Information processing device, information processing method, and information processing system

Abstract

The present disclosure relates to an information processing device, an information processing method, and an information processing system capable of estimating a future motion sickness state of a passenger. A cumulative factor estimation unit estimates future changes in cumulative factors for motion sickness of a passenger of a mobile body on the basis of route information of the mobile body and changes in the cumulative factors for motion sickness during past movements of the mobile body, and a future index value calculation unit calculates an index value for the future motion sickness of the passenger on the basis of the future changes in the cumulative factors. The invention according to the present disclosure can be applied to, for example, a device for processing information about the motion sickness of a passenger of a moving body.

Description

Information processing device, information processing method, and information processing system

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing system, and more particularly to an information processing device, an information processing method, and an information processing system that enable estimation of a passenger's future motion sickness state.

[0002] Motion sickness is likely to occur during long periods of time on board moving vehicles such as cars and ships, disrupting the travel and riding experience. With the development of autonomous driving technology, it is conceivable that drivers may also develop motion sickness. Furthermore, with changes in the way people spend time in vehicles and the entertainment provided, such as viewing video content in vehicles, it will become necessary to address motion sickness.

[0003] Most attempts to detect motion sickness have been made using a medical approach, and the use of biometric data sensing technology has been considered. However, because motion sickness is a factor that depends on the individual, it is difficult to accurately sense biological reactions and understand the symptoms. Furthermore, biometric data sensing can only detect motion sickness after symptoms appear, so it has not been possible to know the state of motion sickness before symptoms appear.

[0004] Meanwhile, there is known a technique for determining whether or not a passenger on a moving object is likely to develop motion sickness on the route of the moving object (see Patent Documents 1 and 2).

[0005] JP 2019-95257 A JP 2004-301692 A

[0006] However, it has not been possible to estimate the passenger's future motion sickness state in real time while the vehicle is moving.

[0007] The present disclosure has been made in light of such circumstances, and makes it possible to estimate a passenger's future motion sickness state.

[0008] The information processing device of the present disclosure is an information processing device that includes an accumulation element estimation unit that estimates future changes in the accumulation element of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation element of motion sickness of the passenger of the moving body during past movements of the moving body, and a future index value calculation unit that calculates a future index value of motion sickness of the passenger based on future changes in the accumulation element.

[0009] The information processing method disclosed herein includes estimating future changes in the accumulation factor of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation factor of motion sickness of the passenger of the moving body during past movements of the moving body, and calculating an index value of future motion sickness of the passenger based on the future changes in the accumulation factor.

[0010] The information processing system of the present disclosure is an information processing system that includes an accumulation element estimation unit that estimates future changes in the accumulation element of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation element of motion sickness of the passenger of the moving body during past movements of the moving body, and a future index value calculation unit that calculates a future index value of motion sickness of the passenger based on the future changes in the accumulation element.

[0011] In the present disclosure, future changes in the accumulation factor of motion sickness of a passenger of a moving body are estimated based on route information of the moving body and changes in the accumulation factor of motion sickness of the passenger of the moving body during past movements of the moving body, and an index value of future motion sickness of the passenger is calculated based on the future changes in the accumulation factor.

[0012] 1 is a diagram showing a filter transfer function for converting acceleration into a weighted effective acceleration. FIG. 1 is a block diagram showing an example configuration of an information processing system for estimating motion sickness. FIG. 1 is a block diagram showing an example functional configuration of a current motion sickness index value calculation unit. FIG. 2 is a flowchart showing the processing flow of the current motion sickness index value calculation unit. FIG. 2 is a block diagram showing an example functional configuration of a future motion sickness index value calculation unit. FIG. 3 is a flowchart showing the processing flow of the future motion sickness index value calculation unit. FIG. 3 is a block diagram showing another example functional configuration of the future motion sickness index value calculation unit. FIG. 4 is a flowchart showing the processing flow of the future motion sickness index value calculation unit. FIG. 4 is a block diagram showing an example functional configuration of a visual information influence degree calculation unit. FIG. 5 is a diagram illustrating an exterior camera and an interior camera. FIG. 6 is a flowchart showing the processing flow of the visual information influence degree calculation unit. FIG. 6 is a flowchart showing a specific example of visual information acquisition processing. FIG. 7 is a diagram illustrating changes in pixel information and the effect on motion sickness. FIG. 7 is a block diagram showing an example functional configuration of a motion sickness onset time calculation unit. FIG. 8 is a flowchart showing the processing flow of the motion sickness onset time calculation unit. FIG. 8 is a diagram showing an example hardware configuration of a computer.

[0013] Modes for carrying out the present disclosure (hereinafter referred to as embodiments) will be described below in the following order.

[0014] 1. Overview 2. Method for calculating motion sickness index 3. Elements of motion sickness recovery 4. System configuration and operation 4-1. Configuration and operation of current motion sickness index value calculation unit 4-2. Configuration and operation of future motion sickness index value calculation unit 4-3. Configuration and operation of visual information influence calculation unit 4-4. Configuration and operation of motion sickness onset time calculation unit 5. Other 6. Example of computer hardware configuration

[0015] <1. Overview> When a passenger boards a vehicle such as a car, the vehicle's behavior can affect the passenger's motion sickness symptoms. When motion sickness occurs, the passenger finds it difficult to continue riding the vehicle, causing significant inconvenience. This disclosure relates to motion sickness prediction, which predicts a passenger's motion sickness level before the onset of motion sickness, leading to appropriate processing such as controlling vehicle motion (such as driving instructions) or notifying the passenger or driver.

[0016] Regarding motion sickness, methods have been used to calculate a motion sickness index by integrating the head movement of a passenger over time, as disclosed in Japanese Patent Application Laid-Open No. 2007-236644 and Japanese Patent Application Laid-Open No. 2004-286502. However, actual experimental evaluations have confirmed that even if the time integral value of head movement is the same, the symptoms of motion sickness vary depending on the driving style of the vehicle (i.e., motion sickness symptoms may or may not occur). This experimental evaluation suggests that when head movement is minimal while riding in a vehicle, there is a tendency for recovery from motion sickness. The tendency for recovery from motion sickness can also be generally explained by the empirical formula. Therefore, this disclosure proposes a technology for calculating a motion sickness index taking into account factors for recovery from motion sickness.

[0017] Furthermore, when predicting motion sickness based on the head movement of a passenger, it is difficult to directly detect the head movement, and there are problems with detection accuracy. Therefore, in this disclosure, in consideration of the difficulty of directly detecting head movement, a technology is also proposed for calculating the head movement of a passenger from the movement of the vehicle through simulation. For example, the behavior of the vehicle can be accurately and easily detected based on an on-board sensor such as an acceleration sensor, and the head movement can be calculated through simulation prediction based on physical information such as the passenger's weight and sitting height.

