A method and system for anti-motion sickness guidance, a vehicle and a storage medium

Abstract

This invention discloses a motion sickness prevention guidance method, a motion sickness prevention guidance system, a vehicle, and a storage medium, relating to the field of vehicles. The motion sickness prevention guidance method includes: acquiring vehicle dynamic information; calculating a first motion sickness risk index based on the vehicle dynamic information; acquiring environmental road condition information; predicting a second motion sickness risk index based on the environmental road condition information; and guiding the driver to drive based on the first and second motion sickness risk indices. This invention provides a forward-looking, collaborative method that can stimulate the driver's initiative, fundamentally preventing motion sickness.

Description

Technical Field

[0001] This invention belongs to the field of vehicle technology, and specifically relates to a motion sickness prevention guidance method, system, vehicle, and storage medium. Background Technology

[0002] With the increasing popularity of automobiles, especially electric vehicles, their rapid power response and strong energy recovery braking characteristics amplify the impact of driver behavior on passenger comfort. Sudden acceleration and deceleration are major causes of motion sickness in passengers.

[0003] There are some existing evaluation or reminder schemes related to driving behavior, but they have obvious limitations: slow response, limited perception range, and poor user engagement. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, vehicle, and storage medium for preventing motion sickness, in order to solve one or more technical problems existing in the prior art.

[0005] The technical solution adopted to solve the above-mentioned technical problems is as follows: This invention discloses a motion sickness prevention guidance method, characterized by comprising: Obtain vehicle dynamic information and calculate a first motion sickness risk index based on the vehicle dynamic information; Obtain environmental road condition information and predict a second motion sickness risk index based on the environmental road condition information; The driver is guided to drive based on the first motion sickness risk index and the second motion sickness risk index.

[0006] The present invention has at least the following beneficial effects: by acquiring vehicle dynamic information and calculating the first motion sickness risk index, the risk level of motion sickness caused by the driver's current driving behavior can be intuitively seen through the data, which is convenient for guiding the driver when the driver has high motion sickness behavior and reducing the risk level of motion sickness for passengers.

[0007] By acquiring environmental road condition information and predicting the second motion sickness risk index, the risk level of motion sickness caused by the road conditions ahead can be intuitively seen through data. This facilitates proactive guidance for drivers before the vehicle enters roads where passengers are prone to motion sickness, enabling the vehicle to smoothly transition through motion sickness-prone sections. This greatly improves the effectiveness of preventing motion sickness and fundamentally prevents it.

[0008] As a further improvement to the above technical solution, the vehicle dynamic information includes the rate of change of acceleration, the standard deviation of acceleration, and the frequency of acute events. The calculation of the first motion sickness risk index based on the vehicle dynamic information includes: The first motion sickness risk index is calculated by weighting and summing the rate of change of acceleration, the standard deviation of acceleration, and the frequency of acute events.

[0009] As a further improvement to the above technical solution, the acute events include acute acceleration events and acute deceleration events, and the acquisition of vehicle dynamic information includes the following steps: Set thresholds and scheduled times; The frequency of acute events is the number of times the absolute value of the rate of change of acceleration exceeds a threshold within a predetermined time period.

[0010] As a further improvement to the above technical solution, the environmental road condition information includes the curves, slopes, elevation changes, and real-time traffic flow data of the road ahead. The step of predicting the second motion sickness risk index based on the environmental road condition information includes the following steps: Obtain data on curves, gradients, elevation changes, and real-time traffic flow on the road ahead; Get the current vehicle speed; Calculate the acceleration generated when passing through the road ahead at the current vehicle speed, and calculate the second motion sickness risk index based on the acceleration.

[0011] As a further improvement to the above technical solution, the environmental road condition information also includes the relative distance and speed between the vehicle and the vehicle in front, as well as lane line information. The prediction of the second motion sickness risk index based on the environmental road condition information includes: The potential risk of emergency deceleration of the vehicle ahead is predicted based on the relative distance, relative speed, and lane information between the vehicle and the vehicle ahead.

[0012] As a further improvement to the above technical solution, the third motion sickness risk index is the average first motion sickness risk index of vehicles passing through the road ahead over a preset length and preset time. The motion sickness prevention guidance method includes: Obtain the third motion sickness risk index and guide the driver's driving based on the third motion sickness risk index.

[0013] As a further improvement to the above technical solution, guiding the driver to drive based on the first motion sickness risk index and the second motion sickness risk index includes the following steps: When the first motion sickness risk index reaches the level of high motion sickness behavior, guide the driver to slow down. When the second motion sickness risk index reaches the high motion sickness section, guide the driver to slow down in advance.

