Motion sickness treatment methods, devices and equipment

Abstract

This application provides a method, apparatus, and device for treating motion sickness, comprising: acquiring a motion sickness sensitivity index and motion sickness reaction data of a target passenger at a target time, as well as the rate of change of vehicle motion state at the target time; performing a weighted summation on the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate to obtain a weighted sum value; obtaining a predicted motion sickness state value based on the weighted sum value, and controlling the vehicle's motion control parameters according to the predicted motion sickness state value. The predicted motion sickness state value characterizes the predicted probability of motion sickness occurring in the target passenger at the target time. This achieves effective vehicle control, reduces the impact of vehicle motion on passenger comfort, adapts to individual passenger differences, and improves vehicle riding comfort.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, apparatus and device for treating motion sickness. Background Technology

[0002] With the development of intelligent connected vehicles and autonomous driving technology, the way passengers behave in vehicles is gradually changing. For example, passengers can read, watch screens, or use mobile devices while riding in a vehicle.

[0003] However, these behaviors increase the probability of passengers experiencing motion sickness. Motion sickness is usually caused by a mismatch between the vehicle's motion and the human sensory system, especially when there are significant changes in the vehicle's motion. Therefore, how to control the vehicle's motion control parameters—that is, how to control the vehicle to reduce the impact of vehicle motion on passenger comfort—is a technical problem that this application needs to solve. Summary of the Invention

[0004] This application provides a method, apparatus, and device for treating motion sickness, which can achieve effective vehicle control, reduce the impact of vehicle movement on passenger comfort, adapt to individual passenger differences, and improve vehicle riding comfort.

[0005] In a first aspect, this application provides a motion sickness treatment method, comprising: acquiring a motion sickness sensitivity index and motion sickness reaction data of a target passenger at a target time, as well as the vehicle motion state change rate at the target time; performing a weighted summation on the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate to obtain a weighted summation value; obtaining a predicted motion sickness state value based on the weighted summation value, and controlling the vehicle's motion control parameters based on the predicted motion sickness state value, wherein the predicted motion sickness state value characterizes the predicted probability of the target passenger experiencing motion sickness at the target time.

[0006] Secondly, this application provides a motion sickness treatment device, comprising: a data acquisition module for acquiring the motion sickness sensitivity index and motion sickness reaction data of a target passenger at a target time, as well as the vehicle motion state change rate at the target time; a weighted summation module for weighted summation of the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate to obtain a weighted summation value; and a predictive control module for obtaining a predicted motion sickness state value based on the weighted summation value, and controlling the vehicle's motion control parameters based on the predicted motion sickness state value, wherein the predicted motion sickness state value characterizes the predicted probability of the target passenger experiencing motion sickness at the target time.

[0007] Thirdly, this application provides an electronic device, including: a processor and a memory, the memory for storing a computer program, and the processor for calling and running the computer program stored in the memory to perform the methods as described in the first aspect or its various implementations.

[0008] Fourthly, this application provides a computer-readable storage medium for storing a computer program that causes a computer to perform the methods described in the first aspect or its various implementations.

[0009] Fifthly, this application provides a computer program product including computer program instructions that cause a computer to perform the methods as described in the first aspect or its various implementations.

[0010] Sixthly, this application provides a computer program that causes a computer to perform the methods described in the first aspect or its various implementations.

[0011] The technical solution of this application can obtain the motion sickness sensitivity index and motion sickness reaction data of a target passenger at a target time, as well as the vehicle motion state change rate at the target time. The motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate are weighted and summed to obtain a weighted sum value. Based on the weighted sum value, a predicted motion sickness state value is obtained, and the vehicle's motion control parameters are controlled according to the predicted motion sickness state value. The predicted motion sickness state value represents the predicted probability of the target passenger experiencing motion sickness at the target time. In this process, by weighting and summing the passenger's real-time motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate, a comprehensive assessment of the passenger's motion sickness risk is achieved. This allows for proactive adjustment of the vehicle's motion control parameters, thereby reducing motion stimulation and minimizing the impact of vehicle motion on passenger comfort. Therefore, effective vehicle control can be achieved, adapting to individual passenger differences and improving vehicle ride comfort.

[0012] Other technical features and effects involved in the technical solution of this application will be described in subsequent embodiments, and will not be repeated here to avoid repetition. Attached Figure Description

[0013] The accompanying drawings used in the following description of the embodiments are introduced.

[0014] Figure 1 A flowchart illustrating a motion sickness treatment method provided in this application embodiment; Figure 2A A schematic diagram illustrating a motion sickness treatment method provided in an embodiment of this application; Figure 2B A schematic diagram illustrating another motion sickness treatment method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the motion sickness treatment device provided in the embodiments of this application; Figure 4 A schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0017] In one embodiment, the technical solution of this application can be used in vehicle control scenarios, for example, in vehicle control scenarios that suppress passenger motion sickness, or in comfort control scenarios in autonomous driving (or intelligent driving, assisted driving) vehicles (e.g., comfort control scenarios that suppress passenger motion sickness), passenger experience optimization scenarios in ride-hailing or shared mobility services, motion sickness prevention scenarios in long-distance passenger vehicles, and personalized driving mode adaptive adjustment scenarios.

[0018] Among them, the vehicle control scenario for suppressing motion sickness can refer to: by monitoring motion sickness reaction data such as passengers' physiological reactions and behavioral characteristics in real time, as well as the rate of change of vehicle motion state, dynamically adjusting motion control parameters such as vehicle acceleration, jerkiness, and yaw rate, and actively reducing motion stimuli that induce motion sickness.

[0019] In one embodiment, the solution provided in this application can be executed by any electronic device with data processing capabilities. For example, the electronic device can be a server, specifically a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Alternatively, the electronic device can be a terminal device, specifically a tablet computer, laptop computer, or desktop computer. Furthermore, the electronic device can be a combination of a server and a terminal device, wherein the server and terminal device in the combination can communicate wirelessly or via wired means. This application does not impose specific limitations on the electronic device.

[0020] Alternatively, the electronic device can be an autonomous vehicle, an intelligent driving vehicle, or a driver-assisted vehicle. For example, the electronic device can be a vehicle equipped with an in-vehicle perception system (e.g., which may include a passenger monitoring system or an occupant monitoring system (OMS) camera, a voice interaction system, or a biosignal sensor) and actuators for adjustable vehicle dynamics (e.g., which may include brake-by-wire, throttle-by-wire, active suspension, variable steering ratio system, etc.), but is not limited to these. The actuators for adjustable vehicle dynamics can correspondingly adjust the vehicle's motion control parameters.

[0021] It should be noted that all technical solutions in this application can be combined in any way to form optional embodiments of this application. To avoid repetition, these will not be elaborated upon.

[0022] Furthermore, the specific embodiments of this application involve data, information, or instructions such as motion sickness sensitivity index, motion sickness reaction data, vehicle motion state change rate, and motion sickness state value. When the embodiments of this application are applied to specific products or technologies, user permission, consent, or authorization is required, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards.

