Vehicle cabin motion sickness relieving method and system, electronic equipment and computer readable medium

Abstract

The invention provides a method for relieving motion sickness of a vehicle cabin, and belongs to the field of automobiles. Physiological data, behavior data and environmental data of passengers are collected; evaluating the motion sickness level according to the data; according to the motion sickness level, different systems are linked for adjustment; according to the method, the physiological state and the environmental parameters of the passenger are monitored in real time, an air conditioner, a seat, acoustics and a display system are dynamically linked through the AI algorithm to actively relieve the motion sickness, active, dynamic and personalized relieving of the motion sickness is achieved, and the comfort level of the passenger is improved. The whole system is integrated in an intelligent cabin, and low delay and high reliability are ensured by relying on an AI algorithm and an Internet of Things technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobiles, in particular to a vehicle cabin motion sickness relief method and system, an electronic device and a computer readable medium. BACKGROUND

[0002] With the increasing maturity of automatic driving technology, more and more people choose to travel by car, however, during the journey, many people will have car sickness reaction, which can easily cause extreme discomfort of the body, generally manifested as upper abdominal discomfort, accompanied by nausea, vomiting, dizziness, sweating, pale face, increased saliva and other symptoms. Car sickness is difficult to relieve immediately and needs a certain period of time to gradually relieve, especially for severe car sickness passengers who need more time, which greatly reduces the user's travel experience. At present, car sickness medicine is mainly used to solve the problem of car sickness, but car sickness medicine contains a large amount of narcotic drugs, and frequent use will have a bad impact on the body, and car sickness medicine is not useful for everyone. SUMMARY

[0003] The present application aims to solve at least one of the technical problems existing in the prior art, and provides a vehicle cabin motion sickness relief method and system.

[0004] In a first aspect, the embodiments of the present application provide a vehicle cabin motion sickness relief method, which comprises:

[0005] Collecting physiological data, behavior data and environmental data of the passenger;

[0006] Evaluating the motion sickness level according to the above data;

[0007] According to the motion sickness level, the different systems are linked to adjust;

[0008] Monitoring the adjustment effect and switching the adjustment strategy according to the effect.

[0009] In some embodiments, the step of collecting physiological, behavioral and environmental data of the passenger comprises:

[0010] The physiological data includes skin electricity response, heart rate variability and skin temperature;

[0011] The behavior data includes head micro-shaking frequency and eye movement trajectory disorder degree;

[0012] The environmental data includes vehicle longitudinal / lateral acceleration fluctuation intensity and vehicle acceleration / deceleration frequency.

[0013] In some embodiments, the step of evaluating the motion sickness level according to the above data comprises:

[0014] Feature extraction is performed on the above data;

[0015] Adjusting the weight of different features dynamically according to the current context;

[0016] Mapping the weighted features to four levels of motion sickness using an AI model, the four levels of motion sickness including:

[0017] Level0: No symptoms, i.e. all features are within normal range;

[0018] Level1: Potential risk, i.e. some features are slightly abnormal;

[0019] Level2: Mild motion sickness, i.e. multiple features are significantly abnormal;

[0020] Level3: Severe motion sickness, i.e. features are severely abnormal.

[0021] In some embodiments, the step of adjusting different systems in linkage according to the level of motion sickness includes adjusting air conditioning system, seat system, acoustic system and display system in linkage according to the level of motion sickness.

[0022] In some embodiments, for the air conditioning system, the adjustment is as follows:

[0023] Level1: Face-oriented air supply by controlling air conditioning damper;

[0024] Level3: Adjust multiple air outlets to form a surrounding airflow for surrounding air supply;

[0025] Fragrance release: Control the menthol release of the fragrance diffuser;

[0026] For the seat system, the adjustment is as follows:

[0027] Level2: Start low-frequency massage of waist and back;

[0028] Level3: Control seat vibration to synchronize with vehicle movement;

[0029] For the acoustic system, the adjustment is as follows:

[0030] Level1: Generate adaptive white noise;

[0031] Level3: Inject sound waves with the same frequency as engine vibration;

[0032] For the display system, the adjustment is as follows:

[0033] Level2: Adjust the virtual image position of augmented reality head-up display to reduce visual conflict;

[0034] Level3: Display stable horizon anchor point.

