Motion sickness prediction and prompting methods, devices, electronic equipment and computer-readable media

Abstract

This application discloses a motion sickness prediction and prompting method, device, electronic device, and computer-readable medium applied to a vehicle system. The method includes: acquiring motion sickness level information of a target user within a specified time period; configuring at least one parameter in a motion sickness prediction model based on the motion sickness level information to obtain a target motion sickness prediction model matching the target user; predicting the motion sickness prediction value of the target user at a specified time based on the target motion sickness prediction model; and sending a prompt message to the target user's target device if the motion sickness prediction value exceeds a specified threshold, wherein the target device is determined based on the target user's user behavior. Since the target motion sickness prediction model is obtained by configuring the parameters of the motion sickness prediction model based on the target user's motion sickness level information, the accuracy of the predicted motion sickness value of the target user predicted by the target motion sickness prediction model is high, and it can promptly prompt the target user, thereby achieving timely prompting before the target user has already become dizzy.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a motion sickness prediction and alerting method, device, electronic device, and computer-readable medium. Background Technology

[0002] Currently, with the development of automotive technology, vehicle systems can measure the degree of motion sickness in users and then adjust the vehicle's driving style based on the passenger's level of motion sickness. However, current methods for measuring the degree of motion sickness have low accuracy and make it difficult to warn users before they become dizzy. Summary of the Invention

[0003] This application proposes a motion sickness prediction and alerting method, device, electronic device, and computer-readable medium.

[0004] In a first aspect, embodiments of this application provide a motion sickness prediction and prompting method applied to a vehicle system. The method includes: acquiring motion sickness level information of a target user within a specified time period; configuring at least one parameter in a motion sickness prediction model based on the motion sickness level information to obtain a target motion sickness prediction model matching the target user; predicting a motion sickness prediction value of the target user at a specified time based on the target motion sickness prediction model; and if the motion sickness prediction value exceeds a specified threshold, sending a prompt message to a target device of the target user, wherein the target device is determined based on the user behavior of the target user.

[0005] Secondly, embodiments of this application also provide a motion sickness prediction and prompting device applied to a vehicle system. The device includes: a first acquisition unit, a second acquisition unit, a prediction unit, and a prompting unit. The first acquisition unit is used to acquire motion sickness level information of a target user within a specified time period; the second acquisition unit is used to configure at least one parameter in a motion sickness prediction model based on the motion sickness level information to obtain a target motion sickness prediction model matching the target user; the prediction unit is used to predict the motion sickness prediction value of the target user at a specified time based on the target motion sickness prediction model; and the prompting unit is used to send a prompting message to the target device of the target user if the motion sickness prediction value exceeds a specified threshold, wherein the target device is determined based on the user behavior of the target user.

[0006] Thirdly, embodiments of this application also provide an electronic device, including: one or more processors; the one or more processors are configured to acquire prompt information sent by the vehicle system, the prompt information being determined by the vehicle system based on the method described in the first aspect.

[0007] Fourthly, embodiments of this application also provide a computer-readable storage medium storing processor-executable program code, which, when executed by the processor, causes the processor to perform the method described in the first aspect.

[0008] The motion sickness prediction and alerting method, device, electronic device, and computer-readable medium provided in this application first acquire motion sickness level information of a target user within a specified time period. Then, based on the motion sickness level information, at least one parameter in a motion sickness prediction model is configured to obtain a target motion sickness prediction model matching the target user. Based on the target motion sickness prediction model, a motion sickness prediction value for the target user at a specified time is predicted. When the motion sickness prediction value exceeds a specified threshold, an alert message is sent to the target user's target device. Since the target motion sickness prediction model in this application is obtained by configuring the parameters of the motion sickness prediction model based on the target user's motion sickness level information, the accuracy of the motion sickness prediction value for the target user obtained through the target motion sickness prediction model is high. Furthermore, when the motion sickness prediction value obtained through the target motion sickness prediction model exceeds the specified threshold, an alert message can be sent to the target user's target device, thereby timely alerting the target user before they become dizzy, improving the user experience.

[0009] Other features and advantages of the embodiments of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the embodiments of this application. The objects and other advantages of the embodiments of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 The diagram illustrates an application scenario of the motion sickness prediction and alerting method provided in this embodiment.

[0012] Figure 2 A flowchart of the motion sickness prediction and prompting method provided in an embodiment of this application is shown;

[0013] Figure 3 A flowchart of a motion sickness prediction and alert method according to another embodiment of this application is shown;

[0014] Figure 4A flowchart of a motion sickness prediction and alert method according to another embodiment of this application is shown;

[0015] Figure 5 A flowchart of a motion sickness prediction and alert method according to another embodiment of this application is shown;

[0016] Figure 6 A schematic diagram of motion amplitude data processing provided in an embodiment of this application is shown;

[0017] Figure 7 A flowchart of a motion sickness prediction and alert method according to another embodiment of this application is shown;

[0018] Figure 8 A structural block diagram of the motion sickness prediction and alerting device provided in an embodiment of this application is shown;

[0019] Figure 9 A structural block diagram of the electronic device provided in an embodiment of this application is shown;

[0020] Figure 10 This paper shows a structural block diagram of a computer-readable storage medium provided in an embodiment of this application;

[0021] Figure 11 A structural block diagram of a computer program product provided in an embodiment of this application is shown. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. The components of the embodiments of the present application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without inventive effort are within the scope of protection of the present application.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Currently, with the development of automotive technology, vehicle systems can measure the degree of motion sickness in users and then adjust the vehicle's driving style based on this measurement. However, current methods for measuring motion sickness have low accuracy and make it difficult to alert users before they become unconscious. Improving the accuracy of motion sickness measurement and providing alerts before unconsciousness is a pressing issue that needs to be addressed.

[0025] In existing technologies, in order to measure the degree of motion sickness of users, the motion information of the vehicle, such as the vehicle's speed and acceleration, can be obtained through a measuring device installed on the vehicle. The degree of motion sickness of users can then be measured based on the motion information of the vehicle, and the driving style of the vehicle can be adjusted based on the degree of motion sickness.

[0026] However, the inventors discovered in their research that user behavior within the vehicle varies. For example, some users might frequently turn their heads to look out the window, while others might lean back in their seats and close their eyes to rest, and still others might talk to fellow passengers. Therefore, the acceleration of motion varies among different users. Measuring the degree of user motion using vehicle motion information acquired through onboard measuring devices is less accurate. Furthermore, different users have varying susceptibility to motion sickness; relying solely on vehicle motion information to measure the degree of motion sickness makes it difficult to warn users before they become unconscious.

[0027] Therefore, in order to overcome the above-mentioned defects, this application provides a motion sickness prediction and prompting method, device, electronic device, and computer-readable medium.

[0028] Please see Figure 1 , Figure 1 This illustration shows an application scenario diagram of a motion sickness prediction and prompting method provided in an embodiment of this application, namely motion sickness prediction and prompting scenario 100. Scenario 100 includes a vehicle system 110 and a target user 120. The vehicle system 110 can acquire motion sickness level information of the target user 120, and then configure some parameters in the motion sickness prediction model based on the acquired motion sickness level information to obtain a target motion sickness model corresponding to the target user 120. This allows the system to predict the motion sickness prediction value of the target user 120 at a specified time using the target motion sickness model, and promptly prompt the target user 120 when the motion sickness prediction value exceeds a specified threshold. For a detailed description, please refer to subsequent embodiments.

