Vehicle control method and device, electronic equipment and storage medium

Abstract

The present application relates to a kind of vehicle control method, device, electronic equipment and storage medium, vehicle control method includes: obtaining the driving state data of vehicle and user-selected soothing level information;According to driving state data, determine road surface water category, according to driving state data and road surface water category, determine road wet grade;If front road condition data of road in front of vehicle can be obtained from cloud, according to road wet grade, road surface water category and driving state data, determine the confidence of front road condition data;According to front road condition data, confidence and soothing level information, determine the set of anti-drowsy control parameters, according to the set of anti-drowsy control parameters Control vehicle operation.This application embodiment can realize the complete closed loop from initiative prediction to dynamic check to hierarchical execution, so that vehicle can obtain optimal drowsy soothing effect under different road conditions and different user needs, significantly reduce the sensory conflict caused by road condition prediction deviation or water burst.

Description

Technical Field

[0001] This application relates to the field of vehicle intelligent control technology, and in particular to a vehicle control method, device, electronic device and storage medium. Background Technology

[0002] Motion sickness (motion sickness) is caused by a conflict between the motion state perceived by the vestibular system in the inner ear and the information received by the visual and somatic sensory systems, which leads to confusion in the brain's central nervous system and causes a series of physiological symptoms such as dizziness, nausea, and vomiting.

[0003] Slippery road surfaces can lead to longer vehicle braking distances, delayed steering response, or the risk of skidding, causing unstable vehicle movement and further exacerbating sensory conflicts and motion sickness risks for occupants. Existing solutions lack accurate prediction and targeted control for slippery road surfaces. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this application provides a vehicle control method, device, electronic device and storage medium.

[0005] In a first aspect, this application provides a vehicle control method, including: Obtain vehicle driving status data and user-selected comfort level information; The road surface water type is determined based on the driving status data, and the road surface slipperiness level is determined based on the driving status data and the road surface water type. If road condition data ahead of the vehicle can be obtained from the cloud, the confidence level of the road surface slipperiness level, the type of water accumulation on the road surface, and the driving status data can be determined. Based on the road condition data ahead, the confidence level, and the soothing level information, a set of anti-motion sickness control parameters is determined, and the vehicle operation is controlled according to the set of anti-motion sickness control parameters.

[0006] Optionally, the driving status data includes: left wheel speed data of the left wheel of the vehicle, right wheel speed data of the right wheel, wheel slip ratio with the road surface, water film noise data when the wheel runs over water, and vertical acceleration of the suspension. The type of road surface water is determined based on the driving status data, including: The wheel speed difference data is determined based on the left wheel speed data and the right wheel speed data; When the wheel speed difference data changes periodically and the vehicle does not brake, the road surface water category is determined based on the wheel speed difference range where the wheel speed difference data is located, the first slip ratio range corresponding to the slip ratio, the time period corresponding to the duration of the water film slapping noise feature or turbulence noise feature in the water film noise data, and the acceleration range corresponding to the vertical acceleration.

[0007] Optionally, the driving status data includes: wheel slip ratio and water film noise data when the wheel runs over water; The road surface slipperiness level is determined based on the driving status data and the road surface water type, including: The first wet slip level is determined based on the second slip rate range corresponding to the slip rate and the frequency range corresponding to the water film noise data. Determine whether there is standing water on the road surface where the vehicle is currently traveling based on the type of road surface water. If there is standing water on the road surface where the vehicle is currently driving, the first slipperiness level is increased by a preset amount to obtain the road slipperiness level. If there is no standing water on the road surface where the vehicle is currently traveling, the first slipperiness level is determined as the slipperiness level of the road surface.

[0008] Optionally, the driving status data includes: wheel-road slip ratio; determining the confidence level of the road condition data ahead based on the road surface slippage level, the road surface water accumulation type, and the driving status data includes: The type of bumps, curves, and slopes on the current road surface are determined based on the driving status data. The slip ratio, the type of water accumulation on the road surface, the type of bump, the type of curve, and the type of slope are compared with the road condition data ahead to obtain the degree of deviation. The confidence level of the road condition data ahead is determined based on the degree of deviation.

[0009] Optionally, the driving status data includes: vertical acceleration, lateral acceleration and longitudinal acceleration of the suspension, yaw rate of the vehicle and steering wheel angle; Based on the driving status data, the current road surface bump type, curve type, and slope type are determined, including: The type of bump is determined based on the vertical acceleration. The curvature is determined based on the yaw rate, lateral acceleration, and steering wheel angle, and the curve type is determined based on the curvature. The type of ramp is determined based on the longitudinal acceleration.

[0010] Optionally, a set of motion sickness control parameters is determined based on the road condition data ahead, the confidence level, and the soothing level information, including: Obtain the first control parameter corresponding to the road condition data ahead; Determine the confidence level range and the parameter adjustment step size corresponding to the confidence level range; The first control parameter is adjusted based on the soothing level information to obtain the second control parameter; The second control parameter is adjusted based on the step size of the parameter to obtain the set of anti-motion sickness control parameters.

[0011] Optionally, the driving state data includes: wheel-road slip ratio; the method further includes: If it is not possible to obtain road condition data ahead of the vehicle from the cloud, obtain occupant posture data in the vehicle. The type of bumps, curves, and slopes on the current road surface are determined based on the driving status data. The road condition data ahead is determined based on the slip ratio, bump type, curve type, slope type, road surface water type, road surface slipperiness level, and occupant posture data. Based on the road condition data ahead and the soothing level information, a set of anti-motion sickness control parameters is determined, and the vehicle operation is controlled according to the set of anti-motion sickness control parameters.

[0012] Optionally, a set of motion sickness control parameters is determined based on the road condition data ahead and the soothing level information, including: The third control parameter is determined based on the road condition data ahead; The third control parameter is adjusted based on the soothing level information to obtain a set of anti-motion sickness control parameters.

[0013] Optionally, controlling the vehicle operation according to the anti-motion sickness control parameter set includes: The power control parameters, chassis control parameters, seat control parameters, and cabin control parameters are determined based on the set of anti-motion sickness control parameters. The power system of the vehicle is controlled according to the power control parameters; The chassis system of the vehicle is controlled according to the chassis control parameters; The active seat system of the vehicle is controlled according to the seat control parameters; The vehicle's cabin environment system is controlled according to the cabin control parameters.

[0014] Secondly, this application provides a vehicle control device, comprising: The acquisition module is used to acquire vehicle driving status data and user-selected comfort level information; The first determining module is used to determine the type of water accumulation on the road surface based on the driving status data, and to determine the road surface slipperiness level based on the driving status data and the type of water accumulation on the road surface. The second determining module is used to determine the confidence level of the road condition data ahead based on the road surface slipperiness level, the road surface water accumulation type, and the driving status data if the road condition data ahead of the vehicle can be obtained from the cloud. The third determining module is used to determine a set of anti-motion sickness control parameters based on the road condition data ahead, the confidence level, and the soothing level information, and to control the vehicle operation based on the set of anti-motion sickness control parameters.

[0015] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the vehicle control method described in any of the first aspects.

[0016] Fourthly, this application provides a computer-readable storage medium storing a program for a vehicle control method, wherein when the program for the vehicle control method is executed by a processor, it implements the steps of any of the vehicle control methods described in the first aspect.

[0017] The beneficial effects of this invention are: This application embodiment first identifies the type of road surface water and determines the road surface slipperiness level by acquiring driving status data and user relief level. Then, it receives road condition data from the cloud and evaluates its confidence level. Finally, it integrates the three to generate a set of anti-motion sickness control parameters. The joint judgment of road surface water type and road surface slipperiness level provides a more accurate comparison benchmark for confidence level assessment. The confidence level determines the credibility of road condition data and the correction range. The relief level controls the final intervention intensity. It realizes a complete closed loop from active prediction to dynamic verification to graded execution, so that the vehicle can obtain the optimal motion sickness relief effect under different road conditions and different user needs, and significantly reduce sensory conflicts caused by road condition prediction errors or sudden water accumulation. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a vehicle control method provided in an embodiment of this application; Figure 2 for Figure 1 A flowchart of the method for determining the type of road surface water based on the driving status data in step S102; Figure 3 for Figure 1 The flowchart of step S102, "Method for determining the road surface slipperiness level based on the driving status data and the road surface water type," is as follows: Figure 4 for Figure 1 Flowchart of step S103; Figure 5 for Figure 4 Flowchart of step S401; Figure 6 for Figure 1 The flowchart of step S104, which is a method for determining the set of anti-motion sickness control parameters based on the road condition data ahead, the confidence level, and the soothing level information; Figure 7 A flowchart of a vehicle control method provided in an embodiment of this application; Figure 8 for Figure 7 Flowchart of step S704; Figure 9 for Figure 1 The flowchart of step S104, which controls the vehicle operation method according to the set of anti-motion sickness control parameters, is as follows: Figure 10 A structural diagram of a vehicle control device provided in an embodiment of this application; Figure 11 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Because slippery roads can easily lead to increased vehicle braking distance, delayed steering response, or the risk of sideslip, causing unstable vehicle movement, and further exacerbating sensory conflicts and motion sickness risks for occupants, existing solutions lack accurate prediction and targeted control for slippery roads. Therefore, this application provides a vehicle control method, device, electronic device, and storage medium. Based on different user-selected soothing intensities ("mild soothing package" and "extreme soothing package"), it integrates standardized road condition data from chassis cloud maps (generated based on chassis suspension acceleration, rain sensor, intelligent driving, NVH data, etc., including slippery road surface judgment) to execute a preset, differentiated, and refined collaborative control strategy for the vehicle's four major systems: power, chassis, active seats, and cabin environment. Simultaneously, it adaptively adjusts control parameters to compensate for hardware differences between high- and low-spec models, systematically reducing sensory conflicts experienced by drivers and passengers during vehicle operation (including slippery road conditions), effectively preventing and alleviating motion sickness symptoms, and ensuring a consistent driving experience across different vehicle configurations.