[0023] The technical solution of this application will be described in detail below: In one embodiment, Figure 1 This is a flowchart illustrating a motion sickness treatment method provided in an embodiment of this application. The method can be executed by the aforementioned electronic device, but is not limited thereto. Figure 1 As shown, the method may include S110-S130.

[0024] S110: Obtain the motion sickness sensitivity index and motion sickness reaction data of the target passenger at the target time, as well as the rate of change of vehicle motion state at the target time.

[0025] For example, the target passenger can be any passenger in the vehicle. For instance, it could be any passenger in a non-driver's seat (e.g., the second row of seats).

[0026] For example, the target time can be any time during the ride.

[0027] For example, a motion sickness sensitivity index can be used to quantify an individual passenger's innate or long-term predisposition to motion sickness, and can be a value between 0 and 1. The higher the index, the more prone the passenger is to motion sickness, such as being more sensitive to the stimulation of vehicle movement; the lower the index, the stronger the passenger's tolerance to vehicle movement, i.e., the less prone they are to motion sickness.

[0028] For example, before the trip begins, passengers can be asked questions via the in-vehicle screen or voice interaction, such as "Are you prone to motion sickness? Please rate your answer between 0 and 10." The passenger's answer will be mapped to the 0-1 range as the initial value of the motion sickness sensitivity index. The motion sickness sensitivity index can then be dynamically acquired and continuously updated based on an online learning mechanism.

[0029] For example, the motion sickness sensitivity index can be set to an intermediate value, such as 0.5, to facilitate rapid convergence through online learning later.

[0030] For example, motion sickness data can be used to comprehensively describe the motion sickness-related signs currently exhibited by a passenger, and can be a multi-dimensional feature vector.

[0031] For example, the above-mentioned acquisition of motion sickness data includes: collecting the behavioral and facial features of the target passenger during the current journey (for example, the passenger's behavioral and facial features can be monitored by an installed OMS camera; behavioral features may include head-down, eye-closed, forehead-holding, and abnormal head-shaking; facial features may include frowning, abnormal facial expression, and nausea); collecting the motion sickness self-rating level input by the target passenger (for example, the motion sickness self-rating level actively input by the passenger through the in-vehicle screen); and generating motion sickness data based on the behavioral features, facial features, and motion sickness self-rating level.

[0032] For example, behavioral features, facial features, and motion sickness self-rating levels can be organized into a feature vector. This vector can be arranged according to a preset feature dimension order, such as head-down posture, eye-closed posture, forehead-holding posture, abnormal head-shaking posture, frowning posture, abnormal facial expression, nausea expression, and motion sickness self-rating levels, forming a multi-dimensional numerical vector that serves as the passenger's motion sickness response data at the current moment. For each type of behavioral and facial feature, binarization can be used for quantification, such as using 1 to represent the presence of the behavior or expression and 0 to represent its absence. For the motion sickness self-rating level, a continuous value between 0 and 1 input by the passenger can be directly used.

[0033] For example, the rate of change of vehicle motion state can be used to quantify the intensity and abruptness of vehicle motion, and can characterize the degree of motion stimulation that the vehicle causes to passengers at the current moment, thus inducing motion sickness. For instance, the higher the rate of change of vehicle motion state, the more intense and abrupt the vehicle's motion is, and the more likely it is to induce motion sickness in passengers.

[0034] For example, the aforementioned acquisition of the rate of change of vehicle motion includes: vehicle displacement (which can represent the amplitude of vehicle swaying; frequent large displacements (such as continuous lane changes) can easily cause motion sickness), speed (used to represent the speed of vehicle movement; sudden changes in speed can easily cause motion sickness), acceleration (used to represent the rate of change of speed; excessive acceleration can stimulate the vestibular organs and cause dizziness), judder (used to represent the rate of change of acceleration; the greater the judder, the stronger the impact of sudden changes in vehicle motion, and the stronger the discomfort that may occur due to the inability to predict the trajectory of the vehicle), and angular displacement (used to represent the rate of change of acceleration). The rate of change of at least one of the following is used as the rate of change of the vehicle's motion state: the rotation angle of the vehicle body around a certain axis (where continuous or repeated angular displacement can cause motion sickness), angular velocity (used to indicate the speed of the vehicle body's rotation, where excessive angular velocity will produce strong centripetal acceleration, causing lateral pulling on the occupants' bodies and aggravating motion sickness), angular acceleration (used to indicate the rate of change of angular velocity, where excessive angular acceleration can cause rotational vertigo), and angular jerk (used to indicate the rate of change of angular acceleration, where angular jerk reflects the degree of abrupt change in steering torque, which can easily cause nervous tension and discomfort).

[0035] Specifically, one can first determine the absolute value of at least one of the vehicle's displacement, velocity, acceleration, jerk, angular displacement, angular velocity, angular acceleration, and angular jerk. Then, assign a weighting coefficient to each of these parameters, and use the weighted sum of the corresponding absolute values ​​as the rate of change of the vehicle's motion state. Alternatively, one can determine the absolute value of the rate of change per unit time for each of these parameters, assign a weighting coefficient to each of the absolute values, and use the weighted sum of the corresponding absolute values ​​as the rate of change of the vehicle's motion state.

[0036] For example, let MSS_i(t) be the motion sickness sensitivity index of the i-th passenger in the second row at time t, and let the value of the index range from [0,1]. The larger the MSS_i(t), the more prone the passenger is to motion sickness; the smaller the MSS_i(t), the stronger the passenger's tolerance to vehicle motion stimuli.

[0037] The specific construction method for the rate of change of vehicle motion state can be as follows: Select four motion parameters: longitudinal acceleration ax(t), lateral acceleration ay(t), jerk j(t), and yaw rate w(t). Take the absolute value of each parameter and multiply it by the corresponding weighting coefficients λ1, λ2, λ3, and λ4. Then sum the four weighted parameters to obtain: U(t)=λ1∣ax(t)∣+λ2∣ay(t)∣+λ3∣ω(t)∣+λ4∣j(t)∣; U(t) is the quantified value of the rate of change of the vehicle's motion state.

[0038] Motion sickness data (or passenger motion sickness status observations) are used to characterize whether passengers experience motion sickness. The sources of these observations can include three aspects, i.e., observational features: First, active passenger feedback (corresponding to a self-assessment level of motion sickness), i.e., an entry point is set up on the screen in the second row of the vehicle for passengers to input their own level of susceptibility to motion sickness; second, behavioral features, i.e., behaviors such as looking down, closing eyes, touching the forehead, and abnormal head movements monitored by the second-row OMS cameras; and third, facial features, i.e., facial expressions such as frowning, abnormal complexion, and nausea, monitored by the second-row OMS cameras. Based on the above three sources, the observational feature vector (i.e., motion sickness data) for the i-th passenger is determined as follows: Xi(t) = [x i1 (t),x i2 (t),...,x im (t)]; Each component corresponds to a specific observation feature.