[0014] This invention discloses an anti-motion sickness guidance system, comprising: The acquisition module is used to acquire vehicle dynamic information and environmental road condition information; The calculation module is used to calculate the first motion sickness risk index based on vehicle dynamic information; The prediction module is used to predict the second motion sickness risk index based on environmental road condition information; The guidance module is used to guide the driver based on the first and second motion sickness risk indices.

[0015] The present invention discloses a vehicle, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the motion sickness prevention guidance method described above.

[0016] The present invention discloses a computer-readable storage medium storing computer-executable instructions for causing a computer to perform an anti-motion sickness guidance method as described in any of the preceding claims. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is a flowchart of the motion sickness prevention guidance method provided in the embodiments of the present invention; Figure 2 yes Figure 1 Detailed flowchart of step S100; Figure 3 yes Figure 2 Detailed flowchart of step S110; Figure 4 yes Figure 1 Detailed flowchart of step S200; Figure 5 yes Figure 1 Another detailed flowchart of step S200; Figure 6 This is another flowchart of the motion sickness prevention guidance method provided in the embodiments of the present invention; Figure 7 yes Figure 1 Detailed flowchart of step S300. Detailed Implementation

[0018] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0019] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0020] In the description of this invention, the use of terms such as "a number" means one or more, with "more than" meaning two or more. Terms like "greater than," "less than," and "exceeding" are understood to exclude the stated number, while terms like "above," "below," and "within" are understood to include the stated number. The use of terms like "first," "second," and "third" is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, the number of indicated technical features, or the sequential relationship between indicated technical features.

[0021] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0022] In related technologies, the system's response is lagging, resulting in only reactive corrections after undesirable driving behaviors occur, failing to achieve proactive prevention—essentially, an isolated system lacking foresight. Specifically, existing systems evaluate only based on current or historical driving data, unable to incorporate future road condition information for predictive guidance. For example, the system can only issue a warning after the driver brakes suddenly, but cannot provide preventative advice before approaching a downhill slope or entering a curve.

[0023] The system's perception range is limited, unable to transcend the physical limitations of the vehicle's own sensors to acquire broader road comfort information; in other words, it has a single information dimension and lacks collaborative perception. Specifically, the system relies solely on the vehicle's own sensors, forming an information "island." It cannot acquire group driving data from other vehicles on the road ahead, nor can it predict whether there are generally uncomfortable driving conditions ahead, such as continuous bumps or frequent stops and starts.

[0024] Poor user engagement and a lack of intrinsic motivation for drivers to actively and willingly use the system result in low system utilization. This indicates a passive interaction method and a lack of long-term incentives. Specifically, existing reminder methods are mostly passive warnings, lacking positive incentive mechanisms. Drivers are prone to becoming annoyed by continuous, accusatory reminders and shutting down the system, making it difficult to develop long-term, stable, and smooth driving habits.

[0025] Reference Figures 1 to 7 The following are several embodiments of the motion sickness prevention guidance method, system, vehicle and storage medium of the present invention.

[0026] like Figure 1 As shown, an embodiment of the motion sickness prevention guidance method of the present invention includes, but is not limited to, the following steps: Step S100: Obtain vehicle dynamic information and calculate a first motion sickness risk index based on the vehicle dynamic information. The vehicle dynamics are perceived, and the first motion sickness risk index is calculated using the vehicle dynamic information. The first motion sickness risk index is used to intuitively reflect whether the driver's current driving style is prone to causing motion sickness.

[0027] Step S200: Obtain environmental road condition information and predict a second motion sickness risk index based on the environmental road condition information. The system performs predictive perception of the environment and predicts the second motion sickness risk index using environmental road condition information. The second motion sickness risk index is used to visually indicate whether the road conditions ahead are likely to cause motion sickness.

[0028] Step S300: Guide the driver's driving based on the first motion sickness risk index and the second motion sickness risk index. By combining multi-source information fusion, and based on the real-time first motion sickness risk index and the predictive second motion sickness risk index, fundamentally optimize the driver's driving behavior, enabling the driver to drive smoothly and achieving the goal of proactive and intelligent motion sickness prevention.

[0029] Understandably, smooth driving refers to maintaining a stable, uniform, and comfortable driving state by reasonably controlling the vehicle's operation during driving, avoiding sudden acceleration, sudden braking, sharp turns, and other violent actions. It not only improves the driving experience but also reduces vehicle wear and tear, improves fuel economy, enhances driving safety, and reduces motion sickness symptoms among passengers.

[0030] Understandably, the predictive second motion sickness risk index can advance the decision-making time for guiding driving from the "present" to the "future," upgrading from "real-time correction" to "proactive prevention." It guides drivers before potential risks occur, giving them ample time to adjust their driving operations, achieving a smooth transition and truly "preventing problems before they occur," greatly enhancing the effectiveness of proactive motion sickness prevention.

