Patent Description:
Recently, studies have been conducted on systems configured to input information measured in a tire, a vehicle, etc. to a learning type arithmetic operation model to estimate tire physical information such as a tire force.

Patent literature <NUM> discloses a tire physical information estimation system according to the related art. The tire physical information estimation system includes a physical information estimation unit and a data acquisition unit. The physical information estimation unit includes a learning type arithmetic operation model including an input layer through an output layer for estimation of physical information related to a tire produced in association with the movement of the tire. The data acquisition unit acquires input data input to the input layer. The arithmetic operation model includes a feature extraction unit that performs a convolution operation in an operation halfway between the input layer and the output layer to extract a feature amount. <CIT> describes a tire physical information estimation system capable of estimating physical information related to a tire in real time. <CIT> describes a multi-dimensional force sensor correction and decoupling method based on a particle swarm optimization BP neural network. <NPL> [A] <NUM>-<NUM> relates to intelligent tires and the application of machine learning techniques to tire force estimation.

When a system with a small scale of arithmetic operation is built by using the tire physical information estimation system disclosed in patent literature <NUM>, the estimation precision might become poor even if the real time performance of estimation is secured. We have come to realize that the technology disclosed in patent literature <NUM> leaves a room for improvement in terms of increasing the precision of estimation of tire physical information based on a learning type arithmetic operation model.

The present invention addresses the aforementioned issue and a purpose thereof is to provide a tire physical information estimation system and an arithmetic operation model generation system capable of estimating physical information related to a tire with a high precision.

Preferred examples are defined in the dependent claims. An embodiment of the present invention relates to a tire physical information estimation system. The tire physical information estimation system includes: a physical information estimation unit that includes a learning type arithmetic operation model including an input layer through an output layer and estimates tire physical information produced in association with movement of a tire; and a data acquisition unit that acquires input data input to the input layer, wherein the arithmetic operation model includes a feature extraction unit that performs a convolution operation in an operation halfway between the input layer and the output layer, the arithmetic operation model outputting
normalized tire physical information in at least two axial directions from the output layer.

According to the present invention, physical information related to a tire can be estimated with a high precision.

Hereinafter, the invention will be described based on a preferred embodiment with reference to <FIG>. Identical or like constituting elements and members shown in the drawings are represented by identical symbols and a duplicate description will be omitted as appropriate. The dimension of members in the drawings shall be enlarged or reduced as appropriate to facilitate understanding. Those of the members that are not important in describing the embodiment are omitted from the drawings.

(Embodiment) <FIG> is a schematic diagram showing an outline of a tire physical information estimation system <NUM> according to an embodiment. The tire physical information estimation system <NUM> includes a sensor <NUM> provided in a tire <NUM> and a tire physical information estimation apparatus <NUM>. Further, the tire physical information estimation system <NUM> may include a server apparatus <NUM> that acquires and collects, via a communication network <NUM>, the tire physical information such as the tire force F, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM> estimated by the tire physical information estimation apparatus <NUM>.

The sensor <NUM> measures the physical quantity of the tire <NUM> such as the acceleration and strain, tire inflation pressure, and tire temperature of the tire <NUM> and outputs the measured data to the tire physical information estimation apparatus <NUM>. The tire physical information estimation apparatus <NUM> estimates the tire physical information based on the data measured by the sensor <NUM>. The tire physical information estimation apparatus <NUM> uses the data measured by the sensor <NUM> for the operation to estimate the tire physical information but may acquire, from a vehicle control apparatus <NUM>, etc., information such as the vehicle acceleration from the vehicle side and use the information for the operation to estimate the tire physical information.

The tire physical information estimation apparatus <NUM> outputs the estimated tire physical information such as the tire force F, the coefficient of friction on the road surface, the moment around three axes produced in the tire <NUM> to, for example, the vehicle control apparatus <NUM>. The vehicle control apparatus <NUM> uses the tire physical information input from the tire physical information estimation apparatus <NUM> for, for example, estimation of braking distance, application to vehicle control, and notification of the driver of information related to the safe driving of the vehicle. The vehicle control apparatus <NUM> can also use map information, weather information, etc. to provide information related to the future safe driving of the vehicle. In the case the vehicle control apparatus <NUM> has a function of driving the vehicle automatically, the tire physical information estimation system <NUM> provides the estimated tire physical information to the vehicle control apparatus <NUM> as data used for vehicle speed control, etc. in automatic driving.

