JP2024071189A - Tire physical information estimating system and tire physical information estimating method - Google Patents

Abstract

To provide a tire physical information estimating system and a tire physical information estimating method capable of continuously estimating physical information of a tire even when some sensors fail.SOLUTION: A data acquisition unit 31 of a tire physical information estimation system 100 acquires data of a physical quantity measured by a sensor 20 attached to a plurality of tires 10 mounted on a vehicle. A physical information estimation unit 33 estimates tire physical information generated by the movement of the tire 10 with respect to the tire 10 and the other tires 10 by an arithmetic model 33a learned for each tire 10. A physical information estimation unit 33 estimates the tire physical information of the tire 10 to which the sensor 20 having acquired the information that the tire has failed from a sensor fault information acquisition unit 32 is attached, based on the arithmetic model 33a of the tire 10 to which the sensor 20 having acquired the information that the tire has not failed is attached.SELECTED DRAWING: Figure 2

Description

The present invention relates to a tire physical information estimation system and a tire physical information estimation method.

Recently, research has been conducted into systems that input information measured on tires and vehicles into a learning-based computational model to estimate tire physical information such as tire force.

Patent Document 1 describes a conventional tire physical information estimation system. The tire physical information estimation system includes a physical information estimation unit and a data acquisition unit. The physical information estimation unit has a learning-type computation model extending from an input layer to an output layer in order to estimate physical information related to a tire that is generated by the motion of the tire. The data acquisition unit acquires input data for the input layer. The computation model has a feature extraction unit that extracts feature amounts by performing a convolution computation in the intermediate computation from the input layer to the output layer.

JP 2021-46080 A

In the tire physical information estimation system described in Patent Document 1, tire physical information is estimated based on physical quantities measured by a sensor provided on the tire. Conventional tire physical information estimation systems have a problem in that if the sensor fails to operate normally due to a breakdown or dead battery, the system continues to estimate erroneous tire physical information.

The present invention was made in consideration of the above circumstances, and its purpose is to provide a tire physical information estimation system and a tire physical information estimation method that can continuously estimate physical information about a tire even if some of the sensors fail.

One aspect of the present invention is a tire physical information estimation system. The tire physical information estimation system includes a data acquisition unit that acquires data on physical quantities measured by sensors attached to multiple tires mounted on a vehicle, a physical information estimation unit that inputs the data acquired by the data acquisition unit and estimates tire physical information generated by the tire motion for the tire and other tires using a computational model that has been trained for each tire, and a sensor failure information acquisition unit that acquires information on whether the sensor is malfunctioning. The physical information estimation unit estimates tire physical information for a tire attached to a sensor that has been acquired by the sensor failure information acquisition unit as malfunctioning, based on the computational model for a tire attached to a sensor that has been acquired as not malfunctioning.

Another aspect of the present invention is a tire physical information estimation method. The tire physical information estimation method includes a data acquisition step of acquiring data of physical quantities measured by sensors attached to multiple tires mounted on a vehicle, a physical information estimation step of inputting the data acquired by the data acquisition step and estimating tire physical information generated by the tire motion for the tire and other tires using a computational model that has been trained for each tire, and a sensor failure information acquisition step of acquiring information on whether the sensor is malfunctioning. The physical information estimation step estimates tire physical information of a tire attached to a sensor that has been acquired as malfunctioning in the sensor failure information acquisition step, based on the computational model for a tire attached to a sensor that has been acquired as not malfunctioning.

A tire physical information estimation system according to another aspect of the present invention includes a data acquisition unit that acquires data on physical quantities measured by a sensor attached to a tire mounted on a vehicle, and a physical information estimation unit that inputs the data acquired by the data acquisition unit and estimates tire physical information generated by tire motion for the tire and other tires using a trained computational model.

According to the present invention, it is possible to continuously estimate physical information about a tire even if some of the sensors fail.