[0031] Understandably, in step S100, the vehicle dynamic information includes the vehicle's rate of change of acceleration, standard deviation of acceleration, and frequency of acute events. For example... Figure 2 As shown, step S100 includes, but is not limited to, the following steps: Step S110: Obtain the vehicle's rate of change of acceleration, standard deviation of acceleration, and frequency of acute events; Step S120: The acceleration change rate, acceleration standard deviation and acute event frequency are weighted and summed to obtain the first motion sickness risk index.

[0032] Understandably, the Motion Sickness Risk Index (MSRI) is a core quantitative indicator. Based on data from the vehicle's dynamic sensor array, it calculates in real-time the risk of motion sickness caused by current driving behavior. The MSRI calculation comprehensively considers the following factors: Among them, the rate of change of acceleration (Jerk): This is the most critical indicator of motion sickness, used to continuously calculate the rate of change of the vehicle's longitudinal acceleration.

[0033] The standard deviation of acceleration is the degree of fluctuation in acceleration values ​​over a unit of time (e.g., 60 seconds). The greater the fluctuation, the more prone passengers are to motion sickness.

[0034] The frequency of acute events is the number of acute events that occur per unit of time.

[0035] MSRI Overall Score Calculation: Based on the above characteristics, a real-time "First Motion Sickness Risk Index" is calculated using a weighted algorithm or machine learning model (such as a lightweight neural network). average Acceleration standard deviation frequency of acute events in, , and These are the weighting coefficients, obtained through weighted calculations or machine learning models.

[0036] The higher the MSRI value, the more likely the current driving style is to cause motion sickness.

[0037] By using weighted or machine learning models, multiple vehicle dynamic information can be integrated into a single and easy-to-understand MSRI score (for example, setting the MSRI to 0-100 points, with higher scores indicating a greater risk of motion sickness).

[0038] It is understandable that acute events include both acute acceleration events and acute deceleration events. For example... Figure 3 As shown, step S100 includes, but is not limited to, the following steps: Step S111: Set the threshold and the predetermined time.

[0039] Step S112: Count the number of times the absolute value of the rate of change of acceleration exceeds a threshold within a predetermined time period. The resulting statistical result is the frequency of acute events. For example, the threshold is set as follows: Statistics within the scheduled time The number of times.

[0040] In some embodiments, taking the following driving behaviors as an example within a 60-second time window: 10 seconds: A rapid acceleration, the longitudinal acceleration from Rise to within 0.5 seconds ; 30 seconds: A sudden deceleration, longitudinal acceleration from It dropped to [a certain value] in 0.4 seconds. ; The rest of the time: constant speed, acceleration at... Fluctuations within a certain range.

[0041] Then in step S110: (1) Calculation of the rate of change of acceleration (jerk): Rapid acceleration event:

[0042] Sudden deceleration events: (Take the absolute value 7.0) During other time periods: jerk values ​​are close to 0.

[0043] Average jerk (within 60 seconds): | Average jerk |

[0044] (2) Calculation of the standard deviation of acceleration: Calculate the standard deviation of all acceleration sampling points over 60 seconds (assuming a sampling frequency of 10Hz and a total of 600 data points): Mean acceleration: approximately 0.05

[0045] The sum of squared differences between each point and the mean: approximately 15.2 Acceleration standard deviation

[0046] (3) Calculation of the frequency of acute events: Set the threshold to Therefore, when This refers to an acute acceleration time or an acute deceleration event.

[0047] The scheduled time was 60 seconds; within 60 seconds, two acute events occurred (one acute acceleration event and one acute deceleration event). Acute event frequency = 2.0 times / minute In step S120: The weighted calculation formula is used as follows: average Acceleration standard deviation frequency of acute events Basis for determining weighting coefficients: (Jerk Weight): Jerk is the most direct physical quantity causing motion sickness, reflecting the abruptness of driving maneuvers, and therefore is given the highest weight. Set to 50; (Standard deviation weight): Reflects overall driving smoothness, with a moderate impact. Set to 20; (Frequency weight): Reflects the intensity of unsafe driving behaviors; its impact is relatively small. Set it to 10.

[0048]

[0049] The MSRI scores are divided as follows: when At that time, the driver's driving was very smooth, and the risk of motion sickness was extremely low, meaning the MSRI was at the very low level of motion sickness behavior. when At that time, the driver's driving was relatively smooth, with occasional sudden operations, indicating that the MSRI was in a low motion sickness state. when At that time, the driver's driving was not smooth enough, and the risk of motion sickness was moderate, that is, the MSRI was moderate motion sickness behavior; when At that time, the driver's driving style is rapid, and the risk of motion sickness is high, that is, the MSRI is in a high motion sickness behavior state.

[0050] Understandably, if a lightweight neural network model is used to adjust the weights in a machine learning model, the adjustment of the weight coefficients is based on the following factors: Driver historical data: Records the driver's long-term driving behavior. If the driver's baseline level is high (habitual sporty driving), the threshold for triggering reminders will be appropriately lowered to avoid frequent interruptions; if the baseline level is low, the weight will be increased to encourage improvement.

