Control characteristic selection system and control characteristic selection method

Abstract

To enable selection of a control characteristic that is less likely to cause sudden changes in ride comfort while satisfying a user's request.SOLUTION: A control characteristic selection system is a control characteristic selection device that searches for a control characteristic of an actuator contributing to travel characteristics of a mobile body, and includes: a database in which a plurality of combinations of control characteristics and sensory evaluation points are stored; a sensory evaluation point estimation unit that accepts input of a control characteristic as a current characteristic and outputs a sensory evaluation point as a current evaluation point; an input unit that accepts a target value, which is a target sensory evaluation point; and a selection unit that selects, from the control characteristics stored in the database, a control characteristic similar to the current characteristic with the sensory evaluation point similar to the target value, as a selected characteristic.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a control characteristic selection system and a control characteristic selection method. [Background technology]

[0002] When operating an actuator in response to a human input operation, it is essential to make adjustments that take into account human sensations, i.e., adjust the control characteristics of the actuator. Patent Document 1 discloses an electric power steering control device that performs control based on various control parameter data, and includes a storage means that stores evaluation data during steering related to the electric power steering control device, steering situation data indicating under what circumstances the evaluation data was performed and evaluated, steering situation data associated with the evaluation data, control parameter data of the electric power steering control device that is associated with the steering situation data and the evaluation data and causes the evaluation data to be expressed under the associated steering situation data, data related to the control parameter data, and adjustment method data of the control parameter data to resolve the evaluation content of the associated evaluation data, a first input means that inputs evaluation data, a second input means that inputs steering situation data, a readout means that reads out adjustment method data of the control parameter data associated with the input data from the storage means based on the evaluation data and steering situation data input by both input means, and an output means that outputs the adjustment method data of the read control parameter data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2002-173042 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the invention described in Patent Document 1, there is room for improvement in the selection of control characteristics. [Means for solving the problem]

[0005] A control characteristic selection system according to a first aspect of the present invention is a control characteristic selection device that searches for a control characteristic of an actuator that contributes to the driving characteristics of a moving body, and comprises a database in which multiple combinations of the control characteristics and sensory evaluation points are stored, a sensory evaluation point estimation unit that inputs the control characteristic as a current characteristic and outputs the sensory evaluation point as the current evaluation point, an input unit that accepts a target value which is the sensory evaluation point to be targeted, and a selection unit that selects, from the control characteristics stored in the database, a control characteristic whose sensory evaluation point is similar to the target value and is similar to the current characteristic as a selected characteristic. A control characteristic selection method according to a second aspect of the present invention is a control characteristic selection method executed by a computer capable of searching a database in which multiple combinations of control characteristics of actuators that contribute to the driving characteristics of a moving body and sensory evaluation points of the control characteristics are stored, and includes a sensory evaluation point process in which the control characteristic is input as a current characteristic and the sensory evaluation point is output as the current evaluation point, an input process in which a target value which is the sensory evaluation point to be targeted is accepted, and a selection process in which a control characteristic whose sensory evaluation point is similar to the target value and similar to the current characteristic is selected as a selected characteristic from the control characteristics stored in the database, and in the selection process, a control characteristic that satisfies the target value and has a small difference in characteristic value from the current characteristic is selected as the selected characteristic. Effect of the Invention

[0006] According to the present invention, it is possible to select a control characteristic that satisfies the user's requirements while preventing abrupt changes in ride comfort. [Brief description of the drawings]

[0007] [Figure 1] Configuration diagram of control characteristic selection device [Diagram 2]FIG. 1 shows the hardware configuration of a control characteristic selection device. [Diagram 3] A diagram showing an example of sensory evaluation scores [Figure 4] A diagram showing an example of data stored in a database. [Diagram 5] Diagram showing the relationship between control characteristics and parameters [Figure 6] A diagram explaining the learning of the neural network that constitutes the control characteristic selection unit. [Figure 7] A diagram showing the configuration of a neural network with multiple nodes in the output layer. [Figure 8] Functional configuration diagram of the control characteristic selection unit [Figure 9] Flowchart showing the process of the control characteristic selection device [Figure 10] A diagram showing an example of the display on the display unit [Figure 11] FIG. 11 is a diagram showing a display when the control characteristic is changed after the shipment of the vehicle in the first modification example. [Figure 12] Diagram showing the limits of target value input [Figure 13] FIG. 1 is a configuration diagram of a control characteristic selection system according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] --First embodiment-- Hereinafter, a first embodiment of a control characteristic selection device will be described with reference to FIGS.