1 is a schematic diagram for explaining an overview of a tire physical information estimation system according to an embodiment; 2 is a block diagram showing a functional configuration of the tire physical information estimation device. FIG. FIG. 2 is a schematic diagram showing a configuration of a computation model. 4 is a flowchart showing a procedure of a tire physical information estimation process performed by the tire physical information estimation device. 13 is an example of a correlation diagram between an estimated value of tire force Fy of the left front wheel tire calculated using a computation model of the left front wheel tire and a measured value. 13 is an example of a correlation diagram between an estimated value of tire force Fy of a left front wheel tire calculated using a computation model of a right front wheel tire and a measured value. 1 is a chart showing the mean absolute error between the estimated tire force F of all tires calculated by a computational model corresponding to each tire of a vehicle and the measured tire force F.

The present invention will be described below based on a preferred embodiment with reference to Figures 1 to 7. The same or equivalent components and parts shown in each drawing are given the same reference numerals, and duplicated explanations will be omitted as appropriate. Furthermore, the dimensions of the parts in each drawing are enlarged or reduced as appropriate to facilitate understanding. Furthermore, some of the parts that are not important for explaining the embodiment will be omitted in each drawing.

(Embodiment)
1 is a schematic diagram for explaining an overview of a tire physical information estimation system 100 according to an embodiment. The tire physical information estimation system 100 includes a sensor 20 disposed in a tire 10, a tire physical information estimation device 30, and a sensor failure determination device 40. The tire physical information estimation system 100 may also include a server device 80 for acquiring and storing tire physical information, such as tire force F estimated by the tire physical information estimation device 30 and moments about three axes acting on the tire 10, via a communication network 91 and monitoring the tire physical information.

The sensor 20 measures physical quantities of the tire 10, such as the acceleration and strain of the tire 10, the tire air pressure, and the tire temperature, and outputs the measured data to the tire physical information estimation device 30 and the sensor failure determination device 40.

The tire physical information estimation device 30 estimates tire physical information based on data measured by the sensor 20. The tire physical information estimation device 30 uses data measured by the sensor 20 in the calculation to estimate the tire physical information, but may also obtain information from the vehicle side, such as vehicle acceleration, from the vehicle control device 90, etc., and use the information in the calculation to estimate the tire physical information.

The tire physical information estimation device 30 outputs tire physical information such as the estimated tire force F and moments around three axes acting on the tire 10 to, for example, a vehicle control device 90. The vehicle control device 90 uses the tire physical information input from the tire physical information estimation device 30, for example, to estimate braking distance, apply it to vehicle control, and further notify the driver of information related to safe vehicle driving. The vehicle control device 90 can also provide information related to safe vehicle driving in the future using map information, weather information, and the like. Furthermore, when the vehicle control device 90 has a function of automatically driving the vehicle, the tire physical information estimation system 100 provides the estimated tire physical information to the vehicle control device 90 as data to be used for vehicle speed control in automatic driving.

The sensor failure determination device 40 determines whether the sensor 20 is malfunctioning based on the data measured by the sensor 20, and notifies the tire physical information estimation device 30, the server device 80, etc. of the determination result. The sensor failure determination device 40 determines whether the sensor 20 is malfunctioning based on a calculation model that performs encoding and decoding using a convolution operation on the data measured by the sensor 20. The sensor failure determination device 40 also determines that the sensor 20 is malfunctioning when the output data is not normal data due to the battery of the sensor 20 running out.

The tire physical information estimation device 30 is notified of the judgment result regarding the sensor failure from the sensor failure judgment device 40. When a failure occurs in one of the sensors 20, the tire physical information estimation device 30 receives the judgment result that the one of the sensors 20 is faulty from the sensor failure judgment device 40. The tire physical information estimation device 30 replaces the tire physical information of the tire 10 to which the faulty sensor 20 is attached with the tire physical information estimated by the other sensor 20 that is operating normally, and continues estimating the tire physical information.

Figure 2 is a block diagram showing the functional configuration of the tire physical information estimation device 30. The sensor 20 has an acceleration sensor 21, a strain gauge 22, a pressure gauge 23, a temperature sensor 24, etc., and measures physical quantities in the tire 10. These sensors measure physical quantities related to the deformation and movement of the tire 10 as physical quantities of the tire 10.

