Vehicle intelligent chassis domain control system and method for preventing occupant motion sickness

Abstract

The present invention is a vehicle intelligent chassis domain control system and method for preventing occupant motion sickness. It includes (1) collecting the current vehicle driving state and road environment information; (2) using the dynamic window method to calculate the initial expected speed sequence for preventing occupant motion sickness: establishing a preliminary evaluation model based on safety, motion sickness, traffic efficiency, energy saving evaluation function and the previous cycle execution error, and obtaining the value of the evaluation model weight coefficient according to the current vehicle driving state and road environment information to obtain the final evaluation model; using the final evaluation model to evaluate and optimize the constraint vector X in the sampling window to obtain a preliminary expected speed sequence; (3) optimizing the preliminary expected speed sequence through the B-spline curve to obtain the expected speed sequence, calculating the expected acceleration, and outputting it to the execution system. The method of the present invention ensures the smooth driving of the vehicle, reduces the impact vibration during the driving process, and reduces the motion sickness stimulation of the passengers.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle chassis control, and in particular relates to a vehicle intelligent chassis domain control system and method for preventing occupant motion sickness. Background Art

[0002] Intelligentization and electrification are two major trends in the development of automotive technology, but they also bring about the unexpected risk of increased motion sickness (commonly known as "motion sickness") for passengers in the car. The vehicle is constantly affected by the driving environment during driving, producing oscillatory motion, and the integral of the amplitude of the low-frequency segment of its acceleration is expressed as the motion sickness dose value MSDV. Z Considering the safety and comfort requirements of passengers during the start-stop process, it is necessary to reduce the MSDV of the start-stop process. Z .

[0003] In response to the motion sickness problem, patents such as CN112955352B, CN 116198398A, and CN111295307 B have been developed to reduce the motion sickness-related disorder of passengers through automatically adjustable headrests and seats, and patents such as DE 10 2015 011 708A1 and DE 101 56 219C1 have been developed to deal with the problem by dynamically adjusting the in-car environment. Whether it is an active device or adjusting the in-car environment, they are both passive responses to the motion sickness stimulation caused by vehicle driving, and cannot give full play to the advantages of intelligent electrification.

[0004] The current mainstream vehicle longitudinal and lateral motion control, when driving in a complex environment, the control target is easily switched with the switching of different control modes, resulting in frequent jumps in the vehicle's target speed and acceleration. Patents CN115991186B and CN108639061B reduce motion sickness by actively controlling the vehicle's motion state, but there are still problems such as complex online calculations and large computing resource requirements, which are difficult to adapt to actual driving environments. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle intelligent chassis domain control system and method for preventing occupant motion sickness. The domain control method adopts a motion sickness evaluation model to evaluate the degree of stimulation of occupant motion sickness by the current vehicle driving state and road environment information according to the current vehicle driving state, road environment information and restriction conditions, and quickly solves the expected speed in real time through the dynamic window method, and optimizes the expected speed with the B-spline law, so that the car driving process is smoother and the pitch degree of the car caused by speed changes is reduced; and in the specific stimulation frequency band that is easy to cause occupant motion sickness, the expected speed and the corresponding expected acceleration amplitude that are quickly solved and optimized are significantly reduced, which can alleviate occupant discomfort and thus reduce the probability of occupant motion sickness.

[0006] The technical solution to achieve the purpose of the present invention is: a vehicle intelligent chassis domain control method for preventing occupant motion sickness, comprising the following steps:

[0007] Step (1): Collect current vehicle driving status and road environment information;

[0008] Step (2): using the dynamic window method to calculate the initial expected speed sequence for preventing occupant motion sickness: establishing a preliminary evaluation model based on the safety evaluation function, motion sickness evaluation function, traffic efficiency evaluation function, energy saving evaluation function and the previous cycle execution error, and calculating the relative change of the vehicle driving state and the road surface roughness according to the current vehicle driving state and road surface environment information collected in step (1), obtaining the value of the evaluation model weight coefficient, and obtaining the final evaluation model; using the final evaluation model to evaluate and optimize the constraint vector X in the sampling window to obtain the optimal result, and obtaining the initial expected speed sequence;