[0039] In the above-described embodiment of S110, the individualized motion sickness sensitivity index of each passenger, multi-dimensional motion sickness reaction data, and the vehicle's current motion state change rate can be obtained in real time, thereby laying a data foundation for subsequent motion sickness prediction and adaptive vehicle control.

[0040] Furthermore, the S110 embodiment does not rely on a single signal source but integrates multimodal information from both active passenger feedback and passive perception. This ensures data accuracy and enables continuous monitoring of passenger status. Simultaneously, by comprehensively quantifying the vehicle's longitudinal acceleration, lateral acceleration, yaw rate, and jerk, the embodiment can fully reflect the degree to which vehicle motion induces motion sickness, avoiding the limitations of single-parameter assessments. Therefore, this embodiment effectively characterizes individual passenger differences and accurately quantifies vehicle motion stimuli, providing reliable data support for improving the accuracy of motion sickness prediction and the targeted nature of vehicle control.

[0041] S120: The motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate are weighted and summed to obtain the weighted sum value.

[0042] Weighted summation can refer to a technical means of comprehensively evaluating multiple data (or indicators, such as the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate mentioned above). Each data point can be assigned a weight (or coefficient, weight coefficient), which can be used to reflect the importance of each data point in the overall evaluation. Then, the data points are weighted according to their corresponding weights, and the weighted results are summed.

[0043] The weighted summation value mentioned above can refer to the result obtained by performing a weighted summation.

[0044] For example, S120 may include any of the following, but is not limited to: The first step involves assigning weights to the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate, and then summing these weights to obtain a weighted sum.

[0045] Alternatively, weights can be assigned to the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate, and the weighted sum can be obtained by multiplying each data by its corresponding weight and then adding it to a preset constant.

[0046] The second step involves performing a joint query on a pre-defined mapping table based on the motion sickness sensitivity index and the vehicle motion state change rate to obtain an initial weighted parameter. This initial weighted parameter is then multiplied or summed with the motion sickness reaction data to obtain a weighted sum. The row index of the pre-defined mapping table represents different value ranges of the motion sickness sensitivity index, and the column index represents different value ranges of the vehicle motion state change rate. The values ​​stored in the mapping table are the corresponding initial weighted parameters. Similarly, any two of the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate can be jointly queried in the pre-defined mapping table, and the remaining two values ​​can be multiplied or summed with the initial weighted parameter obtained from the joint query to obtain a weighted sum.

[0047] The third step involves determining the preset threshold range for each of the motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate. Based on the threshold range, the corresponding weighting coefficient is matched from the preset weighting rule table. The motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate are multiplied by their respective weighting coefficients and then summed to obtain a weighted sum value. Different threshold ranges in the weighting rule table correspond to different weighting coefficients.

[0048] The fourth step involves using a pre-defined machine learning model to perform a weighted summation of the motion sickness sensitivity index, motion sickness reaction data, and the rate of change in vehicle motion state. For example, this fourth step can be understood as: learning the weighted combination of the motion sickness sensitivity index, motion sickness reaction data, and the rate of change in vehicle motion state through a machine learning model, and then performing a weighted summation.

[0049] For example, motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate can be used as inputs to a machine learning model, and the weighted sum can be used as the output of the machine learning model.

[0050] For example, the machine learning model can be any of the following models, but not limited to: extreme gradient boosting (XGBoost) model, linear regression, multilayer perceptron (MLP), and K-Nearest Neighbors (KNN).

[0051] For example, taking the XGBoost model as an example, the input features of the XGBoost model include the motion sickness sensitivity index, motion sickness reaction data, and the rate of change of vehicle motion state at the target time. The model output is a weighted sum. The reasoning process is as follows: Acquire motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate. Input these data as input feature vectors into the XGBoost model. This model contains K pre-trained decision trees (e.g., a binary tree structure consisting of a root node, intermediate nodes, and leaf nodes; the root and intermediate nodes are each associated with an input feature index and a split threshold, while the leaf nodes store a weight score). For each of the K decision trees, starting from the root node, read the input feature index associated with the current node (this refers to the position number of the input feature being compared within the input feature vector; for example, if the input feature vector is in the order of motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate, then the three...). Each feature has an index of 0, 1, and 2, and a split threshold (the critical value used by the current node to make a branch decision on the input feature; this value is automatically determined based on the training data during model training). The system traverses downwards layer by layer until a leaf node is reached. The weight score stored in the leaf node is read as the output value of that decision tree (the prediction contribution of that decision tree to the current input sample; the sum of the weight scores of all decision trees is the final prediction result of the model). For the same input sample (an input feature vector consisting of a set of motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate obtained at the same target time), each decision tree outputs a corresponding weight score in the above manner. The weight scores output by the K decision trees are summed, and the sum is used as the final output of the model, i.e., the weighted sum value. Optionally, the sum can be non-linearly mapped (e.g., using the Sigmoid or Logit function) before output to constrain it to a preset range of values.

[0052] In addition, the above model can be trained using a training dataset containing motion sickness sensitivity index, motion sickness reaction data and vehicle motion state change rate collected at historical time points (the acquisition process is similar to S110 above) and the corresponding labeled weighted sum (the labeled value can be obtained from the passenger's subjective motion sickness score during the actual ride). Specifically, a gradient boosting serial iterative strategy can be used for training. During the training process, mean squared error can be used as a loss function to measure the difference between the predicted value and the labeled value. This application does not restrict the specific training process.

[0053] In this process, because the machine learning model can automatically learn the nonlinear and high-order interaction relationships between the three input parameters, it does not need to pre-assume a linear superposition relationship. It is suitable for real-world scenarios with complex motion sickness mechanisms (for example, when a vehicle is driving on a series of curves, the rate of change of the vehicle's motion state is at a moderate level, but if the passenger's motion sickness sensitivity index is high and the motion sickness reaction data has accumulated to a certain extent, then the relationship between the three is not a simple linear superposition relationship: the motion sickness reaction of a passenger with higher sensitivity may increase exponentially under the same motion stimulus, and the machine learning model can automatically capture such nonlinear coupling effects through training data without manually setting complex mathematical mapping relationships). Moreover, the inference stage only needs to execute the layer-by-layer condition judgment and score accumulation of multiple decision trees, which has a small computational load and high speed, and can meet the real-time requirements of vehicle control scenarios.

[0054] In the above-mentioned embodiment of S120, the information of three dimensions—personalized motion sickness sensitivity index, motion sickness reaction data, and vehicle motion state change rate—can be weighted and summed to fully consider the differentiated reactions of different passengers to the same vehicle motion, and thus make a prediction in advance before motion sickness actually occurs. Therefore, it is possible to accurately predict the risk of motion sickness in passengers, providing a reliable quantitative basis for subsequent adaptive control and online sensitivity learning, and effectively improving the accuracy of motion sickness prediction and individual adaptability.