The acceleration sensor 21 and strain gauge 22 move mechanically together with the tire 10, and measure the acceleration and strain that occur in the tire 10, respectively. The acceleration sensor 21 is disposed, for example, in the tread, side, bead, wheel, etc. of the tire 10, and measures the acceleration in three axes of the tire 10: the circumferential direction, the axial direction, and the radial direction.

The strain gauges 22 are disposed in the tread, sides, beads, etc. of the tire 10, and measure the strain at the locations where they are disposed. The pressure gauge 23 and temperature sensor 24 are disposed, for example, in the air valve of the tire 10, and measure the tire air pressure and tire temperature, respectively. The temperature sensor 24 may be disposed directly on the tire 10 in order to accurately measure the temperature of the tire 10. The tires 10 may be fitted with, for example, an RFID 11 or the like to which unique identification information is assigned in order to identify each tire.

The tire physical information estimation device 30 has a data acquisition unit 31, a sensor failure information acquisition unit 32, a physical information estimation unit 33, and a communication unit 34. The tire physical information estimation device 30 is an information processing device such as a PC (personal computer). Each unit in the tire physical information estimation device 30 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The data acquisition unit 31 acquires information on acceleration, strain, air pressure, and temperature measured by the sensor 20 through wireless communication or the like. The communication unit 34 communicates with external devices such as the sensor failure determination device 40, the vehicle control device 90, and the server device 80 through wired or wireless communication or the like. The communication unit 34 transmits the physical quantities of the tire 10 measured by the sensor 20 and tire physical information estimated about the tire 10, etc., to the external devices via a communication line, for example a control area network (CAN), the Internet, etc.

The sensor failure information acquisition unit 32 acquires information on whether or not a sensor is malfunctioning from the sensor failure determination device 40 via the communication unit 34, and outputs the information to the physical information estimation unit 33. The sensor failure information acquisition unit 32 acquires information on whether or not a sensor is malfunctioning for each of the sensors 20 attached to each of the multiple tires 10 mounted on the vehicle.

The sensor failure determination device 40 outputs information that the sensor 20 has failed when the sensor 20 has failed due to a broken wire, a short circuit, or a functional failure caused by damage to electronic components or a dead battery, and the output data deviates from the data during normal operation. The sensor failure determination device 40 may have a failure detection function, for example, to detect broken wires and short circuits. The sensor failure determination device 40 may also have a function to monitor the voltage of the battery supplied to the sensor 20 and detect a voltage drop.

The sensor failure determination device 40 may also have a function of detecting failures in the physical quantity data measured by the sensor 20 using an autoencoder type learning model that uses convolution operations or the like. In an autoencoder type learning model that uses convolution operations or the like, feature data is generated from the physical quantity data measured by the sensor 20, the original data is reproduced from the feature data, and a failure in the sensor 20 is determined based on the reproducibility of the data.

The physical information estimation unit 33 has a calculation model 33a, inputs information from the data acquisition unit 31 into the calculation model 33a, and estimates tire physical information such as tire force F and moments about three axes acting on the tire 10. As shown in FIG. 2, the tire force F has three axial components: a longitudinal force Fx in the longitudinal direction of the tire 10, a lateral force Fy in the lateral direction, and a load Fz in the vertical direction. The physical information estimation unit 33 may calculate all of these three axial components, or may calculate at least one of the components or two components in any combination.

The computation model 33a uses a learning model such as a neural network. FIG. 3 is a schematic diagram showing the configuration of the computation model 33a. The computation model 33a is a CNN (Convolutional Neural Network) type, and is a learning model equipped with the convolution and pooling operations used in the so-called DenseNet, which is its prototype. FIG. 3 shows an example in which acceleration data in three axial directions is used as input data to the computation model 33a, and tire forces Fx, Fy, and Fz in three axial directions are output.

The calculation model 33a is provided for each tire 10 mounted on the vehicle. For example, when a vehicle has front and rear axles and one tire 10 is mounted on each side of the axle, the physical information estimation unit 33 has four calculation models 33a corresponding to each of the four tires 10. In one calculation model 33a corresponding to one tire 10, data of a physical quantity measured by a sensor 20 attached to the corresponding tire 10 is input, and the tire physical information of the four tires 10 including the other three tires 10 is estimated by the calculation model 33a.