[0023] The vehicle control method provided in this application embodiment can be applied to an Integrated Telematic Head Unit (iTHU). The iTHU can communicate with the Body Domain Controller (BDC), Power Control Unit (PCU / PDCU), Integrated Brake Control Unit (IBCU), Electric Power Steering System (EPS), Chassis Integrated Controller (VMC), Chassis Cloud Map Module (including cloud communication unit), Active Seat Controller and its supporting sensors (pressure sensor, inflation actuator, damping actuator), Vehicle Communication Module (5G / Vehicle Connectivity), NVH Data Acquisition Module, etc., via the vehicle CAN network. Figure 1 As shown, the vehicle control method may include the following steps: Step S101: Obtain vehicle driving status data and user-selected comfort level information; In this embodiment of the application, driving status data refers to the set of sensor signals that characterize the vehicle's motion state and the road surface interaction state, and soothing level information refers to the level of motion sickness intervention intensity selected by the user, including mild soothing level and extreme soothing level.

[0024] In this step, iTHU receives data from wheel speed sensors, suspension acceleration sensors, yaw rate sensors, brake sensors, steering sensors, and noise, vibration, and harshness (NVH) data acquisition modules, including left wheel speed data, right wheel speed data, wheel slip ratio, water film noise data when the wheels run over water, vertical acceleration of the suspension, lateral acceleration of the suspension, longitudinal acceleration of the suspension, yaw rate of the vehicle, and steering wheel angle, as well as driving status data. At the same time, it obtains the soothing level information selected by the user through soft switches or voice commands via the human-machine interface.

[0025] For example, when a user selects the Extreme Soothing Package through the vehicle's infotainment screen, the iTHU receives driving status data at 100-millisecond intervals, such as a wheel slip ratio of 12%, a suspension vertical acceleration of 0.45g, and significant water film slapping noise or turbulence noise characteristics in the water film noise data.

[0026] In practical applications, the iTHU controller receives user commands for function activation and mode selection. It integrates road condition prediction data from the chassis cloud map (generated by the cloud platform based on driving data from a large number of other vehicles, including information on slippery road surfaces determined by NVH data), real-time vehicle motion data, and occupant posture data to generate a specific and executable set of control parameters. These parameters are then distributed to the actuators of the four major systems: powertrain, chassis, active seats, and cabin environment. This allows them to work collaboratively according to preset mild or extreme comfort package strategies. At the same time, adaptive parameter adjustment ensures a consistent experience across high- and low-spec models.

[0027] Step S102: Determine the road surface water type based on the driving status data, and determine the road surface slipperiness level based on the driving status data and the road surface water type; In this embodiment of the application, the road surface water category refers to the degree of water layer covering the road surface, including no water accumulation, a small amount of water accumulation, obvious water accumulation, or deep water accumulation; the road surface slipperiness level refers to the degree of reduction in the road surface adhesion coefficient, including dry road surface, slightly slippery road surface, or heavily slippery road surface.

[0028] In this step, iTHU determines the type of road surface water based on the periodic fluctuations of wheel speed difference, slip ratio, water film noise characteristics, and vertical acceleration impact values ​​in the driving status data, according to the preset water accumulation discrimination rules; then, it judges the basic wet slip level mainly based on the slip ratio. If water accumulation exists, the basic wet slip level is increased by one level to obtain the final road surface wet slip level.

[0029] For example: if the speed difference between the left and right wheels increases instantaneously and fluctuates periodically, the slip rate is 12%, the water film slapping noise lasts for 0.5 seconds, and there is an instantaneous impact in the vertical acceleration, then it is judged as obvious water accumulation; a slip rate of 12% corresponds to a severely slippery road surface, and the level of severely slippery road surface is maintained due to the presence of water accumulation.

[0030] Step S103: If road condition data of the road ahead of the vehicle can be obtained from the cloud, the confidence level of the road surface slipperiness level, the type of water accumulation on the road surface and the driving status data is determined. In this embodiment, the cloud refers to a remote service platform that stores road condition data uploaded and marked by historical vehicles. The cloud-based road condition platform does not perform road condition prediction; it only serves as a distributed data storage, location marking, and distribution node. Historical vehicles complete road condition identification, verification, and labeling of seven types of conditions and upload them to the cloud, forming a standardized road condition cloud map. Subsequent vehicles obtain data from 50–200 kilometers ahead via 5G / vehicle connectivity modules. The cloud-based tagging data is used to pre-adjust chassis and power parameters before entering the road segment. After the vehicle enters, onboard sensors verify, assess confidence, and correct control parameters in real time, and then transmit the final accurate road conditions back to the cloud to update the tags, achieving crowdsourced iteration. The road condition data ahead refers to the road condition category tags that the vehicle identifies in real time and plans to upload to the cloud. In other words, the road condition data ahead includes one or more of the following: smooth road, slightly bumpy road, small curve, heavy bumpy road, large curve, slope, dry road surface, slightly slippery road surface, and heavily slippery road surface. Confidence refers to the degree of consistency between the road condition data ahead and the actual measured data after the vehicle enters the road, and is divided into high confidence, medium confidence, low confidence, or unreliable.

[0031] In this step, iTHU requests road condition tags for the road ahead from the cloud via the vehicle communication module. If the tag is successfully received, it is used as the road condition data ahead. After entering the road section, the vehicle compares the real-time road surface slipperiness level, road surface water type, bump type, curve type, and slope type with the cloud tags. The confidence level is calculated according to the weighted formula of consistency of slip rate, bump consistency, water accumulation characteristics consistency, and curve and slope consistency.

[0032] For example, if the cloud label indicates a severely slippery road surface, and the vehicle's actual slip rate is 12% and the water accumulation is obvious after entering the area, which is completely consistent with the label, then the confidence level is calculated to be 92%, which is considered a high confidence level.

[0033] Step S104: Determine a set of anti-motion sickness control parameters based on the road condition data ahead, the confidence level, and the soothing level information, and control the vehicle operation based on the set of anti-motion sickness control parameters.

[0034] In this embodiment of the application, the set of anti-motion sickness control parameters refers to the set of specific execution values ​​used to adjust the vehicle's power system, chassis system, active seat system, and cabin environment system, including torque change rate limit, brake pressure gain, suspension damping coefficient, seat side wing inflation volume, fragrance concentration, etc.

[0035] In this step, iTHU queries the preset baseline control parameters based on the road condition data ahead, then selects the corresponding parameter adjustment step size according to the confidence level range to correct the baseline parameters, and finally makes differentiated adjustments to the parameters according to the user's selected soothing level information to generate the final anti-motion sickness control parameter set. The parameters are then distributed to the power control unit, integrated brake control unit, electric power steering system, chassis integrated controller, active seat controller and body domain controller for execution through the vehicle network.

[0036] For example, if the road conditions ahead are severely bumpy, the confidence level is low, and the comfort level is extremely comfortable, then iTHU will adjust the suspension damping from 1000 N·m / s to 1200 N·m / s, adjust the seat side wing inflation from 20% to 60%, and limit the torque change rate to 20 N·m per 0.5 seconds, and then send the command to execute.

[0037] This application embodiment first identifies the type of road surface water and determines the road surface slipperiness level by acquiring driving status data and user relief level. Then, it receives road condition data from the cloud and evaluates its confidence level. Finally, it integrates the three to generate a set of anti-motion sickness control parameters. The joint judgment of road surface water type and road surface slipperiness level provides a more accurate comparison benchmark for confidence level assessment. The confidence level determines the credibility of road condition data and the correction range. The relief level controls the final intervention intensity. It realizes a complete closed loop from active prediction to dynamic verification to graded execution, so that the vehicle can obtain the optimal motion sickness relief effect under different road conditions and different user needs, and significantly reduce sensory conflicts caused by road condition prediction errors or sudden water accumulation.

[0038] In another embodiment of this application, the driving state data includes: left wheel speed data of the left wheel of the vehicle, right wheel speed data of the right wheel, wheel slip ratio, water film noise data when the wheel rolls over water, and vertical acceleration of the suspension; such as Figure 2 As shown, step S102 determines the type of road surface water based on the driving status data, including: Step S201: Determine the wheel speed difference data based on the left wheel speed data and the right wheel speed data; In this embodiment, the left wheel speed data refers to the real-time measured value of the rotational linear velocity of the left front wheel or left rear wheel, the right wheel speed data refers to the real-time measured value of the rotational linear velocity of the right front wheel or right rear wheel, and the wheel speed difference data refers to the absolute value or instantaneous change of the difference between the left and right wheel speeds.

[0039] In this step, the integrated cockpit controller (iTHU) collects wheel speed sensor signals from each wheel at a period of 100 milliseconds, calculates the difference between the left and right wheel speeds of the same axle or the same side wheel, and obtains a wheel speed difference data sequence.

[0040] For example, if the speed of the left wheel is 85 kilometers per hour and the speed of the right wheel is 100 kilometers per hour, then the speed difference is 15 kilometers per hour.

[0041] Step S202: When the wheel speed difference data changes periodically and the vehicle does not brake, the road surface water category is determined based on the wheel speed difference range where the wheel speed difference data is located, the first slip ratio range corresponding to the slip ratio, the time period corresponding to the duration of the water film slapping noise feature or turbulence noise feature in the water film noise data, and the acceleration range corresponding to the vertical acceleration.