[0009] Step (3): Optimizing the preliminary expected speed sequence for preventing occupant motion sickness obtained in step (2) by using a B-spline curve to obtain an expected speed sequence, and calculating an expected acceleration based on the expected speed sequence, and outputting the result to the execution system;

[0010] Step (4): the execution system solves the wheel acceleration and deceleration torque according to the expected acceleration and adjusts the current speed of the vehicle to the expected speed sequence;

[0011] Step (5): Calculate the difference between the actual execution result and the expected control target, that is, the execution error ε of the previous cycle, and bring it into the final evaluation model to act on the next control cycle.

[0012] Furthermore, the current vehicle driving state collected in step (1) includes the vehicle speed v, the vehicle acceleration Vehicle pitch angle θ and vehicle pitch angular velocity The collected road environment information includes the road condition path ahead and the road roughness; the road condition path ahead specifically includes the distance of static road conditions obstructed by obstacles ahead, traffic lights and curves, and the distance and speed of dynamic reference point vehicles and pedestrians.

[0013] Furthermore, the “preliminary evaluation model based on the safety evaluation function, motion sickness evaluation function, traffic efficiency evaluation function, energy saving evaluation function and previous cycle execution error” established in step (2) is specifically:

[0014]

[0015] in, is the safety evaluation function; is the motion sickness evaluation function; η(v p ) is the traffic efficiency evaluation function; is the energy-saving evaluation function; ε is the execution error of the previous cycle; α, β, γ, λ is the weight coefficient; σ is the normalization factor.

[0016] Furthermore, the safety evaluation function is as follows:

[0017]

[0018] Among them, d(N d ) represents the distance between the dynamic reference point and the nearest static road obstacle; D m Indicates the limited distance, which is a preset constant; R s Indicates the collision radius of the vehicle;

[0019] The traffic efficiency evaluation function is:

[0020]

[0021] Among them, v max is the maximum speed of the vehicle;

[0022] The energy-saving evaluation function is:

[0023]

[0024] in, and are functions of vehicle acceleration and pitch angular acceleration, respectively.

[0025] Furthermore, the motion sickness evaluation function is based on the six-component force model of the occupant's head. The two directions with the highest probability of motion sickness stimulation are calculated separately, and the remaining directions are combined for calculation. The expression of the motion sickness evaluation function is as follows:

[0026]

[0027] in, Indicates the vertical motion sickness stimulus; hori(v p ) represents the amount of motion sickness stimulation in the longitudinal direction; Indicates the amount of motion sickness stimulation in other directions;

[0028] Vertical motion sickness stimulus Taking the state change caused by axle load transfer as the variable, calculate as follows:

[0029]

[0030] in, is the weighted function of velocity and pitch rate frequency, is the weighted function of acceleration and pitch acceleration;

[0031] Longitudinal motion sickness stimulus hori(v p ) Take the change of longitudinal motion state as the variable and calculate as follows:

[0032]

[0033] Among them, f h (v p ) is a function of speed, is a function of acceleration.

[0034] Furthermore, in step (2), "calculating the relative change of the vehicle driving state according to the current vehicle driving state and road surface environment information collected in step (1)" is: according to the calculated relative change and the detected road surface roughness, the vehicle driving state and road surface environment information at the next moment are divided into n levels, and the value of the weight coefficient corresponding to each pre-set level is obtained, and brought into the preliminary evaluation model to obtain the final evaluation model; specifically:

[0035] Calculate the relative change:

[0036]

[0037] Calculation level:

[0038]

[0039] Furthermore, step (3) includes the following steps:

[0040] Step (31): Optimize the preliminary expected speed sequence for preventing occupant motion sickness obtained in step (2) by using a B-spline curve to obtain an expected speed sequence, which is specifically:

[0041] v p ∈[v p，k-1 , v p，n+1 ]

[0042] Among them, P i (v p ) is the i-th velocity node vector;

[0043] N i,k (v p ) is the basis function corresponding to the i-th velocity node vector, and the expression is as follows:

[0044]

[0045] Where i is the serial number of the basis function;

[0046] Step (32): Calculate the expected acceleration:

[0047]

[0048] Among them, A p，i is the i-th expected acceleration, V p，i+1 、Vp，i are the i+1th and ith expected speeds respectively, T p，i+1 、T p，i They are the i+1th and ith corresponding moments respectively.