The computational model 33a includes an input layer 50, a Stem block 51, a feature extraction unit 52, an intermediate layer 53, a full connection unit 54, and an output layer 55. Time series data of acceleration in three axial directions acquired by the data acquisition unit 31 from the sensor 20 in one tire 10 is input to the input layer 50. The acceleration data is measured in a time series manner by the sensor 20, and data within a certain time interval is extracted using a window function to be used as input data. The input data may be, for example, 128 pieces of acceleration data included in a certain time interval in each axial direction.

The acceleration measured by the tire 10 is periodic for each rotation of the tire 10. The time interval of the input data extracted by the window function may be, for example, a time period corresponding to the rotation period of the tire 10, and the input data itself may be periodic. The window function may extract input data in a time interval shorter or longer than one rotation of the tire 10, and the calculation model 33a can be trained as long as the extracted input data contains at least periodic information.

The Stem block 51 reduces the size of the input data while maintaining the characteristics of the input data. The Stem block 51 performs, for example, convolution operations and pooling operations.

The feature extraction unit 52 repeats Dense block 52a, convolution operation 52b, and pooling operation 52c multiple times (denoted as n times in FIG. 3) to extract features and transmit them to each node in the intermediate layer 53. In the example of the feature extraction unit 52 shown in FIG. 3, a convolution operation with an appropriate filter length is performed on the input data in Dense block 52a, and the subsequent convolution operation 52b and pooling operation 52c are performed.

The convolution operation in the dense block 52a uses a filter of length 1 to 5 as appropriate, and multiplies and adds the input data arranged in time series while moving the filter. The convolution operation multiplies each value in the filter (f1, f2, f3) by consecutive filter length data (e.g. A1, A2, A3) in the time series input data, and adds the values obtained by multiplication to obtain A1 x f1 + A2 x f2 + A3 x f3. The convolution operation may be performed by performing zero padding, which adds "0 (zero)" data to the end of the input data. The amount of filter movement in the convolution operation is usually one input data, but it can be changed as appropriate to make the calculation model 33a smaller.

The convolution operation 52b executes a convolution operation with a filter length of 1. The pooling operation 52c executes an average value pooling operation on the data after the convolution operation 52b. The pooling operation 52c obtains, for example, the average value of two values arranged in time series. The feature extraction unit 52 repeats the operations using the dense block 52a, the convolution operation 52b, and the pooling operation 52c multiple times, and outputs 64 pieces of data as a result to the intermediate layer 53.

The full connection unit 54 fully connects data from each node in the intermediate layer 53 at multiple levels, and outputs tire forces Fx, Fy, and Fz to each node in the output layer 55. The full connection unit 54 performs calculations using a full connection path that performs linear calculations using weighting, but in addition to linear calculations, it may also perform nonlinear calculations using activation functions, etc.

Each node of the output layer 55 may output tire physical information such as tire forces in the three axial directions and moments about three axes acting on the tire 10. The output layer 55 may output one type of tire physical information or multiple types of tire physical information in any combination of tire forces in the three axial directions and moments about three axes acting on the tire 10.

All the connecting parts 54 and output layers 55 are provided for the number of tires 10 mounted on the vehicle. In the example shown in FIG. 3, all the connecting parts 54 and output layers 55 are provided for the front left wheel (FL), front right wheel (FR), rear left wheel (RL), and rear right wheel (RR) of the vehicle.

The calculation model 33a can learn by mounting a tire 10 with specifications appropriate to the vehicle on an actual vehicle and running the vehicle on a test run. The specifications of the tire 10 include information related to the performance of the tire, such as tire size, tire width, aspect ratio, tire strength, tire outer diameter, load index, and date of manufacture.