[0051] Passenger status: If the in-vehicle camera detects children or elderly people in the back seat, it will automatically... , , The coefficients were increased by 20%, 15%, and 10% respectively, making MSRI more sensitive and enabling earlier intervention.

[0052] Traffic conditions: In congested areas, the system reduces... Weighting is used to avoid misjudgments caused by frequent start-stop cycles.

[0053] Understandably, in step S200, the environmental road condition information includes curves, slopes, elevation changes, and real-time traffic flow data of the road ahead. For example... Figure 4 As shown, step S200 includes, but is not limited to, the following steps: Step S210: Obtain curves, slopes, elevation changes, and real-time traffic flow data of the road ahead. Receive route planning information through the navigation system interface to obtain geometric information of the road ahead, such as curves (curvature) and slopes.

[0054] Step S220: Obtain the current vehicle speed. The current vehicle speed is obtained from the speed sensor.

[0055] Step S230: Calculate the acceleration generated when passing the road ahead at the current speed, and calculate the second motion sickness risk index based on the acceleration. Combining the current speed, predict the possible lateral and longitudinal accelerations the vehicle may generate when passing the road ahead at the current speed, and then calculate the second motion sickness risk index. The second motion sickness risk index is the predicted MSRI value, and the calculation method for the predicted MSRI is the same as that for the MSRI.

[0056] In some embodiments, the prediction is made by taking the example that the navigation system identifies a curve with a radius of curvature of 200 meters 300 meters ahead.

[0057] Distance ahead: 300 meters; Curve radius of curvature: 200 meters; Current speed: 80km / h (approximately 22.2m / s); Vehicle weight: Approximately 2000 kg; Road friction coefficient: 0.8.

[0058] (1) Lateral acceleration prediction: Formula for lateral acceleration (centripetal acceleration):

[0059] (Approximately 0.25g) (2) Longitudinal acceleration prediction: To safely navigate a curve, vehicles need to turn within 300 meters... Reduce speed to the recommended cornering speed. The recommended cornering speed is based on the lateral acceleration comfort threshold (usually taken as...). ,Right now (Reverse reasoning:)

[0060] The recommended cornering speed is approximately 71.3 km / h. .

[0061] Required deceleration

[0062] Available deceleration distance: 300 meters Required average deceleration (longitudinal):

[0063] (3) Predicted Jerk value (longitudinal acceleration rate of change): Assuming the driver begins braking 150 meters from the curve and reaches the target position within 3 seconds... deceleration: predict

[0064] (4) Second Motion Sickness Risk Index (Predicted MSRI Value): Based on the above-predicted longitudinal acceleration ( ) and predicting Jerk ( Substituting into the MSRI formula (assuming...) ; ; ); Predicted average Jerk:

[0065] Predicted acceleration standard deviation: Due to the single, smooth deceleration, the fluctuation is small, and it is estimated to be...

[0066] Predicted frequency of rapid acceleration / deceleration: 0 times / minute (because deceleration is smooth). predict

[0067] Conclusion: The predicted MSRI is 3.8 (very low risk), indicating that it is safe to smoothly decelerate through the curve at the current speed.

[0068] In other embodiments, the slope and elevation changes of the road ahead are used as examples to participate in the prediction.

[0069] (1) Influence of slope: Formula for the effect of slope on longitudinal acceleration:

[0070] (Acceleration due to gravity) For slope angle (e.g., 5% slope corresponds to) , ) If there is a 5% downhill slope ahead ( ): (This is equivalent to the vehicle automatically accelerating without the accelerator pedal being pressed.) The revised formula for calculating the predicted MSRI is as follows:

[0071] in: Influence coefficient of slope change rate (set to 0.3) : Slope change rate (the degree of drastic change in slope). If the slope suddenly changes from 0% to 5%, a jerk increment is generated.

[0072] (2) Effects of altitude change: Altitude changes indirectly affect vehicle power response by influencing air density and motor efficiency. However, the more direct impact is on determining long downhill sections using navigation systems. If the elevation drops by more than 200 meters on a continuous downhill slope ahead, the system will predict an increase in energy recovery demand, which may result in frequent deceleration.

[0073] The altitude change influence coefficient β, with a value ranging from 1.0 to 1.5, needs to be multiplied by the predicted Jerk.

[0074] (3) Implement traffic flow data in forecasting: Traffic flow data is used in predictions via the congestion coefficient γ. predict

[0075] in: MSRI benchmark based on road geometry calculations Congestion coefficient, determined by referring to a table based on real-time traffic flow.