[0055] S130: Based on the weighted summation, the predicted motion sickness state value is obtained, and the motion control parameters of the vehicle are controlled according to the predicted motion sickness state value. The predicted motion sickness state value represents the predicted probability of the target passenger experiencing motion sickness at the target time.

[0056] For example, obtaining the predicted motion sickness state value based on the weighted summation value includes: mapping the weighted summation value to a preset numerical range to obtain the predicted motion sickness state value.

[0057] For example, a motion sickness detection model can be used to predict the motion sickness state value. This model can employ logistic regression, taking the motion sickness sensitivity index, motion sickness reaction data, and the rate of change of vehicle motion as input variables. These input variables are then linearly weighted and summed to obtain a linear combination value, i.e., the weighted sum. This linear combination value is then non-linearly transformed using the Sigmoid function. The Sigmoid function can map any real number input to the interval between 0 and 1; therefore, the function's output value can be used as the model's predicted motion sickness probability, i.e., the predicted motion sickness state value, denoted as Y1_i(t). ; in, σ ( ) represents the Sigmoid function: .

[0058] Alternatively, the weighted sum can be normalized to obtain the predicted motion sickness value. Alternatively, an activation function or probability converter can be used to map the weighted sum to a preset numerical range to obtain the predicted motion sickness value.

[0059] For example, the above-mentioned control of vehicle motion control parameters based on predicted motion sickness values ​​includes: if the predicted motion sickness value is greater than or equal to a preset motion sickness threshold (e.g., 0.6), then the motion control parameters are controlled according to a first control strategy, which is used to control the motion parameters to reduce the degree of motion sickness of the target passenger; if the predicted motion sickness value is less than the motion sickness threshold, then the motion control parameters are controlled according to a second control strategy, which is used to maintain the motion control parameters.

[0060] For example, the first control strategy can be a high-comfort control strategy, which may include a set of vehicle motion control parameters (including parameters used to control the vehicle's motion control parameters) aimed at improving ride comfort. The second control strategy can be a normal control strategy, which may include a set of control parameters aimed at maintaining the standard motion control parameters calibrated by the vehicle.

[0061] For example, motion control parameters include at least one of the following: vehicle acceleration (which can be used to limit the vehicle's maximum starting acceleration and maximum braking deceleration to reduce the stimulation to passengers during acceleration and braking), vehicle yaw rate (used to limit the rate of change of acceleration to smooth the operation of the accelerator and brake pedals and avoid motion sickness caused by the shock of sudden acceleration changes), yaw rate (used to limit the vehicle's rotational rate when turning to reduce the lateral pull on the occupants' bodies caused by the centripetal acceleration generated when cornering), and body roll angle (used to control the degree of body tilt when cornering to suppress the effects of large body roll on the occupants). The components include: passenger stimulation, vehicle pitch angle (used to control the degree of vehicle pitch during acceleration and deceleration to suppress rear-end drop during rapid acceleration and front-end dive during rapid deceleration), suspension damping force (used to adjust the stiffness of the suspension system to absorb vertical vibrations and high-frequency shaking caused by uneven road surfaces, thereby improving ride comfort), drive motor torque (used to control the smoothness of power output, avoiding discomfort caused by rapid acceleration and deceleration by limiting the rate of torque change), and braking pressure (used to control the smoothness of braking force output, avoiding forward lurch and nose-diving during emergency braking by limiting the rate of braking pressure change).

[0062] For example, controlling motion control parameters according to the first control strategy can include: the vehicle control system sending commands to the vehicle to reduce the impact of vehicle motion on passenger comfort by limiting vehicle acceleration, controlling vehicle body posture, controlling measured posture, adjusting suspension damping force, limiting the rate of change of drive motor torque, and adjusting braking pressure at least one of these methods. For example, by controlling the throttle and brakes, the maximum starting acceleration and maximum braking deceleration of the vehicle can be limited to a low level; by using active suspension or shock absorbers, the "nose-up" and "throttle-down" phenomena of the vehicle during acceleration and deceleration, as well as body roll during cornering, can be suppressed.

[0063] In addition, vehicle motion control parameters can also be controlled in the following ways, but are not limited to these: Based on the predicted motion sickness status values ​​of multiple passengers, a first fused predicted motion sickness status value is obtained (for example, it can be obtained by taking the maximum, median, minimum, average, or sum, or a weighted sum, etc., and the first fused predicted motion sickness status value can be used to quantify the overall motion sickness risk level of multiple passengers); the vehicle's motion control parameters are controlled according to the first fused predicted motion sickness status value, where multiple passengers include the target passenger (the corresponding embodiment here can refer to the embodiment above that controls the vehicle's motion control parameters based on the target passenger's predicted motion sickness status value); or... Based on the individual motion sickness sensitivity indices of multiple passengers, a fused motion sickness sensitivity index is obtained (for example, by taking the maximum, median, minimum, average, weighted average, etc., to obtain the fused motion sickness sensitivity index); based on the fused motion sickness sensitivity index, a second fused predicted motion sickness state value corresponding to multiple passengers is obtained (similar to the embodiment where the predicted motion sickness state value of the target passenger is obtained based on the motion sickness sensitivity index of the target passenger); the motion control parameters of the vehicle are controlled based on the second fused predicted motion sickness state value (the corresponding embodiment here can refer to the embodiment where the motion control parameters of the vehicle are controlled based on the predicted motion sickness state value of the target passenger described above).

[0064] For example, multiple passengers can refer to multiple occupants inside the vehicle, including the target passenger. Examples of motion sickness sensitivity indices or predicted motion sickness values ​​for multiple passengers can refer to examples of motion sickness sensitivity indices or predicted motion sickness values ​​for the target passenger.

[0065] In the embodiment of S130 above, when the target motion sickness state value reflects a high level of motion sickness risk, the vehicle's motion control parameters can be actively adjusted to reduce motion stimulation; when the risk level is low, the normal driving mode is maintained to avoid unnecessary changes in driving style that could affect traffic efficiency. Thus, while ensuring passenger comfort, normal vehicle driving efficiency is also taken into account, achieving a balance between motion sickness suppression and driving smoothness.

[0066] In addition, by maintaining a motion sickness sensitivity index for each passenger and conducting a fusion assessment in multi-passenger scenarios, the overall motion sickness risk level of passengers in the vehicle can be accurately quantified, making the motion sickness risk assessment results closer to the passengers' real feelings.

[0067] In one embodiment, the technical solution of this application further includes the following steps: Based on the predicted motion sickness value and at least one of the following: the actual motion sickness value of the target passenger at the target time, the rate of change of vehicle motion state at the target time, the historical rate of change of vehicle motion state, the historical actual motion sickness value, and the historical motion sickness sensitivity index under the historical journey, the updated motion sickness sensitivity index corresponding to the next time at the target time is obtained, so as to obtain the predicted motion sickness value corresponding to the next time based on the updated motion sickness sensitivity index.

