A method and system for evaluating occupant motion sickness during driving of an intelligent chassis

Abstract

The present invention is a method and system for evaluating occupant motion sickness during intelligent chassis driving. The method comprises the following steps: (1) collecting chassis motion information, calculating chassis roll angle motion sickness excitation index, chassis pitch angle motion sickness excitation index, chassis yaw angle motion sickness excitation index, and calculating frequency-weighted acceleration vibration total amount; (2) establishing a chassis driving process occupant motion sickness evaluation model based on chassis motion information and occupant motion sickness state; (3) dividing the occupant motion sickness process into three stages: initial motion sickness stage, acceleration motion sickness stage, and late motion sickness stage, and setting weighting coefficients for the three stages of the motion sickness evaluation model; (4) detecting occupant physiological information, determining the occupant motion sickness stage, retrieving the corresponding weighting coefficients for each stage, and calculating the occupant real-time motion sickness value. The present invention combines occupant motion sickness physiological detection with quantitative estimation to provide a more refined and objective motion sickness detection index for chassis anti-motion sickness feedback control.

Description

Technical Field

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

[0002] Motion sickness refers to the condition in which passengers are prone to dizziness, blurred vision, nausea, vomiting, paleness, fatigue, etc. when riding in a car, boat or airplane. With the rapid development of automobile electrification and intelligence, electric vehicles are gradually replacing traditional fuel vehicles. Compared with fuel engines, the high torque characteristics of electric motors during starting and braking will cause low-frequency and large pitch changes in the vehicle. In addition, high-speed steering and muddy roads will also cause low-frequency and large angle changes in the vehicle's yaw and roll. Similar low-frequency and large changes in attitude angles are the main cause of occupant motion sickness. Therefore, real-time detection of occupants' motion sickness during vehicle operation is conducive to continuously adjusting the intelligent chassis anti-sickness control strategy while ensuring the safety of occupants in the vehicle.

[0003] There have been many in-depth studies on the mechanism of motion sickness, among which the mainstream view is the sensory conflict hypothesis, in which vibrations of special frequencies lead to dysfunction of balance regulation. In terms of occupant motion sickness detection, a series of research processes have been experienced. One type is to determine whether the occupant has entered a motion sickness state by detecting changes in the occupant's physiological state. Japanese patent application JP 2022-175384 discloses "motion sickness detection equipment, motion sickness detection procedures, and motion sickness detection methods." This method uses a temperature sensor to detect the facial temperature distribution of seat occupants and detects changes in the occupant's nose temperature. Then judge whether the occupant is in a motion sickness state based on changes in the temperature of the nose. This method can more accurately detect whether the occupant is in a motion sickness state, but it cannot quantify the changes in the degree of motion sickness of the occupant, and provide conditions for chassis anti-motion sickness feedback control.

[0004] The other is to study the inducing factors of occupant motion sickness through a large number of occupant experiments. Korean patent application KR10-2021-0119239 discloses a "system and method for inducing occupant motion sickness". This method studies the factors that induce occupant motion sickness by constructing an occupant motion sickness inducing system in the vehicle. This method can obtain the inducing factors of occupant motion sickness in a more comprehensive way by changing the changes in various parameters during vehicle driving and comparing them with the motion sickness status reported by the occupants in real time, but these factors are more qualitative studies and cannot provide a quantitative dose of inducing factors for chassis anti-motion sickness feedback control.

[0005] Chinese patent application CN 113212437 A discloses a "method for estimating motion sickness when a vehicle is driving on a curve". This method collects the lateral acceleration of the vehicle when it is driving on a curve and calculates the change in the head tilt angle of the occupants in the vehicle. According to the sensory conflict theory, the perceived angular velocity of the semicircular canals and otoliths in the brain is calculated. This achieves a quantitative estimation of the motion sickness of the occupants when the vehicle is driving on a curve, but there is a large subjective difference in the occupants' perception of motion sickness, and it cannot better reflect the individual differences in the occupants' motion sickness. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for evaluating occupant motion sickness during driving of an intelligent chassis.