[0009] FIG. 1 is a configuration diagram of the control characteristic selection device 1. In this embodiment, a person who uses the control characteristic selection device 1 is called a "user." In this embodiment, a person involved in the manufacture of a vehicle equipped with the control characteristic selection device 1 is assumed to be the user. The control characteristic selection device 1 includes a sensory evaluation point estimation unit 101, a setting unit 103, a control characteristic selection unit 104, a database 102, an input unit 105, and a display unit 106. A current characteristic 51 is input to the sensory evaluation point estimation unit 101 and the setting unit 103. The control characteristic is the characteristic of an actuator that contributes to the running characteristic of a moving object, for example, steering, suspension, and brakes. The control characteristic can also be rephrased as the magnitude of a second physical quantity corresponding to a first physical quantity. There is only one second physical quantity corresponding to a first physical quantity. The control characteristic can be expressed by a polynomial or the like, and a "parameter" to be described later is a simplified version of the control characteristic.

[0010] The sensory evaluation point estimation unit 101 estimates a sensory evaluation point corresponding to the input control characteristic, and outputs a current evaluation point 52. The sensory evaluation point estimation unit 101 includes a neural network, as described below. This neural network has undergone a learning process, i.e., weighting has been determined in advance. A large number of combinations of control characteristics and sensory evaluation points are stored in the database 102. The display unit 106 is a display device, such as a liquid crystal display, that presents information to a user. The display unit 106 displays the current evaluation point 52 output by the sensory evaluation point estimation unit 101 and the selected characteristic 54 output by the setting unit 103. The display unit 106 may further display the current characteristic 51 and the target value 53.

[0011] The input unit 105 is, for example, a keyboard or a mouse. The user inputs a target value 53, which is a sensory evaluation score to be targeted, using the input unit 105 while looking at the display unit 106. Since it is not easy to give an absolute evaluation of the sensory evaluation score, the user inputs the target value 53 based on a current evaluation score 52 of a current characteristic 51. The current characteristic 51, an evaluation score 502 output by the sensory evaluation point estimation unit 101, and the target value 53 input from the input unit 105 are input to the setting unit 103. The input unit 105 and the display unit 106 may be realized by a single touch panel.

[0012] 2 is a diagram showing a hardware configuration of the control characteristic selection device 1. The control characteristic selection device 1 includes a CPU 41 which is a central processing unit, a ROM 42 which is a read-only storage device, a RAM 43 which is a readable and writable storage device, an input / output device 44 which is a user interface, and a communication device 45. The CPU 41 loads a program stored in the ROM 42 into the RAM 43 and executes it to perform the various calculations described above.

[0013] The control characteristic selection device 1 may be realized by a field programmable gate array (FPGA) that is a rewritable logic circuit or an application specific integrated circuit (ASIC) that is an integrated circuit for a specific application, instead of the combination of the CPU 41, the ROM 42, and the RAM 43. Moreover, the control characteristic selection device 1 may be realized by a combination of different configurations, for example, a combination of the CPU 41, the ROM 42, the RAM 43, and an FPGA, instead of the combination of the CPU 41, the ROM 42, and the RAM 43.

[0014] The input / output device 44 is an interface for data input / output with a user. Examples of the input / output device 44 include a display, a mouse, a keyboard, a touch panel, a speaker, and a microphone. The communication device 45 realizes communication with an external device including the database 102. Any communication standard may be used by the communication device 45.

