JP2023017639A - Control device, control method and control program - Google Patents

Abstract

To adjust an environment of an occupant by using attitude state information indicating the attitude state followed by the water content of the occupant of a vehicle seat estimated by using the electrical characteristic of the vehicle seat including a flexible material having the conductivity.SOLUTION: A control device acquires the electrical characteristic from a detection unit that detects the electrical characteristic between a plurality of detection points predetermined in a flexible material of a vehicle seat including the flexible material which has the conductivity and whose electrical characteristic changes according to the change in the given pressure and water, estimates attitude state information indicating the attitude state followed by the water content of an occupant by inputting the time-series electrical characteristic to a learning model that is learned by using the time-series electrical characteristic when the pressure and water are given to the flexible material and the attitude state information indicating the attitude state followed by the water content of the occupant of the vehicle seat in which the pressure and water are given to the flexible material as learning data, and controls an environment adjustment device including at least one of a temperature control device of the vehicle seat and an air-conditioning device of a cabin by using the estimated attitude state information.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to control devices, control methods, and control programs.

2. Description of the Related Art Conventionally, in order to identify the posture of an occupant seated on a vehicle seat, a shape change occurring in the vehicle seat is detected, and the detection result is used to estimate the posture of the occupant. In terms of detecting a shape change that occurs in a vehicle seat, it is difficult to detect deformation without interfering with the deformation of the vehicle seat. In addition, a strain sensor used for detecting deformation of a rigid body such as metal deformation is difficult to use in a vehicle seat, so a special detection device is required to detect deformation of the vehicle seat. For example, there is known a technique of measuring the displacement and vibration of an object with a camera, obtaining a deformed image, and extracting the amount of deformation (see, for example, Patent Document 1). Also known is a technique related to a flexible tactile sensor that estimates the amount of deformation from the amount of light transmission (see Patent Document 2, for example).

In terms of maintaining the posture of an occupant seated on a vehicle seat in an appropriate state, a body pressure sensor detects the body pressure of the occupant seated on the vehicle seat of a vehicle, and the seat is adjusted according to the detected body pressure. A technique is known for maintaining an appropriate distribution of body pressure when a person is seated by driving a support member provided on a back, seat cushion, or the like (see, for example, Patent Document 3).

International Publication No. 2017-029905 JP 2013-101096 A JP 2019-137286 A

However, in the conventional technology described above, it is not possible to estimate the posture state of the vehicle seat that accompanies water content due to sweating or the like of the occupant of the vehicle seat. When estimating the posture of a vehicle seat that accompanies water content due to perspiration of the occupant, it is necessary to detect the shape change and the water content independently, and a special detection device that associates the two is required. be. In addition, it is conceivable to control an environment adjustment device that adjusts the environment of the occupant, such as an air conditioner that air-conditions the interior of the vehicle, using posture state information that indicates the estimated posture state of the occupant with moisture due to perspiration or the like. , is not taken into consideration in the techniques described in the above-mentioned Patent Documents 1 to 3.

The present disclosure uses posture state information that indicates the posture state of the occupant of the vehicle seat with moisture, which is estimated using the electrical characteristics of the vehicle seat that includes a flexible material having conductivity. It is an object to provide a control device, a control method, and a control program that can adjust the

In order to achieve the above object, the first aspect is
A vehicle seat comprising a flexible material that is electrically conductive and whose electrical properties change in response to changes in applied pressure and moisture. a detection unit that detects
Data for learning includes time-series electrical characteristics when pressure and moisture are applied to the flexible material, and posture state information indicating a posture state of the occupant of the vehicle seat with water content that applies pressure and moisture to the flexible material. , the time-series electrical characteristics detected by the detection unit are input to a learning model that has been trained to output the posture state information using the time-series electrical characteristics as input. an estimating unit for estimating posture state information indicating a posture state of the occupant with hydration corresponding to time-series electrical characteristics;
A control unit that uses the posture state information estimated by the estimation unit to control an environment adjustment device including at least one of a temperature control device for the vehicle seat and an air conditioner for a vehicle interior in which the vehicle seat is installed. and,
is a control device including

A second aspect is the control device of the first aspect,
The vehicle seat includes at least one of a seat cushion, a seat back, a headrest, and an armrest,
The posture state includes a sitting state with the occupant moistened in the vehicle seat,
The control unit controls the environment adjustment device using the posture state information estimated by the estimation unit.

A third aspect is the control device of the second aspect,
The seated posture state in which the occupant is hydrated is a seated state in which the occupant sweats.

A fourth aspect is the control device according to any one aspect of the first aspect to the third aspect,
The flexible material is a urethane material having a structure having at least one of fibrous and mesh-like skeletons, or having a structure in which a plurality of fine air bubbles are scattered inside, and at least a portion of which is conductive.

A fifth aspect is the control device according to any one aspect of the first aspect to the fourth aspect,
The learning model includes a model generated by learning using a network by reservoir computing using the flexible material as a reservoir and using the reservoir.

The sixth aspect is
A vehicle seat comprising a flexible material that is electrically conductive and whose electrical properties change in response to changes in applied pressure and moisture. Acquiring the electrical characteristics from a detection unit that detects characteristics,
Data for learning includes time-series electrical characteristics when pressure and moisture are applied to the flexible material, and posture state information indicating a posture state of the occupant of the vehicle seat with water content that applies pressure and moisture to the flexible material. , the time-series electrical characteristics detected by the detection unit are input to a learning model that has been trained to output the posture state information using the time-series electrical characteristics as input. estimating posture state information indicating the occupant's posture state with water content corresponding to the time-series electrical characteristics;
In the estimation method, an environment adjustment device including at least one of a temperature control device for the vehicle seat and an air conditioner for a vehicle interior in which the vehicle seat is installed is controlled using the estimated posture state information.

