector Y DK of the DRY road surface and a reference feature vector Y WK of the WET road surface are extracted, and from these reference feature vectors Y DK and Y WK and the feature vector X i , local kernel ⁇ ij (X i, Y aKj ) was calculated, and with the total sum of the local kernel ⁇ ij (X i, Y aKj ) , calculates GA kernel function K a (X, Y AK), respectively (step S15).
  • the number of digits after the decimal point of the reference feature vector Y DK of the DRY road surface stored in the storage means 15 and the number of digits after the decimal point of the reference feature vector Y WK of the WET road surface are both n.
  • an identification function f DW (x) using the GA kernel function K D of the DRY road surface and the GA kernel function K W of the WET road surface is calculated (step S16).
  • Step S17 the kernel function is calculated from the feature vector X i and the reference feature vectors Y DK and Y WK for each time window in which the number of digits after the decimal point has been changed to n (n ⁇ n 0 ).
  • the calculation time can be shortened without lowering the road surface state determination accuracy.
  • DRY road surface support vector and WET road surface support vector are obtained in advance on the DRY road surface and the WET road surface, and the characteristics for each time window calculated from the time series waveform of the tire vibration when traveling on the DRY road surface and the WET road surface.
  • SVM machine learning
  • Table 1 the used road surface data is divided into those for training (for Train) and those for test (for Test), and the support vector for the DRY road surface and the support vector for the WET road surface are After the determination, the support vector of the DRY road surface and the boundary surface of the support vector of the WET road surface were determined.
  • FIGS. 9A to 9C are diagrams showing examples of numerical values of the feature matrix stored as the reference feature vector Y AKj in the storage means 15, and FIG. 9A shows the numerical accuracy up to the third decimal place.
  • Data (b) shows data with numerical precision up to the first decimal place, and (c) shows data with numerical precision as an integer.
  • Table 2 shows the results of comparing the storage capacities of data of each numerical precision.
  • the graph of FIG. 10 shows the result of comparing the determination accuracy of the road surface state when using the data of each numerical accuracy.
  • a discrimination accuracy of 95% or more was obtained using any numerical accuracy data. Further, since the numerical accuracy may be an integer, it has been confirmed that the discrimination accuracy can be secured even if the specifications of the acceleration sensor 11 are lowered. Therefore, even with an acceleration sensor having an acceleration resolution of about 1 G, the road surface can be sufficiently discriminated, and the calculation time can be shortened while securing the discrimination accuracy.
  • the DW identification model is used to determine whether the road surface on which the tire 20 is traveling is a DRY road surface or a WET road surface. If the model is used, it is possible to determine whether the road surface on which the tire 20 is traveling is a DRY road surface, a WET road surface, a SNOW road surface, or an ICE road surface.
  • the AA ′ identification model is an identification function f AA ′ (x ), A road surface feature vector Y AK and a Lagrangian multiplier ⁇ AA ′ , which are reference feature amounts for separation by A), and an A ′ road surface feature vector Y A′K Lagrangian multiplier ⁇ A ′ A.
  • the reference feature values Y AKV and ⁇ A are calculated based on the tire vibration obtained by running a test vehicle equipped with a tire with an acceleration sensor mounted on the road surface of DRY, WET, SNOW, and ICE at various speeds.
  • Lagrange multiplier ⁇ A corresponding to the reference feature vector Y AK exists for each identification model.
  • three Lagrangian multipliers ⁇ DW , ⁇ DS , and ⁇ DI corresponding to the DRY road surface feature vector Y DK have different values. The same applies to other road surface feature vectors Y WK , Y SK , and Y IKS .
  • the kernel function K I (X, Y IK ) is a GA kernel function for the ICE road surface.
  • the feature vector used for the GA kernel function K (X, Y) is the reference feature vector Y ASV , in equations (6) to (11), Y AA′K is set to Y A A′SV , N AA'K may be set to N A'AK .
  • the tire vibration detecting means is the acceleration sensor 11, but other vibration detecting means such as a pressure sensor may be used.
  • the acceleration sensor 11 may be installed at another location such as one at a position separated from the center of the tire in the width direction by a predetermined distance in the width direction, or may be installed in a block.
  • a feature vector X i and the power value x ik of filtration wave, variance, when the power value x ik of filtration wave (log [x ik (t) 2 + x ik ( t-1) 2 ]) may be used.
  • a feature vector X i Fourier coefficients a vibration level of a particular frequency band when the Fourier transform of the tire vibration time series waveform or may be cepstral coefficients.
  • the cepstrum is obtained by assuming the waveform after Fourier transform as a spectrum waveform and performing Fourier transform again, or assuming that an AR spectrum is a waveform and obtaining an AR coefficient (LPC Cepstrum). Since the shape of the spectrum can be characterized without being affected, discrimination accuracy is improved as compared with the case where a frequency spectrum obtained by Fourier transform is used.
  • the GA kernel is used as the kernel function, but a dynamic time warping kernel function (DTW kernel) may be used.
  • DTW kernel dynamic time warping kernel function
  • a GA kernel and a DTW kernel operation value may be used.
  • the present invention provides a step (a) of detecting a vibration of a tire during running, a step (b) of extracting a time-series waveform of the detected tire vibration, and a step of extracting a predetermined time-series waveform of the tire vibration.
  • the kernel function is calculated from the feature amount for each time window calculated in the above and the reference feature amount selected from the feature amount for each time window calculated from the time series waveform of the tire vibration obtained in advance for each road surface condition.
  • the vibration level of a specific frequency band of the time-series waveform for each time window extracted by applying the window function may be used.
  • the vibration level of the specific frequency band may be a frequency spectrum of a time-series waveform for each time window extracted by applying the window function, or a time-series waveform for each time window extracted by applying the window function.
  • the accuracy of determining the time-series waveform road surface state obtained through the filter can be improved.
  • the kernel function is a global alignment kernel function, a dynamic time warping kernel function, or an operation value of the kernel function, it is possible to improve the determination accuracy of the road surface state.
  • the present invention provides a tire vibration detecting means disposed on an air chamber side of an inner liner part of a tire tread part for detecting vibration of a running tire, and the tire vibration detected by the tire vibration detecting means.
  • Windowing means for windowing the time-series waveform of the predetermined time width to extract a time-series waveform of tire vibration for each time window, and a vibration level of a specific frequency in the extracted time-series waveform for each time window.
  • a feature amount calculating means for calculating a feature amount having a component of the vibration level as a component, and a time window calculated from a time series waveform of tire vibration for each road surface condition calculated in advance.
  • Storage means for storing a reference feature quantity selected from the feature quantities of the above and a Lagrange undetermined multiplier corresponding to the reference feature quantity; a feature quantity for each time window calculated by the feature quantity calculation means; A kernel function calculating unit that calculates a kernel function from the stored reference feature amount; and a road surface state determining unit that determines a road surface state based on a value of an identification function using the kernel function.
  • the storage means changes the number of digits n 0 after the decimal point of the calculated reference feature amount to a number of digits n smaller than n 0 and stores the changed value.
  • Numerical accuracy reducing means for changing the number of digits n 0 of the feature amount for each time window calculated by the amount calculating means to n smaller than n 0 is provided, and the kernel function calculating means changes the number of digits to n.
  • a kernel function is calculated from the feature amount for each time window and the reference feature amount.