[0068] For example, the actual motion sickness status value can refer to the degree of motion sickness actually experienced by the passenger at the current moment. For instance, it can be obtained through human-computer interaction, by asking "How is your current motion sickness status? Please rate it from 0 to 1." The rating given by the passenger is the actual motion sickness status value, where 0 indicates no motion sickness reaction, 1 indicates a strong motion sickness reaction, and the middle value indicates a mild to moderate motion sickness trend.

[0069] For example, the historical vehicle motion state change rate can refer to the rate of change of vehicle motion state within a preset time window before the target time, or in previous trips (i.e., historical trips) before the current trip. The historical vehicle motion state change rate can be used to calculate cumulative stimulus intensity or analyze the trend of stimulus intensity changes. For example, historical actual motion sickness values ​​can refer to the actual motion sickness observations of passengers within a preset time window before the target time, or in previous trips prior to this trip (i.e., actual motion sickness values). Historical actual motion sickness values ​​can be used to calculate cumulative motion sickness values ​​or analyze the changing trends of passengers' motion sickness reactions.

[0070] For example, the historical motion sickness sensitivity index under a historical itinerary can refer to the motion sickness sensitivity index recorded by the passenger based on multiple rides prior to the start of this trip. The motion sickness sensitivity index can represent the passenger's long-term stable motion sickness constitution characteristics and serve as the basis for cross-trip data fusion.

[0071] For example, the above method, based on the predicted motion sickness value and at least one of the following: the target passenger's actual motion sickness value at the target time, the vehicle motion state change rate at the target time, the historical vehicle motion state change rate, the historical actual motion sickness value, and the historical motion sickness sensitivity index for the historical itinerary, obtains the updated motion sickness sensitivity index for the next time corresponding to the target time, including: calculating at least one of the following: The predicted motion sickness value is compared with the actual motion sickness value to obtain the first prediction error (for example, the difference between the two is calculated as the first prediction error). Based on the rate of change of vehicle motion state, calculate the vehicle stimulus intensity correction factor. The vehicle stimulus intensity correction factor is positively correlated with the rate of change of vehicle motion state (for example, the vehicle stimulus intensity correction factor can be set to 1 plus the product of the vehicle stimulus intensity correction coefficient and the rate of change of vehicle motion state). Based on the historical vehicle motion state change rate within the first preset time window, calculate the cumulative vehicle stimulus intensity (for example, the cumulative vehicle stimulus intensity can be the average, maximum, median, sum, or minimum value of the historical vehicle motion state change rate for each trip or time period within the preset time window). Based on the historical actual motion sickness values ​​within the second preset time window, calculate the cumulative motion sickness value (for example, the cumulative motion sickness value can be the average, maximum, median, sum, or minimum value of the historical actual motion sickness values ​​for each trip or time period within the preset time window). Calculate the long-term motion sickness sensitivity index based on the historical motion sickness sensitivity index for each trip (for example, the long-term motion sickness sensitivity index can be the average, maximum, median, sum, or minimum value of the historical motion sickness sensitivity index for each trip or each time period). Based on at least one of the following: first prediction error, vehicle stimulus intensity correction factor, cumulative vehicle stimulus intensity, cumulative motion sickness state value, and long-term motion sickness sensitivity index, the updated motion sickness sensitivity index for the next time corresponding to the target time is obtained.

[0072] Specifically, the updated motion sickness sensitivity index for the next time step corresponding to the target time step is obtained based on at least one of the following: the first prediction error, the vehicle stimulus intensity correction factor, the cumulative vehicle stimulus intensity, the cumulative motion sickness state value, and the long-term motion sickness sensitivity index. This includes at least one of the following: The first update method involves multiplying the product of the first learning rate and the first prediction error by the vehicle stimulus intensity correction factor to obtain the first product. The updated motion sickness sensitivity index is then calculated by summing this first product with the motion sickness sensitivity index. This method allows for rapid response to passengers' instantaneous reactions, and the update magnitude automatically increases when the vehicle motion stimulus is strong.

[0073] The second update method is as follows: Calculate the cumulative vehicle stimulus intensity correction factor based on the cumulative vehicle stimulus intensity. The cumulative vehicle stimulus intensity correction factor is positively correlated with the cumulative vehicle stimulus intensity. Calculate the second prediction error based on the cumulative motion sickness state value and the motion sickness sensitivity index. Multiply the product of the second learning rate and the second prediction error by the cumulative vehicle stimulus intensity correction factor to obtain the second product. The updated motion sickness sensitivity index is obtained by summing the second product and the motion sickness sensitivity index.

[0074] For example, firstly, based on the historical rate of change of vehicle motion state within a preset time window, the cumulative or average stimulus intensity within the window is calculated, and a cumulative stimulus intensity correction factor is calculated based on this cumulative stimulus intensity, which is positively correlated with the cumulative stimulus intensity. Secondly, based on the historical actual motion sickness values ​​within the window, the cumulative or average motion sickness value is calculated, and a second prediction error is calculated by combining this with the current motion sickness sensitivity index. Finally, the set second learning rate is multiplied by the second prediction error, and then multiplied by the cumulative stimulus intensity correction factor to obtain a second product. This product is then added to the current motion sickness sensitivity index to complete the update. This method can reflect the physiological characteristic that motion sickness is induced by the accumulation of stimuli over a period of time, and can effectively smooth out the interference of instantaneous noise.

[0075] The third update method: Based on the long-term motion sickness sensitivity index and the motion sickness sensitivity index of the current trip, the updated motion sickness sensitivity index is obtained.

[0076] For example, after a trip, the average sensitivity index for the entire trip is calculated based on the recorded motion sickness sensitivity indices at various times during the trip. Simultaneously, the long-term motion sickness sensitivity index established by the passenger before this trip is retrieved. Then, the long-term sensitivity index is multiplied by a historical retention coefficient, and the difference between multiplying the average sensitivity index for the current trip by 1 and subtracting the historical retention coefficient is summed to obtain the updated motion sickness sensitivity index. This method allows the system to respond to new sensitivity changes during the current trip without completely discarding the passenger's long-term historical characteristics.

[0077] In addition, any two or three of the above three update methods can be combined. For example, instantaneous error update (corresponding to the first update method) and window cumulative update (corresponding to the second update method) can be used at the same time, or, on the basis of window cumulative update, cross-trip fusion update can be performed after the trip ends (corresponding to the third update method) to achieve comprehensive learning across multiple time scales.

[0078] For example, the motion sickness score, i.e. the actual motion sickness state value (or actual motion sickness observation value), is obtained by the passenger through human-computer interaction: Yi(t). The score ranges from 0 to 1, where 0 indicates that the passenger has no motion sickness at all and 1 indicates that the passenger has a very strong motion sickness.

[0079] Define the first prediction error as: ; The meaning is as follows: This indicates that the actual degree of motion sickness is more severe than predicted, suggesting that the current... The estimate is too low and should be increased. This indicates that the actual degree of motion sickness is more severe than predicted, suggesting that the current... The estimate is too high and should be lowered.