The seventh aspect is
A vehicle seat comprising a flexible material that is electrically conductive and whose electrical properties change in response to changes in applied pressure and moisture. Acquiring the electrical characteristics from a detection unit that detects characteristics,
Data for learning includes time-series electrical characteristics when pressure and moisture are applied to the flexible material, and posture state information indicating a posture state of the occupant of the vehicle seat with water content that applies pressure and moisture to the flexible material. , the time-series electrical characteristics detected by the detection unit are input to a learning model that has been trained to output the posture state information using the time-series electrical characteristics as input. estimating posture state information indicating the occupant's posture state with water content corresponding to the time-series electrical characteristics;
using the estimated posture state information to control an environment adjustment device including at least one of a temperature control device for the vehicle seat and an air conditioner for a vehicle interior in which the vehicle seat is installed; It is an estimation program.

According to the present disclosure, the posture state information indicating the posture state of the occupant of the vehicle seat with water content, which is estimated using the electrical characteristics of the vehicle seat provided with the flexible material having conductivity, is used to determine whether the occupant It has the effect of being able to adjust the environment of

It is a figure which shows an example of a structure of the control apparatus which concerns on embodiment. 1A and 1B are diagrams related to a vehicle seat according to an embodiment; FIG. FIG. 4 is a diagram relating to detection points of a conductive member according to the embodiment; FIG. 4 is a diagram related to a conductive member according to the embodiment; FIG. 4 is a diagram related to a conductive member according to the embodiment; FIG. 4 is a diagram related to a conductive member according to the embodiment; It is a figure regarding the learning process which concerns on embodiment. 6 is a flowchart illustrating an example of learning data collection processing according to the embodiment; It is a figure which shows the characteristic relevant to the vehicle seat which concerns on embodiment. It is a figure which shows the characteristic relevant to the vehicle seat which concerns on embodiment. FIG. 4 is a diagram related to learning processing in a learning processing unit according to the embodiment; 6 is a flowchart showing an example of the flow of learning processing according to the embodiment; FIG. 4 is a diagram related to learning processing in a learning processing unit according to the embodiment; It is a figure which shows an example of a structure of the control apparatus which concerns on embodiment. 6 is a flowchart showing an example of the flow of estimation/control processing according to the embodiment; FIG. 4 is a diagram related to a conductive member according to the embodiment;

Hereinafter, embodiments for implementing the technology of the present disclosure will be described in detail with reference to the drawings.
Components and processes having the same actions and functions are given the same reference numerals throughout the drawings, and overlapping descriptions may be omitted as appropriate. In addition, the present disclosure is not limited to the following embodiments, and can be implemented with appropriate modifications within the scope of the purpose of the present disclosure. In addition, although the present disclosure mainly describes the estimation of physical quantities for members that deform nonlinearly, it goes without saying that the present disclosure can be applied to the estimation of physical quantities for members that deform linearly.

In the present disclosure, the term "vehicle seat" refers to, for example, a seat on which an occupant sits, such as a driver's seat, a passenger's seat, and a rear seat after the second row of a vehicle such as an automobile. The concept includes a seat cushion, a seat back that supports the back of the occupant, a headrest that supports the head of the occupant, and an armrest that the arm of the occupant rests on. A "vehicle seat" is a concept that includes a material that is at least partially deformable, such as by bending, when an external force is applied. An example of an external force is pressure as a stimulus applied to a vehicle seat. "Position of a passenger seated on a vehicle seat with water" is a concept that includes the state of a passenger who gives moisture due to perspiration while applying pressure to a flexible material that deforms. Includes occupant behavior such as seated occupant posture and movement. The posture state is a sitting state in which the occupant sits on the vehicle seat.

In the present disclosure, the term “flexible material” is a concept that includes materials that are at least partially deformable such as bending when an external force is applied. It includes a structure having at least one skeleton and a structure in which a plurality of fine air bubbles are scattered. An example of an external force is pressure. Examples of structures having at least one of fibrous and mesh-like skeletons and structures in which a plurality of fine air bubbles are scattered include polymeric materials such as urethane materials. The term "flexible material to which electrical conductivity is imparted" is a concept that includes materials having electrical conductivity, materials obtained by imparting electrical conductivity to a flexible material to impart electrical conductivity, and materials in which the flexible material has electrical conductivity. including. In addition, the flexible material to which electrical conductivity is imparted has the function of changing electrical properties according to deformation. An example of a physical quantity that causes a function of changing electrical properties in response to deformation is a pressure value that indicates a stimulus due to pressure applied to a flexible material (hereinafter referred to as pressure stimulus). The flexible material deforms according to the distribution of external forces, such as pressure stimuli, that occur when the occupant is seated or the like. An example of a physical quantity representing an electrical characteristic that changes according to this deformation is an electrical resistance value. Other examples include voltage values or current values. The electrical resistance value can be regarded as the volume resistance value of the flexible material.

By imparting electrical conductivity to the flexible material, the electrical properties appear according to deformation due to pressure. That is, in a flexible material to which electrical conductivity is imparted, electrical pathways are intricately linked, and the electrical pathways expand and contract according to deformation. It may also exhibit behavior in which the electrical path is temporarily disconnected, and behavior in which a different connection than before occurs. Therefore, the flexible material behaves differently depending on the magnitude and distribution of the applied force (e.g., pressure stimulus) between positions separated by a predetermined distance (e.g., the positions of the detection points where the electrodes are arranged). indicates Therefore, the electrical properties change according to the magnitude and distribution of force (for example, pressure stimulus) applied to the flexible material. Since a conductive flexible material is used, it is not necessary to provide detection points such as electrodes at all locations where pressure is applied to the flexible material by an object such as a person. It is sufficient that detection points such as electrodes are provided at at least two arbitrary locations sandwiching the location where pressure is applied to the flexible material.

Also, the electrical properties that change according to the deformation of the flexible material are affected by moisture (moisture content) given by perspiration of the occupant. The physical quantity (electrical resistance value) representing the electrical characteristics corresponding to the posture state of the occupant with water content varies from the physical quantity (electrical resistance value) representing the electrical characteristics corresponding to the posture state of the occupant without water content. In other words, even if the occupant is in the same posture, the flexible material has different electrical characteristics depending on the water content, so it is possible to determine whether or not the occupant is perspiring. In addition, it is known that passengers sweat when they get motion sickness. Therefore, it is possible to determine the presence or absence of motion sickness based on the degree of perspiration of the occupant.

