JP2021088225A - Cornering limit prediction system and cornering limit prediction method - Google Patents

Abstract

To provide a cornering limit prediction system and a cornering limit prediction method capable of calculating a safety margin index for supporting safe cornering of a vehicle.SOLUTION: In a cornering limit prediction system 100, a sensor information acquisition section 31 acquires a tire physical amount measured through a sensor arranged on a tire 10. A tire force calculation section 32 calculates tire force F and a maximum friction coefficient between the tire 10 and a road surface by inputting the tire physical amount into a calculation model 32a. A curve information acquisition section 33 acquires information of a curve on a road in a travel direction of a vehicle. A generated tire force estimation section 34 calculates acceleration in a lateral direction of the vehicle generated when the vehicle enters the curve at a current travel speed and estimates the tire force generated at the curve. A safety margin index calculation section 35 calculates a safety margin index when the vehicle travels around the curve from limit tire force calculated on the basis of the maximum friction coefficient and the tire force generated at the curve.SELECTED DRAWING: Figure 1

Description

The present invention relates to a cornering limit prediction system and a cornering limit prediction method.

In order to support the running of the vehicle, it is being considered to estimate the friction value of the road surface and the braking distance of the vehicle, and automatically control the accelerator operation, the brake operation, the steering, etc. in the vehicle on behalf of the driver. ..

Patent Document 1 describes a conventional method for determining an operating limit speed. This conventional method estimates the grip potential between the tire and the road surface as a function of the influencing parameters and determines the driving limit speed so that the grip requirement on the upcoming road event does not exceed the grip potential. To do.

Special Table 2018-517978

In the method for determining the driving limit speed described in Patent Document 1, the tire force, which is a grip requirement, is determined from the acceleration generated in the vehicle based on the analysis model of the vehicle. The present inventor has found that the tire force and the maximum coefficient of friction generated in the tire can be estimated more accurately, and the safety margin index at the time of cornering can be calculated to support the safe cornering of the vehicle.

The present invention has been made in view of such circumstances, and an object of the present invention is a cornering limit prediction system and a cornering limit prediction method capable of calculating a safety margin index to support safe cornering of a vehicle. Is to provide.

One aspect of the present invention is a cornering limit prediction system. The cornering limit prediction system is a sensor information acquisition unit that acquires the physical quantity of the tire measured by a sensor arranged on the tire, and a tire force by inputting the physical quantity of the tire acquired by the sensor information acquisition unit into the calculation model. Based on the tire force calculation unit that calculates the maximum friction coefficient between the tire and the road surface, the curve information acquisition unit that acquires curve information about the curve existing on the road in the direction of travel of the vehicle, and the curve information at the present time in advance. Calculated based on the generated tire force estimation unit that calculates the lateral acceleration of the vehicle that occurs when entering the curve at the running speed in the above curve and estimates the generated tire force on the curve, and the maximum friction coefficient. It is provided with a safety margin index calculation unit that calculates a safety margin index when a vehicle travels on the curve based on the limit tire force and the generated tire force.

Another aspect of the present invention is a cornering limit prediction method. The cornering limit prediction method includes a sensor information acquisition step of acquiring the physical quantity of the tire measured by a sensor arranged on the tire, and a tire force by inputting the physical quantity of the tire acquired by the sensor information acquisition step into a calculation model. Based on the tire force calculation step for calculating the maximum friction coefficient between the tire and the road surface, the curve information acquisition step for acquiring the curve information regarding the curve existing on the road in the traveling direction of the vehicle, and the curve information at the present time in advance. Calculated based on the generated tire force estimation step that calculates the lateral acceleration of the vehicle that occurs when entering the curve at the running speed in the above curve and estimates the generated tire force on the curve, and the maximum friction coefficient. It is provided with a safety margin index calculation step of calculating a safety margin index when a vehicle travels on the curve based on the limit tire force and the generated tire force.

According to the present invention, it is possible to calculate a safety margin index to support safe cornering of a vehicle.

It is a block diagram which shows the functional structure of the cornering limit prediction system which concerns on embodiment. It is a schematic diagram for demonstrating the learning of the calculation model. It is a schematic diagram for demonstrating the safety margin index. It is a flowchart which shows the procedure of the safety margin index calculation processing by a tire force estimation device. It is a schematic diagram for demonstrating the timing of the traveling speed control of a vehicle.