[0018] 2. Calculation method of motion sickness index ISO 2631 describes a calculation method of MSDV (Motion Sickness Dose Value) as one of the discrimination indexes of motion sickness. According to this description, the discrimination index of motion sickness MSDV is calculated using the following formula (1):

[0019]

[0020] In formula (1), a ｗ (t) is the weighted rms acceleration of the vehicle at time t. ｗ(t) is the acceleration obtained by weighting the vehicle acceleration a(t) according to frequency. As shown in FIG. 1, the weighted effective acceleration a ｗ (t) can be obtained by first converting the acceleration a(t) in the time domain into the frequency domain, multiplying it by the filter transfer function H(s) (where s = jω = j2πf), and then converting it back into the time domain. However, the filter transfer function H(s) differs depending on the movement (type of vehicle, etc.). For this reason, ISO 2631 requires a coefficient f ｉ (=ωi) and Q ｉ The filter characteristics are changed by changing

[0021] As described above, in the present disclosure, in consideration of the difficulty of accurately detecting the head movement of a passenger, the head movement of the passenger is calculated from the movement of the vehicle by simulation calculation. Specifically, a configuration is adopted in which a sensor such as an acceleration sensor is attached to a rigid body portion of the vehicle body, such as a car. Furthermore, a camera (such as an in-vehicle monitoring camera) and a body pressure sensor are mounted on the vehicle to detect the passenger's attribute data (physical information such as sitting height h and upper body weight w). The passenger's head acceleration calculated by the simulation calculation is weighted according to frequency, the traveling time of the vehicle is detected, and the passenger's motion sickness (more specifically, the accumulation factor of motion sickness) is integrated using equation (1) described in ISO 2631.

[0022] The head movement of the passenger can be obtained by either of the following methods (a) or (b) in addition to the above simulation calculation.

[0023] (a) Detecting actual head movement using the passenger's image captured by the camera. (b) Estimating head movement from the vehicle's travel route and the actual detected camera movement.

[0024] Furthermore, while the vehicle is traveling, the passenger's recovery from motion sickness also occurs over time. The occurrence of recovery from motion sickness is approximated by a linear function based on experimental results. Therefore, in the present disclosure, motion sickness prediction is performed taking into account both the accumulation factor and the recovery factor of motion sickness based on experimental results.

[0025] The MSDV specified in ISO is an index of motion sickness calculated by integrating head movement over time and can be considered a cumulative factor in motion sickness. On the other hand, the Simulator Sickness Questionnaire (SSQ) is known as a subjective assessment of motion sickness susceptibility. The SSQ is widely used as a questionnaire to measure the degree of motion sickness. Specifically, the SSQ calculates and assesses four items: Nausea (nausea), Oculomotor (eye fatigue), Disorientation (unsteadiness), and Total Score (overall assessment) by weighting the raw scores obtained from answers to 16 questions on a four-point scale from 0 to 3. ISO 2631 clearly states that there is a certain correlation between the MSDV and the subjectively assessed motion sickness susceptibility SSQ. That is, with the horizontal axis representing MSDV and the vertical axis representing SSQ, a certain relationship holds: a higher MSDV corresponds to a higher SSQ, indicating a higher susceptibility to motion sickness.

[0026] For example, even when a vehicle is driven normally (in the case of gentle driving), MSDV increases over time because it is the time integral of head movement. In contrast, SSQ does not increase because motion sickness improves over time during normal driving. On the other hand, when a vehicle is accelerated or decelerated (in the case of vigorous driving), MSDV, which is the time integral of head movement, increases, and SSQ also increases over time because motion sickness does not improve.

[0027] Since there is no (or little) recovery from motion sickness during acceleration / deceleration (in the case of vigorous driving), the relationship SSQ = α MSDV holds (where α is the correlation coefficient between MSDV and SSQ). Also, since there is recovery from motion sickness (R t) over time t during normal driving (in the case of gentle driving), the relationship SSQ = α MSDV - R t holds. Depending on how the vehicle is driven, the relationship between MSDV and SSQ holds between SSQ = α MSDV and SSQ = α MSDV - R t.

[0028] Motion sickness can be predicted based on the idea that motion sickness will not occur as long as SSQ does not exceed a certain threshold (limit). When driving in a way that avoids motion sickness, MSDV and SSQ are distributed within a range that does not exceed the threshold. MSDV can be calculated based on the driving of the vehicle. Therefore, motion sickness can be predicted based on whether SSQ exceeds the threshold from MSDV calculated based on data measured by an on-board acceleration sensor.

[0029] Furthermore, when we checked how the relationship between a person's susceptibility to motion sickness (Motion Sickness Susceptibility Questionnaire: MSSQ) and SSQ changed over time, we found that there was a correlation between susceptibility and SSQ for 10 minutes or more, but no correlation for less than 10 minutes. Therefore, it can be said that measurements for 10 minutes or more are necessary or strongly desirable to improve the accuracy of motion sickness prediction.

[0030] Then, when a passenger's motion sickness is predicted through simulation calculation of the passenger's head movement and calculation of MSDV while taking into account recovery factors, it is possible to take appropriate measures such as controlling vehicle movement (driving instructions, etc.) and notifying the passenger or driver.

[0031] The following three examples can be given as methods for issuing instructions to a vehicle based on predictions of motion sickness.

[0032] (1) In the case of manual driving: When the SSQ threshold is approached, the driver is notified that the passengers are prone to motion sickness. For example, to reduce the SSQ, voice guidance such as "reduce speed changes" or "reduce sudden steering" may be used, or a guidance message may be displayed on a display screen on the instrument panel. Furthermore, voice guidance or route changes may be performed in conjunction with route information from a car navigation system. The driver may also be notified via a UI displayed in an easily visible location, such as a head-up display.

[0033] (2) In the case of autonomous driving: When the SSQ threshold is approached, autonomous driving will be linked to motion sickness prediction by limiting driving speed and acceleration, leading to a reduction in SSQ. In some cases, route changes will be made (to a flatter route with fewer curves, which will reduce SSQ). Furthermore, motion sickness will be predicted based on congestion conditions and fed back to the current driving.

[0034] (3) Occupant Visibility Control Increasing visibility of the outside world through vehicle windows or other means can help occupants recognize vehicle behavior. Therefore, occupant visibility control may be initiated when the SSQ threshold is approached. For example, a display system that displays content video on a screen with adjustable transmittance is installed inside the vehicle cabin. If the SSQ threshold is approached while the occupant is viewing the video, the screen's transmittance may be increased, or the content video display may be reduced, moved to the periphery of the screen, or the contrast may be reduced to increase transparency. While such visibility control reduces the visibility of the content, it allows occupants to more easily view the outside of the vehicle and recognize the vehicle's behavior, thereby reducing SSQ. Note that a display system with an adjustable transmittance screen may be, for example, a combination of a projector and a transparent screen, or may be configured with a display device having a transparent FPD (Flat Panel Display) such as a transparent OLED (Organic Light-Emitting Diode) or a transparent LCD (Liquid Crystal Display). The screen of the display system may be placed on the front, rear, left and right windows or walls of the vehicle cabin, or may be a projection screen or display monitor that flips down from the ceiling of the vehicle cabin.

