Apparatus and method for controlling vehicle using motion sickness model

Abstract

An apparatus and method for controlling a vehicle using a motion sickness model are provided. The apparatus comprises at least one sensor to obtain status data of a user of the vehicle and data relating to motion of the user, and a processor to generate at least one motion sickness model based on previously obtained status data of the passenger and data relating to motion of the passenger, a motion sickness model corresponding to the state data of the user is selected from among the at least one motion sickness model, and a motion sickness index of the user is predicted by inputting data related to the motion of the user into the selected motion sickness model.

Description

[0001] Related Applications

[0002] This application claims the priority of Korean Patent Application No. 10-2023-0133517 filed in the Korean Intellectual Property Office on October 6, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to an apparatus and method for controlling a vehicle, and more particularly, to an apparatus and method for controlling a vehicle capable of controlling vehicle operation by reflecting a motion sickness index predicted by a motion sickness model. Background Art

[0004] During vehicle travel, passengers inside the vehicle may experience motion sickness due to the difference between the information about motion sensed by the passenger's vestibular organs and the passenger's visual information. In other words, motion sickness is usually caused by sensory error or sensory conflict because when the vehicle turns, the vestibular organs sense the turn, but the passenger's vision does not turn at the same time.

[0005] When the passenger inside the vehicle feels motion sickness due to the above reasons, the passenger feels uncomfortable. In severe cases, the passenger may want to get off the vehicle while it is running.

[0006] Recently, a technology for minimizing motion sickness of a passenger in order to improve the comfort of the passenger when the vehicle is moving has been developed. Therefore, a motion sickness model (MS model) has been developed to output a motion sickness index using the motion of the passenger's head.

[0007] However, the motion sickness index indicates the probability of motion sickness based on the behavior generated by the passenger repeating the same operation within a preset time period, which has limitations in terms of accuracy in expressing the degree of motion sickness of the passenger. Therefore, it is necessary to develop a technology that predicts the motion sickness index by more accurately expressing the degree of motion sickness of the passenger based on the passenger's behavior, thereby improving the comfort level of the passenger. Summary of the invention

[0008] An aspect of the present disclosure provides an apparatus and method for controlling a vehicle, which is capable of predicting a motion sickness index according to a user's motion to more accurately represent a degree of motion sickness of the user.

[0009] Another aspect of the present disclosure provides an apparatus and method for controlling a vehicle, which is capable of generating a motion sickness model for predicting a motion sickness index of a user based on a movement of a passenger and a Misery Scale (MISC) measured by the passenger.

[0010] Another aspect of the present disclosure provides an apparatus and method for controlling a vehicle, which is capable of predicting a user's motion sickness index by a motion sickness model generated based on a passenger's movement and a pain scale measured by the passenger, and controlling the operation of the vehicle based on the predicted motion sickness index to minimize the user's motion sickness.

[0011] As used herein, terms such as user, driver, and passenger refer to an occupant of a vehicle, and they may be used interchangeably to reflect motion sickness experienced by a person riding in a vehicle.

[0012] The technical problems to be solved by the present disclosure are not limited to the above-mentioned problems, and any other technical problems not mentioned herein will be clearly understood by those skilled in the art to which the present disclosure belongs from the following description.

[0013] According to one aspect of the present disclosure, an apparatus for controlling a vehicle may include: at least one sensor configured to obtain status data of a user of the vehicle and data related to the user's movement; and a processor configured to generate at least one motion sickness model based on previously obtained status data of a passenger and data related to the passenger's movement, select a motion sickness model corresponding to the user's status data from the at least one motion sickness model, and predict a motion sickness index of the passenger by inputting the data related to the user's movement into the selected motion sickness model.

[0014] Depending on the implementation, the sensor may obtain status data of the passenger and data related to the movement of the passenger.

[0015] Depending on the implementation, the status data may include seating position and gaze direction.

[0016] According to an embodiment, the processor may generate at least one motion sickness model based on the passenger's seating position and the passenger's gaze direction.

[0017] According to an embodiment, the processor may generate a motion sickness model for preprocessing data related to the movement of a passenger, inputting the preprocessing result into a conflict model, transforming a conflict vector output from the conflict model into a motion sickness severity, and calculating a motion sickness index of the passenger based on the motion sickness severity.

