JP2011158402A - Method and apparatus for evaluating tire performance - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To accurately obtain biological information about an occupant and accurately evaluate tire performance based on the obtained biological information. <P>SOLUTION: A method for evaluating the tire performance includes the steps of repeatedly implementing a combination of optimization processing with a fitness function and a multiple discriminant analysis on an electric signal obtained from an electrode attached to a frontal pole part of the occupant based on an International Law 10-20, calculating an adaptive frequency having a strength peculiar to the occupant out of the electric signal obtained from the occupant, calculating an adaptive frequency band containing the adaptive frequency, selecting an emotional state matched with a frequency band corresponding to the adaptive frequency band from a database with which the emotional state of the occupant and a frequency band of the electric signal corresponding to the emotion are matched, and determining the emotional state selected in an evaluation process as an evaluation result of the tire performance. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a tire performance evaluation method and a tire performance evaluation apparatus that evaluate tire performance using a signal that can be acquired from an occupant of a vehicle in which a tire to be evaluated is mounted.

  Conventionally, performance including the steering stability and ride comfort of a tire mounted on a vehicle such as an automobile (hereinafter abbreviated as “tire performance” as appropriate) has, for example, a wealth of knowledge about tires, It is evaluated based on the feeling (feeling) of a test driver who has a long driving experience.

  However, in the feeling of the test driver, different evaluations may be made for different test drivers, and a stable evaluation result is not always obtained even with the same test driver.

  Therefore, an attempt has been made to evaluate tire performance that has been evaluated based on the feeling of a test driver based on objective data (for example, Patent Document 1). Specifically, the fact that the ratio between the low frequency component and the high frequency component contained in the electrocardiogram acquired from the occupant represents the occupant's feeling of tension and fatigue is used for the evaluation of tire performance.

JP 2007-139499 A (Page 4, FIG. 1)

  As described above, for example, in a method of evaluating tire performance using a signal such as a heartbeat (hereinafter referred to as biometric information) that can be acquired from an occupant as objective data, a measurement device that acquires biometric information is a large scale. Become. In addition, there is a problem that it takes time to prepare for starting the measurement.

  In addition, an occupant (a person to be measured) may feel uncomfortable or stressful in running the vehicle with the measuring device attached. In this case, the biological information may include information other than information related to steering stability and riding comfort, for example, a sense of discomfort and stress caused by wearing the measuring device. Therefore, it cannot be said that appropriate biological information can be acquired from all occupants.

  Then, an object of this invention is to provide the tire performance evaluation method and tire performance evaluation apparatus which can evaluate tire performance correctly from the acquired biometric information.

  In order to solve the above-described problem, the first feature of the present invention has the following feature. That is, a tire performance evaluation method for evaluating tire performance when a vehicle travels on a predetermined road surface, the adaptation having an intensity specific to the occupant from an electrical signal (for example, an electroencephalogram) acquired from the occupant of the vehicle An adaptive frequency setting step for calculating a frequency, an adaptive frequency band including the adaptive frequency is calculated, and the adaptive state is calculated from a database in which an emotional state of an occupant and a frequency band of the electrical signal corresponding to the emotion are linked. An evaluation step for selecting an emotional state associated with a frequency band corresponding to a frequency band, and a determination step for determining the emotional state selected in the evaluation step as an index representing the tire performance And

  According to the first feature of the present invention, it is possible to determine the state of the occupant's emotion represented by an electrical signal (for example, an electroencephalogram) acquired from the occupant. Thereby, the state of the passenger's emotion can be determined as an index of the evaluation result of the tire performance. Therefore, the tire performance can be accurately evaluated from the biological information acquired from the occupant.

  A second feature of the present invention relates to the first feature of the present invention, wherein the adaptive frequency setting step receives an electric signal from an electrode disposed at the frontal pole portion of the occupant based on the International Law 10-20. A signal acquisition step of acquiring, an analysis step of performing a time-frequency analysis on the electrical signal according to an elapsed time from the time when the travel was started, and a frequency component of the electrical signal obtained by the time-frequency analysis An optimization process for selecting a frequency component from the Nth group by applying an optimization process to the Nth group using a fitness function, and a multiple discrimination for the (N + 1) th group consisting of the frequency components selected in the optimization process An analysis step of calculating a discriminant probability indicating the likelihood of each frequency component included in the (N + 1) th group by performing an analysis, and applying the discriminant probability calculated in the analysis step to the adaptation Substituting into the function, the optimization step is executed again, and in the optimization step executed again, the individual frequency components included in the N + 1th group using the fitness function into which the discriminant probability is substituted The frequency component is selected from the N + 2 group, and the likelihood of each frequency component included in the N + 2 group is expressed by performing a multiple discriminant analysis on the N + 2 group consisting of the frequency component selected in the optimization step executed again. The gist is to calculate the adaptive frequency as a combination of the frequency components of the electric signal by calculating a discriminant intermediate rate and repeatedly executing the analysis step and the optimization step.