[0015] The control characteristic selection device 1 may receive input from an external device via the communication device 45. For example, a touch panel or buttons may be mounted on the instrument panel of the vehicle in which the control characteristic selection device 1 is mounted, and the control characteristic selection device 1 may receive an input operation by the user to the instrument panel. The communication device 45 may also communicate with a mobile terminal carried by the user, and the control characteristic selection device 1 may receive an input operation by the user to the communication device 45. Furthermore, instead of the control characteristic selection device 1 being provided with the input / output device 44, the control characteristic selection device 1 may communicate with an external device of the control characteristic selection device 1 via the communication device 45, and the external device may be used in place of the input / output device 44. In this case, data output by the control characteristic selection device 1 is displayed on the external device.

[0016] FIG. 3 is a diagram showing an example of sensory evaluation scores. FIG. 3(a) shows a case where there are five evaluation items, and FIG. 3(b) shows a case where there is one evaluation item. In FIG. 3(a), a pentagonal radar chart is used to display the evaluation values ​​of the five evaluation items at once. In FIG. 3(b), a bar graph is used. The user can refer to the current evaluation score 52 on the display shown in FIG. 2, and input a target value 53 using the input unit 105 while viewing this display.

[0017] In this example, the sensory evaluation score ranges from 0 to 7 points, with auxiliary lines set at every 0.25 points. The numerical range of the sensory evaluation score is merely an example and may be different. In the example of FIG. 3(a), the range of 3.50 to 4.50 points, which is the most frequently answered range, is expanded, but the display method is not limited. Also, although the illustration shows a case where there is one evaluation item and a case where there are five evaluation items, there is no upper limit as long as there is one or more evaluation items.

[0018] Fig. 4 is a diagram showing an example of data stored in database 102. In database 102, characteristics and evaluation points 502 for each evaluation item are stored in association with each other. However, Fig. 4 shows a case where there are multiple evaluation items. When there is one evaluation item as shown in Fig. 3(b), there is one value for the evaluation item corresponding to each characteristic.

[0019] FIG. 5 is a diagram showing the relationship between the control characteristics and parameters. Three curves 504-1 to 504-3 shown in FIG. 5 show different characteristics. These characteristics show the relationship between the steering angle (or the pinion angle during steering) and the friction torque, with the x-axis showing the steering angle and the y-axis showing the friction torque. Both characteristics have a similar tendency that the friction torque increases with an increase in the steering angle. In this embodiment, the "control characteristics" shown by the curves are converted into "parameters" by sampling at a predetermined value interval on the horizontal axis. When converting the control characteristics of the steering angle and the friction torque into calculation parameters as shown in this figure, the friction torque value is sampled at a predetermined steering angle value interval, for example, at 1 degree intervals, and the friction torque value from 0 degrees to 90 degrees is taken as the "parameter."

[0020] FIG. 6 is a diagram for explaining the learning of the neural network 1041 constituting the control characteristic selection unit 104. First, a test driver drives a vehicle to which a characteristic created in advance is applied, and a sensory value is determined. By repeating this process multiple times, a combination of the characteristic and the sensory value, that is, teacher data for the neural network 1041, is created. The neural network 1041 includes an input layer 305, a hidden layer 306, and an output layer 307. The number of nodes in the input layer 305 is uniquely determined by a parameter determination method. For example, in the example described with reference to FIG. 5, the input layer 305 has 91 nodes since the increments are 0 degrees to 90 degrees. Although only one hidden layer 306 is shown in FIG. 6 for convenience of drawing, multiple hidden layers 306 may exist. The number of nodes in the hidden layer 306 is arbitrary. The number of nodes in the output layer 307 may be one as shown in this figure, or multiple nodes as described later.

[0021] In the learning phase of the neural network 1041, parameters created based on each characteristic are input to the input layer 305, and the weights of the neural network 1041 are determined so that the value of the output layer 307 becomes the sensory value determined by the test driver. A known method, such as the backpropagation method, can be used to determine the weights. The configuration of the hidden layer 306 may be determined by learning. Since the learning process requires a high computation load, it is preferable to use a device with high computational power and loose restrictions on power consumption, such as a server, instead of the control characteristic selection device 1.