[0080] The motion sickness sensitivity index can be updated online using the following formula: MSS_i(t+1)=MSS_i(t)+α ei(t) g(U(t)); Where α is the first learning rate, and g(U(t)) is the stimulus intensity correction function, g(U(t)) = 1 + ηU(t), where η is the stimulus intensity correction coefficient. This means that when the vehicle motion stimulus is stronger, the passenger's motion sickness response contributes more to the sensitivity learning; when the vehicle stimulus is weaker, the update amplitude is smaller.

[0081] A time window of length Tw can be defined, i.e., a preset time window. The cumulative stimulus amount, i.e., the cumulative stimulus intensity, can be calculated within the window Tw. ; Calculate the average motion sickness observation value within this window, i.e., the cumulative motion sickness status value: ; Then update based on window level: ; ; The boundary constraints are as follows: ; The sensitivity is constrained to the interval [0,1].

[0082] After this trip, the short-term results will be integrated with historical long-term models: ; in, This represents the long-term sensitivity before the nth trip. β represents the average sensitivity of this trip, and β is the historical retention coefficient.

[0083] In the above embodiments, the prediction error can be calculated in real time based on the actual motion sickness experience reported by passengers, and the motion sickness sensitivity index can be dynamically adjusted based on the error, so that the sensitivity model continuously converges to the actual motion sickness physical characteristics of passengers, thereby achieving accurate modeling of individual differences among passengers.

[0084] Furthermore, by introducing a correction factor positively correlated with the rate of change of vehicle motion state, the sensitivity update amplitude can be increased when the vehicle motion is intense and decreased when the vehicle motion is stable. This ensures learning efficiency while avoiding erroneous updates due to noise interference under low-stimulus conditions. Secondly, by introducing a time window accumulation mechanism, learning can be based on accumulated stimuli and responses over a continuous period of time, accurately reflecting the physiological characteristic that "motion sickness is induced by accumulated stimuli," effectively avoiding the interference of instantaneous noise on model training and improving the stability and accuracy of learning. In addition, through a cross-trip historical memory update mechanism, it can quickly respond to changes in the passenger's state during the current trip while retaining long-term accumulated historical characteristics, achieving a unity of short-term adaptability and long-term memory capacity. Even if passengers experience temporary changes in sensitivity due to fatigue, fasting, or fluctuations in health status, they will not completely forget their historical physical characteristics.

[0085] In other words, the above embodiments can achieve continuous, accurate, and stable online learning of the motion sickness sensitivities of different passengers, thereby providing a reliable individualized basis for subsequent vehicle adaptive control, and ultimately achieving the technical effects of effectively suppressing motion sickness, adapting to individual passenger differences, and improving vehicle ride comfort.

[0086] It is understood that the above embodiment determines the motion sickness sensitivity index MSSi(t+1) at the next moment based on the motion sickness sensitivity index MSSi(t) at the current moment, and then determines the predicted motion sickness state value at the next moment based on the motion sickness sensitivity index at the next moment. Similarly, the motion sickness sensitivity index MSSi(t) at the current moment can also be determined based on the motion sickness sensitivity index MSSi(t-1) at the previous moment. To avoid repetition, this application will not elaborate on this.

[0087] In some embodiments, the technical solution of this application will be described again below with reference to schematic diagrams.

[0088] For example, in conjunction with the above embodiments, such as Figure 2A As shown, the vehicle motion adaptive control system based on individual passenger motion sickness sensitivity provided in this application involves the following module architecture and data flow: First, the vehicle motion state acquisition module collects real-time data on the vehicle's longitudinal acceleration, lateral acceleration, yaw rate, and jerk, among other vehicle motion state change rates. Simultaneously, the passenger state acquisition module collects passenger facial expressions, head postures, and other information through in-vehicle sensors (such as OMS cameras) and obtains active feedback data from passengers through a human-machine interface. Both types of data are input into the motion sickness state recognition module, which internally deploys a motion sickness probability estimation model to comprehensively process the vehicle motion state change rate and individual passenger state data (such as the target passenger's motion sickness sensitivity index and motion sickness reaction data at the target time), outputting the passenger's predicted motion sickness probability at the current time, i.e., the predicted motion sickness state value. Subsequently, the predicted motion sickness probability and the actual motion sickness status reported by passengers are transmitted to the online learning and updating module for motion sickness sensitivity. This module iteratively updates the passengers' motion sickness sensitivity index according to a preset update rule based on the prediction error and the rate of change of vehicle motion state, realizing the dynamic evolution from MSS_i(t) to MSS_i(t+1). The updated motion sickness sensitivity indices of each passenger are input into the multi-passenger comprehensive motion sickness assessment module. This module uses a preset fusion strategy (e.g., taking the maximum value of each passenger's sensitivity index, or performing a weighted average calculation) to comprehensively assess the overall motion sickness risk of all passengers in the vehicle and generate a unified target motion sickness status value. Finally, the vehicle control strategy generation module decides and executes the corresponding vehicle motion control strategy (such as a high comfort control strategy or a normal control strategy) based on the target motion sickness status value to control the vehicle's motion control parameters, forming a complete closed loop from perception, learning to decision-making and execution.

[0089] For example, in conjunction with the above embodiments, such as Figure 2B As shown, the online update process for the motion sickness sensitivity index in this application specifically includes the following steps: Step 1: Obtain the passenger's initial motion sickness sensitivity, i.e., the motion sickness sensitivity index. An initial motion sickness sensitivity index is assigned to the passenger upon system startup or when a new passenger takes their first ride.

[0090] Step two: Collect the vehicle's motion state, i.e., the rate of change of the vehicle's motion state. Real-time acquisition of motion parameters such as longitudinal acceleration, lateral acceleration, yaw rate, and jerk.

[0091] Step 3: Collect passenger status information, i.e., motion sickness data. Through in-vehicle sensors and human-machine interface, collect passengers' facial expressions, posture characteristics, and self-reported motion sickness information in real time.

[0092] Step four: Calculate the motion sickness prediction probability, i.e., the predicted motion sickness state value. Combine the vehicle motion state data collected in step two with the passenger state information collected in step three, along with the motion sickness sensitivity index at the current moment, and input them into the motion sickness probability estimation model to calculate the passenger's predicted motion sickness probability at the current moment.

[0093] Step 5: Obtain the actual motion sickness observation value, i.e., the actual motion sickness state value. This is achieved through passenger feedback (e.g., voice rating or screen input) to obtain the passenger's actual motion sickness state value at the current moment, serving as a monitoring signal.

[0094] Step Six: Calculate the prediction error. The difference between the actual motion sickness observations obtained in Step Five and the predicted motion sickness probability calculated in Step Four is used to calculate the prediction error. The sign and magnitude of this error reflect the direction and degree of deviation in the current motion sickness sensitivity index estimate.