  According to the 2nd characteristic, since an electrical signal is acquired from the electrode arrange | positioned at the frontal pole part of a passenger | crew based on the international law 10-20 method, an excessive discomfort and stress are not given to a passenger | crew. Therefore, it is possible to acquire correct biological information without disturbance such as uncomfortable feeling and stress.

  A third feature of the present invention relates to the first or second feature of the present invention, wherein the evaluation step sets an adaptive frequency band including the adaptive frequency in the adaptive frequency, and sets the adaptive frequency band to the set adaptive frequency band. A frequency band for evaluation is calculated by superimposing a plurality of weighting coefficients prepared in advance in a weighting coefficient database, and an emotional state and a frequency band of the electrical signal when the occupant is in the emotional state The gist is to select a state of emotion linked to a frequency band that matches the frequency band for evaluation by referring to a database stored linked.

  A fourth feature of the present invention relates to the second feature of the present invention, and includes a weighting factor generation step for generating the plurality of weighting factors from an electrical signal acquired from an arbitrary occupant, and the weighting factor generation step Sets an initial value of a combination of weighting coefficients in a frequency band including the adaptive frequency calculated in the adaptive frequency setting step, and updates the combination of weighting coefficients by repeatedly executing a fitness function and multiple discriminant analysis. Then, referring to a database in which the state of emotion and the frequency band of the electrical signal when the occupant is in the state of emotion are linked and stored, the combination of the updated weighting factors is applied to the adaptation Weight when the frequency band obtained by applying to the frequency band including the frequency matches the frequency band stored in the database The combination of only coefficient is required to be stored in the weighting factor database.

  The fifth feature of the present invention relates to the second feature of the present invention, and the gist of the signal obtaining step is to obtain an electrical signal from an electrode arranged at Fp1 based on the International Law 10-20. .

  A sixth feature of the present invention relates to the second feature of the present invention, and is summarized in that the fitness function is a genetic algorithm.

  The gist of a seventh feature of the present invention is a tire performance evaluation device for executing the tire performance evaluation method according to any one of the first to sixth features of the present invention.

  According to the present invention, it is possible to provide a tire performance evaluation method and a tire performance evaluation apparatus that can accurately acquire the biological information of the occupant and can accurately evaluate the tire performance from the acquired biological information. .

FIG. 1 is a schematic diagram illustrating a tire performance evaluation test performed using the tire performance evaluation apparatus according to the present embodiment. FIG. 2 is a configuration diagram illustrating the tire performance evaluation apparatus according to the present embodiment. FIG. 3 is a flowchart for explaining the tire performance evaluation apparatus. FIG. 4 is a flowchart for explaining an adaptive frequency setting step included in the tire performance evaluation method. FIG. 5 is a schematic diagram for explaining an example of a test course for driving a vehicle on which a tire to be evaluated is mounted. FIG. 6 is a flowchart for explaining the analysis process. FIG. 7 is a diagram illustrating an example of a gene expression (first generation) initially assigned to a plurality of frequency bands (4 Hz to 22 Hz). FIG. 8 is a diagram for explaining an example of the occupant's adaptive frequency obtained by the adaptive frequency setting step. FIG. 9 is a schematic diagram for simply explaining the occupant adaptive frequency obtained from the occupant's brain wave measured over a predetermined period. FIG. 10 is a flowchart for explaining the evaluation process. FIG. 11 is a schematic diagram illustrating a set of weighting coefficients to be superimposed on the adaptive frequency band. FIG. 12 is a schematic diagram for schematically explaining a routine for repeatedly applying the weighting coefficient set stored in the weighting coefficient database DB2. FIG. 13 is a schematic diagram for explaining the reference electroencephalogram stored in the reference electroencephalogram database DB1. FIG. 14 is a flowchart for explaining a database creation phase for creating a database.

  Embodiments of a tire performance evaluation method and a tire performance evaluation apparatus according to the present invention will be described with reference to the drawings.

  Specifically, (1) Configuration of tire performance evaluation device, (2) Description of tire performance evaluation method, (3) Database creation phase, (4) Action and effect, (5) Other embodiments will be described.