[0022] FIG. 7 is a diagram showing the configuration of the neural network 1041 when the output layer 307 has a plurality of nodes. In this case, the output layer 307 employs one-hot encoding. That is, each node is assigned a sensory evaluation value in advance, and only one of the nodes is set to "1", and all the others are set to "0". For example, when the output layer 307 has five nodes and the evaluation score 502 is "3.50" to "4.50", the combination shown in the lower part of FIG. 5 is obtained. For example, when a test driver evaluates the sensory value of a certain test characteristic as "3.50", the weights of the neural network 1041 are learned so that only "Y1" at the top of the output layer 307 is "1" and the others are "0".

[0023] It is not essential to use an actual vehicle and a test driver to create the teacher data. A simulator that imitates the operation of an actual vehicle may be used instead of an actual vehicle, and the output of a sensor may be used instead of the sensory value determined by a test driver. In other words, in addition to the combination of an actual vehicle and a test driver, any of the other three combinations of a simulator and a test driver, an actual vehicle and a sensor, and a simulator and a virtual sensor may be used.

[0024] In the case of a combination of a simulator and a test driver, the test driver operates the simulator and determines the sensory value. In the case of a combination of an actual vehicle and a sensor, the output of the sensor mounted on the actual vehicle is classified using a predetermined category to obtain the sensory value. In the case of a combination of a simulator and a sensor simulator, a virtual sensor is installed in the simulator that mimics the operation of the vehicle, and the output of this sensor is classified using a predetermined category to obtain the sensory value.

[0025] 8 is a functional configuration diagram of the control characteristic selection unit 104. The control characteristic selection unit 104 includes a presentation number setting unit 701, a mode setting unit 702, a control unit 703, and a reading unit 704. A presentation number command 58 is input to the presentation number setting unit 701. The presentation number command 58 may be input by a user from the input unit 105, or a predetermined value may be input. The presentation number setting unit 701 outputs a presentation number 71 to the control unit 703 based on the presentation number command 58. The presentation number command 58 is, for example, a natural number, and the presentation number setting unit 701 may output the presentation number command 58 as the presentation number 71 to the control unit 703 as it is.

[0026] A mode command 59 is input to the mode setting unit 702. The mode command 59 may be input by the user from the input unit 105, or a predetermined value may be input. The mode setting unit 702 selects one of three modes, a parameter similarity priority mode, a trade-off effect minimum mode, and a specified item priority mode, based on the mode command 59 as the determined mode 72. The mode command 59 is a value indicating one of the three modes.

[0027] The presentation number 71 output by the presentation number setting unit 701 and the decision mode 72 output by the mode setting unit 702 set the operation of the reading unit 704. The presentation number 71 sets the number of characteristics output by the reading unit 704. For example, when the presentation number 71 is "1", the reading unit 704 outputs one selected characteristic 54 that best matches the conditions. When the presentation number 71 is "5", the reading unit 704 outputs the top five selected characteristics 54 that best match the conditions. The difference in operation of the reading unit 704 depending on the decision mode 72 is as follows.

[0028] In the parameter similarity priority mode, the reading unit 704 selects a characteristic that satisfies the target value 53 and has a high similarity to the current characteristic 51. Similarity is the difference in characteristic values, and the smaller the difference in the characteristic values, the higher the similarity. For example, when the characteristic 504-1 shown in FIG. 5 is the current characteristic 51, and the characteristics 504-2 and 504-3 are characteristics that satisfy the target value 53, the characteristic 504-2 is selected as the most suitable characteristic. This is because the difference in the friction torque value between the characteristic 504-3 and the characteristic 504-1 at each steering angle is smaller than that between the characteristic 504-3 and the characteristic 504-1.

[0029] The trade-off effect minimum mode and the designated evaluation item priority mode, which will be described next, are premised on the existence of multiple evaluation items for the sensory evaluation points as shown in Fig. 3(a). Among the multiple evaluation items, one or more will be the representative items. The representative items may be specified by the user each time, or may be preset in the input unit 105.