[0095] Step 7: Update motion sickness sensitivity. Based on the prediction error calculated in Step 6 and the vehicle motion state change rate collected in Step 2, the motion sickness sensitivity index is updated according to the preset online learning rules, so that the updated index more accurately reflects the passenger's true motion sickness sensitivity.

[0096] Step 8: Implement boundary constraints and smoothing constraints. Perform boundary checks on the updated motion sickness sensitivity index: set it to 0 if the value is less than 0, and set it to 1 if it is greater than 1. Optional smoothing constraints are used to prevent drastic changes in the sensitivity index, ensuring the stability of the learning process.

[0097] Step 9: Output the updated motion sickness sensitivity. The motion sickness sensitivity index, processed with boundary constraints and smoothing constraints, is output for subsequent motion sickness prediction and vehicle control strategy decisions.

[0098] Figure 3 This is a schematic diagram of a motion sickness treatment device provided in an embodiment of this application. Figure 3 As shown, the motion sickness treatment device 300 includes: The data acquisition module 310 is used to acquire the motion sickness sensitivity index and motion sickness reaction data of the target passenger at the target time, as well as the rate of change of vehicle motion state at the target time. The weighted summation module 320 is used to perform weighted summation on the motion sickness sensitivity index, motion sickness reaction data and vehicle motion state change rate to obtain a weighted summation value; The predictive control module 330 is used to obtain a predicted motion sickness state value based on a weighted summation value, and to control the motion control parameters of the vehicle based on the predicted motion sickness state value. The predicted motion sickness state value represents the predicted probability that the target passenger will experience motion sickness at the target time.

[0099] In some embodiments, the prediction control module 330 is specifically used for: The weighted sum is mapped to a preset range to obtain the predicted motion sickness value.

[0100] In some embodiments, the data acquisition module 310 is specifically used for: The rate of change of at least one of the vehicle's displacement, velocity, acceleration, jerk, angular displacement, angular velocity, angular acceleration, and angular jerk per unit time is taken as the rate of change of the vehicle's motion state.

[0101] In some embodiments, the data acquisition module 310 is specifically used for: Collect behavioral and facial features of the target passenger during the current journey; Collect the motion sickness self-rating level input by the target passenger; Motion sickness data is generated based on behavioral characteristics, facial features, and self-rating levels of motion sickness.

[0102] In some embodiments, the motion sickness treatment device 300 further includes an update module for: Based on the predicted motion sickness value and at least one of the following: the actual motion sickness value of the target passenger at the target time, the rate of change of vehicle motion state at the target time, the historical rate of change of vehicle motion state, the historical actual motion sickness value, and the historical motion sickness sensitivity index under the historical journey, the updated motion sickness sensitivity index corresponding to the next time at the target time is obtained, so as to obtain the predicted motion sickness value corresponding to the next time based on the updated motion sickness sensitivity index.

[0103] In some embodiments, the update module is specifically used for: Calculate at least one of the following: The predicted motion sickness value is compared with the actual motion sickness value to obtain the first prediction error; The vehicle stimulus intensity correction factor is calculated based on the rate of change of vehicle motion state. The vehicle stimulus intensity correction factor is positively correlated with the rate of change of vehicle motion state. The cumulative vehicle stimulus intensity is calculated based on the rate of change of historical vehicle motion status within the first preset time window. Calculate the cumulative motion sickness value based on the historical actual motion sickness values ​​within the second preset time window; Calculate the long-term motion sickness sensitivity index based on the historical motion sickness sensitivity index of the historical itinerary; Based on at least one of the following: first prediction error, vehicle stimulus intensity correction factor, cumulative vehicle stimulus intensity, cumulative motion sickness state value, and long-term motion sickness sensitivity index, the updated motion sickness sensitivity index for the next time corresponding to the target time is obtained.

[0104] In some embodiments, the update module is specifically used for at least one of the following: The product of the first learning rate and the first prediction error is multiplied by the vehicle stimulus intensity correction factor to obtain the first product. The updated motion sickness sensitivity index is obtained based on the sum of the first product and the motion sickness sensitivity index. The cumulative vehicle stimulus intensity correction factor is calculated based on the cumulative vehicle stimulus intensity. The cumulative vehicle stimulus intensity correction factor is positively correlated with the cumulative vehicle stimulus intensity. The second prediction error is calculated based on the cumulative motion sickness state value and the motion sickness sensitivity index. The product of the second learning rate and the second prediction error is multiplied by the cumulative vehicle stimulus intensity correction factor to obtain the second product. The updated motion sickness sensitivity index is obtained based on the sum of the second product and the motion sickness sensitivity index. An updated motion sickness sensitivity index is obtained based on the long-term motion sickness sensitivity index and the motion sickness sensitivity index of the current trip.

[0105] In some embodiments, the prediction control module 330 is specifically used for: If the predicted motion sickness value is greater than or equal to the preset motion sickness threshold, the motion control parameters are controlled according to the first control strategy. The first control strategy is used to control the motion parameters to reduce the degree of motion sickness of the target passenger. If the predicted motion sickness value is less than the motion sickness threshold, the motion control parameters are controlled according to the second control strategy, which is used to maintain the motion control parameters.

[0106] In some embodiments, the prediction control module 330 is specifically used for: A first fused predicted motion sickness value is obtained based on the predicted motion sickness values ​​of multiple passengers; the vehicle's motion control parameters are then controlled based on this first fused predicted motion sickness value, where the multiple passengers include the target passenger; or... A fused motion sickness sensitivity index is obtained based on the individual motion sickness sensitivity indices of multiple passengers; a second fused predicted motion sickness state value is obtained based on the fused motion sickness sensitivity index of multiple passengers; and the motion control parameters of the vehicle are controlled based on the second fused predicted motion sickness state value.

[0107] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, further details will not be provided here. Specifically, Figure 3 The apparatus 300 shown can execute the above-described method embodiments, and the aforementioned and other operations and / or functions of each module in the apparatus 300 are respectively for implementing the corresponding processes in the above-described methods. For the sake of brevity, they will not be described in detail here.

[0108] The apparatus 300 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0109] Figure 4 A schematic diagram of an electronic device provided in an embodiment of this application.

[0110] like Figure 4 As shown, the electronic device 400 may include: The system includes a memory 410 and a processor 420. The memory 410 stores computer programs and transfers the program code to the processor 420. In other words, the processor 420 can retrieve and run the computer program from the memory 410 to implement the methods described in the embodiments of this application.

[0111] For example, the processor 420 can be used to execute the above-described method embodiments according to instructions in the computer program.

[0112] In some embodiments of this application, the processor 420 may include, but is not limited to: General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0113] In some embodiments of this application, the memory 410 includes, but is not limited to: Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0114] In some embodiments of this application, the computer program may be divided into one or more modules, which are stored in the memory 410 and executed by the processor 420 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0115] like Figure 4 As shown, the electronic device may further include: Transceiver 430, which can be connected to processor 420 or memory 410.