(1) Configuration of Tire Performance Evaluation Device The tire performance evaluation device 1 will be described with reference to FIG. Specifically, (1-1) a test for acquiring an electric signal from an occupant and (1-2) a configuration of a tire performance evaluation device will be described.

(1-1) Test for Acquiring an Electric Signal from an Occupant FIG. 1 is a schematic diagram illustrating a tire performance evaluation test for road surface conditions performed using the tire performance evaluation apparatus 1.

  The tire performance evaluation device 1 includes an evaluation device body 10 and a signal acquisition unit 20. The tire performance evaluation apparatus 1 evaluates tire performance using an electrical signal (for example, brain wave) that can be acquired from the occupant 3 when the vehicle 100 on which the tire 101 is mounted travels on a predetermined road surface (described later). Details of the evaluation apparatus body 10 will be described later.

  The signal acquisition part 20 acquires an electrical signal from the electrode arrange | positioned at the frontal pole part of the passenger | crew 3 based on the international law 10-20 method. The signal acquisition unit 20 includes an electrode 21 and an electrode 22. The electrode 21 is arrange | positioned at Fp1 and acquires the electric signal in Fp1. The electrode 22 is a reference electrode. The electrode 22 is arrange | positioned at an earlobe etc., for example. The difference between the potential at the electrode 21 and the potential at the electrode 22 is amplified by a differential amplifier. Thereby, noise can be removed and a weak electric signal can be extracted accurately.

  The occupant 3 gets on the vehicle 100 with the signal acquisition unit 20 mounted at the Fp1 location. The tire performance evaluation device 1 acquires an electric signal that can be acquired from the Fp1 of the occupant 3 via the signal acquisition unit 20 while the vehicle travels on a predetermined test course.

(1-2) Configuration of Tire Performance Evaluation Device FIG. 2 is a configuration diagram illustrating the tire performance evaluation device 1. The tire performance evaluation device 1 includes an evaluation device body 10 and a signal acquisition unit 20. The signal acquisition unit 20 is an electrode. The signal acquisition unit 20 is attached to Fp1 of the frontal pole portion of the occupant 3.

  The evaluation apparatus body 10 includes a signal input unit 11 to which the electrical signal acquired by the signal acquisition unit 20 is supplied, a calculation unit 12 that calculates tire performance from the electrical signal supplied to the signal input unit 11, and a calculation unit 12 And a display unit 13 for displaying the result of the analysis.

  The evaluation apparatus main body 10 also includes an input unit 14 such as a keyboard and a mouse that receives input from the user, and a storage unit 15 including a hard disk drive, a semiconductor memory, and the like. The storage unit 15 stores a program for executing the tire performance evaluation method. Further, it may also serve as a reference electroencephalogram database DB1 stored in association with a typical electroencephalogram spectrum corresponding to emotions such as emotions, and a weighting coefficient database DB2 in which a plurality of sets of weighting coefficients are stored. Details of the reference electroencephalogram database DB1 and the weighting coefficient database DB2 will be described later.

  The calculation unit 12 executes a program of a tire performance evaluation method described later. The calculation unit 12 includes a frequency analysis calculation unit 121, an optimization calculation unit 122, and an analysis calculation unit 123 as functional configurations.

  The frequency analysis calculation unit 121 performs time frequency analysis of the electrical signal. In psychology, electroencephalograms are roughly classified into four types, δ waves, θ waves, α waves, and β waves, depending on the frequency band. For example, the δ wave is an electroencephalogram having a frequency band of less than 4 Hz. The δ wave does not appear when awake, but frequently appears during deep sleep. The θ wave is an electroencephalogram having a frequency band of 4 Hz or more and less than 8 Hz. Appears frequently during light sleep. The α wave is an electroencephalogram having a frequency band of 8 Hz or more and less than 13 Hz. Appears conspicuously on the occipital side when the eyes are resting, and the appearance is suppressed when the eyes are opened. It is also an index for evaluating relaxation. The β wave is an electroencephalogram having a frequency band of 13 Hz or higher. It is seen predominantly in the frontal and temporal regions, and is less regular.

  In this embodiment, an electroencephalogram acquired from Fp1 is analyzed to separate an electroencephalogram in a predetermined frequency band from the electroencephalogram spectrum. As the frequency analysis, fast Fourier transform, wavelet transform, or the like can be used. In this embodiment, fast Fourier transform is used.

  Since the electroencephalogram spectrum is a time-series signal in which a plurality of factors are intertwined in a complicated manner, it is difficult to extract a feature quantity included in the electroencephalogram spectrum only by frequency analysis. Therefore, in the present embodiment, feature quantities are extracted using multivariable analysis.