[0030] In the trade-off influence minimum mode, the reading unit 704 selects a characteristic having a value in the representative item closer to the target value 53 than the current characteristic 51. If there are multiple characteristics with the same value in the representative item, the characteristic with the smallest total change in the sensory evaluation score in the evaluation items other than the representative item is selected. If there are multiple characteristics with the same total change in the sensory evaluation score, the characteristic with the least change from the current characteristic 51 is selected as the selected characteristic 54.

[0031] In the designated evaluation item priority mode, the reading unit 704 selects a characteristic of the representative item that exceeds the target value 53. In this mode, evaluation points 502 of items other than the representative item are not taken into consideration. If there are multiple characteristics of the representative item that exceed the target value 53, the characteristic whose representative item value is closest to the target value 53 is selected. If there are multiple characteristics whose representative item value is closest to the target value 53, the characteristic whose change from the current characteristic 51 is the least is selected as the selected characteristic 54.

[0032] The control unit 703 transfers the search conditions to the reading unit 704 based on the outputs of the presentation number setting unit 701 and the mode setting unit 702. The reading unit 704 searches the database 102 for characteristics that match the search conditions and outputs them to the control unit 703. The control unit 703 transfers the characteristics output by the reading unit 704 to the setting unit 103. The reading unit 704 searches the database 102 according to the mode command 59 set by the mode setting unit 702, and outputs the selected characteristics 54 by the number of presentation number commands 58 set by the presentation number setting unit 701. The operation of the reading unit 704 in each mode is as described above.

[0033] 9 is a flowchart showing the processing of the control characteristic selection device 1. In step S801, the current characteristic 51 is input to the sensory evaluation point estimating unit 101 and the setting unit 103. In the following step S802, the sensory evaluation point estimating unit 101 calculates the current evaluation point 52 and outputs it to the display unit 106 and the setting unit 103, and the process proceeds to step S803. In step S803, the display unit 106 displays the current evaluation point 52. In the following step S804, the input unit 105 accepts an input of a target value 53 by the user, and the process proceeds to step S805.

[0034] In step S805, the control characteristic selection unit 104 calculates the selected characteristic 54. In the following step S806, the display unit 106 displays the selected characteristic 54 calculated in step S805. In the following step S807, the setting unit 103 determines whether or not the user has re-input, that is, whether or not the user has input a new target value 53 from the input unit 105. If it is determined that the user has re-input, the process returns to step S805, and if it is determined that the user has not re-input, the process shown in FIG. 9 is terminated.

[0035] Fig. 10 is a diagram showing a display example of the display unit 106. This diagram shows an example in which a mobile terminal is used as the display unit 106. Fig. 10(a) shows a current characteristic 51 and a current evaluation score 52. The current characteristic 51 may be input by the user, or the current setting may be automatically read. When the user inputs the current characteristic, it may be input on the screen with a touch pen or the like, or it may be read from a CSV file or the like.

[0036] Fig. 10(b) shows a target value 53 and a selection characteristic 54. A current evaluation score 52 is shown in a solid line in the upper part of Fig. 10(b), and the user can input a target value 53 in a relative evaluation with the current evaluation score 52. This input may be made using a touch panel or voice input. The display shown in Fig. 10 is only an example, and a different layout may be used, or all the information shown in Fig. 10 may be displayed on one screen.

[0037] According to the above-described first embodiment, the following advantageous effects can be obtained. (1) The control characteristic selection device 1 searches for control characteristics of actuators that contribute to the running characteristics of a moving object. The control characteristic selection device 1 includes a database 102 in which a plurality of combinations of control characteristics and sensory evaluation points are stored, a sensory evaluation point estimation unit 101 that inputs a control characteristic as a current characteristic 51 and outputs a sensory evaluation point as a current evaluation point 52, an input unit 105 that receives a target value 53 that is a target sensory evaluation point, and a control characteristic selection unit 104 that selects, from the control characteristics stored in the database 102, a control characteristic whose sensory evaluation point is similar to the target value 53 and similar to the current characteristic 51 as a selected characteristic 54. Therefore, a characteristic similar to not only the target value 53 specified by the user but also the current characteristic is selected, so that a sudden change in ride comfort is unlikely to occur while satisfying the user's request.