Hereinafter, the present invention will be described with reference to FIGS. 1 to 5 based on a preferred embodiment. The same or equivalent components and members shown in the drawings shall be designated by the same reference numerals, and redundant description will be omitted as appropriate. In addition, the dimensions of the members in each drawing are shown enlarged or reduced as appropriate for easy understanding. In addition, some of the members that are not important for explaining the embodiment in each drawing are omitted and displayed.

(Embodiment)
FIG. 1 is a block diagram showing a functional configuration of the cornering limit prediction system 100 according to the embodiment. The cornering limit prediction system 100 measures tire physical quantities such as acceleration, air pressure, and temperature generated in the tire 10 by a sensor 20 arranged on the tire 10 when the vehicle is running, and at least the road on which the tire 10 is running from the external device 50. Get map information about.

The cornering limit prediction system 100 inputs the acquired physical quantity of the tire 10 into the calculation model 32a, and calculates the tire force F and the maximum friction coefficient. The calculation model 32a is a learning model such as a neural network. The calculation model 32a uses the tire force F actually measured on the tire 10 and the maximum friction coefficient between the road surface and the tire 10 used during learning as training data, and repeats the execution of the calculation and the learning by updating the calculation model. This will increase the accuracy.

The cornering limit prediction system 100 acquires information (referred to as “curve information”) regarding a curve existing on the road in the traveling direction based on the map information of the traveling road acquired from the external device 50. Curve information includes, for example, the radius of the curve and the slope of the road surface. The cornering limit prediction system 100 calculates the lateral acceleration of the vehicle generated when entering a curve at the current traveling speed based on the curve information, and the tire force generated during cornering (referred to as "generated tire force"). ) Is estimated.

The cornering limit prediction system 100 calculates a safety margin index when the vehicle travels on a curve based on the limit tire force and the generated tire force calculated based on the maximum friction coefficient. The limit tire force is the tire force immediately before the tire 10 starts to slide on the road surface, and is a value obtained by multiplying the vertical load of the tire 10 by the maximum coefficient of friction with the road surface.

The cornering limit prediction system 100 controls the traveling speed of the vehicle so that the safety margin index does not exceed a predetermined threshold value. The control of the traveling speed of the vehicle is preferably performed before the curve. Further, the cornering limit prediction system 100 controls the traveling speed of the vehicle so that the safety margin index does not exceed a predetermined threshold value even when the vehicle is traveling on a curve.

The cornering limit prediction system 100 includes a sensor 20 and a tire force estimation device 30. The sensor 20 includes an acceleration sensor 21, a pressure sensor 22, a temperature sensor 23, and the like, and measures physical quantities in the tire 10 such as acceleration, tire pressure, and tire temperature. The sensor 20 may have a strain gauge to measure the strain generated in the tire 10. These sensors measure the physical quantity related to the deformation and movement of the tire 10 as the physical quantity of the tire 10.

The acceleration sensor 21 is arranged on a tire body portion formed of a rubber material of the tire 10 or a wheel 15 forming a part of the tire 10, and accelerates generated in the tire 10 while mechanically moving together with the tire 10. To measure. The acceleration sensor 21 measures the acceleration of the tire 10 in the three axes of the circumferential direction, the axial direction, and the radial direction. The pressure sensor 22 and the temperature sensor 23 are arranged, for example, by mounting the tire 10 on the air valve or fixing the tire 10 to the wheel 15, and measure the air pressure and temperature of the tire 10, respectively. Further, the pressure sensor 22 and the temperature sensor 23 may be arranged on the inner liner or the like of the tire 10.

The sensor 20 measures physical quantities of the tire 10 such as acceleration and strain of the tire 10, tire air pressure, and tire temperature, and outputs the measured data to the tire force estimation device 30. The tire force estimation device 30 estimates the tire force F and the maximum friction coefficient based on the data measured by the sensor 20.

In order to identify each tire, the tire 10 may be attached with, for example, an RFID 11 to which unique identification information is given. For example, the calculation model 32a may be selected and set from a data group prepared in advance according to the unique information of the RFID 11 attached to the tire 10, or may be selected from a database provided by a server device or the like. It may be. Further, the specifications of the tire 10 may be recorded with respect to the unique information of the RFID 11, and the calculation model 32a corresponding to the specifications of the tire 10 may be provided in the database. The specifications of the tire 10 may be called from the unique information of the RFID 11, and the calculation model 32a may be set, or the calculation model 32a according to the specifications of the called tire 10 may be selected from the database.