<FIG> is a block diagram showing a functional configuration of the tire physical information estimation system <NUM> according to the embodiment. The sensor <NUM> of the tire physical information estimation system <NUM> includes an acceleration sensor <NUM>, a strain gauge <NUM>, a pressure gauge <NUM>, a temperature sensor <NUM>, etc. and measures the physical quantity of the tire <NUM>. These sensors measure, as the physical quantity of the tire <NUM>, the physical quantity related to the deformation and movement of the tire <NUM>.

The acceleration sensor <NUM> and the strain gauge <NUM> move mechanically along with the tire <NUM> and measure the acceleration and amount of strain produced in the tire <NUM>, respectively. The acceleration sensor <NUM> is provided in, for example, the tread, side, and bead of the tire <NUM>, in the wheel, etc. and measures the acceleration in the three axes, i.e., the circumferential, axial, and radial directions of the tire <NUM>.

The strain gauge <NUM> is provided in the tread, side, bead, etc. of the tire <NUM> and measures the strain at the location of provision. Further, the pressure gauge <NUM> and the temperature sensor <NUM> are provided in, for example, the air valve of the tire <NUM> and measure the tire inflation pressure and tire temperature, respectively. The temperature sensor <NUM> may be provided directly in the tire <NUM> to measure the temperature of the tire <NUM> accurately. An RFID <NUM>, etc. to which unique identification information is assigned may be attached to the tire <NUM> to identify each tire.

The tire physical information estimation apparatus <NUM> includes a data acquisition unit <NUM>, a physical information estimation unit <NUM>, and a communication unit <NUM>. The tire physical information estimation apparatus <NUM> is an information processing apparatus such as a personal computer (PC). The units in the tire physical information estimation apparatus <NUM> can be realized in hardware by an electronic element such as a CPU of a computer, a mechanical component or the like, and in software by a computer program or the like. Functional blocks realized through collaboration among them are depicted here. Accordingly, those skilled in the art will understand that these functional blocks can be realized in various forms by a combination of hardware and software.

The data acquisition unit <NUM> acquires, by wireless communication, etc., information on the acceleration, strain, inflation pressure, and temperature measured by the sensor <NUM>. The communication unit <NUM> communicates with an external apparatus such as the vehicle control apparatus <NUM> and the server apparatus <NUM> by wire or wirelessly. The communication unit <NUM> transmits the physical quantity of the tire <NUM> measured by the sensor <NUM> and the tire physical information etc. estimated for the tire <NUM>, etc. to the external apparatus via a communication line (e.g., a control area network (CAN)), the Internet, etc.).

The physical information estimation unit <NUM> includes an arithmetic operation model 32a, inputs the information from the data acquisition unit <NUM> to the arithmetic operation model 32a, and estimates the tire physical information such as the tire force F, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM>. As shown in <FIG>, the tire force F has components in the three axial directions, i.e., a longitudinal force Fx in the longitudinal direction of the tire <NUM>, a lateral force Fy in the lateral direction, and a load Fz in the vertical direction. The physical information estimation unit <NUM> may calculate all of these components in the three axial directions, calculate one of the components, or calculate an arbitrary combination of two components.

The arithmetic operation model 32a uses a learning type model such as a neural network. <FIG> is a schematic diagram showing a configuration of the arithmetic operation model 32a. The arithmetic operation model 32a is of a convolutional neural network (CNN) type and is a learning type model provided with convolution operation and pooling operation used in the so-called LeNet, which is a prototype of CNN. <FIG> shows an example in which acceleration data in the three axial directions is used as input data input to the arithmetic operation model 32a, and the normalized tire force in the three axial directions is output.