[0035] 3. Motion Sickness Recovery Factors As described above, in the present disclosure, motion sickness prediction is performed taking into account both the accumulation factor and recovery factor of motion sickness. Here, a use case in which the motion sickness recovery factor is considered to be important will be described.

[0036] (1) When transitioning from city driving to highway driving City driving, which involves frequent stop-and-go traffic at traffic lights and frequent right and left turns at intersections, involves a lot of front-to-back acceleration and deceleration, as well as left-to-right movement. Driving then switches to highway driving, where the vehicle maintains a nearly constant speed and requires less steering. Driving in city driving involves more head movement, which can lead to the accumulation of motion sickness. On the other hand, driving on highways involves less head movement due to smaller changes in speed and course, which reduces motion sickness in passengers. Furthermore, on highways, the vehicle switches to automatic driving, allowing passengers to relax, thereby reducing motion sickness.

[0037] Conventional methods that calculate an index of motion sickness by integrating the time of a passenger's head movement only take into account the accumulation factor of motion sickness, and therefore predict that a passenger is sick even if they are relaxed on a highway. As a result, there is a risk that the vehicle's speed, acceleration, steering, etc. will be unnecessarily restricted, or that unnecessary route changes will be made. In contrast, the present disclosure predicts motion sickness by taking into account both the accumulation factor and recovery factor of motion sickness, thereby enabling more accurate prediction of motion sickness by taking into account the passenger's tendency to relax on a highway.

[0038] For example, by analyzing GPS signals received from GPS satellites to determine the current position of the vehicle and then detecting map information of the planned route of the vehicle, it is possible to determine whether the vehicle is traveling in an urban area or on a highway. It is also possible to determine whether the vehicle is traveling in an urban area or on a highway by performing image recognition or image analysis on images captured by an on-board camera.

[0039] (2) In the case of an autonomously driven vehicle at an extremely low speed, such as a small, single-seater vehicle, the vehicle is generally driven gently, so motion sickness rarely occurs. However, if a motion sickness index is calculated by integrating head movement over time as in the past, motion sickness will inevitably be predicted after a certain period of time has passed. In contrast, the present disclosure predicts motion sickness while also taking recovery factors into account, so that motion sickness can be predicted more accurately by taking into account the relaxed driving style that the passenger experiences.

[0040] (3) Changes in the degree of recovery from motion sickness depending on the passenger seat, second row, third row, and seat orientation In vehicles with three or more rows of seats, the visibility of the area in front of the vehicle varies depending on the row in which the passenger sits, i.e., the seat position. Furthermore, the up / down and left / right movement of the seat surface while driving is not uniform, and head movement varies depending on the position in which the passenger sits. Therefore, it can be said that the degree of recovery from motion sickness varies depending on the position in which the passenger sits, even within the same vehicle.

[0041] For example, the front passenger seat has a wide view ahead, making it easier to recognize the vehicle's behavior, and is therefore highly effective in relieving motion sickness. On the other hand, the further back you are in the seat, the less information you have in the field of view, making it more difficult to recognize the vehicle's behavior, and so the effect of relieving motion sickness is reduced accordingly.

[0042] Therefore, the position of the passenger is detected using an in-vehicle camera or body pressure sensor, etc., to determine which row the passenger is sitting in. Taking into consideration that the effect of recovering from motion sickness is reduced as the passenger sits further back in the seat, such as in the second or third row, compared to the first row, a weighting calculation may be performed on the recovery factors according to the passenger's seating position to calculate a motion sickness prediction for the passenger's seating position. Furthermore, if the passenger is seated facing backwards in a face-to-face seat, for example, the passenger loses visibility information in front of the vehicle, making it difficult to recognize the vehicle's behavior, making it difficult to recover from motion sickness. Therefore, a weighting calculation may be performed on the recovery factors taking into account the passenger's posture, such as the orientation of the seat, in addition to the passenger's seating position, to more accurately calculate a motion sickness prediction.

[0043] (4) Changes in the degree of recovery from motion sickness due to content display control When a display system that displays content video on a screen with adjustable transmittance is installed inside the vehicle and passengers watch the video, the visibility information that can be obtained through the screen changes depending on the screen's transparency and the display size and display position of the content video, which affects the degree of recovery from motion sickness. For example, if the screen's transmittance is high, the content video is small, or the content video is displayed off-center on the screen, the passenger's field of vision is opened up and it becomes easier to recognize the vehicle's behavior, which is highly effective in recovering from motion sickness.

[0044] Therefore, the display system can be queried to obtain the screen transparency and the display size and display position of the content video to grasp the passengers' field of view information, and a weighting calculation can be performed on the recovery factors based on the ease of recognizing the vehicle's behavior, thereby calculating a motion sickness prediction according to the display status of the content video.

[0045] 4. System Configuration and Operation The technology disclosed herein is intended to predict and notify a passenger's level of motion sickness before the onset of motion sickness, and in addition to predicting the current state of motion sickness by taking into account head movement and recovery factors, it also predicts how much time it will take for motion sickness to develop, taking into account the route from the current location and the impact on motion sickness of changes in visibility information outside the vehicle.

[0046] Here, a system configuration and operation when the technology according to the present disclosure is applied to a vehicle as an example of a moving body will be described.

[0047] FIG. 1 is a block diagram showing an example of the configuration of an information processing system 1 that estimates motion sickness in a vehicle passenger.

[0048] The information processing system 1 shown in FIG. 1 is configured to include a current motion sickness index value calculation unit 10, a future motion sickness index value calculation unit 20, a visual information influence calculation unit 30, a motion sickness level calculation unit 40, a motion sickness onset time calculation unit 50, and a motion sickness notification device 60.

[0049] The information processing system 1 can be configured as a single device in which all of these functional blocks are physically housed in a single housing, or each functional block can be configured as a separate device. The functional blocks can also be divided into two or more groups, with each group configured as a device. Furthermore, some of the functional blocks can be mounted on the vehicle, while other parts of the functional blocks can be configured as devices outside the vehicle.

[0050] The information processing system 1 can be configured with an information processing device such as a PC (Personal Computer). Each functional block constituting the information processing system 1 may be a dedicated hardware circuit, or may be realized by executing a predetermined software program on the PC. Furthermore, each functional block may be implemented on a single PC, or may be distributed across two or more PCs.