[0018] According to an embodiment, the processor may transform the conflict vector output from the conflict model into the motion sickness severity through a Hill function.

[0019] According to an embodiment, the processor may calculate the motion sickness index of the passenger using a cumulative function based on the motion sickness severity.

[0020] According to an embodiment, the processor may allow input of a pain scale corresponding to the degree of motion sickness perceived by the passenger to obtain the pain scale.

[0021] According to an embodiment, the processor may set the parameters included in the time accumulation function based on the pain scale input by the passenger.

[0022] According to an embodiment, the processor may control the operation of the vehicle based on the predicted motion sickness index.

[0023] The vehicle may include equipment for controlling the vehicle.

[0024] According to another aspect of the present disclosure, a method for controlling a vehicle may include: obtaining status data of a user of the vehicle and data related to the user's movements by at least one sensor; generating at least one motion sickness model based on previously obtained status data of a passenger and data related to the passenger's movements, selecting a motion sickness model corresponding to the user's status data, and predicting the user's motion sickness index by inputting data related to the user's movements into the selected motion sickness model.

[0025] According to an embodiment, the method may further comprise obtaining, by the at least one sensor, status data of the passenger and data related to the movement of the passenger.

[0026] Depending on the implementation, the status data may include seating position and gaze direction.

[0027] According to an embodiment, the method may further include generating at least one motion sickness model based on a seating position of the passenger and a gaze direction of the passenger.

[0028] According to an embodiment, the method may further include generating a motion sickness model for preprocessing data related to the passenger's motion, inputting the preprocessing result into a conflict model, transforming a conflict vector output from the conflict model into a motion sickness severity, and calculating a motion sickness index of the passenger based on the motion sickness severity.

[0029] According to an embodiment, the method may further include transforming the conflict vector output from the conflict model into the motion sickness severity by a Hill function.

[0030] According to an embodiment, the method may further include calculating a motion sickness index of the passenger using a cumulative function based on the motion sickness severity.

[0031] According to an embodiment, the method may further include allowing input of a distress level corresponding to a degree of motion sickness perceived by the passenger to obtain the distress level.

[0032] According to an embodiment, the method may further include setting parameters included in the time accumulation function based on a pain scale input by the passenger.

[0033] According to an embodiment, the method may further include controlling the operation of the vehicle based on the predicted motion sickness index. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description in conjunction with the accompanying drawings:

[0035] Figure 1 is a view showing a configuration of an apparatus for controlling a vehicle according to an embodiment of the present disclosure;

[0036] Figure 2 is a view schematically showing the position of a sensor according to an embodiment of the present disclosure;

[0037] Figure 3 is a view schematically showing an input device according to an embodiment of the present disclosure;

[0038] Figure 4 is a diagram showing a generated conflict model according to an embodiment of the present disclosure;

[0039] Figure 5 is a diagram schematically illustrating a method for predicting a motion sickness index based on a motion sickness model according to an embodiment of the present disclosure;

[0040] Figure 6 is a view schematically showing a method for generating a plurality of motion sickness models according to an embodiment of the present disclosure;

[0041] Figure 7 is a flowchart illustrating a method for controlling a vehicle according to an embodiment of the present disclosure; and

[0042] Figure 8 is a diagram showing a configuration of a computing system for executing a method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] It should be understood that the term "vehicle" or "vehicular" or other similar terms as used herein include a broad range of motor vehicles, such as passenger cars (including sport utility vehicles (SUVs), buses, trucks, various commercial vehicles), watercraft (including various boats and ships), aircraft, etc., and include hybrid vehicles, electric vehicles, plug-in hybrid electric vehicles, hydrogen-powered vehicles, and other alternative fuel vehicles (e.g., fuels derived from resources other than petroleum). As mentioned herein, a hybrid vehicle is a vehicle having two or more power sources, for example, gasoline-powered and electric-powered vehicles.