[0042] In this embodiment, periodic change refers to the fluctuation of wheel speed difference data with a period related to the tire rotation frequency; no braking operation refers to the integrated braking control unit not issuing a braking request and the brake pedal travel being zero; water film slapping noise characteristics refer to the low-frequency impact sound signal generated when the tire rolls over the water film; turbulence noise characteristics refer to the fluid noise generated when the water film is torn; vertical acceleration refers to the instantaneous impact value of the vehicle body in the vertical direction.

[0043] In this step, iTHU first detects whether the wheel speed difference data shows periodic fluctuations with the same frequency as the wheel speed, and at the same time reads the brake pedal signal to confirm that there is no braking. If the conditions are met, the system comprehensively determines whether there is no water accumulation, a small amount of water accumulation, obvious water accumulation, or deep water accumulation by referring to a table based on the preset range in which the absolute value of the wheel speed difference falls, the range of mild or severe wet skidding corresponding to the slip ratio value, the duration of the water film noise characteristic, and the range of the vertical acceleration impact peak.

[0044] For example, if the wheel speed difference fluctuates with a period of 0.1 seconds and an amplitude of 8 kilometers per hour, the slip rate is 12%, the water film slapping noise lasts for 0.5 seconds, and the vertical acceleration impact is 0.3g, then it is judged as obvious water accumulation.

[0045] This application embodiment integrates four dimensions of features: periodic fluctuations in wheel speed difference, slip ratio, duration of water film noise, and vertical acceleration impact. Under the condition of excluding braking interference, it comprehensively determines the type of water accumulation. Periodic fluctuations in wheel speed difference are a unique characteristic of waterlogged roads, distinguishing them from ordinary slippery roads. The slip ratio provides quantitative evidence of the decrease in the coefficient of adhesion. The duration of water film noise reflects the thickness of the water layer. Vertical acceleration impact confirms the dynamic effect of tire-water collision. The cross-validation of multiple independent physical quantities significantly improves the accuracy and robustness of water accumulation identification, avoiding misjudging braking slippage or unilateral bumps as water accumulation. This provides a reliable basis for subsequent upgrades to road surface slipperiness levels, ensuring timely triggering of higher-level anti-motion sickness and safety controls in waterlogged scenarios.

[0046] In another embodiment of this application, the driving state data includes: the wheel-road slip ratio and water film noise data when the wheel rolls over accumulated water; such as Figure 3 As shown, step S102 determines the road surface slipperiness level based on the driving status data and the road surface water type, including: Step S301: Determine the first wet slip level based on the second slip rate range corresponding to the slip rate and the frequency range corresponding to the water film noise data; In this embodiment of the application, the second slip ratio range refers to the slip ratio value range corresponding to the pre-calibrated dry road surface, slightly slippery road surface, and heavily slippery road surface; the frequency range refers to the spectral characteristics of water film noise, with high-frequency noise dominating on dry road surfaces, mid-frequency resonance enhanced on slippery road surfaces, and low-frequency water film noise being significant on waterlogged road surfaces.

[0047] In this step, the integrated cockpit controller (iTHU) compares the current slip ratio value with a preset threshold: a slip ratio of less than 5% corresponds to dryness, 5% to 10% corresponds to mild wet slip, and greater than or equal to 10% corresponds to severe wet slip. At the same time, it analyzes the water film noise frequency in the noise vibration roughness (NVH) data. If high-frequency noise is dominant, it confirms dryness; if mid-frequency components are enhanced, it helps confirm wet slip; if low-frequency components are dominant, it helps confirm severe wet slip. The two are combined to output the first wet slip level.

[0048] For example, if a slip ratio of 12% indicates severe slipperiness, and the water film noise spectrum shows obvious low-frequency slapping characteristics, then the first level of slipperiness is a severely slippery road surface.

[0049] Step S302: Determine whether there is standing water on the road surface where the vehicle is currently traveling, based on the type of water accumulation on the road surface. In this embodiment of the application, the category of road surface water accumulation is small amount of water accumulation, obvious water accumulation or deep water accumulation, indicating that there is water accumulation, and the category of no water accumulation indicates that there is no water accumulation.

[0050] In this step, iTHU reads the previously determined category of road surface water. If the category is no water accumulation, it is determined that there is no water accumulation; otherwise, it is determined that there is water accumulation.

[0051] For example, if the road surface water type is "obvious water accumulation," then it is determined that there is water accumulation on the current driving surface.

[0052] Step S303: If there is standing water on the road surface where the vehicle is currently driving, increase the first slipperiness level by a preset amount to obtain the road slipperiness level. In this embodiment of the application, the preset quantity can refer to a level increment, usually one level.

[0053] In this step, when water accumulation is detected, iTHU will increase the first slipperiness level by one level: if the first slipperiness level is slightly slippery, it will be increased to heavily slippery; if it is dry, it will be increased to slightly slippery; if it is already heavily slippery, it will remain heavily slippery.

[0054] For example, if the first level of slipperiness is slightly slippery and the road surface water category is significant water accumulation, then the improved road surface slipperiness level will be severely slippery.

[0055] Step S304: If there is no standing water on the road surface where the vehicle is currently traveling, the first slipperiness level is determined as the road slipperiness level.

[0056] In this step, when it is determined that there is no standing water, iTHU directly adopts the first slippery level as the final slippery level of the road surface without making any additional upgrades.

[0057] For example: if the first wet / slippery level is dry and the road surface water category is no water accumulation, then the road surface wet / slippery level is dry.

[0058] This application embodiment determines the basic wet slip level by combining the slip ratio and the water film noise frequency, and then conditionally increases it according to the type of water accumulation. The slip ratio is a direct quantitative indicator of the road surface adhesion coefficient, while the water film noise frequency verifies the wet slip type from an acoustic perspective. The combination of the two makes the first wet slip level more accurate. The presence of water accumulation means that the water film thickness is sufficient to generate lubrication and impact effects. Even if the slip ratio is not high at the moment, the risk of wet slip increases significantly. Therefore, when water accumulation is determined, the wet slip level is increased by one level, which can enhance the strength of anti-motion sickness control in advance (such as further limiting the torque change rate, increasing steering damping, and strengthening the seat side support), effectively dealing with the risk of sudden slippage on watery roads and achieving a balance between safety and comfort.

[0059] In another embodiment of this application, the driving state data includes: the slip ratio between the wheel and the road surface, such as... Figure 4 As shown, step S103 determines the confidence level of the road condition data ahead based on the road surface slipperiness level, the road surface water accumulation type, and the driving status data, including: Step S401: Determine the bump type, curve type, and slope type of the current driving road surface based on the driving status data; In this embodiment of the application, bump type refers to the classification of road surface smoothness, including smooth road, slight bump, heavy bump, etc.; curve type refers to the classification of road curvature, including small curve or large curve, etc.; slope type refers to the classification of road longitudinal slope, including uphill or downhill and whether the slope threshold is reached, etc.

[0060] In this step, the integrated cockpit controller (iTHU) determines the bump level based on the root mean square value of the suspension vertical acceleration and the vibration frequency, classifying it into levels 1–5. It eliminates steering and braking interference, calculates the curvature based on the yaw rate, lateral acceleration and steering wheel angle, and determines the curve type. Small curves are <90°, and large curves are ≥90°. The slope type is determined based on the slope angle calculated by fusing the longitudinal acceleration and gravity components, with a threshold of ±≥3°.

[0061] For example: a vertical root mean square acceleration of 0.8g and a vibration frequency of 8 Hz indicate severe turbulence; a yaw rate of 20 degrees per second and a lateral acceleration of 0.4g, with a calculated curvature corresponding to a curve angle of 120 degrees, indicate a sharp curve; a slope angle of 5 degrees indicates an uphill slope.

[0062] Step S402: Compare the slip ratio, the type of water accumulation on the road surface, the type of bump, the type of curve, and the type of slope with the road condition data ahead to obtain the degree of deviation. In this embodiment of the application, the road condition data ahead includes the wetness level, water accumulation category, bump type, curve type, and slope type marked in the cloud; the degree of deviation refers to the quantitative difference between the actual category measured by the vehicle and the cloud label.

[0063] In this step, iTHU compares each road condition parameter identified by the vehicle in real time with the corresponding label in the cloud. If they are completely consistent, the deviation is 0. If there is a difference of one level, it is a slight deviation. If there is a difference of two levels or more, it is a severe deviation. Finally, the overall deviation level is output.

[0064] For example, the cloud label might be "severely slippery," "no water accumulation," "severe bumps," "sharp curves," or "uphill," while the actual test results for this vehicle might be "severely slippery," "significant water accumulation," "sharp bumps," "sharp curves," or "uphill," indicating a discrepancy in the water accumulation category.

[0065] Step S403: Determine the confidence level of the road condition data ahead based on the multiple degrees of deviation.

[0066] In this embodiment of the application, the confidence level is calculated using the following formula: C=0.4Cslip+0.3Cbump+0.2Cwater+0.1Ccurve Among them, Cslip represents slip ratio consistency (i.e., the degree of deviation of slip ratio from slip ratio in the road condition data ahead), Cbump represents bump consistency (i.e., the degree of deviation of bump type from bump type in the road condition data ahead), Cwater represents water accumulation characteristic consistency (i.e., a corresponding percentage value can be preset for each type of road water accumulation, such as 0% for no water accumulation, 25% for a small amount of water accumulation, 55% for significant water accumulation, and 85% for deep water accumulation, then the degree of deviation of the road water accumulation category determined by this vehicle from the road water accumulation category in the road condition data ahead = |the percentage value corresponding to the road water accumulation category determined by this vehicle - the percentage value corresponding to the road water accumulation category in the road condition data ahead|*100% / 100), and Ccurve represents curve / slope consistency (i.e., the degree of deviation of curve type and slope type from curve type and slope type in the road condition data ahead).