The tire force estimation device 30 includes a sensor information acquisition unit 31, a tire force calculation unit 32, a curve information acquisition unit 33, a generated tire force estimation unit 34, a safety margin index calculation unit 35, and a communication unit 36. The tire force estimation device 30 is an information processing device such as a PC (personal computer). Each part of the tire force estimation device 30 can be realized by an electronic element such as a computer CPU or a mechanical component in terms of hardware, and can be realized by a computer program or the like in terms of software. It depicts a functional block realized by cooperation. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The sensor information acquisition unit 31 acquires physical tire quantities such as acceleration, tire pressure, and tire temperature measured by the sensor 20 by wireless communication or the like. The tire force calculation unit 32 has a calculation model 32a and a correction processing unit 32b. The tire force calculation unit 32 inputs the tire physical quantity input from the sensor information acquisition unit 31 into the calculation model 32a, and calculates the tire force F and the maximum friction coefficient between the tire 10 and the road surface.

As shown in FIG. 1, the tire force F has three axial components of the front-rear front-rear force Fx of the tire 10, the lateral force Fy in the lateral direction, and the load Fz in the vertical direction. The tire force calculation unit 32 may calculate all of these three axial components, or may calculate at least one component or two components by any combination.

The calculation model 32a uses a learning model such as a neural network. The calculation model 32a is, for example, a CNN (Convolutional Neural Network) type, and uses a learning type model having a convolutional operation and a pooling operation used in the so-called LeNet which is the prototype thereof. The calculation model 32a extracts the features of the data input to the input layer by using a convolution operation and a pooling operation, transmits the features to each node of the intermediate layer, and performs a linear operation or the like for each node of the intermediate layer. Is executed to fully connect and connect to each node of the output layer. In the full coupling, in addition to the linear operation, a non-linear operation may be executed by using an activation function or the like. The tire force F and the maximum friction coefficient in the three axial directions are output to each node of the output layer of the calculation model 32a.

FIG. 2 is a schematic diagram for explaining learning of the calculation model 32a. As the input data to the calculation model 32a, in addition to the tire physical quantity acquired by the sensor information acquisition unit 31, external area information and the like can be used. For the physical quantity of the tire, acceleration, tire pressure, tire temperature, strain generated in the tire, and the like are used. As the external area information, meteorological information such as weather, temperature and precipitation, and road surface information such as road surface unevenness, temperature and frozen state are used. In addition to these, the input data may use the vehicle weight, speed, etc. based on the data of the digital tachograph mounted on the vehicle.

When learning the calculation model 32a, the accuracy of the calculation model 32a is improved by comparing the tire force F and the maximum friction coefficient as the calculation result with the teacher data and repeating the update of the calculation model 32a. It is assumed that the teacher data of the maximum coefficient of friction between the tire 10 and the road surface is known for the various road surfaces used during learning. The calculation model 32a may be trained by performing a rotation test by changing the maximum friction coefficient of the ground contact surface where the tire 10 and the tire 10 are grounded. Further, it is also possible to mount the tire 10 on an actual vehicle and run the vehicle on a test run on a road surface having a different maximum friction coefficient to execute the learning of the calculation model 32a.

In the calculation model 32a, for example, the configuration and weighting of the number of layers in all the joints in the model change according to the specifications of the tire 10, but in the rotation test with the tire 10 (including the wheels) of each specification. The learning of the calculation model 32a can be executed.

However, it is not necessary to strictly learn the calculation model 32a for each specification of the tire 10. For example, by learning and constructing a calculation model 32a for each type of tires for passenger cars, tires for trucks, etc., and making the tire force F and the maximum friction coefficient estimated within a certain error range, it is included in a plurality of specifications. One calculation model 32a may be shared with the tire 10 to be used, and the number of calculation models may be reduced. Further, the calculation model 32a can also carry out learning of the calculation model 32a by mounting the tire 10 on an actual vehicle and running the vehicle in a test run. The specifications of the tire 10 include information on tire performance such as tire size, tire width, flatness, tire strength, tire outer diameter, road index, and date of manufacture.