[0007] The technical solution to achieve the purpose of the present invention is: a method for evaluating occupant motion sickness during driving of an intelligent chassis, comprising the following steps:

[0008] Step (1): Collect chassis motion information and calculate chassis roll angle φ motion excitation index Chassis pitch angle θ motion sickness excitation index Chassis yaw angle Motion sickness incentive index Calculate the total frequency-weighted acceleration vibration a v ;

[0009] Step (2): Based on the chassis motion information and the occupant motion sickness status, a chassis driving process occupant motion sickness evaluation model is established:

[0010]

[0011] Where, IMSE is the real-time motion sickness value of the occupant; n1, n2, n3, n4 are weighted coefficients related to the occupant's motion sickness state;

[0012] Step (3): Divide the motion sickness process of the occupant into three stages: early motion sickness stage, accelerated motion sickness stage and late motion sickness stage. According to the individual information of the occupant, set the weight coefficients of the three stages of the motion sickness evaluation model.

[0013] Step (4): Detect the physiological information of the occupant, determine the stage of motion sickness of the occupant, retrieve the weighted coefficient corresponding to each stage, and calculate the real-time motion sickness value of the occupant.

[0014] Furthermore, the chassis motion information collected in step (1) includes chassis roll angle acceleration Pitch angular acceleration Yaw angular acceleration and the x, y, and z axis vibration acceleration a at the center of mass x 、a y 、a z .

[0015] Furthermore, in step (1), the calculation of the chassis roll angle φ motion excitation index Chassis pitch angle θ motion sickness excitation index Chassis yaw angle Motion sickness incentive index Specifically:

[0016] Chassis roll acceleration Pitch angular acceleration Yaw angular acceleration To calculate the frequency, the formula is as follows:

[0017]

[0018] In the formula, t n is the time interval between adjacent angular accelerations of 0, and n is the interval number; is the frequency of the nth time interval, is the name of angular acceleration;

[0019] Calculate the motion sickness incentive index for the nth time interval The formula is as follows:

[0020]

[0021] In the formula, S n is the coefficient of the nth time interval, according to the frequency of the nth time interval It is concluded that △a n is the amplitude of the nth time interval, t n is the time interval between adjacent angular accelerations of 0, is the name of angular acceleration.

[0022] Furthermore, in step (1), the calculation of the frequency-weighted acceleration vibration amount a v Specifically:

[0023] According to the x, y, z axis vibration acceleration a of the center of mass position x 、a y 、a z , calculate the vibration acceleration a of the x, y, and z axes at the i-th seat position i,x 、a i,y 、a i,z ;

[0024] Calculate the vibration acceleration a of the x, y, and z axes at the i-th seat position i,x 、a i,y 、a i,z The weighted root mean square value of

[0025] Synthesize the weighted root mean square values ​​of the x, y, and z axis vibration accelerometers;

[0026]

[0027] In the formula, a v is the total frequency-weighted acceleration vibration; is the weighted root mean square value of the accelerometer on the x, y, and z axes under the orthogonal coordinate axes, w represents the weighting operation; k x , k y , k z is the seat orientation factor.

[0028] Furthermore, according to the x, y, and z axis vibration acceleration a at the center of mass position x 、a y 、a z , calculate the x, y, and z axis vibration acceleration a of the i-th seat position according to the vehicle vibration transfer function i,x 、a i,y 、a i,z .

[0029] Further, calculate the x-axis vibration acceleration a of the i-th seat position i,x The formula for the weighted root mean square value of is as follows:

[0030]

[0031] In the formula, is the weighted root mean square value of the x-axis vibration acceleration of the ith seat position, w represents the weighting operation; T is the frequency weighting time interval;

[0032] Similarly, calculate the vibration acceleration a of the y and z axes of the i-th seat position i,y 、a i,z The weighted root mean square value of .

[0033] Furthermore, the step (3) of “dividing the occupant's motion sickness process into three stages: early motion sickness stage, accelerated motion sickness stage and late motion sickness stage” is specifically as follows:

[0034] Set the facial temperature change threshold X1, and set the pupil diameter average change threshold X2 in adjacent T time periods. The occupant's facial temperature change at time k is |T k -T k-1 |, the average pupil diameter in time segment T is:

[0035]

[0036] In the formula, is the average pupil diameter in a time segment, N is the number of data in a time segment, PD i is the pupil diameter. The average pupil diameter change between the adjacent n and n-1 time periods is

[0037] In the initial stage of motion sickness, |T k -T k-1 | < X1 or When |T k -T k-1 | ≥ X1 or When, the occupant enters the acceleration motion sickness stage; when |T k -T k-1 | < X1 or When, the occupant enters the late stage of motion sickness.

[0038] Furthermore, the individual information of the occupant includes age, gender, and motion sickness experience;

[0039] The motion sickness experience includes sedan i1, bus i2, train i3, airplane i4, small boat i5, ferry i6, swing i7, carousel i8, roller coaster i9, where the values of i1, i2, i3, i4, i5, i6, i7, i8, i9 are 0, 1, 2, 3, and 4.