[0051] The current motion sickness index value calculation unit 10 calculates the passenger's current motion sickness index value (hereinafter also referred to as the current index value) using the passenger's motion sickness accumulation factor calculated based on the passenger's head movement and the vehicle's traveling time, and the passenger's motion sickness recovery factor calculated based on the passenger's attribute data and the vehicle's traveling time. The accumulation factor and recovery factor used to calculate the current index value are associated with the vehicle's traveling history and stored in a memory area (not shown).

[0052] The future motion sickness index value calculation unit 20 calculates the passenger's future motion sickness index value (hereinafter also referred to as the future index value) based on route information indicating the vehicle's travel route and changes in accumulated factors during the vehicle's past travels. The future motion sickness index value calculation unit 20 also calculates the arrival time to the destination on the vehicle's travel route based on the route information from the current location.

[0053] The visual information influence calculation unit 30 calculates an influence level that indicates the extent to which visual information has an effect on the passenger's current state of motion sickness, based on visual information corresponding to changes in the external world (outside the vehicle) as seen by the passenger.

[0054] The motion sickness level calculation unit 40 calculates a motion sickness level that indicates the degree to which the passenger will develop motion sickness, based on the current index value calculated by the current motion sickness index value calculation unit 10 and the future index value calculated by the future motion sickness index value calculation unit 20. Specifically, the motion sickness level is calculated as an evaluation value of motion sickness from the present to the future, which is an integration of the future index value and the current index value reflecting the influence calculated by the visual information influence calculation unit 30.

[0055] The motion sickness onset time calculation unit 50 calculates the time (onset time) until the motion sickness level calculated by the motion sickness level calculation unit 40 reaches a certain threshold. Based on the calculated onset time, the motion sickness onset time calculation unit 50 determines whether or not the passenger will develop motion sickness until the arrival time calculated by the future motion sickness index value calculation unit 20, and outputs notification information indicating the content of the notification to the passenger or driver according to the determination result.

[0056] The motion sickness notification device 60 notifies the passenger or the driver based on the notification information output by the motion sickness onset time calculation unit 50.

[0057] With the above configuration, the information processing system 1, under conditions of riding in a vehicle and driving, can predict current motion sickness based on the degree of vibration stimulus received from the vehicle's acceleration without using biological data sensing, and can also predict current to future motion sickness by detecting future vehicle behavior based on route information from the car navigation system and changes in the external scenery.

[0058] This allows passengers to be notified of predicted motion sickness before it occurs, enabling them to take measures to prevent it. It also enables longer ride experiences and travel. Furthermore, it makes it possible to predict the onset of motion sickness in real time, taking into account changes in the actual driving environment.

[0059] The detailed configuration and operation of each functional block that constitutes the information processing system 1 will be described below.

[0060] (4-1. Configuration and Operation of Current Motion Sickness Index Value Calculation Unit) FIG. 3 is a block diagram showing an example of the functional configuration of the current motion sickness index value calculation unit 10. As shown in FIG.

[0061] As shown in FIG. 3, the current motion sickness index value calculation unit 10 is configured to include a head movement calculation unit 101 , an accumulation factor calculation unit 102 , and a recovery factor calculation unit 103 .

[0062] The head movement calculation unit 101 calculates the head movement of the passenger based on the data detected by the on-board sensors. Essentially, the head movement calculation unit 101 calculates the head movement of the passenger from the vehicle movement by simulation calculation. Specifically, the head movement calculation unit 101 calculates the head acceleration of the passenger by simulation calculation based on the vehicle acceleration detected by an acceleration sensor installed on a rigid body of the vehicle and the passenger's attribute data (physical information such as seated height h and upper body weight w) detected by an in-vehicle monitoring camera and a body pressure sensor installed on the seat. However, in addition to the above simulation calculation, the head movement calculation unit 101 may also detect actual head movement from captured images of the passenger or estimate head movement from the vehicle's travel route and the movement of the camera image.

[0063] The accumulation factor calculation unit 102 weights the head acceleration calculated by the head movement calculation unit 101 according to frequency, detects the traveling time of the vehicle, and calculates the accumulation factor of the passenger's motion sickness by time integration using equation (1) described in ISO2631.

[0064] The recovery factor calculation unit 103 calculates the recovery factor of the passenger from motion sickness based on the detected passenger attribute data and the vehicle travel time.

[0065] Then, the current motion sickness index value calculation unit 10 combines the motion sickness accumulation factor calculated by the accumulation factor calculation unit 102 and the motion sickness recovery factor calculated by the recovery factor calculation unit 103 to calculate the passenger's current motion sickness index value (current index value).

[0066] Here, if the MSDV calculated by the accumulation factor calculation unit 102 is the motion sickness accumulation factor M and the motion sickness recovery factor calculated by the recovery factor calculation unit 103 is R, the current motion sickness index value calculation unit 10 subtracts the motion sickness recovery factor R from the motion sickness accumulation factor M, as shown in the following equation (2), to calculate the passenger's motion sickness index value S. However, in equation (2), the coefficient α by which the accumulation factor M is multiplied is the correlation coefficient between MSDV and SSQ obtained from experiments.

[0067]

[0068] The correlation coefficient α between MSDV and SSQ differs depending on the driving style of the vehicle. For example, the correlation coefficient α differs between accelerating and decelerating (vigorous driving) and normal driving (gentle driving), and also between driving in urban areas and driving on expressways.

[0069] Next, the flow of processing by the current motion sickness index value calculation unit 10 will be described with reference to the flowchart of Fig. 4. The processing of Fig. 4 starts when a person gets into the vehicle and the occupant is detected.

[0070] In step S101, the current motion sickness index value calculation unit 10 acquires attribute data of the occupant. Specifically, the current motion sickness index value calculation unit 10 acquires physical information such as the occupant's sitting height h and upper body weight w using in-vehicle sensors such as an in-vehicle monitoring camera and a body pressure sensor mounted on the seat.

[0071] When the current motion sickness index value calculation unit 10 detects in step S102 that the vehicle has started to move, in step S103, it acquires acceleration information by detecting the acceleration of the vehicle using an acceleration sensor installed in a rigid part of the vehicle.

[0072] In step S104, the head movement calculation unit 101 calculates the head acceleration (head movement) of the occupant through simulation calculation based on the vehicle acceleration information acquired in step S103 and physical information such as the occupant's sitting height h and upper body weight w acquired in step S101.

[0073] In step S105, the accumulation factor calculation unit 102 weights the head acceleration calculated by the head movement calculation unit 101 according to frequency, detects the driving time for which the vehicle is moving, and calculates the accumulation factor M of the passenger's motion sickness by time integration using equation (1) described in ISO 2631.

[0074] In step S106, the recovery factor calculation unit 103 reads the passenger's attribute data, including physical information such as the passenger's sitting height h and upper body weight w obtained in step S101, and calculates the passenger's motion sickness recovery factor R from the elapsed time.