The control device of the present disclosure uses a learned learning model to estimate the posture state of an occupant seated on the vehicle seat with water content from the electrical properties of the conductive flexible material provided on the vehicle seat, Using the estimated posture state information, an environment adjusting device including at least one of a temperature adjusting device for the vehicle seat and an air conditioning device for the passenger compartment in which the vehicle seat is installed is controlled. The posture state accompanied by water content includes, for example, a posture state accompanied by perspiration, a posture state accompanied by water content due to at least one of the occupant's body and clothes being wet, and the like. Moreover, the posture state accompanied by sweating includes a posture state accompanied by motion sickness. The flexible material can be placed on the vehicle seat. The learning model shows time-series electrical characteristics when pressure and moisture are applied to a conductive flexible material, and the posture of an occupant sitting in a vehicle seat that applies pressure and moisture to the flexible material. Posture state information is used as learning data. The learning model receives time-series electrical characteristics as an input, and is trained to output posture state information indicating the posture state of the occupant seated in the vehicle seat, which corresponds to the time-series electrical characteristics, and which is accompanied by water content.

In the following description, as a vehicle seat, a seat member (hereinafter referred to as conductive urethane) in which a conductive material is infiltrated or blended in all or at least a part of a urethane member as a flexible material having conductivity is arranged. A case where a sheet is applied will be described. The conductive urethane is provided on at least one of the seat cushion, seat back, headrest, and armrest. Also, the conductive urethane may be individually provided on the seat cushion, the seat back, the headrest, and the armrest, or may be provided integrally. That is, at least one or more conductive urethanes are provided in at least a part of the contact area when the passenger sits on the vehicle seat. The thickness of the conductive urethane is preferably 1 mm or more, for example. Moreover, the volume resistivity of the conductive urethane is preferably 10 ⁷ Ω·cm or less, for example. As a physical quantity for deforming the conductive urethane, a value indicating a pressure stimulus including water applied to the vehicle seat (hereinafter referred to as a water pressure value) is applied. In this case, the moisture pressure value is generated depending on the posture of the occupant in the vehicle seat, which is accompanied by moisture due to sweating or the like. In this embodiment, as the posture state, a seated state accompanied by perspiration of a passenger seated on a vehicle seat is applied. Also, a case where the electrical resistance value of conductive urethane is applied as a physical quantity that changes in response to a pressure stimulus containing moisture will be described.

FIG. 1 shows an example of the configuration of the control device 1 of the present disclosure.

The estimation processing in the control device 1 uses the learned learning model 51 to determine the position and movement of the occupant OP seated on the vehicle seat 2 as an unknown posture state of the occupant OP seated on the vehicle seat 2. Estimates and outputs the seated state of the occupant. As a result, it is possible to identify the hydrated posture of the occupant seated on the vehicle seat 2 without using a special device or a large-sized device or directly measuring the deformation of the flexible material contained in the vehicle seat 2. It becomes possible. The learning model 51 uses a posture state (e.g., sitting state value) associated with water content of the occupant in the vehicle seat 2 as a label, and uses the electrical characteristics of the vehicle seat 2 in the posture state (that is, the position of the vehicle seat 2). is learned by inputting the electrical resistance value of conductive urethane). Learning of the learning model 51 will be described later.

The control unit 7 in the control device 1 controls the environment adjustment device 8 for adjusting the environment of the occupant OP using the posture state information output in the estimation process. Here, adjusting the environment of the occupant OP means adjusting at least one of the temperature and humidity in the vicinity of the occupant OP. The environment adjustment device 8 includes a temperature adjustment device 8A for the vehicle seat 2 and an air conditioner 8B for the passenger compartment. In addition, the environment adjusting device 8 may be configured to include either one of the temperature adjusting device 8A and the air conditioning device 8B. The temperature control device 8A is a device for adjusting at least one of the temperature and humidity in the vicinity of the occupant OP seated on the vehicle seat 2. For example, a seat cooler for cooling the vehicle seat 2 and the vehicle seat 2 At least one of the seat heaters for warming is included.

The seat cooler, for example, incorporates a fan (not shown) with a variable air volume level into the vehicle seat 2, rotates the fan, and blows cooled air toward the occupant OP, thereby reducing temperature and humidity in the vicinity of the occupant OP. Adjust at least one. By blowing the cooling air, it is possible to suppress the temperature rise of the vehicle seat 2 and remove moisture caused by perspiration, for example. Note that the seat cooler may be configured to adjust at least one of the temperature and humidity near the occupant OP by rotating the fan in the reverse direction to suck in air. Also, the seat cooler may be of a type in which a coolant such as water is circulated, or may be realized by using other known methods.

The seat heater adjusts at least one of the temperature and humidity around the occupant OP by, for example, incorporating a fan into the vehicle seat 2 and blowing warm air toward the occupant OP. In addition, the seat heater is configured to adjust at least one of the temperature and humidity near the occupant OP by, for example, incorporating a heating wire into the vehicle seat 2 and heating the vehicle seat 2 by generating heat by applying an electric current to the heating wire. It's okay.

The air conditioner 8B is a device for adjusting at least one of the temperature and humidity in the vehicle interior in which the vehicle seat 2 is installed, and rotates a fan (not shown) to blow cooled or warmed air. The vehicle interior is cooled or warmed by blowing air into the vehicle interior. Also, the air conditioner 8B has a dehumidification function, and is controlled to achieve set temperature and humidity.

When the posture state of the occupant OP is estimated to be accompanied by sweating, the control unit 7 operates at least one of the temperature control device 8A and the air conditioner 8B so that the temperature around the occupant OP decreases, for example. activate. As a result, the temperature rise of the vehicle seat 2 is suppressed, and the moisture caused by perspiration is also removed. That is, sweating is suppressed when the occupant OP is seated on the vehicle seat 2, and a comfortable seating state can be obtained.

When the environment of the occupant OP is adjusted by the temperature control device 8A, for example, a data table that associates posture state information associated with sweating with air volume levels (eg, high, medium, low, etc.) of the fan of the temperature control device 8A. is stored in advance, and the fan of the temperature control device 8A is operated at an air volume level corresponding to the estimated posture state accompanied by sweating.