When sensor failure information is input from the sensor failure information acquisition unit 32, the physical information estimation unit 33 stops estimating tire physical information using the calculation model 33a corresponding to the tire 10 to which the failed sensor 20 is attached. For example, when the sensor 20 corresponding to the right front wheel fails, the physical information estimation unit 33 stops estimating tire physical information using the calculation model 33a corresponding to the right front tire 10. The physical information estimation unit 33 estimates tire physical information generated in the right front tire 10 using the calculation model 33a corresponding to other tires 10 such as the left front wheel.

The physical information estimation unit 33 estimates the tire physical information of the tire 10 corresponding to the faulty sensor 20 using one of the calculation models 33a of the other tires 10 mounted on the vehicle. The physical information estimation unit 33 may estimate the tire physical information of the tire 10 corresponding to the faulty sensor 20 based on the calculation model 33a of the tire 10 arranged on the same axle of the vehicle. The physical information estimation unit 33 may estimate the tire physical information of the tire 10 corresponding to the faulty sensor 20 based on the calculation model 33a of the tire 10 arranged on the same left-right side of the vehicle.

Next, the operation of the tire physical information estimation system 100 will be described. FIG. 4 is a flowchart showing the procedure of tire physical information estimation processing by the tire physical information estimation device 30. The tire physical information estimation device 30 acquires time-series data of physical quantities such as acceleration, strain, tire air pressure, and tire temperature of the tire 10 measured by the sensor 20 using the data acquisition unit 31 (S1). The sensor failure information acquisition unit 32 acquires information about a failure of the sensor 20 from the sensor failure determination device 40 (S2).

The physical information estimation unit 33 extracts input data for a certain time interval from the data acquired by the data acquisition unit 31 (S3). In estimating tire physical information, at least one axial (e.g., circumferential) acceleration data is required as input data. In estimating tire physical information, for example, two axial acceleration data in the circumferential and axial directions of the tire 10 may be used as input data, or three axial acceleration data may be used as input data. Furthermore, the input data may include time series data of at least one of the strain in the tire 10, the tire pressure, and the tire temperature.

The physical information estimation unit 33 inputs the input data extracted in step S3 to the calculation model 33a and calculates tire physical information such as the tire force F and the moments around the three axes acting on the tire 10 (S4). The physical information estimation unit 33 determines whether the sensor 20 is malfunctioning based on the information acquired by the sensor malfunction information acquisition unit 32 (S5), and ends the process if it is determined that the sensor 20 is not malfunctioning (S5: No).

If it is determined in step S5 that the sensor 20 is faulty (S5: Yes), the physical information estimation unit 33 replaces the tire physical information of the tire 10 with the faulty sensor 20 with the tire physical information calculated by the computation model 33a for the tire 10 with the sensor 20 that is not faulty (S6), and ends the process.

Figure 5 is an example of a correlation diagram between the estimated value of tire force Fy of the left front tire 10 calculated by the calculation model 33a of the left front tire 10 and the measured value. Figure 6 is an example of a correlation diagram between the estimated value of tire force Fy of the left front tire 10 calculated by the calculation model 33a of the right front tire 10 and the measured value. The correlation between the estimated value of tire force Fy and the measured value shown in Figure 5 has a distribution roughly equivalent to the correlation between the estimated value of tire force Fy and the measured value shown in Figure 6. From these examples of correlation diagrams, it can be seen that when the sensor 20 in the left front tire 10 fails, the estimated value of tire force Fy in the left front tire 10 can be replaced with the estimated value of tire force Fy of the left front tire 10 calculated by the calculation model 33a of the right front tire 10.

Figure 7 is a chart showing the mean absolute error between the estimated tire force F of all tires calculated using a calculation model corresponding to each tire of the vehicle and the measured value. In Figure 7, the vehicle is equipped with four tires 10: the left front wheel (FL), right front wheel (FR), left rear wheel (RL), and right rear wheel (RR), and the calculation models corresponding to each tire are denoted as calculation model FL, calculation model FR, etc.

For example, in the calculation model FL, data on physical quantities measured by a sensor 20 attached to the tire 10 of the left front wheel (FL) is input, and the tire forces F of the left front wheel, right front wheel, left rear wheel, and right rear wheel are estimated. The mean absolute errors between the estimated values by the calculation model FL and the measured values are calculated as 255.34, 151.08, and 175.59 for the tire forces Fx, Fy, and Fz of the left front wheel, respectively.