[0067] The calculated confidence levels are percentages between 0% and 100%, with 80% to 100% corresponding to high confidence, 60% to 79% to medium confidence, 40% to 59% to low confidence, and 0% to 39% to unreliable.

[0068] In this step, iTHU directly reads the corresponding confidence value as the confidence level of the road condition data ahead, based on the range in which the comprehensive deviation falls.

[0069] For example: if the vehicle's slip ratio, bumps, water accumulation, and curves / slopes are completely consistent with the cloud data, and the data is fresh, then the confidence level is high (80%–100%), and subsequent control strategies can directly use the cloud parameters without correction or updates; if there is a small deviation in a single dimension (e.g., slip ratio 4%), then the confidence level is high (80%–100%). 6%, Bumpy Level 2 For Level 3 (minor water accumulation), the confidence level is medium (60%–79%). Subsequent control strategies can maintain the main operating condition, making minor adjustments to damping, torque, and vibration reduction parameters without uploading corrections. If there are significant differences between wet / slippery, water accumulation, and bumpy conditions, and the cloud data does not match the actual measurements, the confidence level is low (40%–59%). Subsequent control strategies can use the vehicle's slip ratio plus the water accumulation judgment result as the standard, significantly correct the control parameters, mark it for confirmation, and upload it to the cloud. If the road surface changes (construction, new water accumulation, snow accumulation, ice formation) and the cloud data becomes completely ineffective, the confidence level is unreliable (0%–39%). Subsequent control strategies can discard the cloud data, switch to the first entry judgment logic, re-identify the 7 operating conditions, and forcibly update the cloud labels.

[0070] This application embodiment compares the multiple road condition dimensions (slip rate, slip level, water accumulation type, bump type, curve type, slope type, etc.) identified by the vehicle in real time with the predicted labels in the cloud one by one, and calculates the degree of deviation and confidence level. This multi-dimensional comparison method can accurately identify which type of information in the cloud data is outdated or incorrect. For example, the slip level may be accurate, but the water accumulation type may be incorrect. The confidence level directly determines whether the subsequent control parameters are directly adopted, fine-tuned, or significantly corrected, and whether it is necessary to send the data back to the cloud for updates. This technical solution avoids the misjudgment that may be caused by a single-dimensional confidence level assessment, and enables the vehicle to dynamically adjust its dependence on the cloud prediction according to the actual road conditions. This allows it to quickly switch to a control mode that prioritizes the vehicle's actual test results when road conditions change, ensuring the real-time accuracy and safety of the anti-motion sickness control.

[0071] In another embodiment of this application, the driving state data includes: the vertical acceleration, lateral acceleration, and longitudinal acceleration of the suspension; the yaw rate of the vehicle; and the steering wheel angle. Figure 5 As shown, step S401 determines the bump type, curve type, and slope type of the current driving road surface based on the driving status data, including: Step S501: Determine the type of bump based on the vertical acceleration; In this embodiment of the application, vertical acceleration refers to the vibration acceleration signal of the vehicle body or suspension in the vertical direction, and the bump type includes smooth road, slight bump, and severe bump.

[0072] In this step, the integrated cockpit controller (iTHU) calculates the root mean square value of vertical acceleration and analyzes the vibration frequency. The root mean square value and frequency are compared with preset turbulence level thresholds: those less than the first threshold and with a frequency below 2 Hz are considered smooth roads; those between the first and second thresholds and with a frequency of 5 to 12 Hz are considered slight turbulence; and those greater than the second threshold and with a frequency above 12 Hz are considered severe turbulence.

[0073] For example, if the root mean square value of vertical acceleration is 0.3g and the vibration frequency is 6 Hz, it is judged as a slight bump.

[0074] Step S502: Determine the curvature based on the yaw rate, lateral acceleration, and steering wheel angle; determine the curve type based on the curvature. In this embodiment, yaw rate refers to the angular velocity of the vehicle rotating around its vertical axis, lateral acceleration refers to the acceleration generated by the lateral inertial force of the vehicle, steering wheel angle refers to the absolute angle at which the driver turns the steering wheel, curvature refers to the reciprocal of the radius of curvature of the road, and the type of curve includes small curves or large curves.

[0075] In this step, iTHU multiplies the yaw rate by the vehicle speed to obtain the theoretical curvature, and simultaneously integrates the lateral acceleration and steering wheel angle signals. After Kalman filtering, a stable curvature value is obtained. If the curvature corresponds to a curve angle of less than 90 degrees, it is determined to be a small curve; if it is greater than or equal to 90 degrees, it is determined to be a large curve.

[0076] For example, if the yaw rate is 15 degrees per second and the vehicle speed is 60 kilometers per hour, and the calculated curvature corresponds to a curve angle of 75 degrees, then it is determined to be a small curve.

[0077] Step S503: Determine the ramp type based on the longitudinal acceleration.

[0078] In this embodiment of the application, longitudinal acceleration refers to the acceleration sensor measurement value in the vehicle's forward direction, and the slope type includes no slope, uphill or downhill, and whether the slope threshold has been reached.

[0079] In this step, iTHU subtracts the vehicle acceleration obtained based on the wheel speed derivative from the longitudinal acceleration sensor signal to calculate the gravity component, and then calculates the slope angle; if the absolute value of the slope angle is less than 3 degrees, it is determined that there is no slope; if it is greater than or equal to 3 degrees and is positive, it is uphill; if it is negative, it is downhill.

[0080] For example: the longitudinal acceleration measurement value is 0.2g, the vehicle's motion acceleration is 0.1g, the calculated gravity component is 0.1g, corresponding to a slope angle of 5.7 degrees, which is determined to be uphill.

[0081] This application's embodiments independently identify bumps, curves, and slopes by utilizing three physical quantities: vertical acceleration, yaw rate and lateral acceleration, and longitudinal acceleration. Vertical acceleration directly reflects the intensity and frequency of road impacts and is suitable for bump classification. Multi-source fusion of yaw rate, lateral acceleration, and steering wheel angle can accurately calculate road curvature and avoid interference from driving behavior with a single signal. Longitudinal acceleration, by decoupling motion acceleration and gravity components, can accurately extract the slope angle under any acceleration or deceleration conditions. Accurate identification of these three road condition types is a prerequisite for confidence assessment and multi-system collaborative control. For example, in curves, differential torque control and seat side support need to be adjusted in advance, and in slopes, energy recovery and brake pitch suppression need to be optimized to ensure that anti-motion sickness control can perform the most appropriate physical intervention for different road condition characteristics.

[0082] In yet another embodiment of this application, as Figure 6 As shown, step S104 determines the set of anti-motion sickness control parameters based on the road condition data ahead, the confidence level, and the soothing level information, including: Step S601: Obtain the first control parameter corresponding to the road condition data ahead; In this embodiment of the application, the first control parameter refers to the baseline control value pre-calibrated in the integrated cockpit controller (iTHU) that matches each road condition type, including the limit of power torque change rate, brake pressure gain, suspension damping coefficient, seat inflation amount, etc.

[0083] In this step, iTHU queries its internal preset mapping table based on the category of the road condition data ahead (e.g., severe bumps, light slippery conditions) and directly reads the corresponding set of baseline control parameters as the first control parameter.

[0084] For example: the road conditions ahead are severely bumpy, and the first control parameters obtained from the query are suspension damping of 1100 N·m and seat side wing inflation of 20%.

[0085] Step S602: Determine the confidence level range and the parameter adjustment step size corresponding to the confidence level range; In this application embodiment, the confidence level range is divided into high confidence (80% to 100%), medium confidence (60% to 79%), low confidence (40% to 59%), and unreliable (0% to 39%); the parameter adjustment step size refers to the proportion or absolute value of fine-tuning or significantly correcting the reference parameter.

[0086] In this step, iTHU determines which range the confidence level falls into, and then reads the corresponding preset step size: the step size for high confidence level is 0%, meaning no adjustment; the step size for medium confidence level is 5% to 10%, which is a slight adjustment; the step size for low confidence level is 20% to 30%, which is a significant correction; and when the data is unreliable, the cloud data is discarded and the system switches to real-time judgment for the vehicle.

[0087] For example, a confidence level of 70% falls within the medium confidence range, and the corresponding parameter adjustment step size is 8%.

[0088] Step S603: Adjust the first control parameter according to the soothing level information to obtain the second control parameter; In this embodiment, the soothing level information includes mild soothing level and extreme soothing level, corresponding to different parameter enhancement multiples or threshold limits.

[0089] In this step, before the vehicle enters the road ahead (50 to 200 meters away from the vehicle), iTHU can make differentiated adjustments to the first control parameters according to the user's selected level of comfort: for the mild comfort level, a moderate torque change rate limit (such as 40 Nm per 0.5 seconds), standard steering assist, and medium suspension damping are used; for the extreme comfort level, a more stringent limit (such as 20 Nm per 0.5 seconds), a stable steering mode, high suspension damping, and active seat predictive inflation are used to generate the final set of anti-motion sickness control parameters.

[0090] For example, if the torque change rate limit in the first control parameter is 40 N·m per 0.5 seconds, and the user selects the extremely comfortable level, it will be adjusted to 20 N·m per 0.5 seconds, and the suspension damping will be adjusted from 1188 to 1200 N·m per second.