[0029] In some implementations, the vehicle system 110 can be an autonomous vehicle, thus eliminating the need for a driver in the driver's seat. In other implementations, the vehicle system 110 can also be a vehicle requiring a driver to operate it, thus requiring a driver in the driver's seat; this application does not limit the scope of the implementation.

[0030] In some embodiments, the vehicle system 110 may include an onboard system that can connect to the user device of the target user 120, thereby obtaining information about the degree of motion sickness of the target user 120 based on the user device. The user device may include different types, such as the target user 120's smartphone, laptop, smart tablet, or wireless headset.

[0031] In other embodiments, the vehicle system 110 may also include a sensing device that can connect to the onboard system, allowing the onboard system to obtain information about the degree of motion sickness of the target user 120. The sensing device may include different types, such as a camera, ultrasonic sensor, pressure sensor, temperature sensor, or airflow sensor. The number of each type of sensing device may be one or more; this application does not limit the number.

[0032] Please see Figure 2 , Figure 2 This application illustrates a motion sickness prediction and prompting method provided by an embodiment of the present application. This motion sickness prediction and prompting method can be applied to... Figure 1 The motion sickness prediction prompt scenario 100 shown can specifically use the vehicle system 110 in the motion sickness prediction prompt scenario 100 as the execution subject. The method includes steps S110 to S140.

[0033] Step S110: Obtain the motion sickness level information of the target user within a specified time period.

[0034] In some implementation methods, the parameters in the motion sickness prediction model need to be configured for different target users to ensure that the obtained target motion sickness prediction model matches the target user. Furthermore, it is also necessary to maximize the accuracy of the motion sickness prediction values ​​obtained from the target motion sickness prediction model in representing the target user's motion sickness. Therefore, the parameters in the motion sickness prediction model can be configured using the target user's motion sickness level information.

[0035] In some implementations, motion sickness information can be used to characterize the degree of motion sickness of the target user, or it can be understood as the degree of discomfort of the target user. The larger the value corresponding to this motion sickness information, the greater the degree of motion sickness of the target user, i.e., the greater the degree of discomfort. Therefore, the motion sickness prediction model obtained by configuring parameters based on the target user's motion sickness information can predict motion sickness values ​​with high accuracy. The target user can be a passenger in the vehicle corresponding to the vehicle system.

[0036] To predict the motion sickness value of a target user at a certain time after the current time, parameters can be configured by obtaining motion sickness intensity information within a specified time period before the current time. It's easy to understand that this specified time period can be before the current time; the end time of this specified time period can also be the current time, thus the specified time period can include the current time. It's also easy to understand that the longer the specified time period, the higher the accuracy of the motion sickness prediction value obtained by configuring the parameters in the motion sickness prediction model; conversely, the shorter the specified time period, the less computational resources are required to obtain the motion sickness prediction value and configure the parameters in the motion sickness prediction model. This application does not specifically limit the specified time period and can flexibly set it as needed.

[0037] As an example, the vehicle system can establish a communication connection with the target device held by the target user in advance, thereby allowing the vehicle system to obtain motion sickness information based on the user's device. For details, please refer to the description of the following embodiments.

[0038] Step S120: Configure at least one parameter in the motion sickness prediction model based on the motion sickness level information to obtain a target motion sickness prediction model that matches the target user.

[0039] In some implementation methods, if the same motion sickness prediction model is used to predict motion sickness values ​​for different target users, the accuracy of the predicted values ​​is not high because different target users have different susceptibility to motion sickness. Therefore, by using the motion sickness information of the target user obtained above to configure at least one parameter in the motion sickness prediction model, a target motion sickness prediction model matching the target user can be obtained. The motion sickness prediction value predicted by the target motion sickness prediction model can more accurately reflect the motion sickness level of the target user.

[0040] One example is that the motion sickness prediction model can be the Passenger Unwellness Model (PUM); another example is that the motion sickness prediction model can also be the Net Dose Model (NDM).

[0041] In some examples, the parameters of the motion sickness prediction model may include motion sickness increase parameters and motion sickness recovery parameters. Furthermore, in other examples, the parameters may also include individual motion sickness variability parameters, motion sickness perception change time parameters, and motion sickness perception lag time parameters. For details, please refer to the descriptions in the subsequent implementation methods.

[0042] Furthermore, after configuring the parameters of the motion sickness prediction model, a target motion sickness prediction model corresponding to the target user can be obtained.

[0043] Step S130: Based on the target motion sickness prediction model, predict the motion sickness prediction value of the target user at a specified time.

[0044] After obtaining the target motion sickness prediction model, the motion sickness prediction value of the target user can be predicted using this model. For example, the motion sickness prediction value of the target user at a specified time after the current time can be predicted. This motion sickness prediction value can then be used to determine whether the target user is likely to feel discomfort, and consequently, whether a prompt should be given to the target user.

[0045] One example is that the motion sickness prediction value output by the target motion sickness prediction model can be obtained by inputting the specified time and the acquired motion sickness amplitude data into the model. For details, please refer to the following embodiments.

[0046] Step S140: If the motion sickness prediction value exceeds a specified threshold, a prompt message is sent to the target device of the target user, wherein the target device is determined based on the user behavior of the target user.

[0047] Furthermore, in order to provide advance warning to the target user before they experience discomfort, the motion sickness prediction value obtained from the target motion sickness prediction model can be evaluated. If the prediction value strongly suggests that the target user may experience discomfort at or after a specified time, the user can be promptly alerted.

[0048] Specifically, motion sickness prediction values ​​can be evaluated by specifying a threshold. If the predicted motion sickness value exceeds the specified threshold, it indicates that the target user is highly likely to experience discomfort at or after a specified time. Therefore, in some implementations, it can be determined whether the predicted motion sickness value exceeds the specified threshold. If the predicted motion sickness value exceeds the specified threshold, a prompt message is sent to the target user's target device. This prompt message can be used to alert the target user to potential discomfort, instructing them to adjust their behavior, such as closing their eyes to rest, listening to music, or looking ahead.

[0049] The target device can be a user device owned by the target user, such as the target user's smartphone. Therefore, if the motion sickness prediction value exceeds a specified threshold, the vehicle system can send a notification to the smartphone.

[0050] Optionally, since a target user may own more than one device, the target device can also be determined based on the user's behavior. Specifically, the target device can be the one the target user is currently using. For example, if the target user is currently listening to music with wireless headphones, the wireless headphones can be used as the target device; if the target user is currently using a smart tablet, the smart tablet can be used as the target device.

[0051] In other implementations, if the motion sickness prediction value exceeds a specified threshold, the vehicle system can switch to a comfort driving mode, thereby reducing the discomfort experienced by the target user. For details, please refer to the descriptions of the following implementation methods.

[0052] The motion sickness prediction and alert method provided in this application first obtains motion sickness level information of a target user within a specified time period. Then, based on the motion sickness level information, it configures at least one parameter in a motion sickness prediction model to obtain a target motion sickness prediction model matching the target user. Based on this target motion sickness prediction model, it predicts the motion sickness value of the target user at a specified time. When the predicted motion sickness value exceeds a specified threshold, it sends an alert message to the target user's target device. Since the target motion sickness prediction model in this application is obtained by configuring the parameters of the motion sickness prediction model based on the target user's motion sickness level information, the accuracy of the predicted motion sickness value is high. Furthermore, when the predicted motion sickness value obtained by the target motion sickness prediction model exceeds the specified threshold, it can send an alert message to the target user's target device, thus providing timely alerts before the target user becomes dizzy, improving the user experience.