[0075] Then, in step S107, the current motion sickness index value calculation unit 10 combines the motion sickness accumulation factor αM calculated by the accumulation factor calculation unit 102 and the motion sickness recovery factor R calculated by the recovery factor calculation unit 103 to calculate the passenger's current index value S (=αM-R).

[0076] The current index value S is calculated by the above-described configuration and processing.

[0077] (4-2. Configuration and Operation of Future Motion Sickness Index Value Calculation Unit) FIG. 5 is a block diagram showing an example of the functional configuration of the future motion sickness index value calculation unit 20. As shown in FIG.

[0078] As shown in FIG. 5, the future motion sickness index value calculation unit 20 is configured to include an arrival time calculation unit 201, an approximate expression calculation unit 202, an accumulation element estimation unit 203, a recovery element estimation unit 204, and a future index value calculation unit 205.

[0079] The arrival time calculation unit 201 calculates the arrival time to the destination based on route information from the current location acquired from route information RI that indicates the vehicle's travel route. The route information RI may be acquired in advance from route information of a car navigation system, for example.

[0080] The approximate expression calculation unit 202 acquires the accumulated factors (associated with the past driving history) from past vehicle driving currently held in the motion sickness index value calculation unit 10, and calculates the changes in the accumulated factors. Then, the approximate expression calculation unit 202 calculates an approximate expression for the changes in the accumulated factors in the past.

[0081] The accumulated element estimation unit 203 estimates a change in the accumulated element during future travel of the vehicle based on the change in the accumulated element during past travel of the vehicle. Specifically, the accumulated element estimation unit 203 estimates a change in the accumulated element until the arrival time calculated by the arrival time calculation unit 201 using the approximate formula calculated by the approximate formula calculation unit 202.

[0082] The recovery factor estimation unit 204 estimates changes in recovery factors during future vehicle travel. For example, the recovery factor estimation unit 204 reads attribute data of the passenger, including physical information such as the passenger's sitting height h and upper body weight w, and estimates changes in the passenger's motion sickness recovery factors from traveling from the current location to the destination. The recovery factor estimation unit 204 may also estimate changes in the accumulated factors during future vehicle travel by calculating changes in recovery factors during past vehicle travel, which are currently held in the motion sickness index value calculation unit 10.

[0083] The future index value calculation unit 205 calculates an index value of the passenger's future motion sickness based on the change in the future accumulation factor. Specifically, the future index value calculation unit 205 combines the future accumulation factor estimated by the accumulation factor estimation unit 203 and the future recovery factor estimated by the recovery factor estimation unit 204 to calculate an index value of the passenger's future motion sickness (future index value).

[0084] Next, the flow of processing by the future motion sickness index value calculation unit 20 will be described with reference to the flowchart of Fig. 6. The processing of Fig. 6 is executed in real time while the vehicle is traveling, for example, at regular time intervals.

[0085] In step S201, the arrival time calculation unit 201 acquires route information from the current location from the route information RI.

[0086] In step S202, the arrival time calculation unit 201 calculates the arrival time at the destination based on the acquired route information.

[0087] In step S203, the approximate expression calculation unit 202 acquires the storage element (MSDV) during past travel of the vehicle, and calculates its change.

[0088] In step S204, the approximate expression calculation unit 202 calculates an approximate expression of the calculated past change in the accumulated element by using, for example, the least squares method.

[0089] In step S205 , the storage element estimation unit 203 uses the approximate expression calculated by the approximate expression calculation unit 202 to estimate a change in the storage element in the future up to the arrival time calculated by the arrival time calculation unit 201 .

[0090] In step S206, the recovery factor estimation unit 204 estimates a change in the recovery factor in future travel of the vehicle.

[0091] Then, in step S207, the future index value calculation unit 205 calculates a future index value based on the future accumulation element estimated by the accumulation element estimation unit 203 and the future recovery element estimated by the recovery element estimation unit 204. The future index value may be calculated in the same manner as the current index value S (= αM-R).

[0092] According to the above process, the index value of future motion sickness is calculated based on the route information and the changes in the accumulated factors during past travel, making it possible to estimate the passenger's future motion sickness state.

[0093] (Modification) FIG. 7 is a block diagram showing another example of the functional configuration of the future motion sickness index value calculation unit 20. As shown in FIG.

[0094] The future motion sickness index value calculation unit 20 shown in Figure 7 differs from the future motion sickness index value calculation unit 20 in Figure 5 in that it has a steering information acquisition unit 211, an average value calculation unit 212, and an accumulation element estimation unit 213 instead of the approximate expression calculation unit 202 and the accumulation element estimation unit 203.

[0095] The steering information acquisition unit 211 acquires future steering information for the vehicle based on route information from the current location acquired from the route information RI. The steering information includes the number of right and left turns and the number of steering operations at twists and turns (curves) on the route information from the current location.

[0096] The average value calculation unit 212 acquires the accumulated factors from the vehicle's past travels currently held in the motion sickness index value calculation unit 10, and calculates the average value of the changes in the accumulated factors from past travels similar to the route information from the current location.

[0097] The accumulated element estimation unit 213 estimates a change in the accumulated element during future vehicle travel based on the change in the accumulated element during past vehicle travel. Specifically, the accumulated element estimation unit 213 estimates an increase in the accumulated element according to the steering information acquired by the steering information acquisition unit 211, using the average value calculated by the average value calculation unit 212.

[0098] Next, the flow of processing by the future motion sickness index value calculation unit 20 will be described with reference to the flowchart of FIG.

[0099] In step S211, the steering information acquisition unit 211 acquires route information from the current location from the route information RI.

[0100] In step S212, the steering information acquisition unit 211 acquires future steering information based on the acquired route information.

[0101] In step S213, the average value calculation unit 212 acquires the accumulated factor (MSDV) in the past travel of the vehicle, and calculates the change in the accumulated factor in the past travel similar to the route information from the current location.

[0102] In step S214, the average value calculation unit 212 calculates the average value of the calculated past changes in the accumulation element.

[0103] In step S215, the accumulation element estimation unit 213 uses the average value calculated by the average value calculation unit 212 to estimate the future increase in accumulation elements according to the steering information acquired by the steering information acquisition unit 211.

[0104] In step S216, the recovery factor estimation unit 204 estimates a change in the recovery factor in future travel of the vehicle.

[0105] Then, in step S217, the future index value calculation unit 205 calculates a future index value based on the future accumulation element estimated by the accumulation element estimation unit 213 and the future recovery element estimated by the recovery element estimation unit 204.

[0106] The above process also calculates a future motion sickness index value based on route information and changes in accumulated factors during past travel, making it possible to estimate the passenger's future motion sickness state. Furthermore, the process of Figure 8 only calculates an average value of changes in accumulated factors during past travel, so the calculation load can be reduced compared to the process of Figure 6, which calculates an approximation of changes in accumulated factors during past travel.