[0035] In some embodiments, in the step of monitoring the adjustment effect and switching the adjustment strategy according to the effect, the step comprises:

[0036] The improvement rate of the galvanic skin response and heart rate variability after real-time monitoring is monitored. If the improvement rate does not reach the expected threshold within 10 minutes, the system automatically switches to a backup strategy.

[0037] In some embodiments, the backup strategy comprises:

[0038] Switching from fragrance to strong cold wind stimulation;

[0039] Switching from low-frequency massage to higher-frequency vibration;

[0040] Switching from white noise to different frequency sound waves.

[0041] In a third aspect, the present application also provides a vehicle cabin motion sickness relief system, comprising:

[0042] A collection unit for collecting physiological data, behavior data and environmental data of the occupant;

[0043] An evaluation unit for evaluating the motion sickness level according to the above data;

[0044] An adjustment unit for adjusting different systems according to the motion sickness level;

[0045] A monitoring unit for monitoring the adjustment effect and switching the adjustment strategy according to the effect.

[0046] In a third aspect, the present application also provides an electronic device, comprising:

[0047] One or more processors;

[0048] A memory for storing one or more programs;

[0049] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods.

[0050] In a fourth aspect, the present application also provides a computer readable medium, wherein the computer readable medium stores a computer program, and the computer program is executed by a processor to implement the steps in any of the methods.

[0051] This invention provides a method for alleviating motion sickness in vehicle cabins. It collects physiological, behavioral, and environmental data from occupants; assesses the level of motion sickness based on this data; adjusts different systems according to the level of motion sickness; monitors the adjustment effect; and switches adjustment strategies based on the effect. This invention actively alleviates motion sickness by monitoring occupant physiological states and environmental parameters in real time, integrating AI algorithms to dynamically link air conditioning, seats, acoustics, and display systems. This achieves proactive, dynamic, and personalized relief of motion sickness, improving occupant comfort. The entire system is integrated into a smart cockpit, relying on AI algorithms and IoT technology to ensure low latency and high reliability. Attached Figure Description

[0052] Figure 1 This is a schematic diagram illustrating the steps of an embodiment of the method for alleviating motion sickness in the vehicle cabin according to the present invention;

[0053] Figure 2 This is a schematic diagram of a structure of an embodiment of the present invention for evaluating motion sickness levels based on the above data;

[0054] Figure 3 This is a schematic diagram of an embodiment of the overall architecture of the method for alleviating motion sickness in vehicle cabins according to the present invention;

[0055] Figure 4 This is a schematic diagram illustrating the steps of an embodiment of the vehicle cabin motion sickness relief system of the present invention;

[0056] Figure 5 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0058] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0059] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0060] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0061] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0062] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0063] In related technologies, motion sickness can be alleviated through methods such as ventilation and medication, but these methods are delayed and cannot intervene before motion sickness occurs; single sensors (such as cameras) are prone to misjudgment (e.g., light interference); and fixed adjustment modes ignore individual differences and dynamic road conditions.

[0064] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a method for alleviating motion sickness in vehicle cabins. Figure 1 A flowchart illustrating the steps of a method for alleviating motion sickness in a vehicle cabin, as provided in an embodiment of the present invention.

[0065] like Figure 1 As shown, the method for relieving motion sickness in the vehicle cabin includes the following steps:

[0066] Step S10: Collect physiological, behavioral, and environmental data of the occupants.

[0067] In this embodiment, the specific scheme for collecting the physiological data of the occupants is as follows:

[0068] Electrodermal conductance (EDA): This monitors skin conductance levels using EDA sensors (such as electrodes) integrated into the steering wheel or seat. Motion sickness typically causes an increase in EDA (indicating sympathetic nerve excitation).