[0049] Furthermore, the constraint vector X in step (2) is:

[0050]

[0051] Among them, v p is the expected speed; is the desired pitch angular velocity; v sl is the lower speed limit for safe driving; v sh is the upper limit of safe driving speed; v ml The lower speed limit to prevent motion sickness of passengers; v mh The upper speed limit to prevent motion sickness of passengers; v pl The lower speed limit determined by vehicle performance; v ph The upper speed limit determined by vehicle performance; v cl v is the lower speed limit allowed in the current state; ch The upper speed limit allowed in the current state; The lower limit of pitch angular velocity for safe driving; The upper limit of pitch angular velocity for safe driving; The lower limit of pitch rate to prevent occupant motion sickness; The upper limit of pitch rate to prevent occupant motion sickness; The lower limit of pitch rate determined by vehicle performance; The upper limit of pitch rate is determined by vehicle performance; The lower limit of the pitch angular velocity allowed in the current state; The upper limit of the pitch angular velocity allowed in the current state.

[0052] Furthermore, the domain control method is based on the vehicle two-degree-of-freedom model, which is as follows:

[0053] A two-degree-of-freedom model of the vehicle is established, and the established two-degree-of-freedom model of the vehicle is written as a state equation and discretized as follows:

[0054]

[0055] Among them, M is the vehicle curb weight, F xf With F xr The front and rear wheel drive / braking forces, C d is the drag coefficient, A a is the windward area, is the road slope angle, l is the distance between the occupant position and the pitch axis, is the vehicle body pitch angular velocity, I y is the moment of inertia of the car around the y-axis, H is the height of the vehicle's center of mass, h is the height of the vehicle's pitch center, and g is the acceleration due to gravity;

[0056] Write the above equation as the state equation

[0057] Where x = [vθ] T , u=[F xf F xr ] T , respectively represent the state and input of the vehicle two-degree-of-freedom model system.

[0058] The vehicle intelligent chassis domain control system for preventing occupant motion sickness is equipped with the above method, specifically including:

[0059] Data acquisition module: used to measure the vehicle's motion state and road environment information through sensors;

[0060] Motion sickness evaluation module: determines the motion sickness control level according to the vehicle's driving status data and the status of each obstacle, and calculates the motion sickness evaluation value;

[0061] Expected acceleration calculation module: The constraint vector X in the sampling window is evaluated and optimized using the final evaluation model based on the motion sickness evaluation value, and the B-spline interpolation is used for optimization to calculate the expected acceleration;

[0062] Chassis control module: used to coordinate and control the acceleration and deceleration of the vehicle drive and braking systems according to the desired acceleration.

[0063] Compared with the prior art, the present invention has the following significant advantages:

[0064] (1) The present invention imposes multiple restrictions on the upper and lower limits of the constraint vector according to the current vehicle driving state, road environment information and restriction conditions, thereby reducing the value range, improving the calculation efficiency, and ensuring that the subsequent steps can quickly obtain the initial velocity sequence. After optimization by the B-spline rule, in the specific stimulation frequency band that is prone to cause occupant motion sickness, the expected velocity and the corresponding expected acceleration amplitude are significantly reduced, which can alleviate occupant discomfort and effectively reduce the probability of occupant motion sickness.

[0065] (2) The evaluation mechanism of the present invention adopts an evaluation model, which can adjust the evaluation function according to the motion sickness control level to ensure that the passengers do not get motion sickness while pursuing better traffic efficiency and energy saving performance.