[0044] The terms used herein are only used for the purpose of describing specific embodiments and are not intended to limit the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular forms "one", "an" and "the" are intended to also include plural forms. It should also be understood that when the terms "comprises" and / or "comprising" are used in this specification, they specify the presence of the features, wholes, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, parts and / or combinations thereof. As used herein, the term "and / or" includes any and all combinations of one or more related listed items. Throughout the specification, unless explicitly described to the contrary, the word "comprise" and variants such as "comprises" or "comprising" will be understood to imply the inclusion of the elements, but do not exclude any other elements. In addition, the terms "unit", "... device", "... instrument" and "module" described in the specification mean a unit for processing at least one function and operation, and can be implemented by hardware components or software components and combinations thereof.

[0045] In addition, the control logic of the present disclosure may be embodied as a non-volatile computer-readable medium on a computer-readable medium containing executable program instructions executed by a processor, a controller, etc. Examples of computer-readable media include, but are not limited to, ROM, RAM, compact disk (CD)-ROM, magnetic tape, floppy disk, flash drive, smart card, and optical data storage device. The computer-readable medium may also be distributed in a network-coupled computer system so that the computer-readable medium is stored and executed in a distributed manner, such as by a telematics server or a controller area network (CAN).

[0046] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In addition, when reference numerals are added to the constituent parts of each drawing, the same reference numerals are also marked when the same or equivalent constituent parts are shown in other drawings. In addition, in the following description of the embodiments of the present disclosure, detailed descriptions of well-known features or functions will be excluded so as not to unnecessarily obscure the main purpose of the present disclosure.

[0047] When describing components according to embodiments of the present disclosure, terms such as first, second, "A", "B", "(a)", "(b)", etc. may be used. These terms are intended only to distinguish one component from another, and these terms do not limit the nature, order, or sequence of the constituent components. In addition, unless otherwise defined, all terms (including technical or scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs. Those terms as defined in commonly used dictionaries should be interpreted as having meanings equivalent to the contextual meanings in the relevant art, and should not be interpreted as having ideal or overly formal meanings unless expressly defined as having ideal or overly formal meanings in this application.

[0048] Figure 1 is a view showing a configuration of an apparatus for controlling a vehicle according to an embodiment of the present disclosure.

[0049] like Figure 1 As shown, the apparatus 100 for controlling a vehicle may include a sensor 110 , an input device 120 , an output device 130 , a memory 140 , and a processor 150 .

[0050] The sensor 110 may obtain the state data of the user and the data related to the movement of the user. In this case, the user may be a target of minimizing motion sickness by applying a motion sickness index predicted by a motion sickness model generated according to an embodiment of the present disclosure. Therefore, the sensor 110 may obtain the state data of the passenger and the data related to the movement of the passenger. In this case, the passenger may be a counterpart providing data for generating a motion sickness model for predicting a motion sickness index according to an embodiment of the present disclosure. The state data may include a seating position and a gaze direction. To this end, the sensor 110 may include a seat sensor for sensing the seating position of the user or passenger and an image sensor for obtaining a line of sight image. According to an embodiment, the image sensor may be included in a camera provided inside the vehicle.

[0051] According to an embodiment, the sensor 110 may include an acceleration sensor, a gyroscope sensor, an inertial navigation system (INS), an inertial measurement unit (IMU), an attitude reference system (ARS), or an attitude heading reference system (AHRS) attached to at least one specific position of the user or the passenger to obtain data related to the user's movement. The specific position of the user or the passenger may include the head, the chest, or the abdomen. The data related to the user's movement or the data related to the passenger's movement may include information about six degrees of freedom (6DOF), information about longitudinal acceleration, information about lateral acceleration, information about vertical acceleration, information about velocity, or information about a position related to the specific position of the user or the passenger. Reference will be made to Figure 2Information about a specific location to which the sensor 110 is attached and information about 6DOF are described in detail.

[0052] Figure 2 is a view schematically illustrating the position of a sensor according to an embodiment of the present disclosure.

[0053] like Figure 2 As shown, the 6DOF obtained by the sensor 110 may include information about the direction of motion relative to a specific position of a user or a passenger. In detail, the 6DOF may include X-axis data in the front-back direction of the user or the passenger, Y-axis data in the left-right direction of the user or the passenger, Z-axis data in the up-down direction of the user or the passenger, pitch data indicating a rotation degree based on the X-axis, roll data indicating a rotation degree based on the Y-axis, and yaw data indicating a rotation degree based on the Z-axis.