[0038] (2) In the parameter similarity priority mode, the control characteristic selection unit 104 selects, as the selected characteristic 54, a control characteristic whose sensory evaluation score is similar to the target value 53 and whose difference from the current characteristic 51 is small.

[0039] (3) The sensory evaluation score includes a plurality of evaluation items including a representative item. In the trade-off influence minimization mode, the control characteristic selection unit 104 selects as the selected characteristic 54 a control characteristic whose representative item of the sensory evaluation score is similar to the target value 53 and whose total change in the sensory evaluation score other than the representative item is minimized.

[0040] (4) In the designated evaluation item priority mode, the control characteristic selection unit 104 selects as the selected characteristic 54 a control characteristic whose representative item of the sensory evaluation score exceeds the target value 53 and whose value of the representative item is closest to the target value 53 .

[0041] (5) The control characteristic indicates a second physical quantity corresponding to the first physical quantity, for example, a friction torque corresponding to a steering angle. The sensory evaluation point estimating unit 101 specifies parameters as described with reference to Fig. 5 in order to evaluate the current characteristic 51. That is, the value of the second physical quantity when the first physical quantity is increased by a predetermined value unit is specified as a parameter, and the parameter is input to the neural network 1041 that has been trained in advance.

[0042] (6) The actuators are either steering, suspension, or brakes.

[0043] (Variation 1) In the above-described embodiment, it is assumed that a person involved in the vehicle production process is the “user” who operates the control characteristic selection device 1. However, the control characteristic selection device 1 may be operated by a person who uses the vehicle after the vehicle is shipped.

[0044] FIG. 11 is a diagram showing a display when changing a control characteristic after the shipment of a vehicle. FIG. 11(a) shows a scene where the control characteristic selection device 1 is operated using an instrument panel 1001 of a vehicle equipped with the control characteristic selection device 1. FIG. 11(b) shows a scene where the control characteristic selection device 1 is operated using a mobile terminal 1004 carried by a user. In FIG. 11(a), the communication device 45 communicates with the control device of the instrument panel 1001, so that the instrument panel 1001 functions as an input / output device 44. That is, the target value 53 and the selected characteristic 54 are displayed on the instrument panel 1001. In FIG. 11(b), the target value 53 and the selected characteristic 54 are displayed on the mobile terminal 1004. The user can input the target value 53 using the instrument panel 1001 or the mobile terminal 1004 and obtain the selected characteristic 54.

[0045] FIG. 12 is a diagram showing restrictions on input of the target value 53. When the target value 53 is set by a general user, not by the vehicle manufacturer, after the vehicle is shipped, it is desirable to narrow the range in which the target value 53 can be set for safety reasons. If the characteristics of the suspension, steering, etc. are changed, the driving feel and ride comfort change. Furthermore, if an inappropriate change is applied, the vehicle may behave in a manner that is unexpected for the driver. Therefore, the settable range of the target value 53 is determined in advance, and the user is allowed to input the target value 53 within the settable range.

[0046] In the example shown in Fig. 12(a), the range that cannot be set is indicated by hatching. In the example shown in Fig. 12(b), the settable values ​​are indicated by white circles. In Fig. 12(a) and Fig. 12(b), the values ​​that can be set as target value 53 are the same.

[0047] (Variation 2) In the above-described embodiment, the control characteristic selection device 1 is configured as one hardware device as shown in Fig. 2. However, the functions of the control characteristic selection device 1 may be realized by multiple hardware devices. In this case, a group of multiple devices having the same functions as the control characteristic selection device 1 can be called a "control characteristic selection system."

[0048] (Variation 3) In the above-described embodiment, the control characteristics are converted into parameters by sampling at predetermined horizontal axis value intervals as described with reference to Fig. 5, and these parameters are input to the neural network. However, the control characteristics diagram as shown in Fig. 5 may be treated as image data, and this image data may be input to the neural network 1041. This image is generated after both the friction torque and the steering angle are set to predetermined minimum and maximum values. When this modified example is adopted, it is necessary to re-run the learning of the neural network 1041.