  The optimization calculation unit 122 performs an optimization process using the fitness function on the frequency components (Nth group) of the electroencephalogram spectrum obtained by the time frequency analysis. A frequency component is further selected from the frequency components of the Nth group by the optimization process. The selected frequency component is referred to as the (N + 1) th group. In the present embodiment, the optimization calculation unit 122 uses a genetic algorithm as an example of an optimization processing technique.

  The analysis calculation unit 123 performs a discriminant analysis on the frequency components of the (N + 1) th group selected by the optimization calculation unit 122, thereby calculating a discriminant median representing the likelihood of each frequency component included in the (N + 1) th group. To do.

  In the tire performance evaluation device 1, the discriminant probability calculated in the analysis calculation unit 123 is substituted into the fitness function, and the optimization calculation unit 122 executes the optimization process again. In the optimization process executed again, frequency components are further selected from the individual frequency components included in the (N + 1) th group using the fitness function into which the discriminant probability is substituted.

  In the optimization process executed again, a multiple discriminant analysis is performed on the frequency components selected from the (N + 1) th group frequency components (N + 2 group). As a result, a discriminant median representing the likelihood of each frequency component included in the (N + 2) th group is calculated.

  In the tire performance evaluation device 1, the combination of the frequency components of the finally extracted electric signal is determined by repeatedly executing the discriminant analysis in the analysis calculation unit 123 and the optimization process in the optimization calculation unit 122. Determined as an indicator of the situation.

(2) Description of Tire Performance Evaluation Method Next, the tire performance evaluation method will be described with reference to the drawings. Specifically, (2-1) Overall description of the tire performance evaluation method, (2-2) Adaptive frequency setting step, and (2-3) Evaluation step will be described.

(2-1) General Description of Tire Performance Evaluation Method FIG. 3 is a flowchart illustrating the tire performance evaluation method. As shown in FIG. 3, the tire performance evaluation method includes an adaptive frequency setting step: step S1, an evaluation step: step S2, and a performance determination step: step S3.

  In the adaptive frequency setting step, a frequency component (referred to as an adaptive frequency) having an intensity specific to the occupant is calculated from an electrical signal (electroencephalogram spectrum) acquired from the occupant of the vehicle.

  In the evaluation process, an adaptive frequency band including an adaptive frequency is set, and a database associated with an occupant's feeling and an electroencephalogram spectrum corresponding to an emotion is associated with an electroencephalogram spectrum corresponding to the adaptive frequency band. Select a ring.

  In the tire performance evaluation method, the emotional state selected in the evaluation process is determined as an index representing the tire performance.

(2-2) Adaptive Frequency Setting Step FIG. 4 is a flowchart for explaining the adaptive frequency setting step. The adaptive frequency setting process includes steps S11 to S13.

  Step S11 is a signal acquisition process. In step S <b> 11, an electrical signal is acquired from the electrode disposed on the frontal pole portion of the occupant 3. Step S12 is an analysis process. The analysis process includes an analysis process for performing time frequency analysis on the electrical signal (electroencephalogram spectrum) according to the elapsed time from the time when the running was started, and an fitness function for the frequency component of the electroencephalogram spectrum obtained by the time frequency analysis. Represents the likelihood of each frequency component by performing a multiple discriminant analysis on the optimization step of selecting frequency components by performing optimization processing using and the group of frequency components selected in the optimization step And an analysis step for calculating the discriminant predictive value. Step S13 is a step of determining an adaptive frequency.

(2-2-1) Step S11: Process for Acquiring an Electric Signal from an Occupant In step S11, a process for acquiring an electroencephalogram spectrum from an occupant will be specifically described. The occupant travels on the test course in a vehicle in which the evaluation target tire is mounted with the electrode mounted on the frontal pole portion.

  The test course has a general paved road, a rough road surface that is not flatter than a general paved road, a wavy road surface, a narrow road, cornering, a wet road surface, a snowy road surface, a frozen road surface, and the like. For general paved roads, the driving stability such as straight running stability, lane change performance and cornering stability will be evaluated. For rough roads and wavy roads, steering stability on rough roads is evaluated. In Kushiro, we will evaluate the handling stability in the Kushiro such as wobbling. For wet roads, snowy roads, and frozen roads, steering stability on wet and icy roads will be evaluated. In addition, noise and vibration on general paved roads and rough roads, riding comfort on steps, and riding comfort on uneven roads will be evaluated. Furthermore, the braking performance and acceleration / deceleration performance are evaluated.