[0054] refer to Figure 1 The input device 120 may receive an input corresponding to a touch, motion, or voice of a user or a passenger and transmit the input to the processor 150. The processor 150 may control the operation of a device for controlling the vehicle to correspond to the input information. According to an embodiment, the input device 120 may include a touch input device or a mechanical input device. The details will be referred to in Figure 3 Provide explanation.

[0055] Figure 3 is a view schematically showing an input device according to an embodiment of the present disclosure.

[0056] like Figure 3 As shown, the input device 130 may be provided at a position where an input is made by a passenger sitting on a seat, and may be implemented using buttons marked with numerical values ​​ranging from "0" to "10". In this case, the numerical value of the input device 120 may represent a Misery Scale (MISC) indicating the degree of motion sickness felt by the passenger. A MISC of "0" indicates that the passenger does not feel motion sickness, and a MISC of "10" indicates that the passenger vomits due to severe motion sickness.

[0057] Return to reference Figure 1, the output device 130 may output an image or sound under the control of the processor 150. According to an embodiment, the output device 130 may output a guide message that allows the passenger to input an MSCI to indicate the degree of motion sickness perceived by the passenger. The output device 130 may be implemented using a display device or a sound output device. In this case, the display device may include a head-up display (HUD) or an instrument panel. According to an embodiment, the display device may be implemented using a display using a liquid crystal display (LCD) panel, a light emitting diode (LED) panel, an organic light emitting diode (OLED) panel, or a plasma display panel (PDP). The LCD may include a thin film transistor-LCD (TFT-LCD). The display device may be implemented integrally with the input device 120 through a touch screen panel (TSP).

[0058] The memory 140 may store at least one algorithm to calculate or execute various instructions for the operation of the vehicle control device according to the embodiment of the present disclosure. According to the embodiment, the memory 140 may store at least one instruction executed by the processor 150, and the instruction may allow the vehicle control device to operate according to the embodiment. The memory 140 may include at least one storage medium of a flash memory, a hard disk, a memory card, a read-only memory (ROM), a random access memory (RAM), an electrically erasable programmable ROM (EEPROM), a programmable ROM (PROM), a magnetic memory, a magnetic disk, or an optical disk.

[0059] The processor 150 may be implemented by various processing devices (such as a microprocessor embedded therein) having a semiconductor chip for operating or executing various instructions, and may control the vehicle control device according to the implementation. The processor 150 may be electrically connected to the sensor 110, the input device 120, the output device 130, and the memory 140 through a wired cable or various circuits to send an electrical signal including a control command for performing arithmetic operations or data processing related to control operations and / or communications. The processor 150 may include at least one of a central processing unit, an application processor, a communication processor (CP), or a combination thereof.

[0060] The processor 150 may generate at least one motion sickness model (MSD_Motion Sickness Impairment Model) based on the state data of the passenger obtained from the passenger and the data related to the movement of the passenger.

[0061] The processor 150 may obtain the state data of the passenger and the data related to the movement of the passenger from the sensor 110 to generate a motion sickness model. According to an embodiment, the processor 150 may obtain information about the movement direction of a specific position of the user or passenger. In detail, the information about the movement direction may include X-axis data in the front-back direction of the user or passenger, Y-axis data in the left-right direction of the user or passenger, Z-axis data in the up-down direction of the user or passenger, pitch data indicating the rotation degree based on the X-axis, roll data indicating the rotation degree based on the Y-axis, and yaw data indicating the rotation degree based on the Z-axis.

[0062] The processor 150 may select data necessary for measuring motion sickness of the passenger from among the data obtained from the sensor 110. According to an embodiment, the processor 150 may select data generated from the motion of the head of the user or the passenger through fast Fourier transform.

[0063] The processor 150 may pre-process the selected data. Thus, the processor 150 may remove the noise of the sensor 110 included in the data obtained from the sensor 110. According to an embodiment, the processor 150 may remove the noise of the selected data by filtering based on a filtering algorithm. According to an embodiment, the processor 150 may remove the noise of the selected data by a Kalman filter, a high-pass filter, or a low-pass filter.

[0064] The processor 150 may generate at least one motion sickness model (MSD_Motion Sickness Damage Model) based on the preprocessed data.