The arithmetic operation model 32a includes an input layer <NUM>, a feature extraction unit <NUM>, an intermediate layer <NUM>, a fully-connected unit <NUM>, and an output layer <NUM>. The arithmetic operation model 32a is configured as a multitask type in which the feature extraction unit <NUM> is used commonly for the three axial directions, and the fully-connected unit <NUM> is provided for each of the three axial directions. The output layer <NUM> outputs the normalized values of the tire forces Fx, Fy, and Fz in the three axial directions. The normalized tire forces in the three axial directions output from the output layer <NUM> are denoted by Fxn, Fyn, and Fzn.

Given that each of the tire forces Fx and FY generated while the vehicle is being driven is -<NUM> N or greater and <NUM> N or smaller, for example, the normalized tire forces Fxn and Fyn output from the output layer <NUM> are defined as values derived by dividing the tire forces Fx and Fy by a constant value <NUM>. This causes the normalized tire forces Fxn and Fyn to vary in a range of -<NUM> or greater and <NUM> or smaller.

Given that the tire force Fz generated while the vehicle is being driven is <NUM> N or greater and <NUM> N or smaller, for example, the normalized tire force Fzn output from the output layer <NUM> is defined as a value derived by subtracting <NUM>, the median value, from the tire force Fz and dividing the resultant value by a constant value <NUM>. This causes the normalized tire force Fzn to vary in a range of -<NUM> or greater and <NUM> or smaller.

Normalization of the the tire physical information such as the tire force F in the three axial directions, the coefficient of friction on the road surface, and the moment around the three axes produced in the tire <NUM> may not be performed as described above and could be configured as appropriate in accordance with the property of the tire physical information, the range of values that could be taken, etc..

Time series data for acceleration in the three axial directions acquired by the data acquisition unit <NUM> is input to the input layer <NUM>. The acceleration data is measured by the sensor <NUM> in a time-series manner, and data for a predetermined time segment is extracted by a window function for use as the input data. For example, the input data may be <NUM> items of acceleration data included in a predetermined time segment for each axial direction.

Acceleration measured in the tire <NUM> exhibits periodicity per rotation of the tire <NUM>. The time segment of input data extracted by the window function may be a period of time corresponding to the period of rotation of the tire <NUM> to impart the input data itself with a periodicity. The window function may extract input data in a time segment shorter or longer than one rotation of the tire <NUM>. The arithmetic operation model 32a can be trained so long as the extracted input data at least includes periodical information.

The feature extraction unit <NUM> extracts a feature amount by using a convolution operation 51a and a pooling operation 51b and transmits the feature amount to the nodes of the intermediate layer <NUM>. In the example of the feature extraction unit <NUM> shown in <FIG>, <NUM> filters are used for the input data to perform the first convolution operation. The convolution operation 51a performs the convolution operation by moving the filter relative to the time series input data such as acceleration data. The convolution operation 51a is performed for the acceleration data (the plurality of items of input data) for each of the three axial directions. By using a common filter in the respective axial directions, the scale of arithmetic operation can be reduced.

The filter length in the convolution operation 51a is indicated to be <NUM> but may be set to be <NUM>-<NUM> as appropriate. The convolution operation is performed such that, of the time series input data, data as long as the continuous filter length (e.g., A1, A2, A3) is multiplied by the values (f1, f2, f3) in the filters, respectively. The values obtained by the multiplication are added up so as to obtain A1×f1+A2×f2+A3×f3. Zero padding, whereby "<NUM>" data is appended to the end of the input data, may be performed to perform the convolution operation. The amount of movement of the filter in the convolution operation is, normally, one item of input data but may be modified as appropriate to reduce the scale of the arithmetic operation model 32a.

In the pooling operation 51b, the data from the first convolution operation is subjected to the first maximum pooling operation. In the pooling operation 51b, the larger of the two values arranged in a time sequence is selected by way of example.

In a second convolution operation 51c, the data from the pooling operation 51b is subjected to a convolution operation by using, for example, <NUM> filters. The filter length in the convolution operation 51c may be equal to or different from that of the convolution operation 51a. The scale of arithmetic operation can equally be reduced in the convolution operation 51c by using a common filter in the respective axial directions.

In a pooling operation 51d, the data from the convolution operation 51c is subjected to the second maximum pooling operation. In the pooling operation 51d, as in the pooling operation 51b, the larger of the two values arranged in a time sequence is selected by way of example. The feature extraction unit <NUM> acquires <NUM> items of data in each axial direction, i.e., acquires <NUM>×<NUM> ch data resulting from the convolution operation and the pooling operation and outputs the data to the nodes in the intermediate layer <NUM>.