[0076] Among them, when the road is clear (i.e., vehicle speed) ):

[0077] When the road is slightly congested (i.e., the vehicle speed is...) ):

[0078] When the road is moderately congested (i.e., the vehicle speed is at...), ):

[0079] When the road is severely congested (i.e., vehicle speed) ):

[0080]

[0081] Furthermore, environmental road condition information also includes the relative distance and speed between the vehicle and the vehicle in front, as well as lane marking information. Therefore, such as Figure 5 As shown, step S200 includes, but is not limited to, the following steps: Step S240: Obtain the relative distance, relative speed, and lane line information between the vehicle and the vehicle ahead. The distance, relative speed, and lane line information of the vehicle ahead are perceived through onboard sensors such as cameras and radar of the Advanced Driver Assistance System (ADAS).

[0082] Step S250: Based on the relative distance, relative speed, and lane information between the vehicle and the vehicle ahead, predict the potential risk of emergency deceleration of the vehicle ahead. For example, predict the second motion sickness risk index when the vehicle ahead brakes suddenly.

[0083] In some embodiments, ADAS data is used in the calculation of the second motion sickness risk index as an example: ADAS data includes: Distance to the vehicle ahead (D): The relative distance (in meters) between the vehicle and the vehicle in front; Relative speed (V_rel): The relative speed between the vehicle and the vehicle in front (positive value indicates approaching, negative value indicates moving away), unit: m / s; Lane information: Whether the vehicle has deviated from the lane, lane line type, etc.

[0084] (1) Calculation of Time to Collision (TTC): (when hour) For example: When D = 30 meters, V_rel = 10 m / s (the vehicle in front is moving slower, and this vehicle is approaching).

[0085] (2) Calculation of emergency deceleration risk coefficient (δ): Risk level is determined based on TTC value: when At that time, the risk level was low. ; when At that time, the risk level was medium. ; when At that moment, it was at high risk. ; when At that moment, the situation was at extremely high risk. .

[0086] (3) MSRI prediction formula after ADAS data correction:

[0087] in, MSRI: A benchmark based on road geometry prediction.

[0088] Assumption: Road geometry prediction

[0089] Seconds (high risk)

[0090]

[0091] In other embodiments, the second motion sickness risk index is calculated using the example of sudden braking by the vehicle in front. The sudden braking is not directly output by the ADAS sensors, but is determined through the following logic: (1) Conditions for judging if the vehicle in front brakes suddenly: The relative speed V_rel of the vehicle in front changes drastically over a period of time; The distance D from the vehicle in front decreases rapidly; (2) Calculation of the deceleration of the vehicle in front:

[0092] For example: If the relative speed of the vehicle in front changes from 0 m / s to -8 m / s within 0.5 seconds (the vehicle in front decelerates), then... (Approximately 1.6g, for emergency braking) (3) Predict the vehicle's emergency braking requirements: If the absolute value of the deceleration of the vehicle in front (Approximately 0.5g) was determined to be an emergency braking event of the vehicle in front, and it was predicted that this vehicle would also need to brake accordingly.

[0093] Predict the required deceleration for this vehicle:

[0094] in: D_current: Current distance D_safe: safe distance (usually the following distance for 2 seconds corresponding to the vehicle speed).

[0095] (4) Predicted Jerk value: If it is predicted that this vehicle will need to... The deceleration during emergency braking reaches this deceleration level within 0.3 seconds:

[0096] (5) Direct correction of predicted MSRI by ADAS events: When an emergency braking by the vehicle ahead is detected, an emergency event correction term ε is directly added to the predicted MSRI:

[0097] Where ε is set according to the level of urgency: The car in front slowed down slightly. ): ε=10 The vehicle in front braked moderately suddenly. ): ε=25 The car in front braked suddenly and violently. ): ε=45.

[0098] Understandably, motion sickness prevention guidance methods also include a third motion sickness risk index. This third motion sickness risk index is the average first motion sickness risk index of vehicles passing over the road ahead over a preset length and time. Therefore, if... Figure 6 As shown, the motion sickness prevention guidance method includes, but is not limited to, the following steps: Step S400: Obtain the third motion sickness risk index and guide the driver based on it. Through the V2X communication module, receive group comfort data sent or broadcast from the cloud or vehicles ahead. This group comfort data is the third motion sickness risk index, which is generated by cloud aggregation of the first motion sickness risk indices anonymously uploaded by other vehicles passing by on the road ahead. This index can be marked on specific road sections of the electronic map.

[0099] This setup extends the vehicle's perception range to the entire traffic environment, utilizing the collective intelligence of the third motion sickness risk index to compensate for the lack of information in a single vehicle. This allows the motion sickness prevention guidance method to also provide collaborative guidance based on wide-area information, making the guidance strategy more holistic, precise, and intelligent.