[0040] Furthermore, the weight coefficient in the initial stage of motion sickness is:

[0041] The weight coefficient in the acceleration motion sickness stage is:

[0042]

[0043] In the formula,

[0044] In the formula,

[0045] The weighted coefficient in the late stage of motion sickness is:

[0046]

[0047] An intelligent chassis occupant motion sickness evaluation system during driving includes a chassis motion sickness information acquisition module, an occupant physiological information detection module, and an occupant motion sickness evaluation module;

[0048] The chassis motion sickness information acquisition module is used to acquire chassis motion sickness information;

[0049] The occupant physiological information detection module is used to detect the facial temperature of the occupant and the pupil diameter during a certain period;

[0050] The occupant motion sickness evaluation module calculates the real-time motion sickness value of the occupant based on the above-mentioned occupant motion sickness evaluation model during chassis driving.

[0051] Compared with the prior art, the significant advantages of the present invention are:

[0052] The occupant motion sickness evaluation model during chassis driving includes the chassis 6-degree-of-freedom motion excitation, which more comprehensively evaluates the impact of chassis motion excitation on occupant motion. On the other hand, it collects occupant individual information and motion sickness experience, monitors occupant physiological information during vehicle driving, and achieves a more objective occupant motion sickness evaluation. With the popularization of vehicle intelligence and electrification, the occupant motion sickness evaluation method and system that combines occupant motion sickness physiological detection with quantitative estimation not only provides occupants with more intuitive motion sickness indicators, but also provides more detailed motion sickness detection indicators for chassis anti-motion sickness feedback control, so that chassis anti-motion sickness feedback control can rely on motion sickness excitation indicators of each degree of freedom and occupant real-time motion sickness values ​​to perform targeted chassis anti-motion sickness control. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the evaluation method of the present invention;

[0054] Figure 2 The vehicle coordinates and layout diagram;

[0055] Figure 3 This is the relationship diagram between the occupant's motion sickness state and motion incentive;

[0056] Figure 4 Frequency weight map of motion sickness related excitation indicators. DETAILED DESCRIPTION

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0058] like Figure 1 A method for evaluating occupant motion sickness during driving of an intelligent chassis, characterized in that it comprises the following steps:

[0059] Step (1): Collect chassis motion information and calculate chassis roll angle φ motion excitation index Chassis pitch angle θ motion sickness excitation index Chassis yaw angle Motion sickness incentive index Calculate the total frequency-weighted acceleration vibration a v ;

[0060] Step (2): Based on the chassis motion information and the occupant motion sickness status, a chassis driving process occupant motion sickness evaluation model is established:

[0061]

[0062] Where, IMSE is the real-time motion sickness value of the occupant; n1, n2, n3, n4 are weighted coefficients related to the occupant's motion sickness state;

[0063] Step (3): Divide the motion sickness process of the occupant into three stages: early motion sickness stage, accelerated motion sickness stage and late motion sickness stage. According to the individual information of the occupant, set the weight coefficients of the three stages of the motion sickness evaluation model.

[0064] Step (4): Detect the physiological information of the occupant, determine the stage of motion sickness of the occupant, retrieve the weighted coefficient corresponding to each stage, and calculate the real-time motion sickness value of the occupant.

[0065] The chassis motion information collected in step (1) includes chassis roll angle acceleration Pitch angular acceleration Yaw angular acceleration and the x, y, and z axis vibration acceleration a at the center of mass x 、a y 、a z The x-, y-, and z-axis vibration accelerations of each seat position are obtained by calculating the vibration transfer function from each seat to the vehicle's center of mass.

[0066] Step (11): Calculate the transfer function from the center of mass position of a specific vehicle model to each seat position through experiments

[0067] Step (12): The CAN acquisition tool collects the data of the above signal, and performs mechanical processing of the signal value, IMU parameter calibration and unit conversion in the host computer;

[0068] Step (13): Figure 2 , the x, y, and z axis vibration accelerations of each seat position are obtained by calculating the vibration transfer function from each seat to the vehicle mass center. Taking the x axis vibration of the front right seat as an example, the transfer function is:

[0069]

[0070] In the formula, m st is the mass of the seat and the occupant, x stfr is the displacement of the seat on the x-axis, x s is the displacement of the center of mass on the x-axis, θ is the pitch angle of the center of mass, φ is the roll angle of the center of mass, p is the longitudinal distance from the front seat to the center of mass, q is the lateral distance from the front seat to the center of mass, k sh1 is the horizontal stiffness, c sh1 is the horizontal damping.