For each of the three axial directions, the fully-connected unit <NUM> fully connects the data from the nodes of the intermediate layer <NUM> in two layers and outputs the normalized tire forces Fxn, Fyn, and Fzn to the nodes of the output layer <NUM>. The fully-connected unit <NUM> performs an operation via fully-connected paths on which weighted liner operation, etc. is performed. In addition to a linear operation, the fully-connected unit <NUM> may perform a nonlinear operation by using an activating function, etc..

The physical information estimation unit <NUM> may restore and estimate the tire forces Fx, Fy, and Fz by subjecting the normalized tire forces Fxn, Fyn, and Fzn output to the output layer <NUM> to an operation inverse to normalization.

In addition to the normalized tire forces in the three axial directions, the tire physical information such as the coefficient of friction on the road surface and the moment around three axes produced in the tire <NUM> may be output to the nodes of the output layer <NUM>. The tire physical information such as the coefficient of friction on the road surface and the moment around three axes produced in the tire <NUM> output to the output layer <NUM> may be normalized values. Of the tire physical information such as the tire forces in the three axial directions, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM>, the output layer <NUM> may output one type of tire physical information or an arbitrary combination of plurality of types of tire physical information.

In the estimation of the coefficient of friction on the road surface, the output layer <NUM> may output an estimated value of the coefficient of friction on the road surface. Alternatively, the coefficient of friction on the road surface may be grouped into a category such as dry, wet, snowy, or frozen, and the output layer <NUM> may output which category is applicable.

Reference is made back to <FIG>. The server apparatus <NUM> acquires, from the tire physical information estimation apparatus <NUM>, the physical quantity of the tire <NUM> measured by the sensor <NUM> and the tire physical information such as the tire force F and the coefficient of friction on the road surface estimated for the tire <NUM>. The server apparatus <NUM> may collect, from a plurality of vehicles, the physical quantity measured in the tire <NUM>, the tire physical information estimated by the tire physical information estimation apparatus <NUM>, etc..

<FIG> is a block diagram showing a functional configuration of an arithmetic operation model generation system <NUM>. The arithmetic operation model generation system <NUM> is provided with a tire physical information measurement apparatus <NUM> and an arithmetic operation model generation apparatus <NUM> including a learning processing unit <NUM>. In addition to the features of the tire physical information estimation apparatus <NUM>, the arithmetic operation model generation apparatus <NUM> includes the learning processing unit <NUM>. Those features of the arithmetic operation model generation apparatus <NUM> corresponding to the respective features of the tire physical information estimation apparatus <NUM> have similar functions as those of the tire physical information estimation apparatus <NUM>, but the arithmetic operation model 32a has not been trained or is being trained.

The tire physical information measurement apparatus <NUM> measures the tire physical information such as the tire forces F in the three axial directions, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM>. The learning processing unit <NUM> uses the normalized version of the tire physical information measured by the tire physical information measurement apparatus <NUM> as training data to train the arithmetic operation model 32a. In the process of training the arithmetic operation model 32a, the tire physical information is estimated by the arithmetic operation model 32a based on input information, and the estimated data is compared with the training data.

The learning processing unit <NUM> compares the tire physical information estimated by the arithmetic operation model 32a with the training data and newly sets various coefficients in the arithmetic steps such as weighting in the arithmetic operation model 32a. The arithmetic operation model 32a is trained by repeatedly being updated. The tire physical information estimation system <NUM> estimates the tire physical information by using the arithmetic operation model 32a that has been trained by the arithmetic operation model generation system <NUM>.

The configuration (e.g., the number of layers) and weighting in the fully-connected unit <NUM> of the arithmetic operation model 32a may be changed basically in accordance with the specification of the tire <NUM>. The arithmetic operation model 32a can be trained in rotation tests in the tires <NUM> (including the wheel) with different specifications. It should however be noted that it is not necessary to strictly train the arithmetic operation model 32a for each specification of the tire <NUM>. By training and building the arithmetic operation model 32a for different types (e.g., tires for passenger vehicles, tires for trucks, etc.) to make it possible to estimate the tire force F within a predetermined margin of error, one arithmetic operation model 32a may be shared by the tires <NUM> encompassed by multiple specifications so that the number of arithmetic operation models is reduced.