[0091] In practical applications, vehicles can pre-adjust suspension damping, power torque, and other parameters within a range of 50 to 200 meters based on cloud-based tags. For example, when the comfort level information is mild, the vehicle can pre-adjust suspension damping, power torque, and other parameters within a range of 100 meters; when the comfort level information is extremely comfortable, the vehicle can pre-adjust suspension damping, power torque, and other parameters within a range of 200 meters. After entering the road section, the parameters are then adjusted according to the confidence level.

[0092] Step S604: Adjust the second control parameter based on the parameter adjustment step size to obtain the anti-motion sickness control parameter set.

[0093] In this step, after the vehicle enters the road ahead (50 to 200 meters away from the vehicle), iTHU multiplies each item of the second control parameter by (1 plus the step size) or subtracts the corresponding offset according to the adjustment step size to obtain the corrected second control parameter. That is, for high confidence cases, the second control parameter remains unchanged; for medium confidence cases, the second control parameter is finely adjusted; for low confidence cases, the second control parameter is corrected mainly based on the actual measurement of the vehicle; for unreliable cases, the cloud map can be discarded, re-judged, and the cloud map updated. Finally, the set of multiple corrected second control parameters is determined as the anti-motion sickness control parameter set.

[0094] For example: In the second control parameter, the suspension damping is 1100 N·s per meter with a step size of 8%. After adjustment, the second control parameter becomes 1188 N·s per meter.

[0095] This embodiment first obtains the baseline parameters (first control parameters) corresponding to the road conditions, then adjusts the intensity according to the user's soothing level to obtain the second control parameters, and finally adaptively corrects the second control parameters based on the cloud confidence level. This sequence reflects the design logic of prioritizing subjective preferences and objectively verifying and correcting: first, it meets the user's subjective needs for anti-motion sickness intensity, and then it makes fine adjustments or large corrections based on the matching degree between cloud data and actual road conditions. When the confidence level is high, the parameters after the soothing level adjustment remain basically unchanged; when the confidence level is low, even if the soothing level requires strong intervention, the system will appropriately adjust or enhance the parameters according to the step size to adapt to the current real road conditions, avoiding excessive or insufficient control. This embodiment enables the anti-motion sickness control parameters to not only quickly respond to the user's personalized selection, but also dynamically adapt to the deviation of road condition prediction, improving the robustness and safety of control while ensuring the consistency of the comfort experience.

[0096] In another embodiment of this application, the driving state data includes: the slip ratio between the wheel and the road surface; such as Figure 7 As shown, the method further includes: Step S701: If the road condition data of the road ahead of the vehicle cannot be obtained from the cloud, obtain the occupant posture data of the vehicle. In this embodiment of the application, "unable to obtain from the cloud" means that the vehicle communication module fails to connect with the cloud traffic platform, there is no cloud-marked data for the road ahead, or the data has expired; occupant posture data refers to the lateral offset angle, fore-aft offset angle, and sitting pressure distribution of the occupant's body collected by the pressure sensors of the active seat cushion and backrest.

[0097] In this step, the integrated cockpit controller (iTHU) requests road condition data ahead via 5G or vehicle connectivity module. If the timeout occurs or no data is returned, it is determined that the data cannot be obtained. At the same time, the seat pressure sensor signal is read at a period of 100 milliseconds to obtain occupant posture data.

[0098] For example, when a vehicle enters a newly built road, there is no labeled data in the cloud. iTHU reads that the occupant's body is shifted 5 degrees to the right, and the pressure distribution of the sitting posture is biased to the right.

[0099] Step S702: Determine the bump type, curve type, and slope type of the current driving road surface based on the driving status data; Step S703: Determine the road condition data ahead based on the slip ratio, bump type, curve type, slope type, road surface water type, road surface slipperiness level, and occupant posture data; In this embodiment of the application, the road condition data ahead refers to the road condition category label that the vehicle identifies in real time and intends to upload to the cloud. That is to say, the road condition data ahead includes one or more of the following: smooth road, slightly bumpy road, small curve, heavy bumpy road, large curve, slope, dry road surface, slightly slippery road surface, and heavily slippery road surface.

[0100] In this step, iTHU employs a weighted scoring and Kalman filter fusion algorithm: using slip ratio, bump type, curve type, slope type, road surface water accumulation type, road surface slipperiness level, and occupant posture data as input features, and calculating scores for each of the seven road conditions according to preset weights (slip ratio 0.35, bump 0.20, water accumulation feature 0.15, curve 0.12, slope 0.10, road surface slipperiness level / noise assistance 0.05, occupant posture 0.03). After Kalman filtering and smoothing, one or more road condition category labels with the highest membership degree are selected as the road condition data ahead, and the current position is marked before being uploaded to the cloud.

[0101] For example: a slip ratio of 12% corresponds to a score of 0.56 for severe slippery road, a score of 0.20 for severe bumpy road, and a score of 0.15 for significant water accumulation. After merging, the total score for severe slippery road is the highest, so the road condition data ahead is determined to be a severe slippery road surface.

[0102] Step S704: Determine the anti-motion sickness control parameter set based on the road condition data ahead and the soothing level information, and control the vehicle operation based on the anti-motion sickness control parameter set.

[0103] In this step, the cloud data with confidence level is directly replaced by the road condition data from the front, and then a set of anti-motion sickness control parameters is generated and executed based on the soothing level information.

[0104] For example, if the road conditions ahead are severely slippery and the user selects "extremely gentle", then the system will directly generate and execute a set of control parameters, including a torque change rate limit of 20 N·m per 0.5 seconds, a 30% increase in steering damping, and a 40% increase in seat side wing inflation.

[0105] This application's embodiments address scenarios where cloud-based road condition data is unavailable (e.g., no network coverage, newly constructed roads, or no cloud-marked data). It utilizes the vehicle's sensors to autonomously identify road conditions and generate control parameters. Occupant posture data serves as an auxiliary feature in the weighted scoring, reflecting the impact of current vehicle movement on occupant stability and further improving the comfort-related accuracy of road condition classification. This solution enables the vehicle to perform complete anti-motion sickness control even when entering unfamiliar roads for the first time. Simultaneously, the identification results are uploaded to the cloud to fill data gaps, achieving crowdsourced iteration. By incorporating occupant posture data, the system can detect occupant body shift trends earlier during bumps or curves, thereby adjusting seat support and motion control in advance. This compensates for the disadvantage of lacking cloud-based prediction, ensuring a consistent anti-motion sickness experience for both high- and low-spec models under any road conditions.

[0106] In yet another embodiment of this application, as Figure 8 As shown, step S704 determines the anti-motion sickness control parameter set based on the road condition data ahead and the soothing level information, including: Step S801: Determine the third control parameter based on the road condition data ahead; In this embodiment of the application, the third control parameter refers to the baseline control parameter that is directly related to the real-time identified road condition category and has not been adjusted by the user's easing level. The third control parameter may include the values ​​of multiple sub-parameters, such as the value of the torque change rate limit, the value of the steering damping, the value of the braking pressure, etc.

[0107] In this step, the integrated cockpit controller (iTHU) uses real-time road condition data identified without cloud data as an index to query the internally preset baseline control parameter mapping table to obtain the third control parameter.

[0108] For example: if the identified road condition data ahead is slightly slippery, the third control parameters obtained are: torque change rate limit of 40 N·m per 0.5 seconds, steering damping increased by 20%, and braking pressure increase rate decreased by 25%.

[0109] It should be noted that when the road condition data includes multiple road condition category labels, the third control parameters corresponding to the multiple road condition category labels are obtained. It is then determined whether there are any identical sub-parameters among the multiple third control parameters. If there are identical sub-parameters among the multiple third control parameters, the values ​​of the multiple sub-parameters are further obtained. If the values ​​of the multiple sub-parameters are different, the largest value is used as the value of the sub-parameter, and the third control parameter is updated. The next step of adjustment is then based on the updated third control parameter. If there are no identical sub-parameters among the multiple third control parameters, or if there are identical sub-parameters among the multiple third control parameters and the values ​​of the sub-parameters are the same, the next step of adjustment can be based on the original third control parameter.

[0110] Step S802: Adjust the third control parameter according to the soothing level information to obtain a set of anti-motion sickness control parameters.

[0111] In this step, iTHU makes differentiated adjustments to the third control parameter based on the user's selected level of soothing (mild or extreme): the mild level remains unchanged or slightly enhanced, while the extreme level further reduces the torque change rate limit, increases steering damping, and increases seat inflation, thus generating the final set of anti-motion sickness control parameters.

[0112] For example, the torque change rate limit in the third control parameter is 40 Nm per 0.5 seconds. If the user selects the extremely comfortable level, it will be adjusted to 20 Nm per 0.5 seconds, and the steering damping increase ratio will be increased from 20% to 30%.

[0113] In the absence of cloud data, this application simplifies road condition recognition and parameter adjustment into two levels: First, the third control parameter is directly determined based on the road condition category identified by the vehicle in real time. Then, the intensity is differentiated according to the user's level of relief. Compared with scenarios with cloud data, the confidence correction step is omitted because the road condition data comes entirely from the vehicle's real-time measurement, so there is no reliability issue. This technical solution ensures the real-time nature and simplicity of anti-motion sickness control when cloud data is unavailable, while still retaining the user's personalized level of relief selection. This allows users with severe motion sickness to receive the same level of intervention on any road, regardless of whether the vehicle has cloud prediction capabilities.