[0066] (3) The motion sickness evaluation function is based on the six-component force model of the occupant's head. The two directions with the highest probability of motion sickness stimulation are taken for separate calculation to save computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the flow of the domain control method of the present invention;

[0068] Figure 2 It is a schematic diagram of the vertical-horizontal impact conversion model;

[0069] Figure 3 It is a schematic diagram of B-spline curve fitting between two adjacent sampling points;

[0070] Figure 4 Comparison of the car pitch frequency before and after using this system; (a) is the amplitude-frequency characteristic after use, and (b) is the amplitude-frequency characteristic before use. DETAILED DESCRIPTION

[0071] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0072] A vehicle intelligent chassis domain control system for preventing occupant motion sickness includes: a data acquisition module for measuring the vehicle body motion state and road environment information through sensors, and evaluating the motion sickness control level; an occupant motion sickness evaluation module for calculating an optimal expected value series based on the vehicle body driving state data and various obstacle states; an acceleration and deceleration process calculation module for calculating an acceleration and deceleration process that can reduce the probability of occupant motion sickness based on the expected value; and a chassis domain control module for coordinating and controlling the vehicle drive and braking systems to achieve acceleration and deceleration based on the expected acceleration.

[0073] A method for preventing occupant motion sickness by a vehicle intelligent chassis domain control system comprises the following steps:

[0074] The S1 data acquisition module obtains the current vehicle driving status and road environment information, including the vehicle speed v, the vehicle acceleration Vehicle pitch angle θ, vehicle pitch angle velocity The execution error ε of the previous cycle, the road conditions ahead and the distance d (N d ) and speed v(N d ), road roughness;

[0075] S2 uses the dynamic window method to calculate the initial expected speed sequence to prevent occupant motion sickness;

[0076] S3 supplements the initial expected speed sequence for preventing occupant motion sickness through the B-spline curve, calculates the supplemented expected acceleration, and then outputs it to the execution system;

[0077] The S4 execution system solves the wheel acceleration and deceleration torque according to the expected acceleration, controls the acceleration system to accelerate, controls the deceleration system to decelerate, and adjusts the vehicle speed to the expected speed sequence.

[0078] S5 calculates the difference ε between the actual execution result and the expected control target and applies it to the next control cycle.

[0079] According to the obtained vehicle driving status and road environment information, the motion sickness control level is evaluated and the evaluation function is adjusted according to the following steps:

[0080] Calculate whether the relative change of the vehicle's driving state is greater than 10%, evaluate whether the road roughness is high or low, and obtain four levels, thereby setting the weight coefficients of 5 corresponding evaluation functions: α, β, γ, λ;

[0081] The calculation formula of relative change is as follows:

[0082]

[0083] The road roughness is assessed as high or low, resulting in four levels, specifically:

[0084]

[0085] The initial expected velocity sequence for preventing occupant motion sickness is calculated according to the dynamic window method, including the following steps:

[0086] S2.1 Firstly, establish the vehicle two-degree-of-freedom model, such as Figure 2 As shown, the established model is written in the form of a state equation and discretized as follows:

[0087]

[0088] Among them, M is the vehicle curb weight, F xf With F xr The front and rear wheel drive / braking forces, C d is the drag coefficient, A a is the windward area, is the road slope angle, l is the distance between the occupant position and the pitch axis, is the vehicle body pitch angular velocity, I y is the moment of inertia of the car around the y-axis, H is the height of the vehicle's center of mass, h is the height of the vehicle's pitch center, and g is the acceleration due to gravity.