[0065] According to an embodiment, the processor 150 may generate a motion sickness model for inputting the preprocessed data into the conflict model, transforming the conflict vector output from the conflict model into the motion sickness severity, and calculating the motion sickness index based on the motion sickness severity. The details will be referred to in Figure 4 Give a description.

[0066] Figure 4 is a view showing a generated conflict model according to an embodiment of the present disclosure.

[0067] like Figure 4 As shown, the processor 150 may generate a conflict model by modeling the mismatch between the passenger motion sensed in the otoliths (OTO) and semicircular canals (SCC) of the passenger based on the vehicle motion and the internal model of the passenger. The processor 150 may input the gravitational acceleration (f), the angular velocity (ω) in the rolling direction, the pitch direction, and the yaw direction, and the pure acceleration (a) into the conflict model, and obtain a conflict vector output from the conflict model.

[0068] When the conflict vector is output from the conflict model, the processor 150 may model the motion sickness model to predict the motion sickness index of the passenger based on the conflict vector. Figure 5 Give a description.

[0069] Figure 5 Schematically illustrates a method for predicting a motion sickness index based on a motion sickness model according to an embodiment of the present disclosure.

[0070] like Figure 5 As shown, when the conflict vector is output from the conflict model, the processor 150 may transform the conflict vector into the severity of the disease. According to an embodiment, the processor 150 may transform the conflict vector (Δv) into the motion sickness severity (h) through a Hill function. In the Hill function, the parameter "b" is a tuning parameter for determining the form of the Hill function.

[0071] When the motion sickness severity is calculated by the Hill function, the processor 150 may calculate the motion sickness index using a cumulative function based on the motion sickness severity.

[0072] The processor 150 may allow input of the MISC corresponding to the degree of motion sickness perceived by the passenger through the input device 120 to obtain the MISC perceived by the passenger. The processor 150 may adjust the parameters included in the time accumulation function so that the motion sickness index calculated using the time accumulation function based on the severity of motion sickness conforms to the MISC obtained by the passenger. According to an embodiment, the processor 150 may set the parameter "τ" included in the time accumulation function to the time when the movement of the passenger is sensed, and may set the parameter "P" to the maximum value of the MISC input at the time when the movement of the passenger is sensed.

[0073] According to an embodiment, the processor 150 may generate at least one motion sickness model (or multiple motion sickness models) according to the passenger's seat position and the passenger's gaze direction. The details will be referred to in Figure 6 Give a description.

[0074] Figure 6 is a view schematically illustrating a method for generating a plurality of motion sickness models according to an embodiment of the present disclosure.

[0075] like Figure 6As shown, the processor 150 may divide the passenger's sitting position into a front seat position and a rear seat position, and generate a first motion sickness model and a second motion sickness model according to a situation where the passenger sits in the front seat and the passenger's gaze direction is toward the interior (a state where the passenger's sight is focused on a specific thing inside, for example, a situation where the passenger is watching a video; there is a task in the front seat), and a situation where the passenger sits in the front seat and the passenger's gaze direction is toward the exterior (a state where the passenger is staring at the front of the vehicle, in a comfortable state, and there is no task in the front seat). In addition, the processor 150 may generate a third motion sickness model and a fourth motion sickness model according to a situation where the passenger sits in the rear seat and the passenger's gaze direction is toward the interior (there is a task in the rear seat), and a situation where the passenger sits in the rear seat and the passenger's gaze direction is toward the exterior (there is no task in the rear seat).

[0076] The processor 150 may obtain state data of a target to be predicted on the motion sickness index, data related to the motion of the target, that is, state data of the user and data related to the motion of the user. According to an embodiment, the processor 150 may obtain a user's seat position and gaze direction.

[0077] The processor 150 may select a motion sickness model corresponding to the state data (seating position and gaze direction) of the user from among a plurality of motion sickness models.

[0078] For example, when the user sits in the back seat and the user's gaze direction is toward the inside, the processor 150 may select a third motion sickness model from among the plurality of motion sickness models previously generated.

[0079] When a motion sickness model corresponding to a user's sitting position and gaze direction is selected, the processor 150 may predict a motion sickness index of the user by inputting data related to the user's motion into the selected motion sickness model.