[0053] Please see Figure 3 , Figure 3 This application illustrates a motion sickness prediction and prompting method provided by an embodiment of the present application. This motion sickness prediction and prompting method can be applied to... Figure 1 The motion sickness prediction prompt scenario 100 shown can specifically use the vehicle system 110 in the motion sickness prediction prompt scenario 100 as the execution subject. The method includes steps S210 to S260.

[0054] Step S210: Obtain the dizziness level information of the target user within a specified time period.

[0055] Step S210 has been described in detail in the foregoing embodiments and will not be repeated here.

[0056] Step S220: Configure the motion sickness increase parameters and motion sickness recovery parameters of the motion sickness prediction model based on the motion sickness dose value and the current discomfort level value.

[0057] Step S230: Configure the motion sickness individual difference parameters of the motion sickness prediction model based on the pre-acquired demographic information of the target user.

[0058] Step S240: Based on the configured motion sickness increase parameters, the configured motion sickness recovery parameters, and the configured motion sickness individual difference parameters, obtain a target motion sickness prediction model that matches the target user.

[0059] As described in the foregoing embodiments, motion sickness information can be used to characterize the degree of motion sickness of a target user, or it can be understood as the degree of discomfort experienced by the target user. Therefore, in some implementations, motion sickness information may include the motion sickness dose value and the target user's current degree of discomfort value. The motion sickness dose value is related to the acceleration experienced by the target user; for example, the greater the acceleration experienced by the target user, the greater the motion sickness dose value will generally be. Similarly, the greater the change in acceleration experienced by the target user, the greater the motion sickness dose value will generally be. The target user's degree of discomfort is determined based on their subjective feeling of comfort. It is understandable that even under the same motion sickness dose value, different target users may have different current degree of discomfort values. For example, if one target user is a young person who frequently travels by car, and another target user is an elderly person who rarely travels by car, and both target users are simultaneously under the same motion sickness dose value, the elderly person's current degree of discomfort may be higher, while the young person's degree of discomfort may be relatively lower. In this context, the target user being at a certain motion sickness dose value can be understood as the target user being at the acceleration corresponding to that motion sickness dose value.

[0060] The motion sickness dosage data can be determined based on the motion sickness amplitude data of the target user, while the current level of discomfort of the target user can be obtained based on feedback from the target user. For details, please refer to the description of the subsequent embodiments.

[0061] In some implementations, the motion sickness prediction model may include motion sickness increase parameters and motion sickness recovery parameters. The motion sickness increase parameters can be used to characterize the relationship between motion amplitude data and user comfort; the motion sickness recovery parameters can be used to characterize the rate at which user discomfort decreases after being subjected to vibration. It is easy to understand that, as one implementation, this rate of discomfort reduction is positively correlated with the degree of discomfort; that is, the higher the degree of user discomfort, the faster the rate of discomfort reduction generally is.

[0062] Therefore, by configuring at least one parameter in the motion sickness prediction model based on the motion sickness program information, a target motion sickness prediction model matching the target user is obtained. Specifically, the motion sickness increase parameter and motion sickness recovery parameter of the motion sickness prediction model can be configured using the motion sickness dose value and the current discomfort level value. The motion sickness increase parameter is positively correlated with both the motion sickness dose value and the current discomfort level value; the motion sickness recovery parameter is also positively correlated with both the motion sickness dose value and the current discomfort level value.

[0063] Furthermore, motion sickness prediction models can also include individual motion sickness parameters. These parameters characterize a user's tolerance to vibration, or their susceptibility to motion sickness. A higher individual motion sickness parameter indicates a higher tolerance to vibration, meaning the user is less likely to feel uncomfortable under the same vibration conditions.

[0064] It is easy to understand that the individual differences in motion sickness parameters are significantly influenced by the demographic information of the target users. Therefore, the individual differences in motion sickness parameters of the motion sickness prediction model can be configured based on the pre-acquired demographic information of the target users. This demographic information can include at least one of the following: gender, age, driving experience, driving frequency, and traffic violation records.

[0065] Optionally, when the vehicle system establishes a communication connection with the user device held by the target user, it can obtain the target account corresponding to the target user, and thus read the pre-stored demographic information of the target user from the storage space corresponding to the target account.

[0066] As an example, if the user device corresponds to a smartphone, the vehicle system can read the target user's demographic information from other applications on the smartphone, such as from the smartphone's health application.

[0067] Optionally, when the vehicle system establishes a communication connection with the user device held by the target user, if it fails to obtain the target account corresponding to the target user, it can also instruct the target user to create the corresponding target account and input the corresponding demographic information, which is then stored in the storage space corresponding to the target account.

[0068] Furthermore, a target motion sickness prediction model matching the target user can be obtained based on the configured motion sickness increase parameters, the configured motion sickness recovery parameters, and the configured motion sickness individual difference parameters.

[0069] In other embodiments, the motion sickness prediction model may also include a motion sickness perception change time parameter and a motion sickness perception lag time parameter. The motion sickness perception change time parameter can be used to characterize the time interval that affects the user's comfort; while the motion sickness perception lag time parameter can be used to characterize the time corresponding to the user's hysteresis effect on the vibration.

[0070] Therefore, when configuring at least one parameter in the motion sickness prediction model based on the motion sickness severity information to obtain a target motion sickness prediction model matching the target user, the motion sickness increase parameter and motion sickness recovery parameter of the motion sickness prediction model can also be configured based on the motion sickness dose value and the current discomfort level value. Similarly, the motion sickness individual difference parameter, motion sickness perception change time parameter, and motion sickness perception lag time parameter of the motion sickness prediction model can also be configured based on the pre-acquired demographic information of the target user.

[0071] Therefore, based on the configured motion sickness increase parameters, the configured motion sickness recovery parameters, the configured motion sickness individual difference parameters, the configured motion sickness perception change time parameters, and the configured motion sickness perception lag time parameters, a target motion sickness prediction model matching the target user can be obtained.

[0072] As an example, the target motion sickness prediction model can be a ride discomfort prediction model PUM, which can be represented by the following formulas.

[0073]

[0074] in,

[0075] In the above formula, t s Used to characterize the moment when the vehicle system begins to feel sluggish; t e D0 is used to characterize the moment when the vehicle system stops moving; D0 is used to characterize individual differences in motion sickness parameters; D N A numerical value used to characterize the level of discomfort of the target user; Used to characterize the target user in t e The numerical value of discomfort at any given moment; A(t) is used to characterize the motion amplitude data of the target user; c A Parameters used to characterize increased motion sickness; c L Used to characterize motion sickness recovery parameters; offset t The parameter used to characterize the time parameter of motion sickness perception change is Δt; the parameter used to characterize the lag time parameter of motion sickness perception is Δt.

[0076] Therefore, by configuring the parameters, the target motion prediction model can predict the motion sickness of the target user at a specified time.