  FIG. 5 is a schematic diagram illustrating an example of a test course. The test course 200 includes at least a straight paved straight road surface R1, a rough road surface R2, a rough road surface R3 that is more undulating than R2, a narrow road R4, a corner R5 having a large bank angle, and a wavy road surface. R6 and a corner R7 having a small bank angle.

  When the vehicle 100 reaches each section (R1 to R7) during traveling, a time stamp indicating the start of brain wave measurement may be recorded together with the brain wave. When the section has been passed, a type stamp indicating the end may be recorded together with the electroencephalogram. This makes it easier to extract brain waves during a period in which the vehicle travels in a predetermined section (a period from the start time stamp to the end time stamp).

(2-2-2) Step S12: Analysis Step Next, a specific step executed in step S12 will be described. In FIG. 6, step S12 further includes steps S21 to S25.

  First, in step S21, time-frequency analysis of the electroencephalogram spectrum supplied from the signal acquisition unit 20 is performed. Specifically, an electroencephalogram spectrum acquired from Fp1 after a certain time has elapsed from the time when the running is started is decomposed into a plurality of frequency components (4 Hz to 22 Hz) using fast Fourier transform.

  Subsequently, in step S22, an initial gene population is determined for a plurality of frequency components (4 Hz to 22 Hz). That is, a value of 0 or 1 generated at random is assigned to a plurality of frequency components (4 Hz to 22 Hz). Here, it may not be 0 or 1. It may be an analog value including a decimal value.

  FIG. 7 shows an example of gene expression. The gene expression initially assigned to a plurality of frequency components (4 Hz to 22 Hz) is referred to as a first generation (corresponding to the Nth group).

  In step S23, a multiple discriminant analysis is performed on a plurality of frequency bands (4 Hz to 22 Hz) having the gene expression determined in step S22, and a discriminant intermediate rate included in the plurality of frequency components (4 Hz to 22 Hz) is calculated. To do. As the multiple discriminant analysis, discriminant analysis based on Mahalanobis general distance is used. The discriminative predictive value is substituted into the fitness function of the genetic algorithm, and the fitness of the individual genetic algorithm (first generation is the first generation) is calculated.

  In step S24, it is determined whether or not the degree of application has reached an end condition. If the end condition is reached, the process proceeds to step S23.

  On the other hand, if the applicability does not reach the end condition in step S24, the process returns to step S23, and the second generation (N + 1th group) selected from the first generation by performing the multiple discriminant analysis on the first generation. The discriminative probability of the frequency component is calculated. The discriminant predictive value is substituted into the fitness function of the genetic algorithm, and the fitness of a plurality of frequency components (4 Hz to 22 Hz) having the second generation gene expression is calculated.

  In step S24, when the applicability does not reach the end condition, the fitness is adjusted by performing “selection” in the genetic algorithm before returning to the process of step S23. The selection includes a roulette method, a scaling method, a rank method, a tournament method, an elite preservation strategy method, and the like. Also, the gene expression is rearranged by performing “crossover” in the genetic algorithm. Crossover includes one-point crossover, multiple-point crossover, uniform crossover, and the like. In addition, gene expression may be rearranged by performing “mutation” in a genetic algorithm. Steps S23 to S25 are repeated until the end condition is reached.

(2-2-3) Step S13: Adaptive Frequency Determination Step In the adaptive frequency determination step, a group of frequency components represented by the gene expression finally extracted based on the processing in the analysis step S12 is characteristic to the occupant. As a frequency component (referred to as adaptive frequency). FIG. 8 is a diagram illustrating an example of an occupant adaptive frequency.

  In FIG. 8, a frequency component set (CDIQ) to which 1 is assigned represents an occupant's adaptive frequency (adaptive frequency component) when the vehicle has a certain feeling. Therefore, when the brain wave spectrum of the occupant is measured over a predetermined period, the same number of frequency component sets as the number of samples can be obtained.

  FIG. 9 is a schematic diagram for simply explaining the occupant adaptive frequency extracted from the electroencephalogram spectrum measured over a predetermined period. For simplification, FIG. 9 illustrates a case where the adaptive frequency is determined from among the nine frequencies f1 to f9.

  In FIG. 9, F1 is an occupant's brain wave spectrum measured over a predetermined period. F2 represents the occupant's adaptive frequency set at time t1. F3 represents the occupant's adaptive frequency set at time t2. F4 represents the occupant's adaptive frequency set at time t3. In F2 to F4, the adaptive frequency sets are f2, f5, f8, and f9 (white columns).

(2-3) Evaluation Step Subsequently, the evaluation step will be described. In the evaluation process, the feeling represented by the adaptive frequency of the passenger described with reference to FIGS. Specifically, in the evaluation process, the following processing is performed. FIG. 10 is a flowchart for explaining the evaluation process.