[0049] --Second embodiment-- A second embodiment of a control characteristic selection system will be described with reference to Fig. 13. In the following description, the same components as in the first embodiment are given the same reference numerals, and differences will be mainly described. Points that are not specifically described are the same as in the first embodiment. This embodiment differs from the first embodiment mainly in that data is added to a database.

[0050] 13 is a configuration diagram of a control characteristic selection system S1 in the second embodiment. The control characteristic selection system S1 further includes a characteristic adding device 1200 in addition to the control characteristic selection device 1 in the first embodiment. The characteristic adding device 1200 includes a characteristic data generating unit 1201, a database management unit 1203, and a second sensory evaluation point estimating unit 1202. The operation of the second sensory evaluation point estimating unit 1202 is similar to that of the sensory evaluation point estimating unit 101. Like the sensory evaluation point estimating unit 101, the second sensory evaluation point estimating unit 1202 also includes a trained neural network.

[0051] The characteristic data generating unit 1201 generates a large amount of automatically generated control characteristics 1251, which are control characteristics. The automatically generated control characteristics 1251 may be generated randomly, or a rough trend, for example, a monotonically increasing trend, may be specified in advance. The second sensory evaluation point estimating unit 1202 generates parameters from each of the automatically generated control characteristics 1251, inputs the parameters to a built-in neural network, and outputs an automatically generated evaluation value 1252.

[0052] The database management unit 1203 receives automatically generated control characteristics 1251 from the characteristic data generation unit 1201 and automatically generated evaluation values ​​1252 from the second sensory evaluation point estimation unit 1202. The database management unit 1203 evaluates the validity of each automatically generated control characteristic 1251. If the database management unit 1203 determines that there is no problem with the validity, it adds the combination of the automatically generated control characteristic 1251 and the automatically generated evaluation value 1252 to the database 102. The evaluation of the validity of the automatically generated control characteristic 1251 by the database management unit 1203 includes a monotonically increasing tendency, upper and lower limits of the minimum value, upper and lower limits of the maximum value, and an upper limit of the amount of change.

[0053] The hardware configuration of the characteristic adding device 1200 may be the same as the hardware configuration of the control characteristic selection device 1 shown in Fig. 2. However, since the characteristic adding device 1200 does not require input from a user, the characteristic adding device 1200 does not need to include the input / output device 44.

[0054] According to the above-described second embodiment, the following advantageous effects can be obtained. (7) A database management unit 1203 is provided that adds to the database 102 a combination of an automatically generated control characteristic 1251, which is an automatically generated control characteristic, and an automatically generated evaluation value 1252, which is a sensory evaluation score obtained by evaluating the automatically generated control characteristic 1251. Therefore, the number of records in the database 102 can be expanded, and it is possible to obtain a new control characteristic that is closer to the target value 53 than before.

[0055] (Modification of the second embodiment) If the automatically generated evaluation value 1252 for the automatically generated control characteristic 1251 added to the database 102 does not match the sensory evaluation value perceived by the user himself, the user may perform the following process. That is, the user adds the automatically generated control characteristic 1251 and the sensory evaluation value perceived by the user as a new combination to the learning data of the sensory evaluation point estimation unit 101 and the second sensory evaluation point estimation unit 1202. This improves the estimation accuracy of the sensory evaluation point estimation unit 101 and the second sensory evaluation point estimation unit 1202.

[0056] In each of the above-mentioned embodiments and modifications, the configuration of the functional blocks is merely an example. Some functional configurations shown as separate functional blocks may be integrated, or a configuration shown in one functional block diagram may be divided into two or more functions. In addition, some of the functions of each functional block may be provided by other functional blocks.