[0077] Optionally, as described above, the target user's current level of discomfort can be obtained based on the target user's feedback. However, the target user may not necessarily provide feedback, so the motion sickness information may not include the current level of discomfort. Therefore, if the motion sickness information includes the target user's motion sickness dose value but not the target user's current level of discomfort, then when configuring at least one parameter in the motion sickness prediction model based on the motion sickness information to obtain a target motion sickness prediction model matching the target user, this may include searching for a preset motion sickness prediction model corresponding to the target user from a plurality of pre-stored preset motion sickness prediction models based on the pre-acquired demographic information of the target user, and using this as the target motion sickness prediction model.

[0078] For example, the age, gender, etc. of the target user can be determined by the demographic information of the target user, and then a preset motion sickness prediction model corresponding to the target user can be found from a plurality of preset motion sickness prediction models stored in advance. For example, each preset motion sickness prediction model has an age label and a gender label, so that the preset motion sickness prediction model that matches the target user can be determined as the target motion sickness prediction model.

[0079] It should be noted that the above-described target motion sickness prediction model based on gender and age is only one example. Any other reasonable demographic information can be used to determine a target motion sickness prediction model that matches the target user. This application does not limit this approach.

[0080] Step S250: Based on the target motion sickness prediction model, predict the motion sickness prediction value of the target user at a specified time.

[0081] Step S260: If the motion sickness prediction value exceeds a specified threshold, a prompt message is sent to the target device of the target user, wherein the target device is determined based on the user behavior of the target user.

[0082] Steps S250 and S260 have been described in detail in the foregoing embodiments and will not be repeated here.

[0083] The motion sickness prediction and alerting method provided in this application configures motion sickness increase parameters and motion sickness recovery parameters of the motion sickness prediction model based on the motion sickness dose value and the current discomfort level value; then, based on the pre-acquired demographic information of the target user, it configures motion sickness individual difference parameters, motion sickness perception change time parameters, and motion sickness perception lag time parameters of the motion sickness prediction model. Based on the configured motion sickness increase parameters, configured motion sickness recovery parameters, configured motion sickness individual difference parameters, configured motion sickness perception change time parameters, and configured motion sickness perception lag time parameters, a target motion sickness prediction model matching the target user is obtained. In the embodiments provided in this application, the parameters of the target motion sickness prediction model are configured based on the target user's motion sickness dose value, current discomfort level value, and demographic information, showing a strong correlation with the target user, thus enabling relatively accurate prediction of the target user's motion sickness value.

[0084] Please see Figure 4 , Figure 4 This application illustrates a motion sickness prediction and prompting method provided by an embodiment of the present application. This motion sickness prediction and prompting method can be applied to... Figure 1 The motion sickness prediction prompt scenario 100 shown can specifically use the vehicle system 110 in the motion sickness prediction prompt scenario 100 as the execution subject. The method includes steps S310 to S360.

[0085] Step S310: Obtain the motion sickness level information of the target user within a specified time period.

[0086] Step S310 has been described in detail in the foregoing embodiments and will not be repeated here.

[0087] Step S320: Search among the pre-stored preset motion sickness prediction models to see if there is a preset motion sickness prediction model corresponding to the target user.

[0088] In some implementations, the target user may ride in the same vehicle more than once. Therefore, the vehicle system corresponding to the vehicle may also search among a plurality of pre-stored preset motion sickness prediction models to see if there is a preset motion sickness prediction model corresponding to the target user before configuring at least one parameter in the motion sickness prediction model based on the motion sickness degree information.

[0089] For example, the existence of a preset motion sickness prediction model corresponding to the target user can be determined by checking whether the target account corresponding to the target user stores a motion sickness prediction model. Specifically, step S320 may also include steps S321 to S324.

[0090] Step S321: Obtain the target account corresponding to the target user.

[0091] Step S322: Check whether at least one of the pre-stored preset motion sickness prediction models is stored in the storage space corresponding to the target account.

[0092] Step S323: If yes, then determine that there is a preset motion sickness prediction model corresponding to the target user among the multiple preset motion sickness prediction models stored in advance.

[0093] Step S324: If not, determine that there is no preset motion sickness prediction model corresponding to the target user among the multiple preset motion sickness prediction models stored in advance.

[0094] In some implementations, the target user's corresponding target account can be obtained first. Then, it is determined whether at least one of the pre-stored motion sickness prediction models exists in the storage space corresponding to the target account. That is, if the target user has previously obtained a target motion sickness prediction model, that model can be stored as a pre-stored motion sickness prediction model in the target user's corresponding target account. Therefore, when the target user subsequently rides the vehicle again, the pre-stored motion sickness prediction model in the target account can be directly used as the target motion sickness prediction model.

[0095] Therefore, if at least one of the preset motion sickness prediction models is stored in the storage space corresponding to the target account, it is determined that there is a preset motion sickness prediction model corresponding to the target user among the multiple preset motion sickness prediction models stored in advance.

[0096] If the preset motion sickness prediction model is not stored in the storage space corresponding to the target account, then it is determined that there is no preset motion sickness prediction model corresponding to the target user among the multiple preset motion sickness prediction models stored in advance.

[0097] Step S330: If not, configure at least one parameter in the motion sickness prediction model based on the motion sickness degree information to obtain a target motion sickness prediction model that matches the target user.

[0098] Step S340: If it exists, the preset motion sickness prediction model corresponding to the target user is used as the target motion sickness prediction model.

[0099] Furthermore, if among the pre-stored multiple preset motion sickness prediction models, there is a preset motion sickness prediction model corresponding to the target user, then it is not necessary to configure the parameters of the motion sickness prediction model, and the preset motion sickness prediction model corresponding to the target user can be directly used as the target motion sickness prediction model.

[0100] If none of the pre-stored motion sickness prediction models corresponds to the target user, then at least one parameter in the motion sickness prediction model is configured based on the motion sickness severity information to obtain a target motion sickness prediction model that matches the target user. For an explanation of how to configure at least one parameter in the motion sickness prediction model based on the motion sickness severity information to obtain a target motion sickness prediction model that matches the target user, please refer to the foregoing embodiments; it will not be repeated here.

[0101] Step S350: Based on the target motion sickness prediction model, predict the motion sickness value of the target user at a specified time.

[0102] Step S360: If the motion sickness prediction value exceeds a specified threshold, a prompt message is sent to the target device of the target user, wherein the target device is determined based on the user behavior of the target user.

[0103] Steps S350 and S360 have been described in detail in the foregoing embodiments and will not be repeated here.

[0104] The motion sickness prediction and prompting method provided in this application can search among a plurality of pre-stored preset motion sickness prediction models to see if there is a preset motion sickness prediction model corresponding to the target user. Only when no preset motion sickness prediction model corresponding to the target user exists, at least one parameter in the motion sickness prediction model is configured based on the motion sickness degree information to obtain a target motion sickness prediction model matching the target user. This can save system computing resources and optimize the overall process of the motion sickness prediction and prompting method. Furthermore, since the preset motion sickness prediction model corresponding to the target user is used as the target motion sickness prediction model when one exists, the obtained target motion sickness prediction model will not reduce the accuracy of the predicted motion sickness value for the target user.

[0105] Please see Figure 5 , Figure 5 This application illustrates a motion sickness prediction and prompting method provided by an embodiment of the present application. This motion sickness prediction and prompting method can be applied to... Figure 1 The motion sickness prediction prompt scenario 100 shown can specifically use the vehicle system 110 in the motion sickness prediction prompt scenario 100 as the execution subject. The method includes steps S410 to S4100.