The arithmetic operation model 32a may be trained by mounting the tire <NUM> to an actual vehicle and test driving the vehicle on road surfaces. The specification of the tire <NUM> includes information related to tire performance such as tire size, tire width, tire profile, tire strength, tire outer diameter, road index, and year/month/date of manufacturing.

The arithmetic operation model 32a may be trained by conducting rotation tests, changing the coefficient of friction on the ground surface touched by the tire <NUM>. Further, the arithmetic operation model 32a may be trained by mounting the tire <NUM> to an actual vehicle and test driving the vehicle on road surfaces with different coefficients of friction.

A description will now be given of the operation of the tire physical information estimation system <NUM>. <FIG> is a flowchart showing a sequence of steps of the tire physical information estimation process performed by the tire physical information estimation apparatus <NUM>. The tire physical information estimation apparatus <NUM> acquires the physical quantity such as the acceleration, strain, tire inflation pressure, and tire temperature of the tire <NUM> measured by the sensor <NUM> (S1).

The physical information estimation unit <NUM> extracts input data in a predetermined time segment from the data acquired by the data acquisition unit <NUM> (S2). For estimation of tire physical information, acceleration data for at least one axis (e.g., the circumferential direction) is necessary as input data. Further, acceleration data for two axes, i.e., the circumferential direction and axial direction of the tire <NUM>, may be used as input data, or acceleration data for three axes may be used as input data for estimation of tire physical information. Further, the time series data for at least one of the strain, tire inflation pressure, tire temperature of the tire <NUM> may be included in the input data.

The feature extraction unit <NUM> of the arithmetic operation model 32a performs a process of extracting the feature amount by the convolution operation and the pooling operation on the input data (S3). The fully-connected unit <NUM> of the arithmetic operation model 32a performs a fully-connected operation on the feature amount extracted by the feature extraction unit <NUM> and input to the nodes of the intermediate layer <NUM> (S4). Parameters for weighting, etc. used in the fully-connected operation are determined as a result of training the arithmetic operation model 32a. The fully-connected operation outputs, for example, the normalized tire physical information such as the tire force F, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM> to the nodes of the output layer <NUM>.

The physical information estimation unit <NUM> estimates the tire physical information by subjecting the normalized tire physical information output to the output layer <NUM> to an operation inverse to normalization (S5) and terminates the process.

<FIG>, <FIG> and <FIG> are graphs showing the correlation between estimated values and measured values found by the tire physical information estimation system <NUM>. <FIG> shows the correlation between estimated values and measured values of the tire force Fx, <FIG> shows the correlation between estimated values and measured values of the tire force Fy, and <FIG> shows the correlation between estimated values and measured values of the tire force Fz.

<FIG>, <FIG> and <FIG> are graphs showing the correlation between estimated values and measured values found when a learning model according to a comparative example is used. The correlation between estimated values and measured values of the tire forces Fx, Fy, and Fz in the comparative example is respectively shown in <FIG>, <FIG>, and <FIG>. In the comparative example, a single-task learning model (referred to as "related-art model") is built. The related-art model has a high estimation precision but requires a large scale of arithmetic operation and so is not suitable for mounting in a vehicle.

The correlation of the tire force Fx estimated by the tire physical information estimation system <NUM> according to the embodiment shown in <FIG> shows a distribution substantially identical to the correlation of the tire force Fx according to the comparative example shown in <FIG>. A comparison between <FIG> and <FIG> and a comparison between <FIG> and <FIG> also reveal that the correlation of the tire forces Fy and Fz estimated by the tire physical information estimation system <NUM> according to the embodiment shows a distribution substantially identical to the correlation of the tire forces Fy and Fz according to the comparative example, respectively.

<FIG> shows mean absolute errors of estimated values and measured values. The mean absolute values of the tire forces Fx, Fy, and Fz are substantially equal in the multitask model of the embodiment and in the related-art model of the comparative example. In other words, it is demonstrated that the estimation precision of the tire force F commensurate with that of the related-art model of the comparative example can be obtained by the multitask learning model of the embodiment in which the output is normalized.