Further, when the environment of the occupant OP is adjusted by the air conditioner 8B, a data table in which posture state information associated with sweating is associated with each of the set temperature and humidity of the air conditioner 8B is stored in advance, and the estimated The air conditioner 8B is operated at the set temperature and set humidity according to the posture state accompanied by perspiration. For example, when it is estimated that the posture state of the occupant OP seated on the vehicle seat 2 is a posture state accompanied by sweating, control is performed to operate the air conditioner 8B so as to lower the temperature and humidity in the vehicle compartment. do. However, if the temperature in the vehicle interior is relatively high and the humidity is within an appropriate range, only the temperature in the vehicle interior may be lowered. Further, when the temperature in the vehicle interior is within an appropriate range and the humidity is relatively high, only the humidity in the vehicle interior may be lowered. This control suppresses sweating when the passenger is seated, and a comfortable seated state can be obtained.

Further, when the estimated posture state accompanied by sweating is a posture state accompanied by motion sickness, the control unit 7 outputs a message announcing the state of motion sickness from the speaker provided in the vehicle. can be controlled as follows.

In this embodiment, for example, the vehicle seat 2 includes a seat cushion 21A, a seat back 21B, and a headrest 21C. A conductive urethane 22A is arranged on the seat cushion 21A, a conductive urethane 22B is arranged on the seat back 21B, and a conductive urethane 22C is arranged on the headrest 21C. The conductive urethanes 22A, 22B, and 22C are connected to an electrical characteristic detector 76 (see FIG. 3), which is an example of a detector. The conductive urethanes 22A, 22B, and 22C are made of the same conductive urethane. The seat cushion 21A, the seat back 21B, and the headrest 21C are hereinafter collectively referred to as the seat 21 . Also, the conductive urethanes 22A, 22B, and 22C are regarded as one and called the conductive urethane 22. FIG. It is assumed that the conductive urethane 22 is formed by either internal addition or impregnation of a conductive material, but the impregnation method is more desirable than the internal addition method because of its higher conductivity.

The vehicle seat 2 constituted by the seat 21 on which the conductive urethane 22 is arranged functions as a detector. The conductive urethane 22 may be arranged on at least a part of the sheet 21 as shown in FIG. 2, and may be arranged inside or outside. In addition, in FIG. 2, the vehicle seat 2 is represented by a simple planar shape for the sake of simplicity of explanation. As a specific example, the interior of the seat 21 may be entirely made of conductive urethane 22, as shown in the AA cross section of the vehicle seat 2 as a seat cross section 2-1. Further, as shown in the seat cross section 2-2, the conductive urethane 22 may be formed on the passenger side (surface side) inside the seat 21, and as shown in the seat cross section 2-3, the inside of the seat 21 may be The conductive urethane 22 may be formed on the side opposite to the passenger side (back side). Furthermore, as shown in the sheet section 2-4, the conductive urethane 22 may be formed inside the sheet 21. FIG.

Further, as shown in the seat section 2-5, the conductive urethane 22 may be arranged on the outside of the passenger side (surface side) of the seat 21, and as shown in the seat section 2-6, the conductive urethane 22 may be arranged opposite to the passenger side. A conductive urethane 22 may be arranged on the outside of the side (rear side). When the conductive urethane 22 is arranged outside the sheet 21, the conductive urethane 22 and the sheet 21 may be simply laminated, or the conductive urethane 22 and the sheet 21 may be integrated by adhesion or the like. Even when the conductive urethane 22 is arranged outside the sheet 21, the flexibility of the sheet 21 is not hindered because the conductive urethane 22 is a conductive urethane member.

To simplify the explanation, an example of forming the vehicle seat 2 by arranging the conductive urethane 22 on the outer side of the seat 21 on the occupant side (surface side) will be explained below (seat cross section 2-5).

In this embodiment, as shown in FIG. 3, signals from a plurality of (two in FIG. 3) detection points 75 arranged at a distance are used to determine the electrical properties (that is, the electrical resistance value) of the conductive urethane 22. A certain volume resistance value) can be detected. In the example shown in FIG. 3, a first detection set #1 is shown that detects electrical resistance values from signals from a plurality of detection points 75 arranged diagonally with a distance on the conductive urethane 22. there is The arrangement of the plurality of detection points 75 is not limited to the positions shown in FIG. The electrical characteristics of the conductive urethane 22 can be obtained by connecting an electrical characteristics detector 76 for detecting electrical characteristics (that is, a volume resistance value, which is an electrical resistance value) to the detection point 75 and using the output thereof.

The posture state described above includes an urging state indicating the urging of the occupant against the vehicle seat 2 with water. The urged state is a state indicating the urged force of the occupant against the vehicle seat 2 among the occupant's posture states involving moisture. For example, the electrical resistance value detected in the vehicle seat 2 composed of the seat 21 provided with the conductive urethane 22 is the deformation of the conductive urethane 22 when the vehicle seat 2 is subjected to a pressure stimulus containing moisture. changes before and after a pressure stimulus containing at least water is applied. Therefore, the electric resistance value changes before and after the occupant urges the vehicle seat 2 with a pressure stimulus containing moisture. Therefore, detection of a time-series electrical resistance value, that is, detection of a change in the electrical resistance value from a state in which a pressure stimulus containing moisture is not applied to the vehicle seat 2 (for example, an electrical resistance value exceeding a predetermined threshold) is detected. is detected), it is possible to detect the urging force of the occupant on the vehicle seat 2 . Specifically, the urging state indicating the urging of the occupant against the vehicle seat 2 with moisture includes the contact state because even the contact of the occupant with the vehicle seat 2 is accompanied by pressure stimulation including moisture. Therefore, by arranging the conductive urethane 22 on the vehicle seat 2 , it is possible to detect the contact of the passenger with the vehicle seat 2 . Also, the electric resistance value changes even if any one of the position, distribution, and magnitude of the pressure stimulus containing moisture applied to the vehicle seat 2 changes. Therefore, it is not impossible to detect the contact state including the contact position of the passenger with respect to the vehicle seat 2 from the electrical resistance value that changes in time series.