[0106] Step S410: Based on the user device held by the target user, obtain the motion amplitude data of the target user within a specified time period.

[0107] Step S420: Determine the motion sickness dose value based on the motion amplitude data.

[0108] Step S430: Send a second inquiry message to the target device of the target user within a specified time period, and obtain the current discomfort level value fed back by the target user based on the second inquiry message.

[0109] As described above, motion sickness information can include motion sickness dose and current level of discomfort, thus allowing the acquisition of motion sickness dose and current level of discomfort separately.

[0110] Specifically, since the vehicle system can be connected to the user's device, it can obtain the motion amplitude data of the target user within a specified time period based on the user's device.

[0111] In some implementations, the motion amplitude data can be acceleration data, which may include acceleration data in the x, y, and z directions based on the world coordinate axes.

[0112] As an example, the user equipment may be equipped with an inertial measurement unit (IMU). Since the user equipment is held by the target user, the motion amplitude data obtained by the IMU of the user equipment can approximately represent the motion amplitude data of the target user. Thus, the vehicle system can obtain motion amplitude data from the user equipment.

[0113] In this scenario, there may be more than one user device. In some implementations, the user device closest to the user can be selected, and its motion amplitude data can be acquired. This can maximize the accuracy of representing the target user's motion amplitude data using this motion amplitude data.

[0114] Optionally, the user equipment may not be constantly operational; for example, it may be in a sleep state. In this case, the user equipment's inertial measurement unit (IMU) may also enter sleep mode, thus preventing the vehicle system from acquiring motion amplitude data through the user equipment.

[0115] Therefore, in some implementations, the vehicle system can first detect the operating status of each user device and obtain motion amplitude data only from the user devices that are in operation.

[0116] Optionally, if each user device is in a dormant state, motion amplitude data can be obtained through sensors installed within the vehicle system.

[0117] Furthermore, the motion sickness dose can be determined based on motion amplitude data within a specified time period. For example, motion amplitude data within a specified time period can be accumulated to obtain the motion sickness dose.

[0118] Furthermore, as described above, the current level of discomfort can be fed back by the target user. Therefore, the vehicle system can send a second query to the target user's target device within a specified time period and obtain the current level of discomfort reported by the target user based on the second query.

[0119] The second inquiry information may include a description of the target user's current comfort level, so that the target user can provide feedback based on the second inquiry information, and the vehicle system can obtain the current discomfort level value based on the target user's feedback.

[0120] For an example, please refer to Table 1 below.

[0121] Table 1

[0122]

[0123] Table 1 illustrates a second query information provided in an embodiment of this application. This second query information includes different symptoms and the severity of each symptom. Each severity of each symptom can correspond to a current level of discomfort. Users can then provide feedback based on this second query information, allowing the vehicle system to determine the current level of discomfort. For example, users can select the symptom and its severity applicable to their current discomfort based on the descriptions of "symptom" and "severity" in Table 1, and provide feedback through corresponding boxes, such as checking or marking the boxes.

[0124] For example, if a user reports that their symptom is "feeling nauseous" and the degree is "somewhat", then the current discomfort level of the target user can be set to 6.

[0125] In some implementations, the current level of discomfort can be measured using the Misery Scale (MISC).

[0126] Step S440: Configure at least one parameter in the motion sickness prediction model based on the motion sickness severity information to obtain a target motion sickness prediction model that matches the target user.

[0127] Step S440 has been described in detail in the foregoing embodiments and will not be repeated here.

[0128] Step S450: Input the motion amplitude data and the specified time into the target motion sickness prediction model to obtain the motion sickness prediction value of the target user at the specified time output by the target motion sickness prediction model.

[0129] In some implementations, motion amplitude data and a specified time can be input into the target motion prediction model, thereby enabling the prediction of motion sickness values ​​based on the target motion sickness prediction model.

[0130] Optionally, as described in the foregoing embodiments, the motion amplitude data obtained by the user equipment approximates the motion amplitude data of the target user. Therefore, in some embodiments, the user behavior of the target user can also be obtained, and the motion amplitude data can be corrected based on the user behavior to obtain more accurate motion amplitude data of the target user. Specifically, when performing step S450, steps S451 to S453 may also be included.

[0131] Step S451: Obtain the current behavior information corresponding to the target user's user behavior at the current moment.

[0132] Step S452: Correct the motion amplitude data based on the current behavior information.

[0133] Step S453: Input the corrected motion amplitude data and the specified time into the target motion sickness prediction model to obtain the motion sickness prediction value of the target user at the specified time output by the target motion sickness prediction model.

[0134] In some implementations, please refer to Figure 6 , Figure 6 A schematic diagram of motion amplitude data processing according to an embodiment of this application is shown. Figure 6 The system includes a sensing component 610, a data processing component 620, and a motion sickness prediction model component 630. The sensing component 610 acquires motion amplitude data and the current behavior information of the target user. The data processing component 620 processes the motion amplitude data using the current behavior information, including correction, filtering, or synthesis. The motion sickness prediction model component 630 acquires the processed motion amplitude data from the data processing component 620, thereby predicting the motion sickness value of the target user at a specified time. For more details, please refer to the subsequent descriptions.

[0135] In some implementations, user behavior may include closing one's eyes to rest, listening to music, or looking ahead, thereby obtaining the target user's current behavior as current behavior information through the vehicle system's sensing devices.

[0136] As the foregoing analysis shows, motion amplitude data can include accelerations in the x, y, and z directions in the world coordinate system. Therefore, as an example, it can be achieved through... as well as To represent the acquired motion amplitude data respectively; through a xa y and a z To represent the motion amplitude data after correction based on the target user's current behavior information; through A 3x3 The matrix represents the correction matrix, which can be determined based on the target user's current behavior information.

[0137] Thus, it can be achieved Calculate the motion amplitude data after correction by the current behavior information.

[0138] Furthermore, the corrected motion amplitude data and the specified time can be input into the target motion sickness prediction model to obtain the motion sickness prediction value of the target user at the specified time, output by the target motion sickness prediction model. The motion prediction value of the target user obtained through the corrected motion amplitude data has higher accuracy.

[0139] Optionally, since motion amplitude data can be acquired via an inertial measurement unit (IMU), both low and high frequency bands may be acquired simultaneously. These low and high frequency bands generally do not cause discomfort to the target user, i.e., they do not induce motion sickness. Therefore, in some embodiments, the acquired motion amplitude data can be filtered to remove frequency bands that are unlikely to cause motion sickness in the target user.

[0140] One example is that it can be done via W x To filter out the modified a from the above example x ; through W y To filter out the modified a from the above example y W z To filter out the modified a from the above example z .

[0141] As the foregoing analysis shows, the obtained motion amplitude data can be viewed as accelerations in the x, y, and z directions in the world coordinate system, and the proportion of acceleration in different directions in the motion amplitude data is different. Therefore, the proportion of acceleration in different directions can be adjusted by using a coefficient k to synthesize and obtain the weighted acceleration 'a'. w An example, Among them, a wx Used to characterize W x Filtered a x Similarly, a wy Used to characterize W y Filtered a y ;a wz Used to characterize W zFiltered a z .

[0142] Filtering can be achieved through a combination of bandpass filters, high-pass filters, and low-pass filters, and is not limited in the embodiments of this application.