The arithmetic operation model 32a of the tire physical information estimation system <NUM> can increase the estimation precision of the tire physical information by being configured to output the normalized tire physical information in at least two axial directions from the output layer <NUM>. The arithmetic operation model 32a can ensure that the normalized tire forces vary within similar ranges and increase the estimation precision, by normalizing the tire force Fz in the vertical direction by an arithmetic operation different from that of the tire forces Fx and Fy in the other two axial directions.

The tire physical information estimation system <NUM> can reduce the scale of arithmetic operation by providing the arithmetic operation model 32a with the fully-connected unit <NUM> for each of the plurality of items of tire physical information output and configuring the arithmetic operation model 32a as a multitasking system accordingly. The feature extraction unit <NUM> of the arithmetic operation model 32a is configured to reduce the scale of arithmetic operation by using a filter common to the convolution operations 51a and 51c. Of the tire forces F in the three axial directions, the physical information estimation unit <NUM> uses the arithmetic operation model 32a to estimate the tire force F in at least two axial directions as tire physical information. Further, the physical information estimation unit <NUM> can provide information necessary to analyze the behavior of the tire <NUM> such as slip, by estimating all of the tire forces F in the three axial directions.

The arithmetic operation model generation system <NUM> can generate the arithmetic operation model 32a having a favorable estimation precision by normalizing the tire physical information measured by the tire physical information measurement apparatus <NUM> and using the normalized information as training data to train the arithmetic operation model 32a. The learning processing unit <NUM> of the arithmetic operation model generation system <NUM> can generate an arithmetic operation model with a reduced scale of arithmetic operation by training the arithmetic operation model 32a configured as a multitasking system.

(Variation) <FIG> is a block diagram showing a functional configuration of the tire physical information estimation system <NUM> according to a variation. In the variation shown in <FIG>, data input to the arithmetic operation model 32a is acquired from the vehicle control apparatus <NUM>. Data from the vehicle control apparatus <NUM> and data from the sensor <NUM> (see <FIG>) can both be used as data input to the arithmetic operation model 32a.

The vehicle control apparatus <NUM> acquires, in the digital tachometer etc. of the vehicle, traveling data such as the traveling speed of the vehicle, acceleration in the three axial directions, and triaxial angular speed, and load data such as the weight of the vehicle and axle load applied to the axle shaft. The vehicle control apparatus <NUM> outputs the traveling data and load data to the tire physical information estimation apparatus <NUM>.

The tire physical information estimation apparatus <NUM> estimates, by means of the arithmetic operation model 32a, the tire physical information such as the tire force F, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM> in response to the data input from the vehicle control apparatus <NUM>. The arithmetic operation model 32a is built by, for example, test-driving an actual vehicle to train the arithmetic operation model 32a to learn to estimate the tire physical information in response to the data input from the vehicle control apparatus <NUM> in advance.

In the embodiment and the variation described above, the tire physical information estimated by the arithmetic operation model 32a is exemplified by the tire force F, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM>. Alternatively, the looseness of a fastening component such as a wheel nut used to mount the tire <NUM> can be estimated. The vibration due to the looseness of the fastening component such as a wheel nut is reflected in the acceleration data measured in the tire <NUM>, and so the arithmetic operation model 32a of a CNN type for estimating the looseness of the fastening component by way of comparison between tire forces F is built and trained. The tire physical information estimation system <NUM> can estimate the looseness of the fastening component of the tire <NUM> in real time by running the operation in the arithmetic operation model 32a based on the input data such as the acceleration data acquired while an actual vehicle is being driven.

Further, the sensor <NUM> is not limited to the sensors described with reference to <FIG>, and a microphone provided in the tire <NUM> or the neighborhood thereof may be used. The arithmetic operation model 32a may estimate the tire physical information by using audio data collected by the microphone.

In the embodiment and the variation described above, the arithmetic operation model 32a of a CNN type built on the LeNet model is used. Alternatively, a model structure such as the Dense Net model, Res Net model, Mobile Net model, and Peleel Net Model may be used. A module structure such as Dense Block, Residual Block, Stem Block, etc. may be incorporated into the arithmetic operation model 32a to build the model.