It should be noted that the biased state is a seated state indicating that an occupant is seated on the vehicle seat 2 . That is, the sitting state can be identified by the position, distribution, and magnitude of the pressure stimulus containing moisture, and it may be impossible to detect the sitting state, including the start of sitting, from the electrical resistance value that changes over time. do not have.

In order to improve the detection accuracy of the electrical characteristics of the conductive urethane 22, more detection points than the two detection points shown in FIG. 3 may be used.

As an example, the conductive urethane 22 may be formed by arranging one or more rows of a plurality of conductive urethane pieces each having a detection point, and the electrical characteristics may be detected for each of the plurality of conductive urethane pieces. good. For example, the conductive urethane pieces 23 (FIG. 4) may be arranged to form the conductive urethane 22 (FIGS. 5 and 6). The example shown in FIG. 4 includes a first detection set #1 that detects an electrical resistance value from a signal from a detection point 75A arranged diagonally with a distance therebetween, and another detection set #1 arranged diagonally. A second detection set #2 is shown which detects the electrical resistance value from the signal from point 75B. In the example shown in FIG. 5, the conductive urethane pieces 23 (FIG. 4) are arranged (4×1) in the longitudinal direction of the sheet 21 to form the conductive urethane 22, and sequentially from the first detection set #1. It shows constructing an eighth detection set #8. Furthermore, in the example shown in FIG. 6, the first detection set #1 is adopted in each of the conductive urethane pieces 23 (FIG. 4), and the conductive urethane pieces 22 are arranged (4×2) in the longitudinal direction and the width direction of the sheet 21. to form the first detection set #1 to the eighth detection set #8. Further, as shown in FIG. 16, a seat made of conductive urethane 22 is provided with eight detection sets #1 to #8 from the top to the bottom of the seat back 21B and from the back to the front of the seating surface of the seat cushion 21A. 21 may be configured.

As another example, the detection range on the conductive urethane 22 may be divided, a detection point may be provided for each divided detection range, and the electrical characteristics may be detected for each detection range. For example, a region corresponding to the size of the conductive urethane piece 23 shown in FIGS. 5 and 6 is set in the conductive urethane 22 as a detection range, a detection point is arranged for each set detection range, What is necessary is just to detect an electrical characteristic.

As shown in FIG. 1 , the control device 1 includes an estimator 5 . Time-series input data 4 representing the magnitude of electrical resistance (electrical resistance value) in the conductive urethane 22 is input to the estimation unit 5 . The input data 4 corresponds to the wet seating state 3 of the occupant, which indicates the behavior of the occupant, such as the posture and movement of the occupant seated on the vehicle seat 2. The estimating unit 5 also outputs output data 6 representing a physical quantity (seating state value) indicating a hydrated seating state of the occupant seated on the vehicle seat 2 as an estimation result. Note that the estimation unit 5 includes a learning model 51 that has been trained.

The learning model 51 calculates the seating state with moisture of the occupant, i.e., the vehicle seat, from the electrical resistance of the conductive urethane 22 (input data 4) that changes due to the pressure stimulus containing moisture according to the seating condition 3 with moisture of the occupant. 2 is a model that has completed learning for deriving the seating state of the occupant (output data 6) that indicates the behavior of the occupant, such as the posture and movement of the occupant with water content. The learning model 51 is, for example, a model that defines a trained neural network, and is expressed as a set of information on weights (strengths) of connections between nodes (neurons) that make up the neural network.

The learning model 51 is generated by learning processing of the learning processing unit 52 (FIG. 7). The learning processing unit 52 performs learning processing using the electrical characteristics (input data 4) of the conductive urethane 22 that change due to the pressure stimulus containing water caused by the seated state 3 with the water content of the occupant. That is, a large amount of data obtained by measuring the electrical resistance of the conductive urethane 22 in chronological order using the seated state 3 with water content of the occupant as a label is used as learning data. Specifically, the learning data is a set of input data including an electrical resistance value (input data 4), and information (output data 6) indicating the seating state 3 with water content of the occupant corresponding to the input data. contains a large amount of Here, time-series information is associated with each of the electrical resistance values (input data 4) of the conductive urethane 22 by adding information indicating the measurement time. In this case, information indicating the time of measurement may be added to the set of time-series electrical resistance values of the conductive urethane 22 to associate the time-series information with the period determined as the sitting state 3 with the passenger's moisture content.

Next, the learning processing section 52 will be described.
In the learning process performed by the learning processing unit 52, the vehicle seat 2 configured by the seat 21 on which the conductive urethane 22 is arranged is applied as a detection unit, and the seated state 3 with the occupant containing water and the conductive urethane 22 (input data 4) is used as learning data. For example, the occupant OP is instructed to maintain or move a seated state that exhibits a behavior such as a posture or movement accompanied by a predetermined water content on the vehicle seat 2, the electric resistance value at that time is detected, and the seated state accompanied by water content is detected. Learning data is associated with the state. Moreover, at the time of learning, various water content patterns may be reproduced by spraying water or the like.

In addition, the seated state includes a state indicating the behavior of the occupant in chronological order. The state indicating the behavior of the occupant in this time series includes a static state due to the occupant's posture accompanied by water content in which the movement is stable on the vehicle seat 2, and a plurality of water content changes in time series on the vehicle seat 2. Includes dynamic states due to posture and movement. Also, the electrical characteristics (that is, the volume resistance value, which is the electrical resistance value) can be detected by connecting the electrical characteristics detection section 76 (FIG. 3) to the detection point 75 .

Specifically, the learning processing unit 52 can be configured including a computer including a CPU (not shown), and executes learning data collection processing and learning processing. FIG. 8 shows an example of learning data collection processing executed by a CPU (not shown). In step S100, the learning processing unit 52 instructs the occupant OP about a sitting state involving water in the vehicle seat 2 (conductive urethane 22). Acquire the electrical resistance value that changes due to time series. In the next step S104, the acquired time-series electrical resistance values are labeled with the sitting state 3 with water content and stored. The learning processing unit 52 continues until the set of the sitting state 3 with water content of the seated person and the electrical resistance value of the conductive urethane 22 reaches a predetermined number or a predetermined time (Yes in step S106). negative judgment until it is judged), and the above processing is repeated. As a result, the learning processing unit 52 can acquire and store the electrical resistance value of the conductive urethane 22 in chronological order for each sitting state with water content of the occupant. A set of time-series electrical resistance values of the conductive urethane 22 for each state becomes learning data.