[0107] (4-3. Configuration and Operation of Visual Information Influence Calculation Unit) FIG. 9 is a block diagram showing an example of the functional configuration of the visual information influence calculation unit 30. As shown in FIG.

[0108] As shown in FIG. 9, the visual information influence calculation unit 30 is configured to include external world information acquisition units 301 and 302 , a visual information acquisition unit 303 , and an influence calculation unit 304 .

[0109] The external environment information acquisition unit 301 acquires digital twin data corresponding to the current location as external environment information that represents the state of the external environment (outside the vehicle) at the current location. The digital twin data is configured as three-dimensional map information that models the real space in which the vehicle can travel, and may be stored within the information processing system 1 or may be acquired from the cloud via a network.

[0110] The external environment information acquisition unit 302 acquires camera images captured from the vehicle as external environment information that indicates the state of the external environment (outside the vehicle) at the current location.

[0111] 10, a vehicle is provided with an exterior camera CAM1 used for autonomous driving and the like in front of it (for example, above the windshield inside the vehicle), and an interior camera CAM2 used for occupant sensing behind it (for example, above the rear window inside the vehicle). Both the exterior camera CAM1 and the interior camera CAM2 may be provided, or only one of them may be provided.

[0112] The external environment information acquisition unit 302 may acquire both the video captured by the exterior camera CAM1 and the video captured by the interior camera CAM2 as external environment information, or may acquire only one of them. Furthermore, in addition to the camera image itself, information on whether the vehicle is traveling during the day, at night, or at dusk, and information on whether the vehicle is traveling in bad weather, may be acquired from the camera image as external environment information. Furthermore, information on whether the vehicle has good visibility, such as whether it is a vehicle with a panoramic roof or an open car, may be acquired from the camera image as external environment information.

[0113] The visual information acquisition unit 303 acquires visual information corresponding to changes in the external world (outside the vehicle) as seen by the passenger from the external world information acquired by the external world information acquisition unit 301 and the external world information acquisition unit 302. Specifically, the visual information acquisition unit 303 acquires information on changes in the external world information of the vehicle at its current location as visual information.

[0114] The influence degree calculation unit 304 calculates the influence degree of a change in the external environment as seen by the passenger on the current index value, based on the visual information (information on changes in external environment information) acquired by the visual information acquisition unit 303. In other words, the influence degree calculation unit 304 calculates the likelihood of motion sickness on the currently traveling route.

[0115] Next, the flow of processing by the visual information influence calculation unit 30 will be described with reference to the flowchart of Fig. 11. The processing of Fig. 11 is executed in real time while the vehicle is traveling, for example, at regular time intervals.

[0116] In step S301, the visual information influence calculation unit 30 acquires position information of the current location. The position information may be acquired based on a GPS signal received from a GPS satellite, for example.

[0117] In step S302, the outside world information acquisition unit 301 acquires digital twin data corresponding to the current location based on the acquired position information of the current location.

[0118] In step S303, the visual information acquisition unit 303 acquires, as visual information, information on changes in the digital twin data around the current location based on the digital twin data corresponding to the current location acquired by the external world information acquisition unit 301.

[0119] In step S304, the external environment information acquisition unit 302 acquires a camera image captured from the vehicle.

[0120] In step S305, the visual information acquisition unit 303 acquires, as visual information, information on changes in the camera image around the current location, based on the camera image acquired by the external world information acquisition unit 302.

[0121] In this way, information on changes in the external environment of the vehicle at the current location is acquired as visual information. Note that, as visual information, information on changes in both the digital twin data and the camera image around the current location may be acquired, or information on changes in only one of them may be acquired.

[0122] Then, in step S306, the influence calculation unit 304 calculates the influence (the likelihood of motion sickness on the currently traveling route) on the current index value (the current motion sickness state) based on the visual information acquired by the visual information acquisition unit 303.

[0123] Change information as visual information is acquired as changes in the frequency and direction of external world information corresponding to the occupant's field of view. Specifically, the digital twin data and camera images acquired as external world information are converted into pixel information, and the frequency and direction of the changes in the pixel information are calculated. For example, by using optical flow, the changes in pixel information can be frequency analyzed using FFT (Fast Fourier Transform) analysis to calculate the speed and direction of the image change.

[0124] Here, a specific example of the visual information acquisition process in the visual information acquisition unit 303 will be described with reference to the flowchart of FIG.

[0125] In step S311, the visual information acquisition unit 303 divides the image acquired as digital twin data or camera image into a plurality of blocks. The number of blocks into which the image is divided may be determined according to the size of the image. For example, if the image size is large, the number of divisions may be large, and if the image size is small, the number of divisions may be small.

[0126] In step S312, the visual information acquisition unit 303 calculates the representative frequency and direction for each divided block.

[0127] Next, in step S313, the visual information acquisition unit 303 divides the acquired image into an effective field of view and a peripheral field of view. The effective field of view is the area that fits within the field of view of, for example, the passenger in the digital twin data or camera image acquired as external world information, and the peripheral field of view is the area around the effective field of view.

[0128] Then, in step S314, the visual information acquisition unit 303 calculates the difference between the changes in frequency and direction in the effective visual field and the peripheral visual field as a change in pixel information in the acquired video. More specifically, the difference between the fluctuations in the representative frequency and direction of each block in the effective visual field and the fluctuations in the representative frequency and direction of each block in the peripheral visual field is calculated.

[0129] In this case, the greater the difference between the changes in the effective visual field and the peripheral visual field, the more likely it is that a sense of discomfort will occur, and the greater the impact on motion sickness.

[0130] Furthermore, the change (frequency) in pixel information in the acquired video may be calculated separately for the vertical direction and the horizontal direction. In this case, the frequencies that affect motion sickness differ in the vertical direction and the horizontal direction.

[0131] For example, as shown in Figure 13A, low-frequency components of 1 Hz or less in the vertical change in pixel information (vector spectrum) are similar to the shaking that causes seasickness, and have a large effect on motion sickness.Furthermore, frequency components around 10 Hz in the vertical change in pixel information are similar to vibrations from the road surface, and cause a lot of head movement, so they have a large effect on motion sickness.

[0132] On the other hand, as shown in Figure 13B, frequency components of about several Hz in the left-right change in pixel information (vector spectrum) are similar to the shaking that occurs when driving on a road with many curves, and have a significant impact on motion sickness.

[0133] The current degree of influence on motion sickness is calculated based on the visual information (information on changes in external world information) obtained in this manner.

[0134] According to the above process, the current impact on motion sickness can be calculated based on the change information of the external environment of the vehicle acquired as visual information, so that the current motion sickness state of the passenger can be predicted with high accuracy. In other words, it is possible to predict the onset of motion sickness in real time, taking into account the change in the actual driving environment.