The mean absolute errors between the estimated values and the measured values using the calculation model FR corresponding to the right front wheel are calculated as 252.58, 155.07, and 174.18 for the tire forces Fx, Fy, and Fz of the left front wheel, respectively, which are equivalent to the values of the calculation model FL. It can be seen that even if the calculation model for one tire 10 cannot correctly estimate tire physical information due to a sensor failure, it is possible to substitute tire physical information estimated based on the calculation model for the other tire 10.

The tire physical information estimation system 100 estimates tire physical information of a tire 10 to which a sensor 20 is attached, for which information indicating that the sensor 20 is faulty is acquired by the sensor fault information acquisition unit 32, based on a calculation model 33a for a tire 10 to which a sensor 20 is attached, for which information indicating that the sensor 20 is not faulty, so that the tire physical information of the tire 10 can be continuously estimated even if some of the sensors 20 are faulty.

The physical information estimation unit 33 may estimate the tire physical information of the tire 10 to which the faulty sensor 20 is attached based on the calculation model 33a for the tire 10 arranged on the same axle of the vehicle. By using the estimated value based on the calculation model 33a for the tire 10 arranged on the same axle, the tire physical information estimation system 100 can improve the estimation accuracy of the tire physical information by making the effects of vehicle steering, etc. equivalent.

The physical information estimation unit 33 may estimate the tire physical information of the tire 10 to which the faulty sensor is attached based on a calculation model for the tire 10 located on the same left-right side of the vehicle. By using an estimated value based on the calculation model 33a for the tire 10 located on the same left-right side of the vehicle, the tire physical information estimation system 100 can improve the estimation accuracy of the tire physical information by making the influence of the road surface over which the vehicle travels roughly the same.

In the above embodiment, the computation model 33a uses a CNN-type DenceNet model, but a model structure such as the so-called LeNet model, ResNet model, MobileNet model, or PeelNet model may also be used. A model may also be constructed by incorporating a modular structure such as a Residual Block into the computation model 33a.

Next, features of the tire physical information estimation system 100 and the tire physical information estimation method according to the embodiment will be described.
The tire physical information estimation system 100 according to the embodiment includes a data acquisition unit 31, a physical information estimation unit 33, and a sensor failure information acquisition unit 32. The data acquisition unit 31 acquires data of physical quantities measured by sensors 20 attached to a plurality of tires 10 mounted on a vehicle. The physical information estimation unit 33 inputs the data acquired by the data acquisition unit 31, and estimates tire physical information generated by the motion of the tire 10 for the tire 10 and the other tires 10 using a computation model 33a that has been trained for each tire 10. The sensor failure information acquisition unit 32 acquires information on whether the sensor 20 is malfunctioning. The physical information estimation unit 33 estimates tire physical information of the tire 10 to which the sensor 20, which has been acquired by the sensor failure information acquisition unit 32 as being malfunctioning, is attached based on the computation model 33a of the tire 10 to which the sensor 20, which has been acquired as being malfunctioning, is attached. In this way, the tire physical information estimation system 100 can continuously estimate tire physical information regarding the tire 10 even when some of the sensors 20 are malfunctioning.

The physical information estimation unit 33 estimates tire physical information of the tire 10 to which the sensor 20, which has been identified as faulty by the sensor fault information acquisition unit 32, is attached, based on the calculation model 33a for the tire 10 arranged on the same axle of the vehicle. This allows the tire physical information estimation system 100 to improve the accuracy of tire physical information estimation by using estimated values that are equally affected by steering, etc., when estimating tire physical information.

The physical information estimation unit 33 estimates tire physical information of the tire 10 to which the sensor 20, which has been identified as faulty by the sensor fault information acquisition unit 32, is attached, based on the calculation model 33a for the tire 10 located on the same left-right side of the vehicle. This allows the tire physical information estimation system 100 to improve the accuracy of estimating tire physical information by using estimated values that are roughly equivalently influenced by the road surface over which the vehicle travels.