[0116] The processor 420 can control the transceiver 430 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 430 may include a transmitter and a receiver. The transceiver 430 may further include antennas, and the number of antennas may be one or more.

[0117] It should be understood that the various components in the electronic device are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0118] This application also provides a computer storage medium storing a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0119] When implemented using software, it can be implemented entirely or partially as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, the computer can perform all or part of the corresponding processes in the methods of the embodiments of this application, producing the functions achievable by the methods of the embodiments of this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid state disks (SSDs)).

[0120] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0122] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0123] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for treating motion sickness, characterized in that, include: Acquire the motion sickness sensitivity index and motion sickness reaction data of the target passenger at the target time, as well as the rate of change of vehicle motion state at the target time; The motion sickness sensitivity index, the motion sickness reaction data, and the vehicle motion state change rate are weighted and summed to obtain a weighted sum value. Based on the weighted summation value, a predicted motion sickness state value is obtained, and the vehicle's motion control parameters are controlled according to the predicted motion sickness state value. The predicted motion sickness state value represents the predicted probability that the target passenger will experience motion sickness at the target time.

2. The motion sickness treatment method according to claim 1, characterized in that, The step of obtaining the predicted motion sickness value based on the weighted summation includes: The weighted summation value is mapped to a preset numerical range to obtain the predicted motion sickness state value.

3. The motion sickness treatment method according to claim 1, characterized in that, Obtaining the rate of change of the vehicle's motion state includes: The rate of change of at least one of the vehicle's displacement, velocity, acceleration, jerk, angular displacement, angular velocity, angular acceleration, and angular jerk per unit time is taken as the rate of change of the vehicle's motion state.

4. The motion sickness treatment method according to claim 1, characterized in that, Acquiring the motion sickness data includes: Collect the behavioral and facial features of the target passenger during the current journey; Collect the motion sickness self-rating level input by the target passenger; The motion sickness reaction data is generated based on the behavioral characteristics, facial features, and motion sickness self-rating.

5. The motion sickness treatment method according to any one of claims 1-4, characterized in that, Also includes: Based on the predicted motion sickness value and at least one of the following: the actual motion sickness value of the target passenger at the target time, the vehicle motion state change rate at the target time, the historical vehicle motion state change rate, the historical actual motion sickness value, and the historical motion sickness sensitivity index under the historical journey, an updated motion sickness sensitivity index for the next time corresponding to the target time is obtained, so as to obtain the predicted motion sickness value for the next time based on the updated motion sickness sensitivity index.

6. The motion sickness treatment method according to claim 5, characterized in that, The updated motion sickness sensitivity index for the next moment corresponding to the target moment is obtained based on the predicted motion sickness state value and at least one of the following: the actual motion sickness state value of the target passenger at the target time, the vehicle motion state change rate at the target time, the historical vehicle motion state change rate, the historical actual motion sickness state value, and the historical motion sickness sensitivity index under the historical itinerary. Calculate at least one of the following: The predicted motion sickness value and the actual motion sickness value are compared to obtain the first prediction error; Based on the rate of change of the vehicle motion state, a vehicle stimulus intensity correction factor is calculated, and the vehicle stimulus intensity correction factor is positively correlated with the rate of change of the vehicle motion state. The cumulative vehicle stimulus intensity is calculated based on the rate of change of the historical vehicle motion state within the first preset time window. Calculate the cumulative motion sickness value based on the historical actual motion sickness value within the second preset time window; Calculate the long-term motion sickness sensitivity index based on the historical motion sickness sensitivity index of the historical itinerary; The updated motion sickness sensitivity index for the next time moment corresponding to the target time is obtained based on at least one of the first prediction error, the vehicle stimulus intensity correction factor, the cumulative vehicle stimulus intensity, the cumulative motion sickness state value, and the long-term motion sickness sensitivity index.

7. The motion sickness treatment method according to claim 6, characterized in that, The step of obtaining the updated motion sickness sensitivity index for the next time moment corresponding to the target time based on at least one of the first prediction error, the vehicle stimulus intensity correction factor, the cumulative vehicle stimulus intensity, the cumulative motion sickness state value, and the long-term motion sickness sensitivity index includes at least one of the following: The product of the first learning rate and the first prediction error is multiplied by the vehicle stimulus intensity correction factor to obtain the first product. The updated motion sickness sensitivity index is obtained by summing the first product and the motion sickness sensitivity index. A cumulative vehicle stimulus intensity correction factor is calculated based on the cumulative vehicle stimulus intensity. The cumulative vehicle stimulus intensity correction factor is positively correlated with the cumulative vehicle stimulus intensity. A second prediction error is calculated based on the cumulative motion sickness state value and the motion sickness sensitivity index. The product of the second learning rate and the second prediction error is multiplied by the cumulative vehicle stimulus intensity correction factor to obtain a second product. The updated motion sickness sensitivity index is obtained based on the sum of the second product and the motion sickness sensitivity index. The updated motion sickness sensitivity index is obtained based on the long-term motion sickness sensitivity index and the motion sickness sensitivity index of the current trip.

8. The motion sickness treatment method according to any one of claims 1-4, characterized in that, The step of controlling the vehicle's motion control parameters based on the predicted motion sickness value includes: If the predicted motion sickness value is greater than or equal to the preset motion sickness threshold, then the motion control parameters are controlled according to the first control strategy. The first control strategy is used to control the motion parameters to reduce the degree of motion sickness of the target passenger. If the predicted motion sickness value is less than the motion sickness threshold, the motion control parameters are controlled according to the second control strategy, which is used to maintain the motion control parameters.

9. The method for treating motion sickness according to any one of claims 1-4, characterized in that, Also includes: Based on the predicted motion sickness values ​​of multiple passengers, the first fusion predicted motion sickness value is obtained; The vehicle's motion control parameters are controlled based on the first fused predicted motion sickness value, wherein the plurality of passengers includes the target passenger; or... A fused motion sickness sensitivity index is obtained based on the individual motion sickness sensitivity indices of the multiple passengers; a second fused predicted motion sickness state value is obtained based on the fused motion sickness sensitivity index of the multiple passengers. The motion control parameters of the vehicle are controlled based on the second fusion-predicted motion sickness value.

10. A motion sickness treatment device, characterized in that, include: The data acquisition module is used to acquire the motion sickness sensitivity index and motion sickness reaction data of the target passenger at the target time, as well as the rate of change of the vehicle's motion state at the target time. The weighted summation module is used to perform a weighted summation on the motion sickness sensitivity index, the motion sickness reaction data, and the vehicle motion state change rate to obtain a weighted summation value; The predictive control module is used to obtain a predicted motion sickness state value based on the weighted summation value, and to control the vehicle's motion control parameters based on the predicted motion sickness state value. The predicted motion sickness state value represents the predicted probability that the target passenger will experience motion sickness at the target time.

11. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the motion sickness treatment method according to any one of claims 1-9 by executing the executable instructions.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the motion sickness treatment method according to any one of claims 1-9.

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