The correction processing unit 32b corrects the calculation model 32a based on the state of the tire 10. Alignment error occurs when the tire 10 is mounted on a vehicle, physical property values such as rubber hardness change with time, and wear progresses as the tire runs. The state of the tire 10 including elements such as alignment error, physical property value, and wear changes depending on the usage conditions, and an error occurs in the calculation of the tire force F and the maximum friction coefficient by the calculation model 32a. The correction processing unit 32b performs a process of adding a correction term according to the state of the tire 10 to the calculation model 32a in order to reduce the error of the calculation model 32a.

The curve information acquisition unit 33 acquires the current position information of the vehicle from the GPS receiver 51, and acquires at least map information about the traveling road from the external device 50. The curve information acquisition unit 33 acquires curve information regarding a curve existing on the road in the traveling direction based on the map information of the traveling road acquired from the external device 50, and outputs the curve information to the generated tire force estimation unit 34. The curve information includes, for example, the radius of the curve and the slope of the road surface.

The generated tire force estimation unit 34 calculates the lateral acceleration of the vehicle from the centrifugal force generated when entering the curve at the current traveling speed by using the radius of the curve included in the curve information. Further, from the gradient included in the curve information, it is possible to calculate the acceleration acting in the front-rear direction and the vertical direction of the vehicle that occurs when entering the curve. The generated tire force estimation unit 34 estimates the generated tire force at the time of cornering by correcting the tire force currently generated before the curve travel by the acceleration in the three axial directions generated when entering the calculated curve. , Output to the safety margin index calculation unit 35.

The generated tire force estimation unit 34 calculates the lateral acceleration of the vehicle using the radius of the curve without considering the slope of the curve, and the calculated lateral acceleration is generated at the present time before traveling on the curve. The existing tire force F may be corrected to obtain the generated tire force.

Further, the tire force generated when entering a curve, that is, the tire force generated during cornering is the acceleration measured by the acceleration sensor 21 using the acceleration in the lateral direction or the triaxial direction of the vehicle generated while traveling on the curve. The data may be corrected on a trial basis, and the corrected acceleration data may be input to the calculation model 32a to obtain the corrected acceleration data.

The safety margin index calculation unit 35 calculates the limit tire force based on the maximum friction coefficient calculated by the tire force calculation unit 32 and the vertical component Fz of the tire force F. The safety margin index calculation unit 35 calculates the safety margin index when the vehicle travels on the curve based on the calculated limit tire force and the generated tire force estimated by the generated tire force estimation unit 34.

FIG. 3 is a schematic diagram for explaining the safety margin index. In FIG. 3, the horizontal axis represents the front-rear force Fx of the tire force F, the vertical axis represents the lateral force Fy of the tire force F, and the solid circle centered on the origin indicates the limit tire force. Let R (the radius of the circle of the limit tire force). Assuming that the magnitude of the tire force f1 generated when entering a curve is R1, the safety margin index is obtained as the value of R / R1 obtained by dividing the limit tire force by the generated tire force. If the safety margin index is less than 1, the generated tire force may exceed the limit tire force and the vehicle may slip while traveling on a curve. Therefore, by controlling the traveling speed so that the safety margin index is at least 1 or more, it is possible to corner the vehicle without slipping.

The safety margin index calculation unit 35 sends the calculated safety margin index to the in-vehicle control device 60 via the communication unit 36. The in-vehicle control device 60 controls the vehicle so that the safety margin index obtained from the safety margin index calculation unit 35 does not become less than 1 when the vehicle is automatically driven. The in-vehicle control device 60 controls so as to reduce the traveling speed when the safety margin index is less than 1. Further, the in-vehicle control device 60 notifies the driver by displaying the safety margin index acquired from the safety margin index calculation unit 35 on the display device or outputting the sound from the speaker for safe driving. It may be designed to draw the driver's attention.

The in-vehicle control device 60 may set a predetermined threshold value for the safety margin index and control the traveling speed of the vehicle so as to be equal to or higher than the threshold value. The predetermined threshold value is, for example, about 1 to 2, but may be a value of 2 or more in consideration of the safety of the occupants of the vehicle.