[0069] Heart rate variability (HRV): HRV is monitored using a heart rate sensor (such as photoplethysmography, PPG). HRV decreases during motion sickness (indicating autonomic nervous system disorder).

[0070] Skin temperature: Skin temperature changes are monitored using temperature sensors (such as thermistors). Motion sickness may cause sudden changes in skin temperature (such as a sudden increase or decrease).

[0071] It is understandable that sensor data is sent to the central processing unit via an in-vehicle network (such as CAN bus) or wireless transmission (such as Bluetooth).

[0072] In this embodiment, the specific scheme for collecting behavioral data includes:

[0073] Head micro-movement frequency: Head movement frequency is captured using an in-cabin camera (such as an infrared camera) combined with computer vision algorithms (such as optical flow or pose estimation). The head micro-movement frequency increases during motion sickness.

[0074] Eye movement trajectory disorder: The regularity of eye movement trajectory is analyzed by eye tracking technology (such as pupil detection algorithm). Motion sickness causes irregular eye movements or tremors.

[0075] It is understandable that camera data is processed in real time by an embedded processor (such as a GPU) to extract motion features.

[0076] In this embodiment, the specific scheme for collecting environmental data includes:

[0077] Longitudinal / lateral acceleration fluctuation intensity: Acceleration data is acquired using vehicle dynamic sensors (such as IMU inertial measurement units), and fluctuation intensity (such as standard deviation or spectral energy) is calculated.

[0078] Acceleration and deceleration frequency: Read acceleration and braking signals from the vehicle's CAN bus and count the number of accelerations and decelerations per unit time.

[0079] It is understandable that sensor data fusion algorithms (such as Kalman filtering) process noise and extract stable environmental parameters.

[0080] This step is understandable as the system's data input layer, providing raw signals for subsequent feature extraction and model evaluation. The real-time performance and accuracy of data acquisition directly affect the overall system's response speed and decoding effectiveness.

[0081] Step S20: Assess the motion sickness level based on the above data.

[0082] Please see Figure 2 In this embodiment, features are extracted from the raw data, and the motion sickness level is evaluated through dynamic weight allocation and an AI model. The attached figure illustrates the flow of this step: raw signal → feature extraction → dynamic weight allocator → motion sickness level evaluation model.

[0083] The specific implementation method is as follows:

[0084] Feature extraction is performed on the above data.

[0085] Specifically, the features include the following:

[0086] EDA slope: Calculates the rate of change of the EDA signal, reflecting the activation speed of the sympathetic nervous system.

[0087] HRV low-frequency power: The power of the low-frequency components (e.g., 0.04-0.15Hz) extracted from the HRV signal, representing sympathetic nerve activity. Low-frequency power may vary in motion sickness.

[0088] Instantaneous motion divergence: Calculated from behavioral layer data, the instantaneous motion divergence (such as variance or entropy) of head or eye movements indicates the degree of motion disorder.

[0089] Environmental parameters: Acceleration spectrum, which is the Fourier transform of the acceleration signal to extract spectral features (such as low-frequency energy) to reflect the periodic interference of vehicle motion.

[0090] It is understandable that digital signal processing (DSP) algorithms (such as sliding window computation) are used in practice to extract features in real time.

[0091] The weights of different features are dynamically adjusted based on the current context.

[0092] Specifically, the weights of different features are dynamically adjusted based on the current context (such as vehicle motion status or occupant historical data). For example, during rapid acceleration, the weight of environmental parameters increases; when stationary, the weight of physiological parameters is higher.

[0093] It is understood that this embodiment uses a rule engine or a lightweight machine learning model (such as linear regression) to calculate weights, ensuring that the model adapts to different scenarios.

[0094] The AI ​​model was used to map the weighted features to four levels of motion sickness.