[0135] (4-4. Configuration and Operation of Motion Sickness Onset Time Calculation Unit) FIG. 14 is a block diagram showing an example of the functional configuration of the motion sickness onset time calculation unit 50. As shown in FIG.

[0136] As shown in FIG. 14 , the motion sickness onset time calculation unit 50 is configured to include a threshold setting unit 501 and an onset time calculation unit 502 .

[0137] The threshold setting unit 501 sets a threshold for the motion sickness level, which indicates the degree to which the passenger is susceptible to motion sickness, according to the passenger's attribute data. The passenger's attribute data here may be physical information such as the passenger's sitting height h and upper body weight w, or may be discrimination information indicating whether the passenger is prone to motion sickness.

[0138] The onset time calculation unit 502 calculates the time (onset time) required for the motion sickness level calculated by the motion sickness level calculation unit 40 to reach the threshold value set by the threshold value setting unit 501 .

[0139] In addition, the onset time calculation unit 502 determines whether the passenger will develop motion sickness by the arrival time calculated by the future motion sickness index value calculation unit 20 based on the calculated onset time, and outputs notification information indicating the content of the notification to the passenger or driver depending on the determination result.

[0140] Next, the flow of processing by the motion sickness onset time calculation unit 50 will be described with reference to the flowchart of FIG.

[0141] In step S501, the threshold setting unit 501 sets a threshold for the motion sickness level according to the attribute data of the passenger. Alternatively, the threshold setting unit 501 may set the threshold based on the results of machine learning of the passenger's past motion sickness level. Furthermore, the threshold for the motion sickness level may be a value previously set, or a value input by the user.

[0142] In step S502 , the onset time calculation unit 502 calculates the onset time until the motion sickness level reaches the threshold value set by the threshold value setting unit 501 .

[0143] In step S503, the onset time calculation unit 502 determines whether the passenger will develop motion sickness until the arrival time calculated by the future motion sickness index value calculation unit 20, based on the calculated onset time.

[0144] If it is determined in step S503 that motion sickness will occur before the arrival time, the process proceeds to step S504, where the onset time calculation unit 502 outputs notification information indicating the content of a notification to the passenger or driver. The notification information may include the driving instructions described above, the onset time itself, the time when motion sickness is predicted to occur, etc.

[0145] On the other hand, if it is determined in step S503 that motion sickness will not occur before the arrival time, step S504 is skipped and no notification information is output.

[0146] According to the above process, whether or not motion sickness will occur before the arrival time is determined based on the motion sickness level calculated by integrating the current index value and the future index value, so that the passenger can be notified of the predicted onset of motion sickness before it actually occurs, and measures can be taken to prevent the onset of motion sickness, thereby enabling long-term riding experiences and travel.

[0147] 5. Others Although the above description has focused on embodiments in which the technology according to the present disclosure is applied to car sickness, the gist of the present disclosure is not limited thereto. The technology according to the present disclosure can also be applied to motion sickness, such as seasickness, which is caused by the movement of a vehicle (mobile body) in passengers on board various vehicles (mobile bodies).

[0148] 6. Example of Computer Hardware Configuration The above-described series of processes can be executed by hardware or software. When the series of processes is executed by software, the program constituting the software is installed from a program recording medium into a computer incorporated in dedicated hardware, a general-purpose personal computer, or the like.

[0149] 16 is a block diagram showing an example of the hardware configuration of a computer that executes the above-described series of processes using a program. The above-described information processing system 1 can be configured, for example, by a computer having a configuration similar to that shown in FIG.

[0150] A CPU (Central Processing Unit) 801 , a ROM (Read Only Memory) 802 , and a RAM (Random Access Memory) 803 are interconnected by a bus 804 .

[0151] An input / output interface 805 is further connected to the bus 804. An input unit 806 including a keyboard, a mouse, etc., and an output unit 807 including a display, a speaker, etc. are connected to the input / output interface 805. Also connected to the input / output interface 805 are a storage unit 808 including a hard disk, a nonvolatile memory, etc., a communication unit 809 including a network interface, etc., and a drive 810 that drives removable media 811.

[0152] In a computer configured as described above, the CPU 801 performs the above-described series of processes by, for example, loading a program stored in the memory unit 808 into the RAM 803 via the input / output interface 805 and the bus 804 and executing it.

[0153] The program executed by the CPU 801 is stored on a removable medium 811, or is provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital broadcasting, and installed in the storage unit 808.

[0154] The program executed by the computer may be a program that processes in chronological order according to the order described in this specification, or may be a program that processes in parallel or at the required timing, such as when called.

[0155] In this specification, a system refers to a collection of multiple components (devices, modules (components), etc.), regardless of whether all of the components are contained in the same housing. Therefore, multiple devices housed in separate housings and connected via a network, and a single device housed in a single housing with multiple modules, are both systems.

[0156] The effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0157] The embodiments of the present disclosure are not limited to the above-described embodiments, and various modifications are possible within the scope of the gist of the present disclosure.

[0158] For example, the embodiment of the present disclosure can be configured as a cloud computing system in which a single function is shared and processed collaboratively by multiple devices via a network.

[0159] Furthermore, each step described in the above flowchart can be executed by one device, or can be shared and executed by a plurality of devices.

[0160] Furthermore, when one step includes multiple processes, the multiple processes included in that one step can be executed by one device or can be shared and executed by multiple devices.