  In step S31, the adaptive frequency calculated in the adaptive frequency setting step is input. The frequency set (CDIQ) shown in FIG. 8 is input.

  In step S32, an applied frequency band setting step is executed. That is, an adaptive frequency band including an occupant's adaptive frequency set is set. FIG. 11 is a diagram for explaining a band set for the adaptive frequency. In the present embodiment, the adaptive frequency band is a band including integer frequencies before and after the adaptive frequency. For example, the frequencies 5, 6, 7, 8, 11, 12, 13, 19, 20, 21 Hz are set for the frequency set (CDIQ), that is, the frequencies 6, 7, 12, 20 Hz.

  FIG. 12 is a diagram illustrating a weighting coefficient set stored in the weighting coefficient database DB2. In step S33, the set adaptive frequency band is superimposed using a set of weighting coefficients (w1, w2, w3,...) Stored in the weighting coefficient database DB2, and an evaluation frequency band Sp0 is calculated. In the present embodiment, the following formula (1) is used. The sum of frequencies 5, 6, 7, 8, 11, 12, 13, 19, 20, and 21 Hz multiplied by different weighting coefficients.

但し、Ｐ（ωｋ）,（ｋ＝０，１，２，…，Ｎ）：適応周波数帯域の脳波の強度であり、ωは、周波数であり、Ｎは、窓フーリエ変換で求められる最大周波数であり、ｗｉは、各周波数に対する重み付け係数である。ここで用いるＰ（ωｋ）のデータパターン数は、周波数解析のフレーム数と同じである。重み付け係数ｗは、評価用周波数帯域Ｓｐ０の分類精度が最も高くなるように設定されることが好ましい。重み付け係数ｗを決定するにあたり、本実施形態では、マハラノビス距離、ニューラルネットワーク、遺伝的アルゴリズム、サポートベクターマシン、判別分析など適用可能である。

Where P (ωk), (k = 0, 1, 2,..., N): the intensity of the electroencephalogram in the adaptive frequency band, ω is the frequency, and N is the maximum frequency obtained by the window Fourier transform. Yes, wi is a weighting coefficient for each frequency. The number of P (ωk) data patterns used here is the same as the number of frames in frequency analysis. The weighting coefficient w is preferably set so that the classification accuracy of the evaluation frequency band Sp0 is the highest. In determining the weighting coefficient w, this embodiment can be applied to Mahalanobis distance, neural network, genetic algorithm, support vector machine, discriminant analysis, and the like.

  In step S34, the calculated evaluation frequency band Sp0 is compared with the frequency band stored in the reference electroencephalogram database DB1. FIG. 13 is a schematic diagram for explaining the reference electroencephalogram stored in the reference electroencephalogram database DB1. In the reference electroencephalogram database DB1, for example, general electroencephalogram spectra (Sp1, Sp2, Sp3,..., Spk,...) Corresponding to various emotions including “fun”, “uncomfortable”, “scary (fear)” and the like. Is stored.

  In step S34, it is determined whether or not there is an electroencephalogram spectrum that matches the evaluation frequency band Sp0 in the reference electroencephalogram database DB1, and if there is not, the process returns to step S33 and is superimposed with another weighting coefficient set prepared in the weighting coefficient database DB2. Then, the evaluation frequency band Sp0 is calculated. The comparison in step S34 is repeated until a matching electroencephalogram spectrum is found.

  In step S35, a feeling associated with an electroencephalogram spectrum that matches the evaluation frequency band Sp0 is selected.

(3) Database creation phase According to the tire performance evaluation apparatus according to the present embodiment, the above-described weighting coefficient database DB2 can be created. FIG. 14 is a flowchart for explaining a database creation phase (weighting factor generation step) for creating the weighting factor database DB2.

  In the database creation phase, an electroencephalogram spectrum that is associated with the occupant feeling for each hour is used.

  In step S41, the processing of steps S11 to S13 is performed to determine the adaptive frequency. In step S42, the adaptive frequency setting step is executed to set the adaptive frequency band. Step S42 is in accordance with the processing of steps S31 to S32.

  In step S43, an initial value of a combination of weighting coefficients is set in the frequency band including the adaptive frequency calculated in the adaptive frequency setting step.

  In step S44, an optimization process using a fitness function is performed on the combination of weighting coefficients.

  In step S45, the discriminant predictive value representing the likelihood of each weighting coefficient is calculated by subjecting the optimized combination of weighting coefficients to the multiple discriminant analysis.

  In step S46, the adaptive frequency band is weighted by a weighting coefficient expressed using this likelihood (discriminant probability).