By the way, the sitting state (posture) of the occupant OP with water content is determined by the relative positional relationship of each part of the occupant OP with respect to the vehicle seat 2, the distribution, magnitude, frequency, etc. of the pressure stimulus including water by each part. It can be identified by at least a part of change or maintenance of each physical quantity. Therefore, it is considered that some of these time-series physical quantities include features indicating the sitting state (posture) of the occupant OP with water content. In this embodiment, by using the conductive urethane 22, it is possible to detect the electrical characteristics (volume resistance) reflecting these physical quantities in chronological order.

FIG. 9 shows an example of the electrical characteristics of the vehicle seat 2 composed of the seat 21 on which the conductive urethane 22 is arranged. The example of FIG. 9 shows how the electrical characteristics (resistance) change in time series when water is sprayed on the conductive urethane 22 .

FIG. 10 shows another example of the electrical characteristics of the vehicle seat 2 composed of the seat 21 on which the conductive urethane 22 is arranged. Similar to the example of FIG. 9, the example of FIG. 10 shows how the electrical characteristics (resistance) change in time series when the conductive urethane 22 is sprayed with water. 10(B) is an enlarged view of the X portion of FIG. 10(A), and FIG. 10(C) is an enlarged view of the Y portion of FIG. 10(A).

As shown in FIGS. 9 and 10, the electrical properties of the conductive urethane 22 change depending on the water content of the conductive urethane 22, and the water content is reflected. It can be confirmed that it is possible to estimate the posture of the occupant with moisture from the electrical resistance value that changes over time. That is, even in various posture states involving water content such as sweating, by using the learning model 51 that has been learned, the results regarding the posture state with water content of the occupant can be separated, and the results regarding the posture state with water content of the passenger can be separated. It is possible to determine the posture state.

When the occupant OP changes his or her posture in accordance with the instructed sitting state with water content, the pressure stimulus containing water to the vehicle seat 2 changes, and the electrical characteristics corresponding to the change of the pressure stimulus containing water are shown in time series. , it becomes possible to store the time-series electrical characteristics in association with the sitting state (posture state) of the seated person with water content. A set of the time-series electrical characteristics and the seating state value indicating the instructed sitting state with water content becomes the learning data.

Next, an example of the learning data described above is shown in the following table. Table 1 is an example of static state data in which time-series electrical resistance value data (r) and seating state values are associated with each other as learning data relating to a sitting state with water content.

Next, learning processing in the learning processing section 52 will be described. FIG. 11 is a diagram showing functions of a CPU (not shown) of the learning processing unit 52 in learning processing.
A CPU (not shown) of the learning processing unit 52 includes functional units of the generator 54 and the calculator 56 . The generator 54 has a function of generating an output in consideration of the sequential relationship of the electrical resistance values obtained in time series as an input.

In addition, the learning processing unit 52 uses the above-described input data 4 (electrical resistance value) as learning data, and output data indicating the sitting state 3 with water content of the occupant who gave the conductive urethane 22 a pressure stimulus containing water. I have many sets with 6.

The generator 54 includes an input layer 540, an intermediate layer 542, and an output layer 544 to form a known neural network (NN). Since the neural network itself is a known technology, detailed description is omitted, but the intermediate layer 542 includes a large number of node groups (neuron groups) having inter-node connections and feedback connections. Data from the input layer 540 is input to the intermediate layer 542 , and data resulting from the operation of the intermediate layer 542 is output to the output layer 544 .

The generator 54 is a neural network that generates generated output data 6A representing the seating state of the occupant with moisture from the input data 4 (electrical resistance). The generated output data 6A is data obtained by estimating, from the input data 4 (electrical resistance), the moist seating state of the occupant to whom the conductive urethane 22 was given a pressure stimulus containing water. From the input data 4 (electrical resistance) input in time series, the generator 54 generates generated output data representing a state close to a seated state with water content of the occupant. By learning using a large number of input data 4 (electric resistance), the generator 54 approximates a seated state with water content of an occupant in which a pressure stimulus containing water is applied to the vehicle seat 2, that is, the conductive urethane 22. It becomes possible to generate the generated output data 6A. In another aspect, the vehicle seat 2, that is, the conductive urethane 22 is given a pressure stimulus containing water by capturing the electrical characteristics, which are the input data 4 input in chronological order, as a pattern and learning the pattern. It becomes possible to generate the generated output data 6A that is close to the seated state with the water content of the occupant.

The calculator 56 is a calculator that compares the generated output data 6A with the output data 6 of the learning data and calculates the error of the comparison result. The learning processing unit 52 inputs the generated output data 6A and the output data 6 of the learning data to the calculator 56 . In response to this, the calculator 56 calculates the error between the generated output data 6A and the output data 6 of the learning data, and outputs a signal indicating the calculation result.

The learning processing unit 52 performs learning of the generator 54 that tunes the weight parameter of the connection between nodes based on the error calculated by the calculator 56 . Specifically, the weight parameter of the connection between the nodes of the input layer 540 and the hidden layer 542 in the generator 54, the weight parameter of the connection between the nodes in the hidden layer 542, and the node of the hidden layer 542 and the output layer 544 Each of the weight parameters of the connections between is fed back to the generator 54 using techniques such as gradient descent and error backpropagation. That is, with the output data 6 of the learning data as a target, the connections between all nodes are optimized so as to minimize the error between the generated output data 6A and the output data 6 of the learning data.

The learning model 51 is generated by learning processing of the learning processing unit 52 . The learning model 51 is expressed as a set of information of weight parameters (weights or strengths) of connections between nodes as a result of learning by the learning processing unit 52 .