[0143] Optional, please continue reading Figure 6 , Figure 6 The system may also include an interface 640. After obtaining a motion prediction value based on the processed motion amplitude data provided by the data processing component 620, the motion sickness prediction model component 630 determines whether the motion sickness prediction value exceeds a specified threshold. If the motion sickness prediction value exceeds the specified threshold, a prompt message can be sent to the target device. Therefore, in some embodiments, the interface 640 can be the interface of the target device, allowing the target user to obtain the prompt message through the interface 640. Optionally, when the motion sickness prediction value exceeds the specified threshold, the motion sickness prediction model component 630 can also send a first query message to the interface 640, allowing the target user to input first confirmation information through the interface 640, which is then fed back to the vehicle system.

[0144] Step S460: Obtain the current behavior information corresponding to the target user's user behavior at the current moment.

[0145] Step S470: Determine the target device based on the current behavior information.

[0146] Step S480: Determine the specified threshold based on the pre-acquired demographic information of the target user.

[0147] Furthermore, as the foregoing analysis shows, the target user's user behavior can vary, such as listening to music, using a smartphone, or using a tablet. Therefore, the current behavior information corresponding to the target user's behavior at the current moment can be obtained first. Based on this current behavior information, the target device can be determined. The target device can be at least one user device held by the target user.

[0148] Specifically, the user device currently being used by the target user can be determined through current behavior information, thus identifying the user device being used as the target device. For example, if the target user is using a smartphone, the smartphone can be used as the target device; if the target user is using wireless headphones to listen to music, the wireless headphones can be used as the target device.

[0149] Furthermore, since different users have different susceptibility to motion sickness, a specified threshold can be determined based on the target user's demographic information. This allows the specified threshold to correspond to the target user, enabling a more accurate determination of whether to send a prompt message to the target device. For example, if demographic information indicates that the target user is elderly and has no driving experience, a lower specified threshold can be set; if the target user is young and has a longer driving experience, a higher specified threshold can be set.

[0150] Step S490: If the motion sickness prediction value exceeds a specified threshold, a prompt message and a first inquiry message are sent to the target user's target device. The first inquiry message is used to ask the user whether they need to switch to comfort driving mode.

[0151] Step S4100: If the first confirmation information input by the target user is received, switch to comfort driving mode.

[0152] Furthermore, after identifying the target device, if the motion sickness prediction value exceeds a specified threshold, the vehicle system can send a prompt message to the target device. It is easy to understand that the prompt message can be adjusted according to the different target devices. For example, if the target device is a smartphone, the prompt message can be a text prompt displayed on the smartphone's screen; if the target device is a wireless headset, the prompt message can be a voice prompt played through the wireless headset. The process of determining whether the motion sickness prediction value exceeds the specified threshold has been described in detail in the preceding embodiments and will not be repeated here.

[0153] It should be noted that the specific content of the prompt information in this embodiment is not limited and can be flexibly adjusted as needed.

[0154] Optionally, when the motion sickness prediction value exceeds a specified threshold, a first inquiry message can be sent to the target device, wherein the first inquiry message is used to ask the user whether they need to switch to comfort driving mode. Similar to the prompt message, the first inquiry message can also be a text first inquiry message or a voice first inquiry message, etc.

[0155] If the system receives the first confirmation information from the target user, it can switch to Comfort Driving Mode. In Comfort Driving Mode, the vehicle system provides a smoother ride, thereby reducing motion sickness for the target user. The first confirmation information can be text-based, such as input via a smartphone keyboard or touchscreen; it can also be voice-based, such as input via a smartphone microphone or a wireless headset microphone. This application does not impose any limitations on this method.

[0156] Furthermore, if the first confirmation information input by the target user is not obtained, the vehicle system can continue to maintain the current driving mode.

[0157] The motion sickness prediction and prompting method provided in this application allows for the configuration of at least one parameter in a motion sickness prediction model using motion sickness dosage and current discomfort level values, thereby obtaining a target motion sickness prediction model. The method then corrects motion amplitude data using current behavioral information corresponding to user behavior. Based on the corrected motion amplitude data, the target motion sickness prediction model is used to predict the motion sickness value of the target user, achieving high accuracy. Furthermore, when the motion sickness prediction value exceeds a specified threshold, a first query message can be sent to the target device. Upon receiving the first confirmation message from the target user, the device can switch to a comfort driving mode, thereby reducing the target user's motion sickness before severe motion sickness such as vomiting occurs, thus improving the user experience.

[0158] Please see Figure 7 , Figure 7 This application illustrates a motion sickness prediction and prompting method provided by an embodiment of the present application. This motion sickness prediction and prompting method can be applied to... Figure 1 The motion sickness prediction prompt scenario 100 shown can specifically use the vehicle system 110 in the motion sickness prediction prompt scenario 100 as the execution subject. The method includes steps S510 to S590.

[0159] Step S510: If the target user is detected to have entered the specified range, a communication connection is established with the user equipment held by the target user.

[0160] Step S520: Send a third query message to the target user's target device, the third query message being used to ask the target user whether they need to perform motion sickness prediction prompts.

[0161] Step S530: If the second determination information input by the target user is received, the dizziness level information of the target user within the specified time period is obtained, and subsequent steps are executed.

[0162] In some implementations, before obtaining the motion sickness level information of the target user within a specified time period, it is also possible to detect whether the target user exists and whether the target user needs to perform motion sickness prediction. This way, if the target user does not exist or does not need to perform motion sickness prediction, the motion sickness prediction can be skipped, which can save system resources, such as power resources and computing power resources.

[0163] Specifically, the vehicle system can detect whether a target user has entered a designated range. When the target user is detected to have entered the designated range, a communication connection is established with the user device held by the target user. The designated range can be determined based on the vehicle system; for example, the interior space of the vehicle corresponding to the vehicle system can be used as the designated range. Alternatively, the vehicle can be used as the origin, and the space within a designated range around it can be used as the designated range, such as 1 meter or 1.5 meters.

[0164] Optionally, the vehicle system can also determine whether a target user has entered a specified range by judging whether a pre-set condition has been triggered. For example, the specified condition could be that a car door is opened; if the door is detected to be open, the target user can be considered to have entered the specified range. Another example is that the specified condition could be that the seat pressure is greater than a specified value, such as 200 Newtons or 400 Newtons. In this case, if the seat pressure is detected to be greater than the specified value, the target user can be considered to have entered the specified range.

[0165] An example user device that has established a communication connection with a vehicle system can receive information sent by the vehicle system, and can also obtain information input by a target user and send that information to the vehicle system. For example, the user device can run a specified application, thereby enabling communication with the vehicle system through the specified application; or, for another example, the user device can run a specified background service, thereby enabling communication with the vehicle system through the specified background service.

[0166] The vehicle system can establish a communication connection with the user equipment through short-range wireless communication protocols. For example, the vehicle system can establish a communication connection with the user equipment through Bluetooth; or it can establish a communication connection with the user equipment through the wireless communication protocol Wi-Fi.

[0167] In some implementations, the vehicle system can use sensors to detect whether a target user has entered a designated area. For example, the sensor could be a camera, which can then be used to detect whether a target user has entered a designated area.

[0168] Furthermore, as can be seen from the foregoing analysis, since the vehicle system needs to communicate with the user device held by the target user, a communication connection can be established with the user device held by the target user when the system detects that the target user has entered the specified range.