A description will now be given of the features of the tire physical information estimation system <NUM> and the arithmetic operation model generation system <NUM> according to the embodiment. The tire physical information estimation system <NUM> according to the embodiment includes the physical information estimation unit <NUM> and the data acquisition unit <NUM>. The physical information estimation unit <NUM> includes the learning type arithmetic operation model 32a including the input layer <NUM> through the output layer <NUM> and estimates the tire physical information produced in association with the movement of the tire <NUM>. The data acquisition unit <NUM> acquires the input data input to the input layer <NUM>. The arithmetic operation model 32a includes the feature extraction unit <NUM> that performs the convolution operations 51a and 51c in the operation halfway between the input layer <NUM> and the output layer <NUM>, and the arithmetic operation model 32a outputs the normalized tire physical information in at least two axial directions from the output layer <NUM>. This makes it possible for the tire physical information estimation system <NUM> to increase the estimation precision of the tire physical information such as the tire force F, the coefficient of friction on the road surface, and the moment around three axes produced in the tire <NUM>.

Further, the arithmetic operation model 32a has a plurality of fully-connected units <NUM> corresponding to the respective axial directions, and the output of the feature extraction unit <NUM> is input to the plurality of fully-connected units <NUM>. This allows the tire physical information estimation system <NUM> to reduce the scale of arithmetic operation owing to multitasking in the arithmetic operation model 32a.

Further, the tire physical information comprises tire forces F in the three axial directions. This allows the tire physical information estimation system <NUM> to provide information necessary to analyze the behavior of the tire <NUM> such as slip.

Further, the output layer <NUM> of the arithmetic operation model 32a outputs tire force Fz in the vertical direction normalized by an arithmetic operation different from that of the tire forces Fx and Fy in the other two axial directions. This allows the tire physical information estimation system <NUM> to ensure that the normalized tire forces vary within similar ranges and to increase the estimation precision.

The arithmetic operation model generation system <NUM> includes the physical information estimation unit <NUM>, the data acquisition unit <NUM>, and the learning processing unit <NUM>. The physical information estimation unit <NUM> includes the learning type arithmetic operation model 32a including the input layer <NUM> through the output layer <NUM> and estimates the tire physical information produced in association with movement of the tire <NUM>. The data acquisition unit <NUM> acquires the input data input to the input layer <NUM>. The learning processing unit <NUM> trains the arithmetic operation model 32a based on the training data derived from normalizing the tire physical information measured in the tire <NUM>. The arithmetic operation model 32a includes the feature extraction unit <NUM> that performs the convolution operations 51a and 51c in the operation halfway between the input layer <NUM> and the output layer <NUM>, and the arithmetic operation model 32a outputs the normalized tire physical information in at least two axial directions from the output layer <NUM>. The allows the arithmetic operation model generation system <NUM> to generate the arithmetic operation model 32a having a favorable estimation precision.

Described above is an explanation based on an exemplary embodiment. The embodiments are intended to be illustrative only and it will be understood by those skilled in the art that variations and modifications are possible within the scope of the present invention as defined by the appended claims.

Claim 1:
A tire physical information estimation system (<NUM>), comprising:
a physical information estimation unit (<NUM>) that includes a learning type arithmetic operation model (32a) including an input layer (<NUM>) through an output layer (<NUM>) and estimates tire physical information produced in association with movement of a tire (<NUM>); and
a data acquisition unit (<NUM>) that acquires input data input to the input layer (<NUM>), wherein
the arithmetic operation model (32a) includes a feature extraction unit (<NUM>) that performs a convolution operation (51a, 51c) in an operation halfway between the input layer (<NUM>) and the output layer (<NUM>), the arithmetic operation model (32a) outputting normalized tire physical information in at least two axial directions from the output layer (<NUM>),
characterized in that
the arithmetic operation model (32a) includes a plurality of fully-connected units (<NUM>) corresponding to respective axial directions and inputs an output of the feature extraction unit (<NUM>) to the plurality of fully-connected units (<NUM>).