[0057] In the above-described embodiments and modifications, the programs are stored in the ROM 42, but the programs may be stored in the storage unit 46. The control characteristic selection device 1 may have an input / output interface (not shown), and the programs may be read from another device via the input / output interface and a medium available to the control characteristic selection device 1 when necessary. The medium here refers to, for example, a storage medium that is detachable from the input / output interface, or a communication medium, i.e., a wired, wireless, optical, or other network, or a carrier wave or digital signal that propagates through the network. Some or all of the functions realized by the programs may be realized by a hardware circuit or an FPGA.

[0058] The above-mentioned embodiments and modifications may be combined with each other. Although various embodiments and modifications have been described above, the present invention is not limited to these. Other aspects that are conceivable within the scope of the technical idea of ​​the present invention are also included in the scope of the present invention. [Explanation of symbols]

[0059] 1: Characteristics selection device 44: Input / Output device 45: Communication equipment 51: Current characteristics 52: Current rating 53: Target value 54: Selection characteristics 58: Presentation number command 59: Mode command 101: Sensory evaluation point estimation unit 102: Database 103: Setting section 104: Control characteristic selection unit 105: Input section 106:Display section 502: Evaluation score 703: Control section 704:Reading section 1200: Special feature addition device 1201: Characteristic data generation unit 1203: Database Management Department

Claims

1. A control characteristic selection device that searches for a control characteristic of an actuator that contributes to a running characteristic of a moving object, comprising: a database in which a plurality of combinations of the control characteristics and the sensory evaluation scores are stored; a sensory evaluation point estimating unit that inputs the control characteristic as a current characteristic and outputs the sensory evaluation point as a current evaluation point; An input unit that receives a target value that is a target sensory evaluation score; a selection unit that selects, from the control characteristics stored in the database, a control characteristic whose sensory evaluation score is similar to the target value and similar to the current characteristic as a selected characteristic.

2. 2. The control characteristic selection system according to claim 1, A control characteristic selection system, wherein the selection unit selects, as the selected characteristic, the control characteristic whose sensory evaluation score is similar to the target value and whose difference from the current characteristic is small.

3. 2. The control characteristic selection system according to claim 1, The sensory evaluation score includes a plurality of evaluation items including a representative item, A control characteristic selection system, wherein the selection unit selects as the selected characteristic the control characteristic in which the representative item of the sensory evaluation score is similar to the target value and in which the sum of the changes in the sensory evaluation scores other than the representative item is the smallest.

4. 2. The control characteristic selection system according to claim 1, The sensory evaluation score includes a plurality of evaluation items including a representative item, A control characteristic selection system, wherein the selection unit selects as the selected characteristic the control characteristic for which the representative item of the sensory evaluation score exceeds the target value and the value of the sensory evaluation score of the representative item is closest to the target value.

5. 2. The control characteristic selection system according to claim 1, the control characteristic indicates a second physical quantity corresponding to a first physical quantity, The sensory evaluation point estimating unit identifies, as a parameter, a value of the second physical quantity when the first physical quantity is increased by a predetermined value unit in order to evaluate the current characteristic, and inputs the parameter to a pre-trained neural network.

6. 2. The control characteristic selection system according to claim 1, The control characteristic selection system, wherein the actuator is one of steering, suspension, and brakes.

7. 2. The control characteristic selection system according to claim 1, The control characteristic selection system further includes a characteristic adding device that adds to the database a combination of an automatically generated control characteristic, which is the control characteristic that has been automatically generated, and an automatically generated evaluation value, which is the sensory evaluation point obtained by evaluating the automatically generated control characteristic.

8. A control characteristic selection method executed by a computer capable of searching a database storing a plurality of combinations of control characteristics of actuators that contribute to running characteristics of a moving object and sensory evaluation scores of the control characteristics, the method comprising: a sensory evaluation point processing in which the control characteristic is input as a current characteristic and the sensory evaluation point is output as a current evaluation point; An input process for receiving a target value which is the sensory evaluation score to be targeted; a selection process for selecting, as a selected characteristic, a control characteristic whose sensory evaluation score is similar to the target value and similar to the current characteristic from the control characteristics stored in the database; In the selection process, the control characteristic that satisfies the target value and has a small difference in characteristic value from the current characteristic is selected as the selected characteristic.

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