[0095] Specifically, AI models (such as Support Vector Machines (SVM), Random Forests, or Neural Networks) are used to map weighted features to four levels of sloshing:

[0096] Level 0: Asymptomatic - all characteristics are within the normal range.

[0097] Level 1: Potential Risk - Some characteristics are slightly abnormal (e.g., EDA is slightly elevated).

[0098] Level 2: Mild motion sickness - multiple abnormal features (such as decreased HRV + increased head shaking frequency).

[0099] Level 3: Severe motion sickness - characterized by serious abnormalities (such as sudden changes in skin temperature + eye movement disorder).

[0100] Understandably, the model is pre-trained on historical motion sickness data and deployed on in-vehicle edge computing devices to achieve low-latency inference.

[0101] This step is understandably the core decision-making layer of the system. It receives the raw data from the previous steps and outputs the motion sickness level. The dynamic weight assigner enhances the model's robustness, making the evaluation more adaptable to real-time conditions.

[0102] Step S30: Adjust different systems according to the level of motion sickness.

[0103] This step dynamically adjusts the air conditioning, seats, acoustics, and display systems to implement mitigation measures based on the level of motion sickness. The adjustment measures become more robust as the level increases.

[0104] The specific implementation method is as follows:

[0105] Corresponding air conditioning system:

[0106] Level 1: Directed airflow to the face - By controlling the air conditioning damper, airflow is directed towards the occupant's face to provide a cooling stimulus.

[0107] Level 3: Surround air supply - Adjust multiple air outlets to form a surrounding airflow and increase air flow coverage.

[0108] Fragrance release: Controls the release of menthol by the fragrance diffuser, with the concentration increasing with each level (e.g., Level 1 low concentration, Level 3 high concentration) to refresh and invigorate the mind.

[0109] It is understood that in this embodiment, the air damper and fragrance valve can be adjusted via the vehicle air conditioning control module (such as ECU).

[0110] For the aforementioned seating system, the adjustments are as follows:

[0111] Level 2: Activate 3Hz low-frequency massage for the lower back - Low-frequency massage is performed through the seat's built-in vibration motor to help relax muscles.

[0112] Level 3: Synchronize vehicle yaw rhythm to counteract inertial illusion - Based on data from the vehicle yaw rate sensor, control the seat vibration to synchronize with the vehicle's movement, reducing motion illusion.

[0113] It is understandable that the seat control unit receives vehicle motion data and generates a synchronous vibration signal.

[0114] For the acoustic system, the adjustment is as follows:

[0115] Level 1: Generate adaptive white noise (such as natural wind sound) - Play masking white noise through the vehicle's speakers to reduce environmental interference.

[0116] Level 3: Inject sound waves at the same frequency as engine vibration - Analyze the engine vibration frequency (e.g., obtain the speed from the CAN bus), generate sound waves of the same frequency, and cancel out infrasound interference.

[0117] It is understood that this embodiment uses a digital signal processor (DSP) to generate custom sound waves, which are then played through an audio system.

[0118] For the display system, the adjustments are as follows:

[0119] Level 2: AR-HUD dynamically adjusts the virtual focus position and shortens the depth of field - adjusts the position of the virtual image in the augmented reality head-up display (AR-HUD) to reduce visual clashes.

[0120] Level 3: Display stable horizon anchor point - Overlay a stable horizon onto the AR-HUD to provide a visual reference anchor point.

[0121] It is understandable that the AR-HUD controller renders the displayed content in real time based on vehicle attitude data (such as IMU).

[0122] This step is understandable; it's the system's execution layer, directly affecting the occupants. It receives the motion sickness level output from the previous steps and triggers corresponding adjustment measures. Different systems work together to alleviate motion sickness from a multi-sensory perspective.

[0123] Step S40: Monitor the adjustment effect and switch the adjustment strategy according to the effect.