The tire physical information estimation method includes a data acquisition step, a physical information estimation step, and a sensor failure information acquisition step. The data acquisition step acquires data of physical quantities measured by sensors 20 attached to multiple tires 10 mounted on a vehicle. The physical information estimation step inputs the data acquired in the data acquisition step, and estimates tire physical information generated by the motion of the tire 10 for the tire 10 and the other tires 10 using a computation model 33a that has been trained for each tire 10. The sensor failure information acquisition step acquires information on whether the sensor 20 is malfunctioning. The physical information estimation step estimates tire physical information of a tire 10 to which a sensor 20 that has been acquired as malfunctioning in the sensor failure information acquisition step is attached, based on a computation model for a tire 10 to which a sensor 20 that has been acquired as malfunctioning is attached. According to this tire physical information estimation method, tire physical information regarding the tire 10 can be continuously estimated even if some of the sensors 20 are malfunctioning.

The tire physical information estimation system also includes a data acquisition unit 31 and a physical information estimation unit 33. The data acquisition unit 31 acquires data on physical quantities measured by a sensor 20 attached to one tire 10 mounted on a vehicle. The physical information estimation unit 33 inputs the data acquired by the data acquisition unit 31, and estimates tire physical information generated by the motion of the tire 10 for the one tire 10 and other tires 10 using a trained computation model 33a. This enables the tire physical information estimation system 100 to estimate tire physical information for other tires 10 using the computation model 33a corresponding to the tire 10 to which the sensor 20 is attached.

The above describes the embodiments of the present invention. These embodiments are merely examples, and it will be understood by those skilled in the art that various modifications and changes are possible within the scope of the claims of the present invention, and that such modifications and changes are also within the scope of the claims of the present invention. Therefore, the descriptions and drawings in this specification should be treated as illustrative rather than restrictive.

10 Tire, 20 Sensor, 31 Data acquisition unit,
32 Sensor failure information acquisition unit, 33 Physical information estimation unit, 33a Calculation model,
100 Tire physical information estimation system.

Claims (5)

a data acquisition unit that acquires data of physical quantities measured by sensors attached to a plurality of tires mounted on a vehicle;
a physical information estimation unit that receives the data acquired by the data acquisition unit and estimates tire physical information generated by the tire motion for the tire and other tires using a trained calculation model for each tire;
a sensor failure information acquisition unit that acquires information on whether the sensor is malfunctioning,
the physical information estimation unit estimates tire physical information of a tire attached to a sensor that has been acquired by the sensor failure information acquisition unit as being faulty, based on the calculation model for a tire attached to a sensor that has been acquired as being non-faulty. The tire physical information estimation system according to claim 1, characterized in that the physical information estimation unit estimates tire physical information of a tire to which a sensor that has been detected as faulty by the sensor fault information acquisition unit is attached, based on the computational model for a tire that is located on the same axle of the vehicle. The tire physical information estimation system according to claim 1, characterized in that the physical information estimation unit estimates tire physical information of a tire to which a sensor that has been detected as faulty by the sensor fault information acquisition unit is attached, based on the calculation model for tires that are located on the same left-right side of the vehicle. A data acquisition step of acquiring data of physical quantities measured by sensors attached to a plurality of tires mounted on a vehicle;
a physical information estimation step of inputting the data acquired in the data acquisition step, and estimating tire physical information generated by the tire motion for the tire and other tires using a computation model that has been trained for each tire;
A sensor failure information acquisition step of acquiring information on whether the sensor is malfunctioning,
the physical information estimation step estimates tire physical information of a tire equipped with a sensor that has been acquired as being faulty in the sensor failure information acquisition step, based on the computation model for a tire equipped with a sensor that has been acquired as being non-faulty. a data acquisition unit that acquires data of a physical quantity measured by a sensor attached to one of the tires mounted on the vehicle;
a physical information estimation unit that receives the data acquired by the data acquisition unit and estimates tire physical information generated by tire motion for the one tire and another tire using a trained computation model;
A tire physical information estimation system comprising:

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