[0080] When the motion sickness index of the user is predicted, the processor 150 may control the operation of the vehicle based on the predicted motion sickness index.

[0081] According to an embodiment, the processor 150 may control the driving of the vehicle or output a guidance message through the output device 130 so that the user's motion sickness is minimized based on the predicted motion sickness index. Therefore, the processor 150 may minimize the user's discomfort during vehicle driving by minimizing the user's motion sickness based on the predicted motion sickness index of the user.

[0082] Figure 7 is a flowchart illustrating a method for controlling a vehicle according to an embodiment of the present disclosure.

[0083] like Figure 7As shown, the processor 150 may obtain the passenger's state data and data related to the passenger's movement from the sensor 110 to generate a motion sickness model.

[0084] According to an embodiment, in S110, the processor 150 may obtain information about the movement direction of a specific position of a user or a passenger. In detail, the information about the movement direction may include X-axis data in the front-back direction of the user or the passenger, Y-axis data in the left-right direction of the user or the passenger, Z-axis data in the up-down direction of the user or the passenger, pitch data indicating a rotation degree based on the X-axis, roll data indicating a rotation degree based on the Y-axis, and yaw data indicating a rotation degree based on the Z-axis.

[0085] The processor 150 may select data necessary for measuring motion sickness of the passenger among the data obtained from the sensor 110 ( S120 ).

[0086] According to an embodiment, in S120 , the processor 150 may select data generated by the movement of the head of the user or the passenger through fast Fourier transform.

[0087] The processor 150 may pre-process the selected data (S130). In S130, the processor 150 may remove the noise of the sensor 110 included in the data obtained from the sensor 110. According to an embodiment, the processor 150 may remove the noise of the selected data by filtering based on a filtering algorithm. According to an embodiment, the processor 150 may remove the noise of the selected data by a Kalman filter, a high pass filter, or a low pass filter.

[0088] The processor 150 may generate at least one motion sickness model (MSD_motion sickness injury model) based on the preprocessed data ( S140 ).

[0089] According to an embodiment, in S140, the processor 150 may generate a motion sickness model for inputting the preprocessed data into the conflict model, transforming the conflict vector output from the conflict model into the motion sickness severity, and calculating the motion sickness index based on the motion sickness severity.

[0090] According to an embodiment, the processor 150 may generate a conflict model by modeling the mismatch between the passenger motion sensed in the otolith (OTO) and semicircular canal (SCC) of the passenger based on the vehicle motion and the internal model of the passenger. The processor 150 may input the gravitational acceleration (f), the angular velocity (ω) in the rolling direction, the pitch direction, and the yaw direction, and the pure acceleration (a) into the conflict model, and obtain a conflict vector output from the conflict model.

[0091] When the conflict vector is output from the conflict model, the processor 150 may model a motion sickness model for predicting a motion sickness index of a passenger based on the conflict vector.

[0092] According to an embodiment, when the conflict vector is output from the conflict model, the processor 150 may transform the conflict vector into the severity of the disease. According to an embodiment, the processor 150 may transform the conflict vector (Δv) into the motion sickness severity (h) through a Hill function. In the Hill function, the parameter "b" is a tuning parameter for determining the form of the Hill function.

[0093] When the motion sickness severity is calculated by the Hill function, the processor 150 may calculate the motion sickness index using a cumulative function based on the motion sickness severity.

[0094] The processor 150 may allow input corresponding to the degree of motion sickness MISC perceived by the passenger through the input device 120 to obtain the MISC perceived by the passenger. The processor 150 may adjust the parameters included in the time accumulation function so that the motion sickness index calculated using the time accumulation function based on the severity of motion sickness conforms to the MISC obtained by the passenger. According to an embodiment, the processor 150 may set the parameter "τ" included in the time accumulation function to the time when the movement of the passenger is sensed, and may set the parameter "P" to the maximum value of the MISC input at the time when the movement of the passenger is sensed.

[0095] The processor 150 may generate at least one motion sickness model (a plurality of motion sickness models) according to the seating position of the passenger and the gaze direction of the passenger.