[0124] Understandably, this step involves monitoring the effects of the adjustments and optimizing the strategy through feedback mechanisms to ensure the effectiveness of mitigation measures.

[0125] The specific implementation method is as follows:

[0126] Real-time monitoring of EDA / HRV improvement rate after adjustment: Continue to use the aforementioned sensors to monitor physiological indicators and calculate the improvement rate after adjustment (e.g., EDA decrease rate or HRV increase rate). For example, improvement rate = (previous value - postvious value) / previous value. If the improvement rate does not reach the expected threshold within 10 minutes (e.g., EDA decrease <10%), the system automatically switches to a backup strategy. For example:

[0127] Switch from fragrance to strong cold air stimulation (air conditioning system).

[0128] Switch from low-frequency massage to higher-frequency vibration (seat system).

[0129] Switching from white noise to sound waves of different frequency bands (acoustic system).

[0130] It is understood that this embodiment uses a state machine or decision tree to manage strategy switching, and backup strategies are predefined in the database. If the current strategy is ineffective, the system iteratively adjusts the strategy to ensure continuous optimization.

[0131] Please see Figure 3 This invention provides a method for alleviating motion sickness in vehicle cabins. It collects physiological, behavioral, and environmental data from occupants; assesses the motion sickness level based on this data; adjusts different systems according to the motion sickness level; monitors the adjustment effect; and switches adjustment strategies based on the effect. This invention actively alleviates motion sickness by real-time monitoring of occupant physiological states and environmental parameters, integrating AI algorithms to dynamically link air conditioning, seats, acoustics, and display systems. This achieves proactive, dynamic, and personalized relief of motion sickness, improving occupant comfort. The entire system is integrated into a smart cockpit, relying on AI algorithms and IoT technology to ensure low latency and high reliability.

[0132] Please see Figure 4 The present invention also provides a vehicle cabin motion sickness relief system. It is applied to the vehicle cabin motion sickness relief method provided in the above embodiments, and specifically includes: a data acquisition unit, an evaluation unit, an adjustment unit, and a monitoring unit.

[0133] The data acquisition unit is used to collect physiological, behavioral, and environmental data of the occupants.

[0134] In this embodiment, the specific scheme for collecting the physiological data of the occupants is as follows:

[0135] Electrodermal conductance (EDA): This monitors skin conductance levels using EDA sensors (such as electrodes) integrated into the steering wheel or seat. Motion sickness typically causes an increase in EDA (indicating sympathetic nerve excitation).

[0136] Heart rate variability (HRV): HRV is monitored using a heart rate sensor (such as photoplethysmography, PPG). HRV decreases during motion sickness (indicating autonomic nervous system disorder).

[0137] Skin temperature: Skin temperature changes are monitored using temperature sensors (such as thermistors). Motion sickness may cause sudden changes in skin temperature (such as a sudden increase or decrease).

[0138] It is understandable that sensor data is sent to the central processing unit via an in-vehicle network (such as CAN bus) or wireless transmission (such as Bluetooth).

[0139] In this embodiment, the specific scheme for collecting behavioral data includes:

[0140] Head micro-movement frequency: Head movement frequency is captured using an in-cabin camera (such as an infrared camera) combined with computer vision algorithms (such as optical flow or pose estimation). The head micro-movement frequency increases during motion sickness.

[0141] Eye movement trajectory disorder: The regularity of eye movement trajectory is analyzed by eye tracking technology (such as pupil detection algorithm). Motion sickness causes irregular eye movements or tremors.

[0142] It is understandable that camera data is processed in real time by an embedded processor (such as a GPU) to extract motion features.

[0143] In this embodiment, the specific scheme for collecting environmental data includes:

[0144] Longitudinal / lateral acceleration fluctuation intensity: Acceleration data is acquired using vehicle dynamic sensors (such as IMU inertial measurement units), and fluctuation intensity (such as standard deviation or spectral energy) is calculated.