[0169] Therefore, the vehicle system can send a third inquiry message to the target user's target device, which can be used to inquire whether the target user needs to perform motion sickness prediction prompts. The target device can be at least one user device held by the target user. Specifically, the method for determining the target device can be referred to the description in the foregoing embodiments, and will not be repeated here. Similar to the first inquiry message, the third inquiry message can also be a text third inquiry message or a voice third inquiry message, etc.

[0170] Furthermore, after receiving the third inquiry information, the target user can input the second confirmation information based on the third inquiry information. At this time, the vehicle system can determine that the target user needs to execute the motion sickness prediction prompt, so that the vehicle system can obtain the motion sickness level information of the target user within a specified time period and execute subsequent steps.

[0171] Step S540: Obtain the dizziness level information of the target user within a specified time period.

[0172] Step S550: Configure at least one parameter in the motion sickness prediction model based on the motion sickness severity information to obtain a target motion sickness prediction model that matches the target user.

[0173] Step S560: Based on the target motion sickness prediction model, predict the motion sickness prediction value of the target user at a specified time.

[0174] Step S570: If the motion sickness prediction value exceeds a specified threshold, a prompt message is sent to the target device of the target user, wherein the target device is determined based on the user behavior of the target user.

[0175] Steps S540 to S570 have been described in detail in the foregoing embodiments and will not be repeated here.

[0176] The motion sickness prediction and prompting method provided in this application only acquires the motion sickness level information of the target user within a specified time period and executes subsequent steps when the vehicle system detects that the target user has entered a specified range and obtains the second certain information input by the target user. When the target user does not exist, or when the target user does not need to perform motion sickness prediction, the prediction is not performed, thus saving system resources.

[0177] Please see Figure 8 , Figure 8 The diagram shows a structural block diagram of a motion sickness prediction and prompting device 800 provided in an embodiment of this application. The motion sickness prediction and prompting device 800 is applied to a vehicle system. The device includes: a first acquisition unit, a second acquisition unit, a prediction unit, and a prompting unit.

[0178] The first acquisition unit 810 is used to acquire information on the degree of dizziness of the target user within a specified time period.

[0179] Furthermore, the first acquisition unit 810 can also be used to acquire motion amplitude data of the target user within a specified time period based on the user device held by the target user; determine the motion sickness dose value based on the motion amplitude data; send a second inquiry message to the target device of the target user within the specified time period, and acquire the current discomfort level value fed back by the target user based on the second inquiry message.

[0180] Furthermore, the first acquisition unit 810 can also be used to establish a communication connection with the user device held by the target user if it detects that the target user has entered a specified range; send a third inquiry message to the target user's target device, the third inquiry message being used to ask the target user whether it needs to perform motion sickness prediction prompts; if it receives the second confirmation information input by the target user, acquire the motion sickness degree information of the target user within a specified time period, and execute subsequent steps.

[0181] The second acquisition unit 820 is used to configure at least one parameter in the motion sickness prediction model based on the motion sickness degree information to obtain a target motion sickness prediction model that matches the target user.

[0182] Furthermore, the second acquisition unit 820 can also be used to configure the motion sickness increase parameter and motion sickness recovery parameter of the motion sickness prediction model based on the motion sickness dose value and the current discomfort level value; configure the motion sickness individual difference parameter of the motion sickness prediction model based on the pre-acquired demographic information of the target user; and obtain a target motion sickness prediction model matching the target user based on the configured motion sickness increase parameter, the configured motion sickness recovery parameter and the configured motion sickness individual difference parameter.

[0183] Furthermore, the second acquisition unit 820 can also be used to configure the motion sickness increase parameter and motion sickness recovery parameter of the motion sickness prediction model based on the motion sickness dose value and the current discomfort level value; configure the motion sickness individual difference parameter, motion sickness perception change time parameter, and motion sickness perception lag time parameter of the motion sickness prediction model based on the pre-acquired demographic information of the target user; and obtain a target motion sickness prediction model matching the target user based on the configured motion sickness increase parameter, the configured motion sickness recovery parameter, the configured motion sickness individual difference parameter, the configured motion sickness perception change time parameter, and the configured motion sickness perception lag time parameter. The motion sickness increase parameter is positively correlated with both the motion sickness dose value and the current discomfort level value; the motion sickness recovery parameter is positively correlated with both the motion sickness dose value and the current discomfort level value.

[0184] Furthermore, the second acquisition unit 820 can also be used to search for a preset motion sickness prediction model corresponding to the target user from a plurality of preset motion sickness prediction models stored in advance, based on the demographic information of the target user that has been acquired in advance, and use it as the target motion sickness prediction model.

[0185] Furthermore, the second acquisition unit 820 can also be used to search for whether there is a preset motion sickness prediction model corresponding to the target user among a plurality of pre-stored preset motion sickness prediction models; if not, at least one parameter in the motion sickness prediction model is configured based on the motion sickness degree information to obtain a target motion sickness prediction model matching the target user; if it exists, the preset motion sickness prediction model corresponding to the target user is used as the target motion sickness prediction model.

[0186] Furthermore, the second acquisition unit 820 can also be used to acquire the target account corresponding to the target user; search among the pre-stored multiple preset motion sickness prediction models to see if at least one of the preset motion sickness prediction models is stored in the storage space corresponding to the target account; if so, it is determined that there is a preset motion sickness prediction model corresponding to the target user among the pre-stored multiple preset motion sickness prediction models; if not, it is determined that there is no preset motion sickness prediction model corresponding to the target user among the pre-stored multiple preset motion sickness prediction models.

[0187] The prediction unit 830 is used to predict the motion sickness prediction value of the target user at a specified time based on the target motion sickness prediction model.

[0188] Furthermore, the prediction unit 830 can also be used to input the motion amplitude data and the specified time into the target motion sickness prediction model to obtain the motion sickness prediction value of the target user at the specified time output by the target motion sickness prediction model.

[0189] Furthermore, the prediction unit 830 can also be used to obtain the current behavior information corresponding to the user behavior of the target user at the current moment; correct the motion amplitude data based on the current behavior information; input the corrected motion amplitude data and the specified time into the target motion sickness prediction model, and obtain the motion sickness prediction value of the target user at the specified time output by the target motion sickness prediction model.

[0190] The prompting unit 840 is used to send a prompting message to the target device of the target user if the motion sickness prediction value exceeds a specified threshold, wherein the target device is determined based on the user behavior of the target user.

[0191] Furthermore, the prompting unit 840 can also be used to acquire current behavior information corresponding to the target user's user behavior at the current moment; determine the target device based on the current behavior information; and determine the specified threshold based on the pre-acquired demographic information of the target user. The demographic information includes at least one of the following: gender, age, driving experience, driving frequency, and traffic violation records.

[0192] Furthermore, the prompting unit 840 can also be used to send a prompt message and a first inquiry message to the target device of the target user if the motion sickness prediction value exceeds a specified threshold. The first inquiry message is used to ask the user whether they need to switch to comfort driving mode. If the first confirmation message input by the target user is received, the user switches to comfort driving mode.