  In step S46, the weighted applied frequency band is compared with the spectrum stored in the reference electroencephalogram database DB1, and it is determined whether or not it matches the reference electroencephalogram stored in the reference electroencephalogram database DB1 described with reference to FIG. . If there is no matching spectrum, the process returns to step S44, and the (N + 1) th optimization process and the multiple discriminant analysis in step S45 are repeated.

  If there is a spectrum that matches the selected applied frequency band in the reference electroencephalogram database DB1, in step S47, the set of weighting coefficients at this time is stored in the weighting coefficient database DB2. In this way, a set of weighting coefficients to be stored in the weighting coefficient database DB2 is created.

(4) Action / Effect According to the tire performance evaluation method according to the present embodiment, the state of emotion of the occupant 3 represented by the electroencephalogram acquired from the occupant 3 can be determined. Thereby, conventionally, the tire performance that has been evaluated based on the feeling of the test driver can be linked to objective data such as the brain wave of the occupant when the occupant 3 is on the vehicle 100. Therefore, the emotional state determined from the brain wave of the occupant 3 at the time of boarding can be determined as an index of the evaluation result of the tire performance.

  Moreover, according to the tire performance evaluation apparatus 1, it acquires from the electrode arrange | positioned at the front pole part of the passenger | crew 3 based on the international law 10-20 method. Since only one signal acquisition site is required, the passenger 3 is not excessively discomforted or stressed. Therefore, it is possible to acquire correct biological information without disturbance such as uncomfortable feeling and stress.

  Moreover, in the tire performance evaluation method according to the present embodiment, the frequency component of a desired electroencephalogram can be extracted by performing time frequency analysis on the electroencephalogram spectrum acquired from one location. Furthermore, characteristic frequency components can be extracted by performing optimization processing using a genetic algorithm as a fitness function and multiple discriminant analysis on the frequency components of the electroencephalogram.

  In psychology, brain waves are broadly divided into four types, δ waves, θ waves, α waves, and β waves, depending on the frequency band, and α waves that are generally used as an index for evaluating relaxation are 8 Hz or more. It is said to be less than 13 Hz. However, in reality, these frequency bands have slight differences depending on the individual. For example, when a subject is in a relaxed state, a feature may appear in a frequency band of 7.5 Hz to 13 Hz, or a feature may appear in another frequency band. According to the tire performance evaluation method according to the present embodiment, it is possible to extract a difference in frequency or frequency band in which a characteristic appears or a difference in strength depending on a subject.

  According to the tire performance evaluation method according to the present embodiment, the evaluation frequency band Sp0 calculated by superimposing a plurality of weighting coefficients prepared in advance on the weighting coefficient database on the adaptive frequency band, and the reference electroencephalogram database DB1 are stored. .., Spk,..., Spk,..., Spk,..., Spk,.

  In the tire performance evaluation method according to the present embodiment, as described with reference to FIGS. 4 to 9, the brain wave spectrum of the occupant 3 at a certain time (that is, under a certain condition) is under this condition. Even if the characteristic adaptive frequency at that time can be extracted, it is difficult to know the feeling of the passenger 3 from this adaptive frequency.

  For example, a test for examining the correspondence between the feeling and the characteristic adaptive frequency is separately performed on the occupant 3 to create a database in which the adaptive frequency and the feeling are associated with each other. A method of knowing the feeling of the occupant 3 by referring to the database from the extracted adaptive frequency can be considered. In this method, the feeling of the occupant 3 can be accurately known from the adaptive frequency characteristic for the occupant 3, but since a database is required for each subject (occupant), calibration is performed when the number of subjects increases. Processing becomes complicated.

  In the present embodiment, the weighting coefficient set prepared in the weighting coefficient database DB2 is applied to calculate the evaluation frequency band, and compared with the typical pattern of the electroencephalogram spectrum stored in the reference electroencephalogram database DB1, thereby occupant 3 Can be discriminated. That is, since a database common to all people such as the reference electroencephalogram database DB1 and the weighting coefficient database DB2 can be applied to anyone, analysis can be performed very efficiently.

(5) Other Embodiments As described above, the contents of the present invention have been disclosed through the embodiments of the present invention. However, it is understood that the description and drawings constituting a part of this disclosure limit the present invention. Should not. From this disclosure, various alternative embodiments, examples and operational techniques will be apparent to those skilled in the art.

  In the present embodiment, the case where the method for determining the feeling of the subject based on the brain wave spectrum of the subject is applied to the evaluation of the tire performance has been described. However, it can be applied to other than tire performance evaluation. For example, the present invention can be applied to the evaluation of things that are felt differently depending on the user, such as the convenience of tools such as sports equipment.