FIG. 12 shows an example of the flow of learning processing. In step S110, the learning processing unit 52 acquires the input data 4 (electrical resistance) labeled with the information indicating the seating state of the occupant OP with moisture, which is learning data obtained as a result of chronological measurement. In step S112, the learning processing unit 52 generates the learning model 51 using learning data obtained as a result of time-series measurement. That is, a set of information on weight parameters (weights or strengths) of connections between nodes is obtained as a result of learning using a large amount of learning data as described above. Then, in step S114, data expressed as a set of information on weight parameters (weights or strengths) of connections between nodes of learning results is stored as a learning model 51. FIG.

Note that the generator 54 may use a recursive neural network having a function of generating an output in consideration of the context of time-series inputs, or may use another method.

In the control device 1, the learned generator 54 (that is, data expressed as a set of weight parameter information of connections between nodes of learning results) generated by the above-described method is used as a learning model 51. use. If a sufficiently learned learning model 51 is used, it is possible to identify the seated state of the occupant with water content from the time-series electrical resistance value of the vehicle seat 2, that is, the conductive urethane 22. FIG.

Note that the processing by the learning processing unit 52 is executed by, for example, an external learning model generation device (not shown) different from the control device 1 . The control device 1 includes an estimator 5 and a controller 7 . The output data 6, which is information indicating the sitting state 3 with water content, is an example of the posture state information of the present disclosure.

By the way, as described above, in the conductive urethane 22, the electrical paths are linked in a complicated manner as described above, and the electrical paths expand, contract, expand, contract, temporarily disconnect, and create new connections in response to deformation. behavior, resulting in behavior with different electrical properties depending on the applied force (eg pressure stimulus involving moisture). This allows the conductive urethane 22 to be treated as a reservoir that stores data regarding deformation of the conductive urethane 22 . That is, the control device 1 can apply the conductive urethane 22 to a network model (hereinafter referred to as PRCN) called physical reservoir computing (PRC). Since PRC and PRCN are known techniques, detailed description thereof will be omitted.

FIG. 13 shows an example of the learning processing unit 52 that learns by treating the vehicle seat 2 containing the conductive urethane 22 as a reservoir for storing data relating to deformation of the vehicle seat 2 containing the conductive urethane 22 . The conductive urethane 22 has electrical characteristics (electrical resistance values) corresponding to various pressure stimuli containing moisture, and functions as an input layer for inputting the electrical resistance values, and also provides data on the deformation of the conductive urethane 22. It functions as a reservoir layer to store. Since the conductive urethane 22 outputs different electrical characteristics (input data 4) in response to the pressure stimulus containing water given by the occupant's seated state 3 with water, the electrical resistance of the conductive urethane 22 is estimated at the estimation layer. From the values it is possible to deduce the pressure stimulus 3 (shape of the pressing member) containing the given moisture. Therefore, in the learning process, the estimation layer should be learned.

The control device 1 described above can be realized, for example, by causing a computer to execute a program representing each function described above.

FIG. 14 shows an example of a configuration in which a computer is included as an execution device that executes processing for realizing various functions of the control device 1 .

A computer functioning as the control device 1 has a computer main body 100 shown in FIG. The computer main body 100 includes a CPU 102 , a RAM 104 such as a volatile memory, a ROM 106 , an auxiliary storage device 108 such as a hard disk drive (HDD), and an input/output interface (I/O) 110 . These CPU 102, RAM 104, ROM 106, auxiliary storage device 108, and input/output I/O 110 are connected via a bus 112 so as to exchange data and commands with each other. Also, the input/output I/O 110 is connected to a communication unit 114 for communicating with an external device, and an operation display unit 116 such as a display and a keyboard. The communication unit 114 acquires the input data 4 (electrical resistance) with the vehicle seat 2 including the conductive urethane 22 . That is, the communication unit 114 includes the vehicle seat 2 on which the conductive urethane 22 is arranged, which is a detection unit, and the input data 4 (electricity resistance) can be obtained.

The auxiliary storage device 108 stores a control program 108P for causing the computer main body 100 to function as the control device 1 as an example of the control device of the present disclosure. The CPU 102 reads the control program 108P from the auxiliary storage device 108, develops it in the RAM 104, and executes processing. Thereby, the computer main body 100 that has executed the control program 108P operates as the control device 1 as an example of the control device of the present disclosure.

The auxiliary storage device 108 stores a learning model 108M including the learning model 51 and data 108D including various data. The control program 108P may be provided by a recording medium such as a CD-ROM.

Next, the estimation processing in the control device 1 implemented by a computer will be described.

FIG. 15 shows an example of the flow of estimation/control processing by the control program 108P executed in the computer main body 100. As shown in FIG.
The estimation/control processing shown in FIG. 15 is executed by the CPU 102 when the computer main body 100 is powered on. That is, the CPU 102 reads out the control program 108P from the auxiliary storage device 108, develops it in the RAM 104, and executes the process.

First, in step S200, the CPU 102 reads out the learning model 51 from the learning model 108M of the auxiliary storage device 108 and develops it in the RAM 104, thereby acquiring the learning model 51. FIG. Specifically, a network model (see FIGS. 9 and 11), which is a connection between nodes based on weight parameters expressed as the learning model 51, is developed in the RAM 104. FIG. Therefore, a learning model 51 is constructed in which connections between nodes are realized by weight parameters.

Next, in step S202, the CPU 102 transmits, via the communication unit 114, the unknown input data 4 (electrical resistance), which is the target for estimating the shape of the pressing member due to the pressure stimulus containing moisture given to the conductive urethane 22. obtained in chronological order.

Next, in step S204, the CPU 102 uses the learning model 51 acquired in step S200 to convert the output data 6 (seated state with unknown water content) corresponding to the input data 4 (electrical resistance) acquired in step S202. presume.

Then, in the next step S206, the output data 6 (occupant's seated state with water content) of the estimation result is output to the auxiliary storage device 108. FIG.