[0096] According to an embodiment, the processor 150 may divide the passenger's seat position into a front seat position and a rear seat position, and generate a first motion sickness model and a second motion sickness model according to a situation where the passenger sits in the front seat and the passenger's gaze direction is toward the interior (a state where the passenger's sight is focused on a specific thing inside, for example, a situation where the passenger is watching a video; there is a task in the front seat), and a situation where the passenger sits in the front seat and the passenger's gaze direction is toward the exterior (a state where the passenger is staring at the front, in a comfortable state, and there is no task in the front seat). In addition, the processor 150 may generate a third motion sickness model and a fourth motion sickness model according to a situation where the passenger sits in the rear seat and the passenger's gaze direction is toward the interior (there is a task in the rear seat), and a situation where the passenger sits in the rear seat and the passenger's gaze direction is toward the exterior (there is no task in the rear seat).

[0097] The processor 150 may obtain state data of a target to be predicted on a motion sickness index, data related to a motion of the target, that is, may obtain state data of a user and data related to a motion of the user ( S150 ).

[0098] According to an embodiment, in S150 , the processor 150 may obtain a sitting position and a gaze direction of a user.

[0099] The processor 150 may select a motion sickness model corresponding to the state data (seating position and gaze direction) of the user (S 160 ).

[0100] In S160 , for example, when the user sits in the back seat and the gaze direction of the user is toward the inside, the processor 150 may select a third motion sickness model from among a plurality of previously generated motion sickness models.

[0101] When the motion sickness model corresponding to the sitting position and gaze direction of the user is selected, the processor 150 may predict the motion sickness index of the user by inputting data related to the movement of the user into the selected motion sickness model ( S170 ).

[0102] When the motion sickness index of the user is predicted, the processor 150 may control the operation of the vehicle based on the predicted motion sickness index ( S180 ).

[0103] According to an embodiment, in S180, the processor 150 may control the driving of the vehicle or output a guidance message through the output device 130 so that the user's motion sickness is minimized based on the predicted motion sickness index. Therefore, the processor 150 may minimize the user's discomfort during vehicle driving by minimizing the user's motion sickness based on the predicted motion sickness index of the user.

[0104] Figure 8 is a diagram showing a configuration of a computing system for executing a method according to an embodiment of the present disclosure.

[0105] refer to Figure 8 , the computing system 1000 may include at least one processor 1100 , a memory 1300 , a user interface input device 1400 , a user interface output device 1500 , a storage device 1600 , and a network interface 1700 connected to each other via a bus 1200 .

[0106] The processor 1100 may be a central processing unit (CPU) or a semiconductor device for processing instructions stored in the memory 1300 and / or the storage device 1600. Each of the memory 1300 and the storage device 1600 may include various types of volatile or non-volatile storage media. For example, the memory 1300 may include a read-only ROM 1310 and a RAM 1320.

[0107] Therefore, the operation of the method or algorithm described in conjunction with the embodiments disclosed in the present disclosure may be directly implemented using a hardware module, a software module, or a combination thereof executed by the processor 1100. The software module may reside on a storage medium (i.e., the memory 1300 and / or the storage device 1600), such as a RAM, a flash memory, a ROM, an erasable and programmable ROM (EPROM), an electrical EPROM (EEPROM), a register, a hard disk, a removable disk, or a compact disk ROM (CD-ROM). An exemplary storage medium may be coupled to the processor 1100. The processor 1100 may read information from the storage medium and may write information to the storage medium. Alternatively, the storage medium may be integrated with the processor 1100. The processor and the storage medium may be present in an application specific integrated circuit (ASIC). The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as separate components of the user terminal.

[0108] According to an embodiment of the present disclosure, in an apparatus and method for controlling a vehicle, a motion sickness index accurately showing a user's motion sickness degree may be predicted according to the user's motion.

[0109] According to an embodiment of the present disclosure, in an apparatus and method for controlling a vehicle, a motion sickness model for predicting a motion sickness index of a user may be generated based on a passenger's motion and a pain scale (MISC) measured by the passenger.

[0110] According to an embodiment of the present disclosure, in an apparatus and method for controlling a vehicle, a user's motion sickness index can be predicted by a motion sickness model generated based on a passenger's movement and a pain scale measured by the passenger, and the operation of the vehicle can be controlled based on the predicted motion sickness index to minimize the user's motion sickness.