[0071] Step (14): Chassis roll angle acceleration Pitch angular acceleration Yaw angular acceleration Perform frequency detection and calculate the frequency as follows:

[0072]

[0073] In the formula, t nis the time interval between adjacent angular accelerations of 0, and n is the interval number; is the frequency of the nth time interval, is the name of the angular acceleration, for example Expressed as the frequency of the pitch angular acceleration in the second time interval.

[0074] Step (15): Calculation of motion sickness related incentive index: Calculate the motion sickness related incentive index for the nth time interval The formula is as follows:

[0075]

[0076] In the formula, according to the frequency of the nth time interval Set the coefficient S for the nth time interval n , Figure 4 The coefficient S n With frequency The relationship diagram of △a n is the amplitude of the nth time interval, t n is the time interval between adjacent angular accelerations of 0. is the name of angular acceleration.

[0077] Step (16): Vibration frequency weighting: Considering the impact of axial vibration caused by the road surface, etc. on the occupant's motion sickness, the vibration acceleration a of the x, y, and z axes at the i-th seat position is calculated. i,x 、a i,y 、a i,z Using the frequency-weighted detection method, take the x-axis vibration acceleration as an example:

[0078]

[0079] In the formula, is the weighted RMS value of the x-axis vibration acceleration of the ith seat position, w represents the weighting operation; T is the frequency weighting time interval, which is set to 1s.

[0080] Step (17): Synthesize the weighted root mean square values ​​of the x-, y-, and z-axis vibration accelerometers.

[0081]

[0082] In the formula, a v is the total frequency-weighted acceleration vibration; is the weighted root mean square value of the accelerometer on the x, y, and z axes under the orthogonal coordinate axes, w represents the weighting operation; k x , k y , k z is the seat direction factor, and the common seat is k x =0.8,k y= 0.8, k z = 1.4。

[0083] The "dividing the occupant's motion sickness process into three stages: the initial stage of motion sickness, the acceleration motion sickness stage, and the late stage of motion sickness" in step (3) is specifically as follows:

[0084] Set the facial temperature change threshold X1, and set the average change threshold X2 of the pupil diameter in adjacent T time periods. The facial temperature change of the occupant at time k is |T k - T k-1 |, and the average pupil diameter within the time segment T is:

[0085]

[0086] In the formula, is the average pupil diameter within a time segment, N is the number of data within a time segment, and PD i is the pupil diameter. The change in the average pupil diameter between adjacent n and n - 1 time periods is

[0087] In the initial stage of motion sickness, |T k - T k-1 |< X1 or When |T k - T k-1 |≥ X1 or , the occupant enters the acceleration motion sickness stage; when |T k - T k-1 |< X1 or , the occupant enters the late stage of motion sickness.

[0088] The occupant's individual information includes age, gender, and motion sickness experience;

[0089] The motion sickness experience includes sedan i1, bus i2, train i3, airplane i4, small boat i5, ferry i6, swing i7, carousel i8, roller coaster i9 (not experienced, none: 0, rarely: 1, sometimes: 2, often: 3, always: 4).

[0090] The calculation formula for the motion sickness susceptibility MSSQ is as follows:

[0091]

[0092] In the formula,

[0093] In the initial stage of motion sickness, set the weighting coefficient according to experience: In the acceleration motion sickness state, the weighting coefficient is set according to

[0094] Set, the average MSSQ of Chinese adult males is 4.2, and the weighting coefficient is The average MSSQ of Chinese adult women is 6.1, and the weighted coefficient is In the late stage of motion sickness, the crew's motion sickness value no longer increases, and the weighted coefficient