Then, in step S208, CPU 102 executes environment adjustment control for adjusting the environment in the vicinity of occupant OP using output data 6 as a result of estimation. That is, for example, when the posture state of the occupant OP is estimated to be accompanied by sweating, at least one of the temperature control device 8A and the air conditioner 8B is operated so as to lower the temperature in the vicinity of the occupant OP. Specifically, for example, the fan of the temperature control device 8A is operated at an air volume level corresponding to the estimated posture state accompanied by sweating. Also, for example, the vehicle interior is air-conditioned by setting the set temperature and set humidity of the air conditioner 8B to lower temperatures and lower humidity than the current temperature and humidity in the vehicle interior. Such environment adjustment control suppresses sweating when the passenger is seated, and a comfortable seated state can be obtained. Further, when the estimated posture state accompanied by sweating is a posture state accompanied by motion sickness, control is performed so that a message announcing the state of motion sickness is output from a speaker provided in the vehicle. . As a result, for example, the driver will try to drive carefully in consideration of the passengers who are motion sick, and it will be possible to reduce the motion sickness.

Note that the estimation/control processing illustrated in FIG. 15 is an example of processing executed by the control method of the present disclosure.

As described above, according to the present disclosure, the occupant's It is possible to estimate the seating state with water content and use the estimation result to adjust the occupant's environment. In other words, it is possible to estimate the seating state of an unknown occupant with water content and adjust the occupant's environment using the estimation results without using special or large-sized equipment or directly measuring the deformation of the flexible members. becomes.

In addition, the behavior of the occupant OP changes the electrical characteristics of each detection set, and the electrical characteristics (time-series electrical resistance) reflect the seating state with water content. From the resistance value, it is possible to estimate the seating condition of the occupant with water content and adjust the occupant's environment. That is, even in various seating states with water content, the above-described learning model can be used to identify the seating state with water content of the occupant, estimate the seating state with water content of the occupant, and can be adjusted.

In the control device 1 using the learning model 51 learned by the learning process described above, by inputting the electrical characteristics of the conductive urethane 22 in the seated state with various unknown water content, the water content of the corresponding seated person is input. It was confirmed that the seating state can be estimated.

As described above, in the present disclosure, the case of applying conductive urethane as an example of the flexible member has been described, but the flexible member is of course not limited to conductive urethane.

Moreover, the technical scope of the present disclosure is not limited to the scope described in the above embodiments. Various changes or improvements can be made to the above-described embodiments without departing from the scope of the invention, and forms with such changes or improvements are also included in the technical scope of the present disclosure.

Further, in the above embodiment, the estimation process, the learning process, and the control process have been described as being realized by a software configuration based on processes using flowcharts, but the invention is not limited to this. It may be realized by a software configuration.

Also, part of the control device, for example, a neural network such as a learning model, may be configured as a hardware circuit.

1 Control device 2 Vehicle seat 3 Seated state with water content 4 Input data 5 Estimation unit 6 Output data 6A Generated output data 7 Control unit 8 Environment adjustment device 8A Temperature control device 8B Air conditioner 22 Conductive urethane 23 Conductive urethane piece 51 Learning model 52 Learning processing unit 54 Generator 56 Calculator 75 Detection point 76 Electrical characteristic detection unit

Claims (7)

A vehicle seat comprising a flexible material that is electrically conductive and whose electrical properties change in response to changes in applied pressure and moisture. a detection unit that detects
Data for learning includes time-series electrical characteristics when pressure and moisture are applied to the flexible material, and posture state information indicating a posture state of the occupant of the vehicle seat with water content that applies pressure and moisture to the flexible material. , the time-series electrical characteristics detected by the detection unit are input to a learning model that has been trained to output the posture state information using the time-series electrical characteristics as input. an estimating unit for estimating posture state information indicating a posture state of the occupant with hydration corresponding to time-series electrical characteristics;
A control unit that uses the posture state information estimated by the estimation unit to control an environment adjustment device including at least one of a temperature control device for the vehicle seat and an air conditioner for a vehicle interior in which the vehicle seat is installed. and,
Control device including. The vehicle seat includes at least one of a seat cushion, a seat back, a headrest, and an armrest,
The posture state includes a sitting state with the occupant moistened in the vehicle seat,
The control device according to claim 1, wherein the control unit controls the environment adjustment device using the posture state information estimated by the estimation unit. The control device according to claim 2, wherein the seated state in which the occupant is hydrated is a seated state in which the occupant sweats. the electrical property is volume resistance;
The flexible material is a urethane material having a structure having at least one of fibrous and mesh-like skeletons, or having a structure in which a plurality of fine air bubbles are scattered inside, and at least a portion of the urethane material being electrically conductive. 4. The control device according to any one of 1 to 3. The control according to any one of claims 1 to 4, wherein the learning model includes a model generated by learning using a network by reservoir computing using the flexible material as a reservoir. Device. A vehicle seat comprising a flexible material that is electrically conductive and whose electrical properties change in response to changes in applied pressure and moisture. Acquiring the electrical characteristics from a detection unit that detects characteristics,
Data for learning includes time-series electrical characteristics when pressure and moisture are applied to the flexible material, and posture state information indicating a posture state of the occupant of the vehicle seat with water content that applies pressure and moisture to the flexible material. , the time-series electrical characteristics detected by the detection unit are input to a learning model that has been trained to output the posture state information using the time-series electrical characteristics as input. estimating posture state information indicating the occupant's posture state with water content corresponding to the time-series electrical characteristics;
A control method for controlling an environment adjustment device including at least one of a temperature control device for the vehicle seat and an air conditioner for a vehicle interior in which the vehicle seat is installed, using the estimated posture state information. A vehicle seat comprising a flexible material that is electrically conductive and whose electrical properties change in response to changes in applied pressure and moisture. Acquiring the electrical characteristics from a detection unit that detects characteristics,
Data for learning includes time-series electrical characteristics when pressure and moisture are applied to the flexible material, and posture state information indicating a posture state of the occupant of the vehicle seat with water content that applies pressure and moisture to the flexible material. , the time-series electrical characteristics detected by the detection unit are input to a learning model that has been trained to output the posture state information using the time-series electrical characteristics as input. estimating posture state information indicating the occupant's posture state with water content corresponding to the time-series electrical characteristics;
using the estimated posture state information to control an environment adjustment device including at least one of a temperature control device for the vehicle seat and an air conditioner for a vehicle interior in which the vehicle seat is installed; control program.

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