Next, the operation of the cornering limit prediction system 100 will be described. FIG. 4 is a flowchart showing the procedure of the safety margin index calculation process by the tire force estimation device 30. The sensor information acquisition unit 31 of the tire force estimation device 30 starts acquiring tire physical quantities such as acceleration, tire air pressure, and tire temperature of the tire 10 measured by the sensor 20 (S1).

The tire force calculation unit 32 inputs the tire physical quantity into the calculation model 32a, and calculates the tire force F and the maximum friction coefficient between the tire 10 and the road surface (S2). The curve information acquisition unit 33 acquires curve information regarding a curve existing on the road in the traveling direction based on the map information of the traveling road acquired from the external device 50 (S3). The generated tire force estimation unit 34 estimates the generated tire force generated when the vehicle enters a curve (S4). The safety margin index calculation unit 35 calculates the safety margin index when the vehicle travels on the curve based on the limit tire force calculated based on the maximum friction coefficient and the generated tire force (S5), and ends the process.

The cornering limit prediction system 100 inputs the tire physical quantity measured by the tire 10 into the calculation model 32a, and accurately calculates the tire force F and the maximum friction coefficient between the tire 10 and the road surface. The cornering limit prediction system 100 calculates and provides a safety margin index based on the limit tire force at the time of cornering calculated based on the maximum friction coefficient between the tire 10 and the road surface and the generated tire force, thereby providing the safety of the vehicle. Can support various cornering.

The cornering limit prediction system 100 can accurately estimate the generated tire force by calculating the acceleration in the three axial directions using the radius and the slope of the curve as the curve information. Further, the cornering limit prediction system 100 sets a predetermined threshold value for the safety margin index, and controls the traveling speed of the vehicle by the in-vehicle control device 60 so that the safety margin index becomes equal to or higher than the threshold value. It can increase safety.

FIG. 5 is a schematic diagram for explaining the timing of controlling the traveling speed of the vehicle. In FIG. 5, it is desirable that the traveling speed control of the vehicle is completed before reaching the curve, and the vehicle enters the curve at a traveling speed at which the safety margin index is 1 or equal to or higher than the above-mentioned predetermined threshold value.

The cornering limit prediction system 100 calculates the limit tire force and the generated tire force even during curve driving and obtains the safety margin index, so that the safety margin index during curve driving becomes 1 or equal to or more than the above-mentioned predetermined threshold value. You may want to monitor whether or not. The cornering limit prediction system 100 controls the traveling speed of the vehicle by the in-vehicle control device 60 so that the safety margin index calculated during curve driving becomes 1 or equal to or more than the above-mentioned predetermined threshold value, thereby ensuring the safety of vehicle cornering. The sex can be further increased.

Next, the features of the cornering limit prediction system 100 according to the embodiment will be described.
The cornering limit prediction system 100 according to the embodiment includes a sensor information acquisition unit 31, a tire force calculation unit 32, a curve information acquisition unit 33, a generated tire force estimation unit 34, and a safety margin index calculation unit 35. The sensor information acquisition unit 31 acquires the physical quantity of the tire 10 measured by the sensor arranged on the tire 10. The tire force calculation unit 32 inputs the physical quantity of the tire acquired by the sensor information acquisition unit 31 into the calculation model 32a to calculate the tire force F and the maximum friction coefficient between the tire 10 and the road surface. The curve information acquisition unit 33 acquires curve information regarding a curve existing on the road in the traveling direction of the vehicle. The generated tire force estimation unit 34 calculates the lateral acceleration of the vehicle generated when entering the curve at the current traveling speed in advance based on the curve information, and estimates the generated tire force on the curve. The safety margin index calculation unit 35 calculates the safety margin index when the vehicle travels on a curve based on the limit tire force and the generated tire force calculated based on the maximum friction coefficient. As a result, the cornering limit prediction system 100 can support the safe cornering of the vehicle by calculating the safety margin index at the time of cornering when the vehicle travels on a curve.

The curve information is the radius and slope of the curve. As a result, the cornering limit prediction system 100 can accurately estimate the tire force generated when entering a curve.

Further, an in-vehicle control device 60 as a vehicle control unit that controls the traveling speed of the vehicle so that the safety margin index becomes equal to or higher than a predetermined threshold value is further provided. As a result, the cornering limit prediction system 100 sets a predetermined threshold value for the safety margin index, and controls the traveling speed of the vehicle by the in-vehicle control device 60 so that the safety margin index becomes equal to or higher than the threshold value, thereby cornering the vehicle. Can increase the safety of.