[0161] The effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0162] Furthermore, the technology according to the present disclosure may have the following configurations: (1) An information processing device comprising: an accumulation element estimation unit that estimates a future change in an accumulation element of motion sickness of a passenger of a moving body based on route information of the moving body and a change in the accumulation element of the motion sickness of the passenger of the moving body during past movements of the moving body; and a future index value calculation unit that calculates a future index value of motion sickness of the passenger based on the future change in the accumulation element. (2) The information processing device according to (1), further comprising an approximate expression calculation unit that calculates an approximate expression of a change in the past accumulation element, wherein the accumulation element estimation unit estimates a future change in the accumulation element using the approximate expression. (3) The information processing device according to (2), further comprising an arrival time calculation unit that calculates an arrival time at a destination based on the route information from a current location, wherein the accumulation element estimation unit estimates a change in the accumulation element up to the arrival time using the approximate expression. (4) The information processing device according to (1), further comprising: a steering information acquisition unit that acquires future steering information of the moving object based on the route information from a current location, wherein the accumulated element estimation unit estimates a change in the accumulated element corresponding to the steering information based on past changes in the accumulated element. (5) The information processing device according to (4), further comprising: an average value calculation unit that calculates an average value of changes in the accumulated element in past movements similar to the route information from the current location, wherein the accumulated element estimation unit estimates an increase in the accumulated element corresponding to the steering information using the average value. (6) The information processing device according to (4) or (5), wherein the steering information includes the number of steering operations in right and left turns and twists and turns on the route information from the current location. (7) The information processing device according to any of (1) to (6), further comprising: a threshold setting unit that sets a threshold value of a motion sickness level at which the passenger develops motion sickness; and an onset time calculation unit that calculates a time until the motion sickness level based on the future index value will reach the threshold value. (8) The information processing device according to (7), wherein the threshold setting unit sets the threshold according to an attribute of the passenger. (9) The information processing device according to (7), wherein the threshold setting unit sets the threshold based on a learning result of the motion sickness level of the passenger.(10) The information processing device according to (7), further comprising a motion sickness level calculation unit that calculates the motion sickness level based on the future index value and the current motion sickness index value of the passenger. (11) The information processing device according to (10), further comprising an influence calculation unit that calculates an influence degree on the current index value based on visual information of the passenger, wherein the motion sickness level calculation unit calculates the motion sickness level based on the future index value and the current index value reflecting the influence degree. (12) The information processing device according to (11), wherein the visual information is change information acquired from external world information of the moving body at a current location. (13) The information processing device according to (12), wherein the external world information is digital twin data corresponding to the current location. (14) The information processing device according to (12), wherein the external world information is camera video captured from the moving body. (15) The information processing device according to any one of (12) to (14), wherein the change information is a change in frequency and direction of the external world information corresponding to the field of view of the occupant. (16) The information processing device according to any one of (1) to (15), wherein the past accumulated factor is calculated based on head movement of the occupant and a traveling time of the moving body. (17) The information processing device according to any one of (1) to (16), wherein the index value is calculated by combining the accumulated factor and a motion sickness recovery factor of the occupant. (18) The information processing device according to (17), wherein the recovery factor is calculated based on the traveling time of the moving body and physical information of the occupant. (19) An information processing method comprising: estimating a future change in the accumulated factor based on route information of the moving body and a change in the accumulated factor of motion sickness of the occupant of the moving body during past movements of the moving body; and calculating a future motion sickness index value of the occupant based on the future change in the accumulated factor. (20) An information processing system comprising: an accumulation element estimation unit that estimates future changes in an accumulation element of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation element of motion sickness of the passenger of the moving body during past movements of the moving body; and a future index value calculation unit that calculates a future index value of motion sickness of the passenger based on future changes in the accumulation element.

[0163] REFERENCE SIGNS LIST 1 Information processing system, 10 Current motion sickness index value calculation unit, 20 Future motion sickness index value calculation unit, 30 Visual information influence calculation unit, 40 Motion sickness level calculation unit, 50 Motion sickness onset time calculation unit, 60 Motion sickness notification device, 101 Head movement calculation unit, 102 Accumulation element calculation unit, 103 Recovery element calculation unit, 201 Arrival time calculation unit, 202 Approximation formula calculation unit, 203 Accumulation element estimation unit, 204 Recovery element estimation unit, 205 Future index value calculation unit, 211 Steering information acquisition unit, 212 Average value calculation unit, 213 Accumulation element estimation unit, 301, 302 External information acquisition unit, 303 Visual information acquisition unit, 304 Influence calculation unit, 501 Threshold setting unit, 502 Onset time calculation unit

Claims

1. An information processing device comprising: an accumulation factor estimation unit that estimates future changes in an accumulation factor of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation factor of motion sickness of the passenger of the moving body during past movements of the moving body; and a future index value calculation unit that calculates a future index value of motion sickness of the passenger based on the future changes in the accumulation factor.

2. The information processing device according to claim 1, further comprising an approximation calculation unit that calculates an approximation of a past change in the accumulation element, wherein the accumulation element estimation unit uses the approximation to estimate a future change in the accumulation element.

3. The information processing device according to claim 2, further comprising an arrival time calculation unit that calculates an arrival time to a destination based on the route information from a current location, and the storage element estimation unit uses the approximation formula to estimate a change in the storage element up to the arrival time.

4. The information processing device of claim 1, further comprising a steering information acquisition unit that acquires future steering information of the moving body based on the route information from a current location, and the storage element estimation unit estimates changes in the storage element corresponding to the steering information based on past changes in the storage element.

5. An information processing device as described in claim 4, further comprising an average value calculation unit that calculates an average value of changes in the accumulated element in past movements similar to the route information from the current location, and the accumulated element estimation unit uses the average value to estimate an increase in the accumulated element according to the steering information.

6. The information processing device according to claim 4, wherein the steering information includes the number of right and left turns and steering maneuvers in twists and turns on the route information from the current location.

7. The information processing device according to claim 1, further comprising: a threshold setting unit that sets a threshold for the motion sickness level at which the passenger will develop motion sickness; and an onset time calculation unit that calculates the time until the motion sickness level based on the future index value will reach said threshold.

8. The information processing device according to claim 7, wherein the threshold setting unit sets the threshold according to attributes of the passenger.

9. The information processing device according to claim 7, wherein the threshold setting unit sets the threshold based on a learning result of the motion sickness level of the passenger.

10. The information processing device according to claim 7, further comprising a motion sickness level calculation unit that calculates the motion sickness level based on the future index value and the current motion sickness index value of the passenger.

11. An information processing device as described in claim 10, further comprising an influence calculation unit that calculates the influence on the current index value based on the visual information of the occupant, wherein the motion sickness level calculation unit calculates the motion sickness level based on the future index value and the current index value reflecting the influence.

12. The information processing device according to claim 11, wherein the visual information is change information obtained from external information of the moving body at its current location.

13. The information processing device according to claim 12, wherein the outside world information is digital twin data corresponding to a current location.

14. The information processing device according to claim 12, wherein the external information is a camera image captured from the moving object.

15. The information processing device according to claim 12, wherein the change information is a change in frequency and direction of the external world information corresponding to the occupant's field of vision.

16. The information processing device according to claim 1, wherein the past accumulated elements are calculated based on the head movement of the passenger and the travel time of the moving object.

17. The information processing device according to claim 1, wherein the index value is calculated by combining the accumulation factor and a recovery factor from motion sickness of the passenger.

18. The information processing device according to claim 17, wherein the recovery element is calculated based on a travel time of the moving object and physical information of the passenger.

19. An information processing method comprising: estimating future changes in an accumulation factor of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation factor of motion sickness of the passenger of the moving body during past movements of the moving body; and calculating a future index value of motion sickness of the passenger based on the future changes in the accumulation factor.

20. An information processing system comprising: an accumulation element estimation unit that estimates future changes in an accumulation element of motion sickness of a passenger of a moving body based on route information of the moving body and changes in the accumulation element of motion sickness of the passenger of the moving body during past movements of the moving body; and a future index value calculation unit that calculates a future index value of motion sickness of the passenger based on future changes in the accumulation element.

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