[0114] In yet another embodiment of this application, as Figure 9 As shown, step S104 controls the vehicle operation according to the anti-motion sickness control parameter set, including: Step S901: Determine the power control parameters, chassis control parameters, seat control parameters, and cabin control parameters based on the set of anti-motion sickness control parameters; In this embodiment, the power control parameters include torque change rate limit, energy recovery intensity, anti-yaw differential torque value, etc.; chassis control parameters include brake pressure gain, steering assist mode and damping, suspension damping and height, etc.; seat control parameters include side wing lumbar support headrest inflation volume, damping damping, attitude prediction advance time, etc.; cabin control parameters include air conditioning temperature and air volume, fragrance concentration and ingredients, music volume and rhythm, window opening and closing status, etc.

[0115] In this step, the Integrated Cockpit Controller (iTHU) breaks down the anti-motion sickness control parameter set into four subsets, corresponding to the execution commands of the powertrain, chassis, active seats, and cabin environment systems, respectively.

[0116] For example, the set of anti-motion sickness control parameters includes a torque change rate limit of 20 N·m per 0.5 seconds, suspension damping of 1200 N·m per meter, seat side wing inflation of 60%, and fragrance concentration of 15 mg per cubic meter, which can be extracted as power parameters, chassis parameters, seat parameters, and cabin parameters, respectively.

[0117] Step S902: Control the vehicle's power system according to the power control parameters; In this embodiment, the power system includes a power control unit (PCU) or a power drive control unit (PDCU) responsible for the torque output and energy recovery control of the engine or motor.

[0118] In this step, iTHU sends power control parameters to the power control unit via the vehicle network. The power control unit then switches to a motion sickness relief-specific predictive mode, loads the corresponding power mapping table, limits the torque change rate, adjusts the energy recovery curve, and performs differential torque control to suppress yaw based on cornering information.

[0119] For example, if the power control parameter is a torque change rate limit of 20 Nm per 0.5 seconds, the power control unit will limit the torque rise slope corresponding to the accelerator pedal opening to this range, thereby achieving extremely linear acceleration.

[0120] Step S903: Control the vehicle's chassis system according to the chassis control parameters; In this embodiment, the chassis system includes an integrated brake control unit (IBCU), an electric power steering system (EPS), and a chassis integrated controller (VMC), which respectively control braking, steering, and suspension.

[0121] In this step, iTHU sends chassis control parameters to each chassis system: the integrated brake control unit adjusts the brake pedal damping and comfort parking function, the electric power steering system adjusts the steering assist mode and damping, and the integrated chassis controller adjusts the damping and height of the electronically controlled suspension. If there is no electronically controlled suspension, it is compensated for through software strategies.

[0122] For example, if the chassis control parameters require a suspension damping of 1200 N·s per meter and a height reduction of 20 mm, the chassis integrated controller will adjust the suspension solenoid valve to the corresponding damping setting and lower the vehicle height.

[0123] Step S904: Control the vehicle's active seat system according to the seat control parameters; In this embodiment, the active seat system includes an active seat controller and a matching pressure sensor, an inflation actuator, and a vibration damping actuator, used to adjust the seat side wings, lumbar support, headrest, and seat cushion vibration damping.

[0124] In this step, iTHU sends the seat control parameters to the active seat controller. The active seat controller automatically adjusts the inflation force according to the occupant's weight and, in conjunction with occupant posture data and cloud map prediction data, performs posture correction, posture prediction and adaptation, and vibration buffering in advance.

[0125] For example, if the seat control parameters require the side wing to inflate to 60%, the active seat controller will drive the side wing airbags to inflate to the target pressure, thus fixing the occupant's body in the center of the seat.

[0126] Step S905: Control the vehicle's cabin environment system according to the cabin control parameters.

[0127] In this embodiment, the cabin environment system includes air conditioning, fragrance, audio, windows, and air purification modules managed by the Body Domain Controller (BDC).

[0128] In this step, iTHU sends the cabin control parameters to the body domain controller. The body domain controller sets the air conditioning temperature to 20±1 degrees Celsius and three fan speeds, activates the anti-motion sickness fragrance and adjusts the concentration to 8 to 15 milligrams per cubic meter, plays low-frequency soothing music at 60 to 80 beats per minute and a volume of 15 to 20 decibels, and automatically controls the opening and closing of the windows according to the intelligent ventilation logic.

[0129] For example, the cabin control parameters require a fragrance concentration of 15 mg per cubic meter and the ingredients to be 8% menthol, 5% ginger essential oil, and 3% lemon essential oil. The vehicle domain controller will then activate the fragrance generator and release the essential oils in the specified proportions.

[0130] This application embodiment decomposes the anti-motion sickness control parameter set into four independent subsystem parameters, which respectively control the power system, chassis system, active seat system, and cabin environment system, achieving full-link intervention from physical motion to occupant posture and sensory environment. The power system controls torque smoothness and energy recovery to eliminate longitudinal impact; the chassis system controls braking linearity, steering stability, and suspension vibration filtering to suppress lateral and vertical sway; the active seat system stabilizes the occupant's body in the center of the seat through predictive inflation, reducing information conflict between the body and vision; the cabin environment system lowers the sensory threshold and alleviates nausea through suitable temperature, anti-motion sickness fragrance, low-frequency music, and intelligent ventilation. The coordinated control of the four systems is not a simple superposition, but a unified timing scheduling based on the same set of road condition predictions and user comfort levels, ensuring that the adjustment actions of each system are synchronized and consistent in goal. This technical solution fundamentally addresses multiple dimensions of sensory conflict theory. Real-world test data shows that it can optimize pitch motion by more than 75%, roll motion by more than 30%, and suppress yaw by more than 60%, significantly reducing motion sickness triggers.

[0131] For ease of understanding, this application also provides an embodiment in practical application, as follows: I. System Initialization and Function Activation After the vehicle is powered on, the motion sickness relief function is off by default. The top-level function controller integrated into the iTHU completes a full hardware self-check.

[0132] Users initiate function activation requests via a soft switch provided by iTHU or through voice commands. Upon receiving the user's activation command, iTHU first checks if the vehicle's status meets prerequisites, including: power is on and there are no faults. Once these conditions are met, iTHU will display a secondary confirmation window via the HMI interface. This window has a 5-second countdown timer; if the user does not interact, it automatically confirms activation and simultaneously displays a soothing package selection interface. After the user completes their selection, the system enters the data acquisition phase.

[0133] II. Multi-dimensional Data Acquisition and Fusion Processing Chassis cloud map data acquisition: iTHU communicates with the cloud-based road condition platform via the in-vehicle 5G / vehicle connectivity module to obtain road condition data with a forward reference value of 50 to 200 meters. This data is generated by iTHU's own data and the cloud platform based on a large amount of driving data feedback from other vehicle models, specifically including: Chassis suspension acceleration values ​​collected during the driving of other vehicle models (used to mark the road bumpiness level reference value 1-5). Rain sensor data (preliminary assessment of road surface moisture); Intelligent driving perceives road condition features (used to mark curve angles / curvatures, and slope reference values ​​± ≥ 3°). NVH data (accurately judges the degree of road slippage by the friction noise between the tire and the road surface and the vibration noise of the vehicle body, and classifies it into three levels: dry, slightly slippery, and heavily slippery). Standardized road condition labels are generated after cloud-based analysis. The estimated intervention time for the vehicle to reach the road condition is calculated based on the current vehicle speed (reference value 0.5-3s), and the prediction range is filtered according to the relief package level: 100m for mild relief package and 200m for severe relief package.

[0134] Real-time vehicle data acquisition: Data such as vehicle speed, longitudinal / lateral acceleration, yaw rate, vertical vibration frequency, brake pedal travel, and steering wheel angle are collected through vehicle wheel speed sensors, acceleration sensors, yaw rate sensors, and braking / steering position sensors, and transmitted to iTHU in real time with a reference value period of 100ms; the NVH data acquisition module collects the vehicle's tire friction noise and body vibration noise data to verify the accuracy of the cloud map wet skid level prediction in real time.

[0135] Occupant posture data acquisition: Through pressure sensors on the active seat cushion / backrest, the system collects data on the occupant's left / right / forward / backward offset angles and sitting pressure distribution to determine whether the occupant's sitting posture is correct. The data is transmitted to iTHU in real time with a reference value period of 100ms.

[0136] Data fusion processing: iTHU fuses the three types of data using a Kalman filter algorithm, combines them with the vehicle's NVH data to verify the cloud map's slipperiness level, and classifies seven road condition levels: smooth road, slightly bumpy / small curves (<90° reference value), heavily bumpy / large curves (≥90° reference value), slope (±≥3° reference value), dry road surface, lightly slippery road surface, and heavily slippery road surface. This generates road condition information, predicted intervention time, and an initial control parameter set (including specific parameters for slippery road surfaces), which is then sent to the control layer. Simultaneously, considering the hardware configuration differences between high- and low-spec models, corresponding parameter adaptation strategies are automatically loaded to ensure that the predicted control logic adapts to different hardware levels.

[0137] III. Specific Implementation of Multi-System Differentiated Collaborative Control Based on the user-selected soothing package and the integrated road condition data (including slipperiness level), iTHU issues differentiated control commands via CAN / LIN bus to four major systems: powertrain, chassis, active seats, and cabin environment. The specific execution logic of each system is as follows: 1. Control and execution of the power system (PCU / PDCU) After receiving the instruction from iTHU, the PCU memorizes the current driving mode and immediately switches to the motion sickness relief prediction mode.