[0089] Write the above equation as the state equation

[0090] Where x = [vθ] T , u=[F xf F xr ] T , respectively represent the state and input of the vehicle two-degree-of-freedom model system;

[0091] S2.2 calculates the feasible range of expected speed and expected acceleration through the inherent performance of the vehicle, the comfort of the passengers, the current motion state, and the actual driving environment, and uses it as the sampling window. The constraint vector X is selected from the sampling window as follows:

[0092]

[0093] Among them, v p is the expected speed; is the desired pitch angular velocity; v sl is the lower speed limit for safe driving; v sh is the upper limit of safe driving speed; v ml The lower speed limit to prevent motion sickness of passengers; v mh The upper speed limit to prevent motion sickness of passengers; v pl The lower speed limit determined by vehicle performance; v ph The upper speed limit determined by vehicle performance; v cl v is the lower speed limit allowed in the current state; ch The upper speed limit allowed in the current state; The lower limit of pitch angular velocity for safe driving; The upper limit of pitch angular velocity for safe driving; The lower limit of pitch rate to prevent occupant motion sickness; The upper limit of pitch rate to prevent motion sickness of the passengers; The lower limit of pitch rate determined by vehicle performance; The upper limit of pitch rate is determined by vehicle performance; The lower limit of the pitch angular velocity allowed in the current state; The upper limit of the pitch angular velocity allowed in the current state.

[0094] S2.3 uses an evaluation mechanism to optimize the constraint vector X, and selects the constraint vector X with the highest evaluation value as the optimization result. The basic form of the algorithm's evaluation mechanism is as follows:

[0095]

[0096] in, is a safety evaluation function, which aims at high vehicle and occupant safety during driving; is the motion sickness evaluation function, which aims to reduce the degree of motion sickness stimulation of passengers during driving; η(v p ) is the traffic efficiency evaluation function, with high traffic efficiency as the goal; is the energy-saving evaluation function, with the goal of small driving change; ε is the execution error of the previous cycle, α, β, γ, λ is the weight coefficient of the five evaluation functions; σ represents the normalization factor.

[0097] The safety evaluation function is calculated according to the following formula:

[0098]

[0099] Among them, d(N d ) represents the distance between the dynamic reference point and the nearest "obstacle"; D m Indicates the limited distance, which is a manually preset constant. Obstacles beyond this range are The scoring of R s It represents the collision radius of the vehicle, that is, the distance from the center of mass of the vehicle to the front end contour of the vehicle.

[0100] The motion sickness evaluation function is calculated based on the six-component force model of the occupant's head. To improve the calculation efficiency, the forces in the two directions with the highest probability of motion sickness stimulation are taken for detailed calculation, and the other directions are simply calculated. The motion sickness evaluation function is calculated according to the following formula:

[0101]

[0102] in, Indicates the amount of motion sickness caused by vertical motion; hori(v p ) represents the amount of motion sickness caused by longitudinal motion, which is a weighted function of the velocity, pitch velocity frequency, and the velocity and pitch velocity changes; Indicates the amount of motion sickness caused by movement in other directions.

[0103] The vertical motion sickness stimulus is calculated by taking the state change caused by the axle load transfer as the variable:

[0104]

[0105] in, is the weighted function of velocity and pitch rate frequency, is the weighted function of acceleration and pitch acceleration.

[0106] The longitudinal motion sickness stimulus amount is calculated by the following formula, with the longitudinal motion state change as the variable:

[0107]

[0108] Among them, f h (v p ) is a function of speed, is a function of acceleration.

[0109] The traffic efficiency function is calculated as follows:

[0110]

[0111] Among them, vmax The maximum speed of the vehicle.

[0112] The energy-saving evaluation function is calculated according to the following formula:

[0113]

[0114] Among them, λ 1 and λ 2 are functions of vehicle acceleration and pitch angular acceleration, respectively.

[0115] The initial expected velocity sequence for preventing occupant motion sickness is supplemented by a B-spline curve, and the supplemented expected acceleration is calculated, including the following steps:

[0116] S3.1 The expected velocity sequence calculated by the dynamic window method is supplemented by the B-spline curve law to reduce the amplitude of the corresponding expected acceleration sequence amplitude-frequency characteristic in the frequency range that is easy to stimulate occupant motion sickness. The B-spline curve law velocity sequence is calculated according to the following recursive formula:

[0117]

[0118] Among them, P i (v p ) is the i-th velocity node vector, N i,k (v p ) is the basis function corresponding to the i-th velocity node vector, which is calculated as follows:

[0119]

[0120] Where i is the serial number of the basis function.