[0145] Acceleration and deceleration frequency: Read acceleration and braking signals from the vehicle's CAN bus and count the number of accelerations and decelerations per unit time.

[0146] It is understandable that sensor data fusion algorithms (such as Kalman filtering) process noise and extract stable environmental parameters.

[0147] This step is understandable as the system's data input layer, providing raw signals for subsequent feature extraction and model evaluation. The real-time performance and accuracy of data acquisition directly affect the overall system's response speed and decoding effectiveness.

[0148] The assessment unit is used to assess the level of motion sickness based on the above data.

[0149] In this embodiment, features are extracted from the raw data, and the motion sickness level is evaluated through dynamic weight allocation and an AI model. The attached figure illustrates the flow of this step: raw signal → feature extraction → dynamic weight allocator → motion sickness level evaluation model.

[0150] The specific implementation method is as follows:

[0151] Feature extraction is performed on the above data.

[0152] Specifically, the features include the following:

[0153] EDA slope: Calculates the rate of change of the EDA signal, reflecting the activation speed of the sympathetic nervous system.

[0154] HRV low-frequency power: The power of the low-frequency components (e.g., 0.04-0.15Hz) extracted from the HRV signal, representing sympathetic nerve activity. Low-frequency power may vary in motion sickness.

[0155] Instantaneous motion divergence: Calculated from behavioral layer data, the instantaneous motion divergence (such as variance or entropy) of head or eye movements indicates the degree of motion disorder.

[0156] Environmental parameters: Acceleration spectrum, which is the Fourier transform of the acceleration signal to extract spectral features (such as low-frequency energy) to reflect the periodic interference of vehicle motion.

[0157] It is understandable that digital signal processing (DSP) algorithms (such as sliding window computation) are used in practice to extract features in real time.

[0158] The weights of different features are dynamically adjusted based on the current context.

[0159] Specifically, the weights of different features are dynamically adjusted based on the current context (such as vehicle motion status or occupant historical data). For example, during rapid acceleration, the weight of environmental parameters increases; when stationary, the weight of physiological parameters is higher.

[0160] It is understood that this embodiment uses a rule engine or a lightweight machine learning model (such as linear regression) to calculate weights, ensuring that the model adapts to different scenarios.

[0161] The AI ​​model was used to map the weighted features to four levels of motion sickness.

[0162] Specifically, AI models (such as Support Vector Machines (SVM), Random Forests, or Neural Networks) are used to map weighted features to four levels of sloshing:

[0163] Level 0: Asymptomatic - all characteristics are within the normal range.

[0164] Level 1: Potential Risk - Some characteristics are slightly abnormal (e.g., EDA is slightly elevated).

[0165] Level 2: Mild motion sickness - multiple abnormal features (such as decreased HRV + increased head shaking frequency).

[0166] Level 3: Severe motion sickness - characterized by serious abnormalities (such as sudden changes in skin temperature + eye movement disorder).

[0167] Understandably, the model is pre-trained on historical motion sickness data and deployed on in-vehicle edge computing devices to achieve low-latency inference.

[0168] This step is understandably the core decision-making layer of the system. It receives the raw data from the previous steps and outputs the motion sickness level. The dynamic weight assigner enhances the model's robustness, making the evaluation more adaptable to real-time conditions.

[0169] The adjustment unit is used to adjust different systems in conjunction with the motion sickness level.

[0170] This step dynamically adjusts the air conditioning, seats, acoustics, and display systems to implement mitigation measures based on the level of motion sickness. The adjustment measures become more robust as the level increases.

[0171] The specific implementation method is as follows:

[0172] Corresponding air conditioning system:

[0173] Level 1: Directed airflow to the face - By controlling the air conditioning damper, airflow is directed towards the occupant's face to provide a cooling stimulus.

[0174] Level 3: Surround air supply - Adjust multiple air outlets to form a surrounding airflow and increase air flow coverage.