[0100] Understandably, the V2X communication module communicates wirelessly with the on-board unit (OBU), roadside unit (RSU), and cloud to achieve real-time data exchange. For example, if the received data shows that "the average first motion sickness risk index of vehicles passing through the road section 2 kilometers ahead has been consistently as high as 80 in the past hour," meaning the third motion sickness risk index has been consistently as high as 80, it is determined that there is a risk of general discomfort in that road section, which may be due to poor road conditions or severe congestion.

[0101] It is understandable that, such as Figure 6 As shown, step S400 includes, but is not limited to, the following steps: Step S410: When the third motion sickness risk index reaches the high motion sickness section, guide the driver to slow down in advance.

[0102] Understandably, the determination of high-risk motion sickness sections is based on a combination of the second and third motion sickness risk indices. The second motion sickness risk index serves as the primary criterion, while the third motion sickness risk index acts as a supplementary or corrective factor.

[0103] In this embodiment, when the second motion sickness risk index is greater than or equal to 50, it is directly determined as a high-motion-sickness road segment; When the second motion sickness risk index is greater than 30 and less than 50, and the third motion sickness risk index is greater than or equal to 40, it is determined to be a high motion sickness road segment (double confirmation). When the third motion sickness risk index is greater than or equal to 60, even if the second motion sickness risk index is lower, it is still considered a high motion sickness road segment (in this case, the third motion sickness risk index takes priority).

[0104] It is understandable that, such as Figure 7 As shown, step S300 includes, but is not limited to, the following steps: Step S310: When the first motion sickness risk index reaches the level of high motion sickness behavior, guide the driver to slow down.

[0105] Step S320: When the second motion sickness risk index reaches the high motion sickness section, guide the driver to slow down in advance.

[0106] Understandably, drivers should receive proactive voice guidance. When the second high risk index for motion sickness is reached, indicating the vehicle is about to enter a high-risk section, guiding voice prompts should be played beforehand, such as "Sharp bend ahead, we recommend smoothly reducing speed to 60 km / h to pass" or "Downhill ahead, please ease off the accelerator and use gentle energy recovery," etc. When approaching the third high-risk section for motion sickness, a voice prompt should be given, "Please note that the road ahead is bumpy, please maintain a safe distance and drive cautiously."

[0107] Understandably, voice prompts can use pre-recorded, non-accusatory language, such as "Current road conditions are complex; we recommend driving smoothly to improve comfort" or "For the sake of rear passengers, shall we try gentler braking?" The frequency and tone of the voice prompts can be adjusted according to the severity of the MSRI (Minimum Situational Response Rate). Predictive voice guidance can also be customized to be personalized and contextualized.

[0108] It is understandable that visual guidance is provided to drivers.

[0109] In some embodiments, a gradually changing MSRI progress bar is displayed on the instrument panel or HUD (Head-Up Display). When the driver is driving smoothly, the MSRI progress bar is full in green; as the driving behavior becomes more agile, the green portion gradually shortens, while the yellow or red portion gradually lengthens; the color distinguishes the MSRI values ​​for different driving behaviors.

[0110] In other embodiments, an abstract animation, such as ripples on water, is displayed on the dashboard or HUD. The water is calm when driving smoothly and ripples appear when driving aggressively. This approach is intuitive and non-accusatory.

[0111] Furthermore, tactile guidance is provided to the driver. Specifically, the driver is alerted by vibrating the steering wheel or driver's seat with specific and gentle vibration patterns.

[0112] Understandably, based on the first motion sickness risk index, the driver is guided by voice, visualization, and tactile feedback.

[0113] when At this time, the risk of motion sickness is low, and there is no prompt or only a green progress bar is displayed; when At this time, the risk of motion sickness is low, a yellow progress bar is displayed, and there are no voice prompts; when If there is a risk of motion sickness, an orange-yellow progress bar will be displayed, and one voice prompt will be available. when At times, if there is a higher risk of motion sickness, an orange progress bar will be displayed, triggering predictive voice guidance; when When there is a high risk of motion sickness, a red progress bar will appear, triggering predictive voice guidance accompanied by vibration. when At this time, there is an extremely high risk of motion sickness. A red flashing progress bar will be displayed, continuous voice guidance will be provided, and the incident will be recorded as an adverse driving event.

[0114] It is understandable that predictive voice guidance is triggered 300-500 meters before the vehicle enters a section of road prone to motion sickness. Predictive guidance is also triggered when ADAS detects a TTC (Time To Catch) of less than 3 seconds, indicating a risk of emergency deceleration.

[0115] Furthermore, motion sickness prevention guidance methods incorporate gamified incentives.

[0116] In some embodiments, a points and reward system is set up. Specifically, "comfort points" are awarded based on metrics such as average MSRI per trip, smooth driving duration, and the number of successfully handled predictable scenarios. Drivers can use these points to redeem in-vehicle entertainment services, virtual vehicle appearances, and more.