[0121] S3.2 Calculate the expected acceleration after supplementation, the expected acceleration after supplementation is calculated according to the following formula:

[0122]

[0123] Among them, A p，i is the i-th expected acceleration, V p，i+1 、V p，i are the i+1th and ith expected speeds respectively, T p，i+1 、T p，i They are the i+1th and ith corresponding moments respectively.

[0124] The wheel acceleration and deceleration torque is calculated as follows:

[0125] M=mgA p,i r

[0126] Where r is the wheel radius.

Claims

1. A vehicle intelligent chassis domain control method for preventing occupant motion sickness, characterized in that: The steps include: Step (1): Collect current vehicle driving status and road environment information; Step (2): using the dynamic window method to calculate the initial expected speed sequence for preventing occupant motion sickness: establishing a preliminary evaluation model based on the safety evaluation function, motion sickness evaluation function, traffic efficiency evaluation function, energy saving evaluation function and the previous cycle execution error, and calculating the relative change of the vehicle driving state and the road surface roughness according to the current vehicle driving state and road surface environment information collected in step (1), obtaining the value of the evaluation model weight coefficient, and obtaining the final evaluation model; using the final evaluation model to evaluate and optimize the constraint vector X in the sampling window to obtain the optimal result, and obtaining the initial expected speed sequence; Step (3): Optimizing the preliminary expected speed sequence for preventing occupant motion sickness obtained in step (2) by using a B-spline curve to obtain an expected speed sequence, and calculating an expected acceleration based on the expected speed sequence, and outputting the result to the execution system; Step (4): the execution system solves the wheel acceleration and deceleration torque according to the expected acceleration and adjusts the current speed of the vehicle to the expected speed sequence; Step (5): Calculate the difference between the actual execution result and the expected control target, that is, the execution error ε of the previous cycle, and bring it into the final evaluation model to act on the next control cycle.

2. The method according to claim 1, characterized in that The current vehicle driving state collected in step (1) includes the vehicle speed v, the vehicle acceleration Vehicle pitch angle θ and vehicle pitch angular velocity The collected road environment information includes the road condition path ahead and the road roughness; the road condition path ahead specifically includes the distance of static road conditions obstructed by obstacles ahead, traffic lights and curves, and the distance and speed of dynamic reference point vehicles and pedestrians.

3. The method according to claim 2, characterized in that The "preliminary evaluation model based on safety evaluation function, motion sickness evaluation function, traffic efficiency evaluation function, energy saving evaluation function and previous cycle execution error" established in step (2) is specifically: Among them, v p is the expected speed; is the expected pitch angular velocity; is the safety evaluation function; is the motion sickness evaluation function; η(v p ) is the traffic efficiency evaluation function; is the energy-saving evaluation function; ε is the execution error of the previous cycle; α, β, γ, λ is the weight coefficient; σ is the normalization factor.

4. The method according to claim 3, characterized in that The safety evaluation function is as follows: Where d(Nd) represents the distance between the dynamic reference point and the nearest static road obstacle; v(N d ) is the difference between the speed of the road obstacle and the expected speed, D m Indicates the limited distance, which is a preset constant; R s Represents the collision radius of the vehicle, N d The nearest road obstacle point; The traffic efficiency evaluation function is: Among them, v max is the maximum speed of the vehicle; The energy-saving evaluation function is: in, and are functions of vehicle acceleration and pitch angular acceleration, respectively.

5. The method according to claim 4, characterized in that The motion sickness evaluation function is based on the six-component force model of the occupant's head. The two directions with the highest probability of motion sickness stimulation are calculated separately, and the remaining directions are combined for calculation. The expression of the motion sickness evaluation function is as follows: in, Indicates the vertical motion sickness stimulus; hori(v p ) represents the amount of motion sickness stimulation in the longitudinal direction; Indicates the amount of motion sickness stimulation in other directions; Vertical motion sickness stimulus Taking the state change caused by axle load transfer as the variable, calculate as follows: in, is the weighted function of velocity and pitch rate frequency, is the weighted function of acceleration and pitch acceleration; Longitudinal motion sickness stimulus hori(v p ) Take the change of longitudinal motion state as the variable and calculate as follows: Among them, f h (v p ) is a function of speed, is a function of acceleration.