[0175] Fragrance release: Controls the release of menthol by the fragrance diffuser, with the concentration increasing with each level (e.g., Level 1 low concentration, Level 3 high concentration) to refresh and invigorate the mind.

[0176] It is understood that in this embodiment, the air damper and fragrance valve can be adjusted via the vehicle air conditioning control module (such as ECU).

[0177] For the aforementioned seating system, the adjustments are as follows:

[0178] Level 2: Activate 3Hz low-frequency massage for the lower back - Low-frequency massage is performed through the seat's built-in vibration motor to help relax muscles.

[0179] Level 3: Synchronize vehicle yaw rhythm to counteract inertial illusion - Based on data from the vehicle yaw rate sensor, control the seat vibration to synchronize with the vehicle's movement, reducing motion illusion.

[0180] It is understandable that the seat control unit receives vehicle motion data and generates a synchronous vibration signal.

[0181] For the acoustic system, the adjustment is as follows:

[0182] Level 1: Generate adaptive white noise (such as natural wind sound) - Play masking white noise through the vehicle's speakers to reduce environmental interference.

[0183] Level 3: Inject sound waves at the same frequency as engine vibration - Analyze the engine vibration frequency (e.g., obtain the speed from the CAN bus), generate sound waves of the same frequency, and cancel out infrasound interference.

[0184] It is understood that this embodiment uses a digital signal processor (DSP) to generate custom sound waves, which are then played through an audio system.

[0185] For the display system, the adjustments are as follows:

[0186] Level 2: AR-HUD dynamically adjusts the virtual focus position and shortens the depth of field - adjusts the position of the virtual image in the augmented reality head-up display (AR-HUD) to reduce visual clashes.

[0187] Level 3: Display stable horizon anchor point - Overlay a stable horizon onto the AR-HUD to provide a visual reference anchor point.

[0188] It is understandable that the AR-HUD controller renders the displayed content in real time based on vehicle attitude data (such as IMU).

[0189] This step is understandable; it's the system's execution layer, directly affecting the occupants. It receives the motion sickness level output from the previous steps and triggers corresponding adjustment measures. Different systems work together to alleviate motion sickness from a multi-sensory perspective.

[0190] The monitoring unit is used to monitor the adjustment effect and switch the adjustment strategy according to the effect.

[0191] Understandably, this step involves monitoring the effects of the adjustments and optimizing the strategy through feedback mechanisms to ensure the effectiveness of mitigation measures.

[0192] The specific implementation method is as follows:

[0193] Real-time monitoring of EDA / HRV improvement rate after adjustment: Continue to use the aforementioned sensors to monitor physiological indicators and calculate the improvement rate after adjustment (e.g., EDA decrease rate or HRV increase rate). For example, improvement rate = (previous value - postvious value) / previous value. If the improvement rate does not reach the expected threshold within 10 minutes (e.g., EDA decrease <10%), the system automatically switches to a backup strategy. For example:

[0194] Switch from fragrance to strong cold air stimulation (air conditioning system).

[0195] Switch from low-frequency massage to higher-frequency vibration (seat system).

[0196] Switching from white noise to sound waves of different frequency bands (acoustic system).

[0197] It is understood that this embodiment uses a state machine or decision tree to manage strategy switching, and backup strategies are predefined in the database. If the current strategy is ineffective, the system iteratively adjusts the strategy to ensure continuous optimization.

[0198] The vehicle cabin motion sickness relief system provided by this invention collects physiological, behavioral, and environmental data of occupants; assesses the motion sickness level based on the data; adjusts different systems in conjunction with the motion sickness level; monitors the adjustment effect and switches adjustment strategies accordingly. This invention actively alleviates motion sickness by monitoring occupant physiological states and environmental parameters in real time, integrating AI algorithms to dynamically link air conditioning, seats, acoustics, and display systems, thus achieving proactive, dynamic, and personalized relief of motion sickness and improving occupant comfort. The entire system is integrated into a smart cockpit, relying on AI algorithms and IoT technology to ensure low latency and high reliability.