[0117] In other embodiments, an achievement system is set up. Specifically, a series of achievement goals are established, such as "Master of Continuous Curves" or "Seven-Day Smooth Driving Challenge," to increase the fun and sense of purpose in driving and enhance the driver's motivation to drive smoothly.

[0118] In some other embodiments, social interaction features are included. A comfort driving leaderboard is set up within the car owner's mobile app or in-vehicle system, allowing friendly competition between friends or owners of the same model. Drivers can generate and share "comfort driving reports" to social media platforms with a single click, showcasing their driving achievements and smoothness data.

[0119] This setup introduces gamification and social elements into the motion sickness prevention guidance method, upgrading it from "passively receiving guidance" to "actively participating in the driver's role." It transforms the essence of guidance from "requiring drivers not to drive hastily" to "encouraging drivers to drive smoothly for more fun," turning smooth driving into an interesting and accumulative achievement. This stimulates the driver's intrinsic motivation and sense of participation, ensuring the long-term effectiveness and user stickiness of the motion sickness prevention guidance method.

[0120] Understandably, taking a complete workflow in a comprehensive scenario as an example: 1. The driver sets the destination, and the navigation system plans the route. The navigation system shows a long downhill slope and continuous curves 15 kilometers ahead.

[0121] The vehicle system receives cloud data via the V2X communication module, showing that the third motion sickness risk index for this continuous curved road section is as high as 80 (red alert).

[0122] 3. When the distance to the start of the downhill section is 2 kilometers, the system will begin providing guidance: Visual guidance: Downhill and curved sections on the navigation path are displayed as a gradient from yellow to red.

[0123] The voice prompts predictive guidance: "There is a long downhill slope and a curve ahead. The road conditions are quite bumpy. It is recommended to slow down in advance and maintain a speed of less than 70 km / h."

[0124] Fourth, the driver releases the accelerator in advance as instructed, and the vehicle smoothly glides downhill. The system maintains a steady speed by predicting the intensity of energy recovery.

[0125] 5. After successfully passing through this section of road, the driver will receive a voice prompt saying "The cornering was very smooth!" and will be rewarded with 50 "Comfort Points" and "Cornering Master" achievement fragments (collecting several fragments can be exchanged for the complete achievement).

[0126] After the trip, the car's infotainment system displays a report stating, "You have outperformed 92% of car owners nationwide and earned the title of 'Comfort Star'!" The driver can then share the report to their WeChat Moments with a single click.

[0127] Understandably, this approach responds to personalized incentives. Since novice drivers generally have a higher risk of motion sickness upon first encounter, the motion sickness prevention guidance method sets a more lenient threshold for earning points and provides more frequent encouraging voice prompts to help them build confidence. For experienced drivers with a high baseline level, the method sets a "challenge mode," where only those who achieve extremely high stability standards can earn top-tier points and rare achievements, satisfying their desire for excellence.

[0128] An embodiment of the present invention provides a motion sickness prevention guidance method system, which includes an acquisition module, a calculation module, a prediction module, and a guidance module.

[0129] Understandably, the acquisition module is used to acquire vehicle dynamic information and environmental road condition information. For example, the acquisition module includes inertial measurement units (IMUs) or vehicle control units (VCUs) to collect core data such as the vehicle's longitudinal acceleration, and obtain vehicle dynamic information through calculation.

[0130] Furthermore, the acquisition module also includes a navigation system interface to obtain environmental and road condition information such as the curves ahead, slopes, altitude changes, and real-time traffic flow data.

[0131] In addition, the acquisition module also includes ADAS sensors to acquire environmental road condition information such as the relative distance and speed between the vehicle and the vehicle in front, as well as lane line information.

[0132] Understandably, the calculation module is used to calculate the first motion sickness risk index based on vehicle dynamic information, so as to confirm whether the driver's driving behavior reaches the level of high motion sickness behavior that is likely to cause motion sickness in passengers.

[0133] Understandably, the prediction module is used to predict the second motion sickness risk index based on environmental road condition information, so as to confirm whether the road ahead is a high-risk section of road that is likely to cause motion sickness in passengers.

[0134] Understandably, the guidance module is used to guide the driver based on the first and second motion sickness risk indices. For example, when the first motion sickness risk index reaches a high level, the driver is guided to slow down to reduce the likelihood of passengers experiencing motion sickness; when the second motion sickness risk index reaches a high level, the driver is guided to slow down in advance to avoid passengers experiencing motion sickness due to road conditions.

[0135] Understandably, the guidance module includes a voice unit, a visual unit, a tactile unit, and an incentive unit, guiding the user to drive through voice broadcasts, visual reminders, tactile vibrations, and incentive encouragement.