[0193] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0194] In the several embodiments provided in this application, the coupling between the units can be electrical, mechanical, or other forms of coupling. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0195] Please see Figure 9 , Figure 9 This diagram illustrates a structural block diagram of an electronic device 900 according to an embodiment of this application. The electronic device 900 can be a smartphone, laptop, desktop computer, tablet computer, wireless headset, etc. The electronic device 900 in this application may include one or more processors 910. These processors 910 can be used to acquire prompt information sent by the vehicle system, the prompt information being determined by the vehicle system based on the methods described in the foregoing embodiments.

[0196] Optionally, the electronic device 900 may also include a memory ( Figure 9 (Not shown in the image), this memory can be connected to the processor 910.

[0197] The processor 910 may include one or more processing cores. The processor 910 connects to various parts within the electronic device 900 using various interfaces and lines, and performs various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets, or instruction sets stored in memory, and by calling data stored in memory. Optionally, the processor 910 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 910 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 910 and may be implemented separately using a communication chip.

[0198] The memory may include random access memory (RAM) or read-only memory (ROM). The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the electronic device 900 during use.

[0199] Please refer to Figure 10 This diagram illustrates a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. The computer-readable medium 1000 stores program code that can be invoked by a first processor or a second processor to execute the methods described in the above method embodiments.

[0200] The computer-readable storage medium 1000 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 1000 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 1000 has storage space for program code 1010 that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code 1010 may be compressed, for example, in a suitable form.

[0201] Please refer to Figure 11 The diagram illustrates a structural block diagram of a computer program product 1100 provided in an embodiment of this application. The computer program product 1100 includes a computer program / instructions 1110, which, when executed by a first processor or a second processor, implements the steps of the aforementioned method.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A motion sickness prediction prompting method characterized by comprising: The method is applied to a vehicle system, and the method comprises: obtaining motion sickness degree information of a target user in a specified time period, the motion sickness degree information comprising a motion sickness dose value of the target user and a current discomfort degree value of the target user; configuring a motion sickness increase parameter and a motion sickness recovery parameter of the motion sickness prediction model based on the motion sickness dose value and the current discomfort degree value; configuring a motion sickness individual difference parameter of the motion sickness prediction model based on pre-obtained demographic information of the target user; obtaining a target motion sickness prediction model matched with the target user based on the configured motion sickness increase parameter, the configured motion sickness recovery parameter and the configured motion sickness individual difference parameter; predicting a motion sickness prediction value of the target user at a specified time based on the target motion sickness prediction model; if the motion sickness prediction value exceeds a specified threshold, sending prompt information to a target device of the target user, wherein the target device is determined based on user behavior of the target user.

2. The method of claim 1, wherein: the configuring of the motion sickness individual difference parameter of the motion sickness prediction model based on the pre-obtained demographic information of the target user comprises: configuring a motion sickness individual difference parameter, a motion sickness perception change time parameter and a motion sickness perception lag time parameter of the motion sickness prediction model based on the pre-obtained demographic information of the target user; the obtaining of the target motion sickness prediction model matched with the target user based on the configured motion sickness increase parameter, the configured motion sickness recovery parameter and the configured motion sickness individual difference parameter comprises: obtaining the target motion sickness prediction model matched with the target user based on the configured motion sickness increase parameter, the configured motion sickness recovery parameter, the configured motion sickness individual difference parameter, the configured motion sickness perception change time parameter and the configured motion sickness perception lag time parameter.

3. The method according to claim 1 or 2, characterized in that, the motion sickness increase parameter is positively correlated with the motion sickness dose value and the current discomfort degree value respectively, and the motion sickness recovery parameter is positively correlated with the motion sickness dose value and the current discomfort degree value respectively.

4. The method of claim 1, wherein, before the sending of the prompt information to the target device of the target user if the motion sickness prediction value exceeds the specified threshold, the method further comprises: obtaining current behavior information corresponding to user behavior of the target user at a current time; determining the target device based on the current behavior information; determining the specified threshold based on the pre-obtained demographic information of the target user.

5. The method of any one of claims 1, 2, and 4, the demographic information comprising: at least one of gender, age, driving age, driving frequency and violation record.

6. The method of claim 1, wherein, the sending of the prompt information to the target device of the target user if the motion sickness prediction value exceeds the specified threshold comprises: if the motion sickness prediction value exceeds the specified threshold, sending prompt information and first inquiry information to the target device of the target user, the first inquiry information being used for inquiring whether the user needs to switch to a comfortable driving mode; if the first determination information input by the target user is received, switching to the comfortable driving mode.

7. The method of claim 1, wherein The motion sickness degree information of the target user in a specified time period includes: Based on the user equipment held by the target user, the motion amplitude data of the target user in a specified time period is obtained; Based on the motion amplitude data, the motion sickness dose value is determined; In a specified time period, second inquiry information is sent to the target device of the target user, and the current discomfort degree value fed back by the target user based on the second inquiry information is obtained.

8. The method of claim 7, wherein, The target motion sickness prediction model is based on the target motion sickness prediction model, and the motion sickness prediction value of the target user at a specified time is predicted, including: The motion amplitude data and the specified time are input into the target motion sickness prediction model, and the motion sickness prediction value of the target user at a specified time output by the target motion sickness prediction model is obtained.

9. The method of claim 8, wherein, The motion amplitude data and the specified time are input into the target motion sickness prediction model, and the motion sickness prediction value of the target user at a specified time output by the target motion sickness prediction model is obtained, including: The current behavior information corresponding to the user behavior of the target user at the current time is obtained; Based on the current behavior information, the motion amplitude data is corrected; The corrected motion amplitude data and the specified time are input into the target motion sickness prediction model, and the motion sickness prediction value of the target user at a specified time output by the target motion sickness prediction model is obtained.

10. The method of claim 1, wherein, Before the motion sickness degree information of the target user in a specified time period is obtained, it further includes: If it is detected that the target user enters a specified range interval, a communication connection is established with the user equipment held by the target user; Third inquiry information is sent to the target device of the target user, and the third inquiry information is used to inquire whether the target user needs to perform motion sickness prediction prompt; If the second determination information input by the target user is received, the motion sickness degree information of the target user in a specified time period is obtained, and the subsequent steps are performed.

11. A motion sickness prediction prompting device, characterized by, Applied to a vehicle system, the device includes: A first obtaining unit is configured to obtain motion sickness degree information of a target user in a specified time period, the motion sickness degree information including a motion sickness dose value of the target user and a current discomfort degree value of the target user; A second obtaining unit is configured to configure a motion sickness increase parameter and a motion sickness recovery parameter of the motion sickness prediction model based on the motion sickness dose value and the current discomfort degree value, configure a motion sickness individual difference parameter of the motion sickness prediction model based on pre-obtained demographic information of the target user, and obtain a target motion sickness prediction model matched with the target user based on the configured motion sickness increase parameter, the configured motion sickness recovery parameter, and the configured motion sickness individual difference parameter; A prediction unit is configured to predict a motion sickness prediction value of the target user at a specified time based on the target motion sickness prediction model; A prompt unit is configured to send prompt information to a target device of the target user if the motion sickness prediction value exceeds a specified threshold, wherein the target device is determined based on user behavior of the target user.

12. An electronic device, comprising: It includes: One or more processors; The one or more processors are configured to acquire prompt information sent by the vehicle system, the prompt information being determined by the vehicle system based on the method of any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores program codes, and the program codes can be called and executed by the processor to perform the method of any one of claims 1-10.

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