[0111] The above description is merely an embodiment of the technical concept of the present disclosure, and various modifications and changes may be made by those skilled in the art without departing from the essential features of the present invention.

[0112] Therefore, the exemplary embodiments of the present disclosure are provided to explain the spirit and scope of the present disclosure, but not to limit them, so that the spirit and scope of the present disclosure are not limited by the embodiments. The scope of the present disclosure should be interpreted based on the attached claims, and all technical concepts within the scope equivalent to the claims should be included in the scope of the present disclosure.

[0113] Although the present disclosure has been described above with reference to exemplary embodiments and the accompanying drawings, the present disclosure is not limited thereto but various modifications and changes may be made by those skilled in the art without departing from the spirit and scope of the present disclosure as claimed in the appended claims.

Claims

1. A device for controlling a vehicle, the device comprising: at least one sensor configured to obtain status data of a user of the vehicle and data related to the movement of the user; as well as A processor is configured to generate at least one motion sickness model based on previously obtained state data of a passenger and data related to the movement of the passenger, select a motion sickness model corresponding to the state data of the user from the at least one motion sickness model, and predict a motion sickness index of the user by inputting the data related to the movement of the user into the selected motion sickness model.

2. The device according to claim 1, wherein: The sensors obtain status data of the passenger and data related to the movement of the passenger.

3. The device according to claim 1, wherein: The status data includes: Seating position and gaze direction.

4. The device according to claim 1, wherein: The processor is configured to: The at least one motion sickness model is generated based on a seating position of the passenger and a gaze direction of the passenger.

5. The device according to claim 1, wherein: The processor is configured to: A motion sickness model is generated for preprocessing data related to the motion of a passenger, inputting the preprocessing result into a conflict model, transforming a conflict vector output from the conflict model into a motion sickness severity, and calculating a motion sickness index of the passenger based on the motion sickness severity.

6. The device according to claim 5, wherein: The processor is configured to: The conflict vector output from the conflict model is transformed into the motion sickness severity by a Hill function.

7. The device according to claim 5, wherein: The processor is configured to: Based on the motion sickness severity, a cumulative function is used to calculate the motion sickness index of the passenger.

8. The device according to claim 7, wherein: The processor is configured to: allowing input of a pain scale corresponding to a degree of motion sickness perceived by the passenger to obtain the pain scale; as well as Parameters included in a time accumulation function are set based on the pain scale input by the passenger.

9. The device according to claim 1, wherein: The processor is configured to: Operation of the vehicle is controlled based on the predicted motion sickness index.

10. A vehicle comprising the apparatus according to claim 1.

11. A method for controlling a vehicle, the method comprising: obtaining, by at least one sensor, status data of a user of the vehicle and data related to the movement of the user; generating at least one motion sickness model based on previously acquired status data of a passenger and data related to movement of the passenger; selecting a motion sickness model corresponding to the state data of the user from the at least one motion sickness model; as well as A motion sickness index of the user is predicted by inputting data related to the motion of the user into a selected motion sickness model.

12. The method according to claim 11, further comprising: The status data of the passenger and the data related to the movement of the passenger are obtained by the at least one sensor.

13. The method according to claim 11, wherein: The status data includes seating position and gaze direction.

14. The method according to claim 11, further comprising: The at least one motion sickness model is generated based on a seating position of the passenger and a gaze direction of the passenger.

15. The method according to claim 11, further comprising: A motion sickness model is generated for preprocessing data related to the motion of the passenger, inputting the preprocessing result into a conflict model, transforming a conflict vector output from the conflict model into a motion sickness severity, and calculating a motion sickness index of the passenger based on the motion sickness severity.

16. The method according to claim 15, further comprising: The conflict vector output from the conflict model is transformed into the motion sickness severity by a Hill function.

17. The method according to claim 15, further comprising: Based on the motion sickness severity, a cumulative function is used to calculate the motion sickness index of the passenger.

18. The method according to claim 17, further comprising: An input of a pain scale corresponding to a degree of motion sickness perceived by the passenger is allowed to obtain the pain scale.

19. The method according to claim 18, further comprising: Parameters included in a time accumulation function are set based on the pain scale input by the passenger.

20. The method according to claim 11, further comprising: Operation of the vehicle is controlled based on the predicted motion sickness index.

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