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  • Mechanical Engineering (AREA)
  • Environmental & Geological Engineering (AREA)
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Abstract

Selon la présente invention, lors du calcul d'une fonction noyau KA à partir d'un vecteur de caractéristiques Xi pour chaque fenêtre temporelle et d'un vecteur de caractéristiques de référence YASVJ, qui est un vecteur de caractéristiques pour chaque fenêtre temporelle trouvée pour chaque état de surface de chaussée calculé au préalable, la présente invention active le nombre de chiffres n0 de la partie décimale pour un vecteur de caractéristiques Xi, pour chaque fenêtre temporelle, et le nombre de chiffres n0 de la partie décimale, pour qu'à la fois un vecteur de caractéristiques de référence YAKJ, soit modifié en n, qui est inférieur à n0, et qu'une fonction noyau KA soit calculée à partir du vecteur de caractéristiques Xi et du vecteur de caractéristiques de référence YAKJ, dont le nombre de chiffres après la partie décimale a été modifié à cet effet, ledit calcul de fonction noyau KA étant effectué après : le fenêtrage de formes d'onde chronologiques pour une vibration de pneu détectée par un capteur d'accélération, pour le temps T, à l'aide d'un moyen de fenêtrage ; l'extraction de la forme d'onde chronologique pour la vibration du pneu pour chaque fenêtre temporelle ; et le calcul du vecteur de caractéristiques Xi pour chaque fenêtre temporelle.
PCT/JP2018/047164 2018-06-22 2018-12-21 Procédé de détermination d'état de surface de chaussée et dispositif de détermination d'état de surface de chaussée Ceased WO2019244380A1 (fr)

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JP2018119176A JP2019218026A (ja) 2018-06-22 2018-06-22 路面状態判別方法及び路面状態判別装置
JP2018-119176 2018-06-22

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008261431A (ja) * 2007-04-12 2008-10-30 Kurashiki Kako Co Ltd アクティブ除振装置及びそれに用いられる制振ユニット
JP2016107833A (ja) * 2014-12-05 2016-06-20 株式会社ブリヂストン 路面状態判別方法

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008261431A (ja) * 2007-04-12 2008-10-30 Kurashiki Kako Co Ltd アクティブ除振装置及びそれに用いられる制振ユニット
JP2016107833A (ja) * 2014-12-05 2016-06-20 株式会社ブリヂストン 路面状態判別方法

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