[0138] Power MAP Calibration: A dedicated predictive power MAP is applied based on the corresponding soothing package. The mild soothing package moderately limits the torque change rate during start-up and acceleration; the extreme soothing package adjusts torque output 0.5 seconds in advance of the predicted intervention time, ensuring the torque change rate is ≤ the reference value across the entire speed range, achieving extremely linear acceleration; if the cloud map indicates a mild / severe slippery road surface, the torque change rate is further reduced, with the mild soothing package ≤ the reference value by 40N. m / 0.5s, Extreme Soothing Pack ≤ 20N (reference value) m / 0.5s. To address the differences in powertrain hardware between high- and low-spec models, the torque adjustment range is precisely matched using cloud-based road condition data (including slipperiness level) to ensure consistent acceleration smoothness and stability on slippery surfaces across different configurations.

[0139] Energy recovery control: The mild relief package is set to low, with a deceleration reference value of 0.005-0.01G. When the vehicle speed is below 20km / h, the deceleration is reduced to the reference value of 0.005G. The extreme relief package anticipates slopes / curves based on cloud maps and adjusts the recovery intensity 1 second in advance. It is turned off on flat roads / curves and only activated at a reference value of 0.005G for micro-recovery on downhill slopes. When switching recovery modes, the PCU compensates for the reference value by 5-10N in advance. m torque, to avoid the vehicle body from feeling dragged or lurching.

[0140] Anti-yaw suppression: When the Extreme Soothing Package is activated, this function intervenes 0.5 seconds in advance based on the cornering angle and anticipated intervention time. Differential torque is generated through distributed power distribution, outputting 50-80N at small-angle turns (reference value 0-90°). Differential torque (m), large-angle turns (reference value above 90°), output reference value 80-120N. The differential torque automatically disengages when the yaw rate is ≤ 3° / s of the reference value. If the cloud map indicates a light / heavy slippery road surface, the differential torque output amplitude is reduced by 30%-50% of the reference value to prevent wheel slippage.

[0141] 2. Coordinated control execution of the chassis system (1) Braking system (IBCU) After receiving the instruction from the iTHU, the IBCU sets the brake pedal damping parameter to ≤ the reference value 2N. mm / ° (minimum allowed by the platform), braking force gain ≤ 0.1G / 10% of the reference value before pedal travel (30% of the reference value), achieving a linear and soft braking feel; simultaneously, the corresponding comfort parking function is activated according to the level of the comfort package: Mild Soothing Package: Activate Comfort Parking 1.0. When the vehicle speed is ≤5km / h, reduce the brake pressure by 0.5-1MPa to alleviate vehicle pitching. If the cloud map indicates a mild / severe wet road surface, adjust the brake pressure 0.3s in advance to reduce the pressure increase rate by 25%.

[0142] Extreme Comfort Package: Activate Comfort Parking 2.0. Based on the cloud map markings of intersections / slopes / slippery conditions, adjust brake pressure 1-2 seconds in advance according to the reference value. When the vehicle speed is ≤3km / h of the reference value, reduce the brake pressure to below 0.3MPa of the reference value. At the same time, send a torque request to the PCU to control the motor output to 50-80N. m represents positive torque; the reference value for stopping positive torque output is 0.3s after the pedal is released, with an additional 0.5s for slope conditions, resulting in a reference value of 5-10N. m Micro-torque compensation; if the cloud map marks a light / severe slippery road surface, adjust the braking pressure 0.5s in advance based on the reference value, reduce the pressure increase rate reference value by 40%, and coordinate with the ESP system to optimize braking force distribution.

[0143] (2) Power Steering System (EPS) EPS adjusts the steering assist mode via the HU_EPSTorqueModeSet signal sent by iTHU: Mild relief package: Assist mode set to "Standard", assist torque maintained at the reference value of 8-12N. m, the reference value for the aligning torque coefficient is 0.5N. m / °; if the cloud map indicates a light / heavy slippery road surface, increase the steering damping by 20% of the reference value.

[0144] Extreme Soothing Package: With the power assist mode set to "Stable," the power assist torque reference value is 15N when the vehicle speed is below 40km / h. When the vehicle speed is ≥ 40 km / h, the value should be increased to 20 N. Based on the curve / slippery level prediction indicated by the cloud map, increase the steering damping by 0.5-1 seconds in advance; if the cloud map indicates a light / heavy slippery road surface, further increase the steering damping by 30% to reduce steering sensitivity.

[0145] (3) Suspension system (VMC) If the vehicle is equipped with electronically controlled suspension, after receiving instructions from iTHU, VMC adjusts the suspension damping and height based on the level of the comfort package and the road condition data predicted by cloud maps: Mild soothing pack: Damping set to the reference value of 800-1000N When the vertical acceleration is ≥ 0.8G, it automatically adjusts to the reference value of 1000N. s / m; if the cloud map indicates a light / heavy slippery road surface, the suspension height should be reduced by a reference value of 5mm.

[0146] Extreme Soothing Pack: Features both predictive damping and height adjustment. When the bump level is marked as ≥3 on the cloud map, adjust the damping to the reference value of 1100N. s / m, curve operation condition set to reference value 1200N s / m, and at the same time lower the suspension height by 20-30mm; if the cloud map marks a light / heavy slippery road surface, further lower the suspension height by 5-10mm, and keep the damping hard setting.

[0147] If the vehicle is a low-spec model without electronically controlled suspension, iTHU compensates for the hardware deficiencies by adjusting the power torque output and braking pressure control rhythm (especially strengthening the braking linear control on wet and slippery roads) to ensure that the bump filtering, roll suppression and stability on wet and slippery roads are consistent with the high-spec model.

[0148] 3. Control and execution of the active seat system After receiving instructions from iTHU, the active seat controller combines occupant posture data and cloud map prediction data (including slip resistance level) to drive the pressure sensor, inflation actuator, and damping actuator to complete coordinated execution. All actions are pre-emptive interventions based on prediction, and the inflation force and damping can be automatically adapted according to the occupant's weight (identified by the pressure sensor). Posture correction: The mild comfort package only reminds occupants to correct their posture through the HMI interface; if the cloud map marks a mild / severe slippery road surface, the seat side wings will automatically inflate to a reference value of 10%-20%; under the extreme comfort package, when the occupant's body deviation is ≥3° of the reference value, the side wings, lumbar support, and headrest will inflate together to a reference value of 30%-60%, and if the cloud map marks a mild / severe slippery road surface, the inflation volume will increase to a reference value of 40%-70%.

[0149] Posture prediction and adaptation: Under the extreme comfort package, the seat posture is adjusted 1-2 seconds in advance based on the predicted data of curves / bumps / slopes / slippery conditions marked by cloud map; if the cloud map marks a light / severe slippery road surface, the lumbar support is increased by 30% of the reference value, and the headrest is adjusted to fit the sides of the head.

[0150] Vibration damping: The mild soothing package seat damping module maintains "standard" damping; the extreme soothing package damping module is adjusted to "ultimate damping", increasing the damping force by 50% of the reference value, which can completely counteract vertical vibrations ≤1 of the reference value.

[0151] 4. Intelligent linkage execution of the cabin environment system (BDC) After receiving instructions from iTHU, BDC implements basic functions always-on + differentiated predictive collaborative adjustment: Basic functions: Air conditioning is on by default, with the temperature set at a reference value of 20±1℃, three fan speeds, and an air outlet speed of 2-3m / s; the air purification module is on by default, with a PM2.5 filtration efficiency of ≥99%; soothing music is on by default, playing low-frequency soothing music at a reference value of 60-80BPM, with a default volume of 15-20dB; if the cloud map indicates a light / heavily slippery road surface, the music volume is reduced by a reference value of 3-5dB, and the air conditioning air outlet mode is switched to blowing on the face and feet.

[0152] Intelligent ventilation: The mild relief package does not intervene by default, but allows users to manually turn it on; the extreme relief package enables fully automatic predictive intelligent ventilation by default, which automatically opens windows when conditions are met and automatically closes windows within 0.5 seconds when triggered by bumpy / curved / severely slippery road conditions.

[0153] Fragrance System: The Mild Soothing Pack is not activated by default and requires manual activation by the user; the Extreme Soothing Pack is activated by default, releasing a fragrance containing 5%-8% menthol, 3%-5% ginger essential oil, and 2%-3% lemon essential oil, with a concentration of 8-15 mg / m³. If the cloud map indicates a mild / severe slippery road surface, the fragrance concentration is adjusted to a medium level, and the menthol content is increased to 7%-10% of the reference value. If the system detects that no dedicated anti-motion sickness fragrance is inserted, the HMI interface will prompt the user to select the appropriate model and purchase it.

[0154] IV. Functional Operation Management and Dynamic Adjustment iTHU collects real-time road condition update data (including wetness level updates), vehicle motion feedback data, occupant posture change data, and vehicle NVH data with a reference value period of 100ms. It then compares and analyzes this data with the initial prediction data to achieve dynamic adjustment across the entire closed loop. Road condition deviation adjustment: If the deviation between the road condition ahead and the predicted data is ≤10% of the reference value, maintain the original control parameters; if the deviation is >10% of the reference value (including changes in slipperiness level), immediately recalculate the road condition level and the predicted intervention time, and dynamically adjust the control parameters of the four major systems to ensure that the control strategy matches the actual road conditions.

[0155] Occupant posture deviation adjustment: If the occupant's body offset angle is ≥5° (the threshold of the extreme comfort package), the active seat controller immediately increases the inflation correction force, and at the same time, the iTHU sends a fine-tuning command to the chassis system to appropriately reduce the vehicle's lateral / vertical acceleration and reduce body sway until the occupant's body returns to a centered position.

[0156] System synchronization adjustment: Real-time monitoring of the execution status of the four major systems. If the execution delay of a certain system exceeds the reference value of 200ms, iTHU sends a delay instruction to the other systems to ensure that the adjustment actions of each system are synchronized and to avoid passenger discomfort caused by asynchronous execution.