Further, the in-vehicle control device 60 controls the traveling speed of the vehicle so that the safety margin index does not exceed a predetermined threshold value while traveling on a curve. As a result, the cornering limit prediction system 100 controls the traveling speed of the vehicle by the in-vehicle control device 60 so that the safety margin index calculated during curve driving becomes equal to or higher than a predetermined threshold value, thereby ensuring the safety of vehicle cornering. Can be further increased.

The cornering limit prediction method includes a sensor information acquisition step, a tire force calculation step, a curve information acquisition step, a generated tire force estimation step, and a safety margin index calculation step. The sensor information acquisition step acquires the physical quantity of the tire measured by the sensor 20 arranged on the tire 10. In the tire force calculation step, the physical quantity of the tire acquired by the sensor information acquisition step is input to the calculation model 32a to calculate the tire force F and the maximum friction coefficient between the tire 10 and the road surface. The curve information acquisition step acquires curve information regarding a curve existing on the road in the traveling direction of the vehicle. In the generated tire force estimation step, the lateral acceleration of the vehicle generated when entering the curve at the current running speed is calculated in advance based on the curve information, and the generated tire force on the curve is estimated. The safety margin index calculation step calculates the safety margin index when the vehicle travels on a curve based on the limit tire force and the generated tire force calculated based on the maximum friction coefficient. According to this cornering limit prediction method, it is possible to support the safe cornering of the vehicle by calculating the safety margin index at the time of cornering when the vehicle travels on a curve.

The above description has been made based on the embodiment of the present invention. It will be appreciated by those skilled in the art that these embodiments are exemplary and that various modifications and modifications are possible within the claims of the invention, and that such modifications and modifications are also within the claims of the present invention. It is about to be done. Therefore, the descriptions and drawings herein should be treated as exemplary rather than limiting.

10 tires, 20 sensors, 31 sensor information acquisition unit,
32 Tire force calculation unit, 32a calculation model, 33 Curve information acquisition unit,
34 Generated tire force estimation part, 35 Safety margin index calculation part,
60 In-vehicle control device (vehicle control unit), 100 Cornering limit prediction system.

Claims (5)

A sensor information acquisition unit that acquires the physical quantity of the tire measured by a sensor placed on the tire,
A tire force calculation unit that inputs the physical quantity of the tire acquired by the sensor information acquisition unit into the calculation model and calculates the tire force and the maximum friction coefficient between the tire and the road surface.
A curve information acquisition unit that acquires curve information related to curves existing on the road in the direction of travel of the vehicle,
Based on the curve information, the generated tire force estimation unit that calculates the lateral acceleration of the vehicle generated when entering the curve at the current running speed in advance and estimates the generated tire force on the curve, and the generated tire force estimation unit.
A safety margin index calculation unit that calculates a safety margin index when a vehicle travels on the curve based on the limit tire force calculated based on the maximum friction coefficient and the generated tire force.
A cornering limit prediction system characterized by being equipped with. The cornering limit prediction system according to claim 1, wherein the curve information is a radius and a slope of the curve. The cornering limit prediction system according to claim 1 or 2, further comprising a vehicle control unit that controls the traveling speed of the vehicle so that the safety margin index becomes equal to or higher than a predetermined threshold value. The cornering limit prediction system according to claim 3, wherein the vehicle control unit controls the traveling speed of the vehicle so that the safety margin index does not exceed a predetermined threshold value while traveling on the curve. A sensor information acquisition step that acquires the physical quantity of the tire measured by a sensor placed on the tire, and
A tire force calculation step in which the physical quantity of the tire acquired in the sensor information acquisition step is input to the calculation model to calculate the tire force and the maximum friction coefficient between the tire and the road surface, and the tire force calculation step.
The curve information acquisition step to acquire the curve information about the curve existing on the road in the direction of travel of the vehicle, and
Based on the curve information, the generated tire force estimation step for calculating the lateral acceleration of the vehicle generated when entering the curve at the current running speed in advance and estimating the generated tire force on the curve, and the generated tire force estimation step.
A safety margin index calculation step for calculating a safety margin index when a vehicle travels on the curve based on the limit tire force calculated based on the maximum friction coefficient and the generated tire force.
A cornering limit prediction method characterized by providing.

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