6. The method according to claim 5, characterized in that In step (2), "calculating the relative change of the vehicle driving state according to the current vehicle driving state and road surface environment information collected in step (1)" means: according to the calculated relative change and the detected road surface roughness, classifying the vehicle driving state and road surface environment information at the next moment into n levels, obtaining the value of the weight coefficient corresponding to each pre-set level, and bringing it into the preliminary evaluation model to obtain the final evaluation model; specific: Calculate the relative change: Calculation level:

7. The method according to claim 6, characterized in that Step (3) comprises the following steps: Step (31): Optimize the preliminary expected speed sequence for preventing occupant motion sickness obtained in step (2) by using a B-spline curve to obtain an expected speed sequence, which is specifically: Among them, P i (v p ) is the i-th velocity node vector; N i,k (v p ) is the basis function corresponding to the i-th velocity node vector, and the expression is as follows: Where i is the serial number of the basis function; Step (32): Calculate the expected acceleration: Among them, A p，i is the i-th expected acceleration, V p，i+1 、V p，i are the i+1th and ith expected speeds respectively, T p，i+1 , T p，i They are the i+1th and ith corresponding moments respectively.

8. The method according to claim 7, characterized in that The constraint vector X in step (2) is: Among them, v p is the expected speed; is the desired pitch angular velocity; v sl is the lower speed limit for safe driving; v sh is the upper limit of safe driving speed; v ml The lower speed limit to prevent motion sickness of passengers; v mh The upper speed limit to prevent motion sickness of passengers; v pl The lower speed limit determined by vehicle performance; v ph The upper speed limit determined by vehicle performance; v cl v is the lower speed limit allowed in the current state; ch The upper speed limit allowed in the current state; The lower limit of pitch angular velocity for safe driving; The upper limit of pitch angular velocity for safe driving; The lower limit of pitch rate to prevent occupant motion sickness; The upper limit of pitch rate to prevent occupant motion sickness; The lower limit of pitch rate determined by vehicle performance; The upper limit of pitch rate is determined by vehicle performance; The lower limit of the pitch angular velocity allowed in the current state; The upper limit of the pitch angular velocity allowed in the current state.

9. The method according to claim 8, characterized in that The domain control method is based on the vehicle two-degree-of-freedom model, which is as follows: A two-degree-of-freedom model of the vehicle is established, and the established two-degree-of-freedom model of the vehicle is written as a state equation and discretized as follows: Among them, M is the vehicle curb weight, F xf With F xr The front and rear wheel drive / braking forces, C d is the drag coefficient, A a is the frontal area, v is the vehicle speed, is the road slope angle, l is the distance between the occupant position and the pitch axis, is the vehicle body pitch angular velocity, I y is the moment of inertia of the car around the y-axis, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, H is the height of the center of mass of the vehicle, h is the height of the pitch center of the vehicle body, g is the acceleration of gravity, and θ is the pitch angle of the vehicle body; Write the above equation as the state equation Where x = [vθ] T , u=[F xf F xr ] T , respectively represent the state and input of the vehicle two-degree-of-freedom model system.

10. A vehicle intelligent chassis domain control system for preventing occupant motion sickness, characterized in that: The method according to any one of claims 1 to 9 specifically comprises: Data acquisition module: used to measure the vehicle's motion state and road environment information through sensors; Motion sickness evaluation module: determines the motion sickness control level according to the vehicle's driving status data and the status of each obstacle, and calculates the motion sickness evaluation value; Expected acceleration calculation module: The constraint vector X in the sampling window is evaluated and optimized using the final evaluation model based on the motion sickness evaluation value, and the B-spline interpolation is used for optimization to calculate the expected acceleration; Chassis control module: used to coordinate and control the acceleration and deceleration of the vehicle drive and braking systems according to the desired acceleration.

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