  In the present embodiment, it has been described that the signal acquisition unit 20 acquires an electrical signal from the electrode arranged at Fp1 of the frontal pole portion of the occupant 3 based on the International Law 10-20. However, the acquisition site is not limited to Fp1.

  It goes without saying that the present invention includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention specifying matters according to the scope of claims reasonable from the above description.

  DESCRIPTION OF SYMBOLS 1 ... Tire performance evaluation apparatus, 3 ... Crew, 10 ... Evaluation apparatus main body, 11 ... Signal input part, 12 ... Calculation part, 13 ... Display part, 14 ... Input part, 15 ... Memory | storage part, 20 ... Signal acquisition part, 21 DESCRIPTION OF SYMBOLS ... Electrode, 22 ... Electrode, 100 ... Vehicle, 101 ... Tire, 121 ... Frequency analysis calculation part, 122 ... Optimization calculation part, 123 ... Analysis calculation part, 200 ... Test course

Claims (7)

A tire performance evaluation method for evaluating tire performance when a vehicle travels on a predetermined road surface,
An adaptive frequency setting step for calculating an adaptive frequency having an intensity specific to the occupant from an electrical signal acquired from the occupant of the vehicle;
An adaptive frequency band including the adaptive frequency is calculated, and is linked to a frequency band corresponding to the adaptive frequency band from a database in which the state of the passenger's emotion and the frequency band of the electrical signal corresponding to the emotion are linked. An evaluation process for selecting the state of the sent emotion,
And a determination step of determining an emotional state selected in the evaluation step as an index representing the tire performance. The adaptive frequency setting step includes
A signal acquisition step of acquiring an electrical signal from an electrode disposed at the frontal pole portion of the occupant based on the International Law 10-20;
An analysis step of performing a time-frequency analysis on the electrical signal according to an elapsed time from the time when the traveling was started;
An optimization step of selecting a frequency component from the Nth group by applying an optimization process to the Nth group of frequency components of the electrical signal obtained by the time-frequency analysis using an fitness function;
An analysis step of calculating a discriminant median representing the likelihood of each frequency component included in the N + 1 group by performing a multiple discriminant analysis on the N + 1 group of frequency components selected in the optimization step. Have
Substituting the discriminant probability calculated in the analysis step into the fitness function, and executing the optimization step again,
In the optimization step executed again, a frequency component is selected from the individual frequency components included in the N + 1th group using a fitness function into which the discriminant probability is substituted,
A discriminant median representing the likelihood of each frequency component included in the N + 2 group is calculated by performing multiple discriminant analysis on the N + 2 group consisting of the frequency components selected in the optimization step executed again. The tire performance evaluation method according to claim 1, wherein the adaptive frequency is calculated as a combination of frequency components of the electrical signal by repeatedly executing the analysis step and the optimization step. The evaluation step includes
Setting an adaptive frequency band including the adaptive frequency to the adaptive frequency;
A frequency band for evaluation is calculated by superimposing a plurality of weighting coefficients prepared in advance in a weighting coefficient database on the set adaptive frequency band,
By referring to a database in which the state of emotion and the frequency band of the electrical signal when the occupant is in the state of emotion are linked and stored, the frequency band that matches the frequency band for evaluation is obtained. The tire performance evaluation method according to claim 1 or 2, wherein a state of emotions linked is selected. A weighting coefficient generating step of generating the plurality of weighting coefficients from an electrical signal obtained from an arbitrary occupant;
The weighting coefficient generation step includes:
An initial value of a combination of weighting coefficients is set in a frequency band including the adaptive frequency calculated in the adaptive frequency setting step, and the combination of weighting coefficients is updated by repeatedly executing an fitness function and multiple discriminant analysis,
Reference is made to a database in which the state of emotion and the frequency band of the electrical signal when the occupant is in the state of emotion are linked, and the combination of the updated weighting factors is used as the adaptive frequency. The tire performance evaluation method according to claim 2, wherein a combination of weighting coefficients when a frequency band obtained by applying to a frequency band to be included matches the frequency band stored in the database is stored in the weighting coefficient database.   The tire performance evaluation method according to claim 2, wherein in the signal acquisition step, an electrical signal is acquired from an electrode arranged at Fp1 based on the International Law 10-20.   The tire performance evaluation method according to claim 2, wherein the fitness function is a genetic algorithm.   The tire performance evaluation apparatus which performs the tire performance evaluation method as described in any one of Claims 1 thru | or 6.

Images