[0136] Furthermore, the motion sickness guidance system also includes a coordination module for receiving a third motion sickness risk index. This allows the system to assess the comfort level of passengers traveling on the road ahead based on the third motion sickness risk index. When the third motion sickness risk index reaches a high-risk section, the guidance module will guide the driver.

[0137] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0138] This invention also provides a vehicle, including a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the aforementioned motion sickness prevention guidance method. The vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. The vehicle can also be a commercial vehicle, such as a van, bus, small truck, or large trailer. The vehicle needs to have an electric motor capable of outputting power or acting as a generator to store mechanical energy. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle.

[0139] Since the vehicle applies all the technical solutions of the above-mentioned anti-motion sickness guidance method or anti-motion sickness guidance system, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0140] Furthermore, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for performing the aforementioned motion sickness prevention guidance method. Exemplarily, the above-described method is executed... Figures 1 to 7 The methods and steps in the text.

[0141] It is worth noting that, since the computer-readable storage medium of the present invention can execute the motion sickness prevention guidance method of any of the above embodiments, the specific implementation and technical effects of the computer-readable storage medium of the present invention can be referred to the specific implementation and technical effects of the motion sickness prevention guidance method of any of the above embodiments.

[0142] Furthermore, one embodiment of the present invention also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the aforementioned motion sickness prevention guidance method. Exemplarily, the above-described method is executed... Figures 1 to 7 The methods and steps in the text.

[0143] It is worth noting that, since the computer program product of this embodiment can execute the motion sickness prevention guidance method of any of the above embodiments, the specific implementation method and technical effect of the computer program product of this embodiment can refer to the specific implementation method and technical effect of the motion sickness prevention guidance method of any of the above embodiments.

[0144] Those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital multi-stage chassis (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0145] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for guiding motion sickness prevention, characterized in that, include: Obtain vehicle dynamic information and calculate a first motion sickness risk index based on the vehicle dynamic information; Obtain environmental road condition information and predict a second motion sickness risk index based on the environmental road condition information; The driver is guided to drive based on the first motion sickness risk index and the second motion sickness risk index.

2. The motion sickness prevention guidance method according to claim 1, characterized in that, The vehicle dynamic information includes the rate of change of acceleration, the standard deviation of acceleration, and the frequency of acute events. The calculation of the first motion sickness risk index based on the vehicle dynamic information includes: The first motion sickness risk index is calculated by weighting and summing the rate of change of acceleration, the standard deviation of acceleration, and the frequency of acute events.

3. The motion sickness prevention guidance method according to claim 2, characterized in that, The acute events include acute acceleration events and acute deceleration events, and the acquisition of vehicle dynamic information includes the following steps: Set thresholds and scheduled times; The frequency of acute events is the number of times the absolute value of the rate of change of acceleration exceeds a threshold within a predetermined time period.

4. The motion sickness prevention guidance method according to claim 1, characterized in that, The environmental road condition information includes curves, slopes, elevation changes, and real-time traffic flow data of the road ahead. The prediction of the second motion sickness risk index based on the environmental road condition information includes the following steps: Obtain data on curves, gradients, elevation changes, and real-time traffic flow on the road ahead; Get the current vehicle speed; Calculate the acceleration generated when passing through the road ahead at the current vehicle speed, and calculate the second motion sickness risk index based on the acceleration.

5. The motion sickness prevention guidance method according to claim 1, characterized in that, The environmental road condition information also includes the relative distance and speed between the vehicle and the vehicle in front, as well as lane marking information. The prediction of the second motion sickness risk index based on the environmental road condition information includes: The potential risk of emergency deceleration of the vehicle ahead is predicted based on the relative distance, relative speed, and lane information between the vehicle and the vehicle ahead.

6. The motion sickness prevention guidance method according to claim 1, characterized in that, The third motion sickness risk index is the average first motion sickness risk index of vehicles passing through the road ahead over a preset length and preset time. The motion sickness prevention guidance method includes: Obtain the third motion sickness risk index and guide the driver's driving based on the third motion sickness risk index.

7. The motion sickness prevention guidance method according to claim 1, characterized in that, Guiding the driver based on the first and second motion sickness risk indices includes the following steps: When the first motion sickness risk index reaches the level of high motion sickness behavior, guide the driver to slow down. When the second motion sickness risk index reaches the high motion sickness section, guide the driver to slow down in advance.

8. A motion sickness prevention guidance system, characterized in that, Including: The acquisition module is used to acquire vehicle dynamic information and environmental road condition information; The calculation module is used to calculate the first motion sickness risk index based on vehicle dynamic information; The prediction module is used to predict the second motion sickness risk index based on environmental road condition information; The guidance module is used to guide the driver based on the first and second motion sickness risk indices.

9. A vehicle, characterized in that, It includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements a motion sickness prevention guidance method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a motion sickness prevention guidance method as described in any one of claims 1 to 7.

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