[0157] Meanwhile, iTHU locks the manual switching of driving modes and chassis settings (such as suspension mode and steering mode). If the user attempts to operate, the HMI interface will prompt "Please exit the motion sickness relief function before operating"; if the system detects that the vehicle has entered assisted driving, emergency mode or super power saving mode, iTHU immediately sends a forced exit command to each subsystem, the motion sickness relief function is turned off, and each system returns to its initial state.

[0158] V. Exit Function Exiting Function and Restoring Status The motion sickness relief function can be exited either manually or automatically, with precise parameter restoration in both cases. Active Exit: Users can initiate an exit request via a vehicle infotainment system soft switch or voice command (such as "turn off motion sickness relief"). Upon receiving the command, iTHU will immediately send a reset command to each subsystem.

[0159] Automatic Exit: When the vehicle is powered off, iTHU automatically triggers the exit process and sends reset commands to each subsystem.

[0160] After receiving the reset command, each subsystem precisely restores all control parameters to the initial state remembered before the function was activated. Specifically, this includes: the power system is restored to the original driving mode, the chassis system is restored to the original braking, steering, and suspension parameters, the active seats complete the deflation of side wings / lumbar support / headrests, the reset of damping, and the return of headrests / lumbar support to their original positions, and the cabin environment system is restored to the user's original air conditioning, music, and fragrance settings to ensure that it does not affect the user's vehicle usage habits next time.

[0161] In yet another embodiment of this application, as Figure 10 As shown, a vehicle control device is also provided, comprising: The acquisition module 11 is used to acquire vehicle driving status data and user-selected comfort level information; The first determining module 12 is used to determine the type of water accumulation on the road surface based on the driving status data, and to determine the road surface slipperiness level based on the driving status data and the type of water accumulation on the road surface. The second determining module 13 is used to determine the confidence level of the road condition data ahead based on the road surface slipperiness level, the road surface water accumulation type and the driving status data if the road condition data ahead of the vehicle can be obtained from the cloud. The third determining module 14 is used to determine a set of anti-motion sickness control parameters based on the road condition data ahead, the confidence level, and the soothing level information, and to control the vehicle operation based on the set of anti-motion sickness control parameters.

[0162] In another embodiment of this application, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the vehicle control method described in any of the foregoing method embodiments.

[0163] The electronic device provided in this invention, through its processor executing a program stored in its memory, acquires driving status data and user comfort level information. It first identifies the type of road surface water and determines the road surface slipperiness level, then receives road condition data from the cloud and assesses its confidence level. Finally, it integrates these three data points to generate a set of anti-motion sickness control parameters. The joint judgment of the road surface water type and road surface slipperiness level provides a more accurate benchmark for confidence assessment, while the confidence level determines the reliability and correction range of the road condition data. The comfort level controls the final intervention intensity, achieving a complete closed loop from proactive prediction to dynamic verification to graded execution. This enables the vehicle to achieve optimal motion sickness relief under different road conditions and user needs, significantly reducing sensory conflicts caused by road condition prediction errors or sudden water accumulation.

[0164] The communication bus 1140 mentioned in the above-mentioned electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0165] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0166] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0167] The processor 1110 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0168] In another embodiment of this application, a computer-readable storage medium is also provided, on which a program for a vehicle control method is stored, wherein when the program for the vehicle control method is executed by a processor, it implements the steps of the vehicle control method described in any of the foregoing method embodiments.

[0169] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0170] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A vehicle control method, characterized in that, include: Obtain vehicle driving status data and user-selected comfort level information; The road surface water type is determined based on the driving status data, and the road surface slipperiness level is determined based on the driving status data and the road surface water type. If road condition data ahead of the vehicle can be obtained from the cloud, the confidence level of the road surface slipperiness level, the type of water accumulation on the road surface, and the driving status data can be determined. The driving status data includes: wheel-road slip ratio; determining the confidence level of the road condition data ahead based on the road surface slippage level, the road surface water accumulation type, and the driving status data includes: The type of bumps, curves, and slopes on the current road surface are determined based on the driving status data. The slip ratio, the type of water accumulation on the road surface, the type of bump, the type of curve, and the type of slope are compared with the road condition data ahead to obtain the degree of deviation. The confidence level of the road condition data ahead is determined based on multiple degrees of deviation. Based on the road condition data ahead, the confidence level, and the soothing level information, a set of anti-motion sickness control parameters is determined, and the vehicle operation is controlled according to the set of anti-motion sickness control parameters.

2. The vehicle control method according to claim 1, characterized in that, The driving status data includes: left wheel speed data of the left wheel of the vehicle, right wheel speed data of the right wheel, wheel slip ratio with the road surface, water film noise data when the wheel runs over water, and vertical acceleration of the suspension. The type of road surface water is determined based on the driving status data, including: The wheel speed difference data is determined based on the left wheel speed data and the right wheel speed data; When the wheel speed difference data changes periodically and the vehicle does not brake, the road surface water category is determined based on the wheel speed difference range where the wheel speed difference data is located, the first slip ratio range corresponding to the slip ratio, the time period corresponding to the duration of the water film slapping noise feature or turbulence noise feature in the water film noise data, and the acceleration range corresponding to the vertical acceleration.

3. The vehicle control method according to claim 1, characterized in that, The driving status data includes: the slip ratio between the wheel and the road surface and the water film noise data when the wheel runs over water accumulation; The road surface slipperiness level is determined based on the driving status data and the road surface water type, including: The first wet slip level is determined based on the second slip rate range corresponding to the slip rate and the frequency range corresponding to the water film noise data. Determine whether there is standing water on the road surface where the vehicle is currently traveling based on the type of road surface water. If there is standing water on the road surface where the vehicle is currently driving, the first slipperiness level is increased by a preset amount to obtain the road slipperiness level. If there is no standing water on the road surface where the vehicle is currently traveling, the first slipperiness level is determined as the slipperiness level of the road surface.

4. The vehicle control method according to claim 1, characterized in that, The driving status data includes: vertical acceleration, lateral acceleration and longitudinal acceleration of the suspension, yaw rate of the vehicle and steering wheel angle; Based on the driving status data, the current road surface bump type, curve type, and slope type are determined, including: The type of bump is determined based on the vertical acceleration. The curvature is determined based on the yaw rate, lateral acceleration, and steering wheel angle, and the curve type is determined based on the curvature. The type of ramp is determined based on the longitudinal acceleration.

5. The vehicle control method according to claim 1, characterized in that, Based on the road condition data ahead, the confidence level, and the soothing level information, a set of motion sickness control parameters is determined, including: Obtain the first control parameter corresponding to the road condition data ahead; Determine the confidence level range and the parameter adjustment step size corresponding to the confidence level range; The first control parameter is adjusted based on the soothing level information to obtain the second control parameter; The second control parameter is adjusted based on the step size of the parameter to obtain the set of anti-motion sickness control parameters.

6. The vehicle control method according to claim 1, characterized in that, The driving status data includes: the slip ratio between the wheels and the road surface; the method further includes: If it is not possible to obtain road condition data ahead of the vehicle from the cloud, obtain occupant posture data in the vehicle. The type of bumps, curves, and slopes on the current road surface are determined based on the driving status data. The road condition data ahead is determined based on the slip ratio, bump type, curve type, slope type, road surface water type, road surface slipperiness level, and occupant posture data. Based on the road condition data ahead and the soothing level information, a set of anti-motion sickness control parameters is determined, and the vehicle operation is controlled according to the set of anti-motion sickness control parameters.

7. The vehicle control method according to claim 6, characterized in that, Based on the road condition data ahead and the soothing level information, a set of motion sickness control parameters is determined, including: The third control parameter is determined based on the road condition data ahead; The third control parameter is adjusted based on the soothing level information to obtain a set of anti-motion sickness control parameters.

8. The vehicle control method according to claim 1 or 6, characterized in that, Controlling the vehicle operation according to the aforementioned anti-motion sickness control parameter set includes: The power control parameters, chassis control parameters, seat control parameters, and cabin control parameters are determined based on the set of anti-motion sickness control parameters. The power system of the vehicle is controlled according to the power control parameters; The chassis system of the vehicle is controlled according to the chassis control parameters; The active seat system of the vehicle is controlled according to the seat control parameters; The vehicle's cabin environment system is controlled according to the cabin control parameters.

9. A vehicle control device, characterized in that, include: The acquisition module is used to acquire vehicle driving status data and user-selected comfort level information; The first determining module is used to determine the type of water accumulation on the road surface based on the driving status data, and to determine the road surface slipperiness level based on the driving status data and the type of water accumulation on the road surface. The second determining module is used to determine the confidence level of the road condition data ahead based on the road surface slipperiness level, the road surface water accumulation type, and the driving status data if the road condition data ahead of the vehicle can be obtained from the cloud. The driving status data includes: wheel-road slip ratio; determining the confidence level of the road condition data ahead based on the road surface slipperiness level, the road surface water accumulation type, and the driving status data includes: determining the bump type, curve type, and slope type of the current driving road surface based on the driving status data; comparing the slip ratio, the road surface water accumulation type, the bump type, the curve type, and the slope type with the road condition data ahead to obtain the degree of deviation; and determining the confidence level of the road condition data ahead based on multiple degrees of deviation. The third determining module is used to determine a set of anti-motion sickness control parameters based on the road condition data ahead, the confidence level, and the soothing level information, and to control the vehicle operation based on the set of anti-motion sickness control parameters.

10. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the vehicle control method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for a vehicle control method, which, when executed by a processor, implements the steps of the vehicle control method according to any one of claims 1-8.

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