Claims

1. A method for evaluating occupant motion sickness during driving of an intelligent chassis, characterized in that: The steps include: Step (1): Collect chassis motion information and calculate chassis roll angle φ motion excitation index Chassis pitch angle θ motion sickness excitation index Chassis yaw angle Motion sickness incentive index Calculate the total frequency-weighted acceleration vibration a v ; Step (2): Based on the chassis motion information and the occupant motion sickness status, a chassis driving process occupant motion sickness evaluation model is established: Where, IMSE is the real-time motion sickness value of the occupant; n1, n2, n3, n4 are weighted coefficients related to the occupant's motion sickness state; Step (3): Divide the motion sickness process of the occupant into three stages: early motion sickness stage, accelerated motion sickness stage and late motion sickness stage. According to the individual information of the occupant, set the weight coefficients of the three stages of the motion sickness evaluation model; specifically: Set the facial temperature change threshold X1, set the pupil diameter average change threshold X2 in adjacent T time periods, and the occupant's facial temperature change at time k is |T k -T k-1 |, the average pupil diameter in time segment T is: In the formula, is the average pupil diameter in a time segment, N is the number of data in a time segment, PD i is the pupil diameter; the average pupil diameter change between adjacent n and n-1 time periods is In the early stages of motion sickness, |T k -T k-1 |<X1 or When |T k -T k-1 |≥X1 or When |T k -T k-1 |<X1 or When the occupants enter the late stage of motion sickness; Step (4): Detect the physiological information of the occupant, determine the stage of motion sickness of the occupant, retrieve the weighted coefficient corresponding to each stage, and calculate the real-time motion sickness value of the occupant.

2. The method according to claim 1, characterized in that The chassis motion information collected in step (1) includes chassis roll angle acceleration Pitch angular acceleration Yaw angular acceleration and the x, y, and z axis vibration acceleration a at the center of mass x 、a y 、a z .

3. The method according to claim 2, characterized in that Step (1) "Calculate chassis roll angle φ motion excitation index Chassis pitch angle θ motion sickness excitation index Chassis yaw angle Motion sickness incentive index Specifically: Chassis roll acceleration Pitch angular acceleration Yaw angular acceleration To calculate the frequency, the formula is as follows: Where, t n is the time interval between adjacent angular accelerations of 0, and n is the interval number; is the frequency of the nth time interval, is the name of angular acceleration; Calculate the motion sickness incentive index for the nth time interval The formula is as follows: In the formula, S n is the coefficient of the nth time interval, according to the frequency of the nth time interval It is concluded that Δα n is the amplitude of the nth time interval, t n is the time interval between adjacent angular accelerations of 0, is the name of angular acceleration.

4. The method according to claim 3, characterized in that Step (1) "Calculate the frequency-weighted acceleration vibration amount a v Specifically: According to the x, y, z axis vibration acceleration a of the center of mass position x 、a y 、a z , calculate the vibration acceleration a of the x, y, and z axes at the i-th seat position i,x 、a i,y 、a i,z ; Calculate the vibration acceleration a of the x, y, and z axes at the i-th seat position i,x 、a i,y 、a i,z The weighted root mean square value of Synthesize the weighted root mean square values ​​of the x, y, and z axis vibration accelerometers; In the formula, a v is the total frequency-weighted acceleration vibration; is the weighted root mean square value of the accelerometer on the x, y, and z axes under the orthogonal coordinate axes, w represents the weighting operation; k x , k y , k z is the seat orientation factor.

5. The method according to claim 4, characterized in that According to the x, y, z axis vibration acceleration a of the center of mass position x 、a y 、a z , calculate the x, y, and z axis vibration acceleration a of the i-th seat position according to the vehicle vibration transfer function i,x 、a i,y 、a i,z .

6. The method according to claim 5, characterized in that Calculate the x-axis vibration acceleration a of the i-th seat position i,x The formula for the weighted root mean square value of is as follows: In the formula, is the weighted root mean square value of the x-axis vibration acceleration of the ith seat position, w represents the weighting operation; T is the frequency weighting time interval; Similarly, calculate the vibration acceleration a of the y and z axes of the i-th seat position i,y 、a i,z The weighted root mean square value of .

7. The method according to claim 6, characterized in that Individual occupant information includes age, gender, and motion sickness experience; The motion sickness experiences include car i1, bus i2, train i3, airplane i4, small boat i5, ferry i6, swing i7, carousel i8, and roller coaster i9, where the values ​​of i1, i2, i3, i4, i5, i6, i7, i8, and i9 are 0, 1, 2, 3, and 4.

8. The method according to claim 7, characterized in that The weight coefficient at the initial stage of motion sickness is: The weight coefficient of the accelerated motion sickness period is: In the formula, In the formula, The weighting coefficient in the late stage of motion sickness is:

9. An intelligent chassis driving process occupant motion sickness assessment system, characterized in that: It includes chassis motion sickness information collection module, occupant physiological information detection module and occupant motion sickness evaluation module; The chassis motion sickness information collection module is used to collect chassis motion sickness information; The passenger physiological information detection module is used to detect the passenger's facial temperature and pupil diameter during a certain period of time; The occupant motion sickness evaluation module calculates the occupant's real-time motion sickness value based on the chassis driving process occupant motion sickness evaluation model according to any one of claims 1 to 8.

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