[0199] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the vehicle cabin motion sickness relief methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory, configured to enable information interaction between the processors and the memory.

[0200] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0201] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0202] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0203] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the vehicle cabin motion sickness relief methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0204] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described method for relieving vehicle cabin motion sickness.

[0205] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0206] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0207] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0208] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0209] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0210] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0211] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0212] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0213] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0214] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for alleviating motion sickness in a vehicle cabin, characterized in that, It includes: Collect physiological, behavioral, and environmental data from occupants; Assess the level of motion sickness based on the above data; Adjustments are made to different systems based on the level of motion sickness. Monitor the adjustment effect and switch the adjustment strategy according to the effect.

2. The method for alleviating motion sickness in a vehicle cabin according to claim 1, characterized in that, The steps involved in collecting physiological, behavioral, and environmental data from occupants include: The physiological data include: skin conductance response, heart rate variability, and skin temperature; The behavioral data includes: the frequency of head micro-shaking and the degree of eye movement trajectory disorder; The environmental data includes: the intensity of longitudinal / lateral acceleration fluctuations of the vehicle and the frequency of vehicle acceleration and deceleration.

3. The method for alleviating motion sickness in a vehicle cabin according to claim 2, characterized in that, The steps for assessing the level of motion sickness based on the above data include: Feature extraction is performed on the above data; The weights of different features are dynamically adjusted based on the current context. An AI model is used to map weighted features to four motion sickness levels, which include: Level 0: Asymptomatic, meaning all characteristics are within the normal range; Level 1: Potential risk, i.e., slight abnormalities in some characteristics; Level 2: Mild motion sickness, characterized by multiple obvious abnormalities; Level 3: Severe motion sickness, characterized by serious abnormalities.

4. The method for relieving motion sickness in a vehicle cabin according to claim 3, characterized in that, The step of adjusting different systems in conjunction with the motion sickness level includes: adjusting the air conditioning system, seat system, acoustic system and display system in conjunction with the motion sickness level.

5. The method for relieving motion sickness in a vehicle cabin according to claim 4, characterized in that, For the air conditioning system, the adjustments are as follows: Level 1: Directed airflow to the face by controlling the air conditioning damper; Level 3: Adjust multiple air outlets to form a surrounding airflow and provide surrounding air delivery; Fragrance release: Controls the release of menthol from the fragrance diffuser; For the aforementioned seating system, the adjustments are as follows: Level 2: Initiate low-frequency massage of the lower back; Level 3: Controls seat vibration to synchronize with vehicle movement; For the acoustic system, the adjustment is as follows: Level 1: Generate adaptive white noise; Level 3: Inject sound waves that resonate with the engine vibration; For the display system, the adjustments are as follows: Level 2: Adjust the position of the virtual image in the augmented reality head-up display to reduce visual clashes; Level 3: Displays a stable horizon anchor point.

6. The method for relieving motion sickness in a vehicle cabin according to claim 5, characterized in that, The steps of monitoring the adjustment effect and switching the adjustment strategy according to the effect include: The system monitors the improvement rate of skin conductance response and heart rate variability after adjustment in real time. If the improvement rate does not reach the expected threshold within 10 minutes, the system automatically switches to the backup strategy.

7. The method for relieving motion sickness in a vehicle cabin according to claim 6, characterized in that, The backup strategy includes: Switching from fragrance to a strong, cool breeze; Switch from low-frequency massage to higher-frequency vibration; Switch from white noise to sound waves of different frequencies.

8. A vehicle cabin motion sickness relief system, characterized in that, include: The data acquisition unit is used to collect physiological, behavioral, and environmental data of the occupants. An assessment unit is used to assess the level of motion sickness based on the above data; An adjustment unit is used to adjust different systems in conjunction with the motion sickness level. The monitoring unit is used to monitor the adjustment effect and switch the adjustment strategy according to the effect.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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