Motion sickness evaluation system and method based on instantaneous field sickness value

Abstract

The invention relates to the field of motion sickness detection, in particular to a motion sickness evaluation system and method based on an instantaneous motion sickness value, and aims to simulate the state of a passenger through a test dummy and collect acceleration, inclination angle and vehicle speed data collected by head and chest sensors in real time. The data processing module calculates each axial basic seasickness value based on the product of the vehicle speed, the axial acceleration of the part, the gravity and the cosine value of the inclination angle, and distributes negative correlation seasickness coefficients according to the seasickness value interval. The system counts the number of data points in each interval in a preset time period, and the score rate of each axial direction of the head and chest is obtained by dividing the sum of the number of interval points * the corresponding coefficient by the number of total points * the first-level coefficient. The method comprises the following steps: firstly, carrying out coaxial scoring on a three-axis motion sickness evaluation table, then carrying out weighted summation on the coaxial scoring rates to obtain a single-axis total evaluation, then fusing a three-axis total evaluation to calculate a comprehensive motion sickness scoring rate, and finally, outputting a three-axis motion sickness evaluation score according to the preset score table, thereby realizing multi-dimensional riding comfort quantitative evaluation, and solving the problem that a real person evaluation result is greatly influenced by subjective influence factors of a tester.

Description

Technical Field

[0001] The present invention relates to the field of motion sickness detection, and in particular to a motion sickness evaluation system and method based on instantaneous field sickness values. Background Art

[0002] Motion sickness (MS) is a physiological discomfort experienced by occupants due to vehicle motion. Its core mechanism is the perceptual conflict theory: when the motion signals input to the cerebellum by the visual, vestibular, and somatosensory systems are inconsistent, the higher the conflict, the more pronounced the motion sickness symptoms. In recent years, the high responsiveness of smart electric vehicles (EVs) due to their drive motors (e.g., 0-100% torque response time <200ms) and frequent regenerative braking has resulted in a 40%-60% increase in longitudinal acceleration jerk compared to traditional fuel-powered vehicles. This, combined with the lack of smooth lateral oscillation controlled by the algorithm, has led to a surge in the incidence of motion sickness. Industry research indicates that over 30% of users experience moderate to severe motion sickness in Level 3 autonomous vehicles, making motion sickness comfort a key bottleneck restricting user experience and market adoption. Motion sickness performance is now increasingly a key consideration for vehicle owners when choosing a vehicle.

[0003] Currently, the mainstream motion sickness assessment methods for vehicles include two categories: 1. Subjective evaluation methods: such as the Motion Sickness Symptom Questionnaire (SSQ) or the Visual Analog Scale (VAS), which rely on occupant self-reporting and have the problems of strong subjectivity and poor real-time performance; 2. Physiological monitoring methods: using contact sensors to collect vital sign parameters such as heart rate variability (HRV) and skin conductance (EDA). For example, the invention patent with application number CN202411434473.2 discloses a motion sickness sensing device, a seat, a vehicle, and a motion sickness detection method. The motion sickness sensing device includes: a seat cover; a sensor, the sensor is provided on the seat cover; and a motion sickness detection integrated module, the motion sickness detection integrated module is provided on the seat cover and connected to the sensor, and the motion sickness detection integrated module is configured to receive the sensor signal and determine the user's physical sign information based on the sensor signal and send it to the terminal device. According to the motion sickness sensing device of the present invention, the motion sickness detection integrated module integrates the physical sign signal detection function, the sensor signal processing function, and the information transmission function.

[0004] However, these technologies still have some drawbacks, including: 1. Indirectness and hysteresis: Physiological signals are the final indicators of motion sickness, and significant changes typically occur some time after the motion stimulus; 2. Differences in age, physical condition, and mood swings can lead to misjudgment; 3. Intrusive design flaws: Forced contact sensing compromises passenger freedom and can put pressure on passengers' mental and physiological states, all of which affect the objective assessment of motion sickness in vehicles. Therefore, a technology is needed that can replace human motion sickness assessment and provide objective motion sickness assessments. Summary of the Invention

[0005] The present invention provides a motion sickness evaluation system and method based on instantaneous motion sickness value, which can improve the accuracy of vehicle motion sickness rating and reduce the subjective influence of the test subject on the test results.

[0006] In order to solve the above technical problems, the present application provides the following technical solutions: a motion sickness assessment system based on instantaneous motion sickness value, comprising:

[0007] Test dummy: The test dummy is used to replace the passenger in the car seat, and several sensors are installed on its head and chest;

[0008] Data acquisition module: used to acquire sensor data at each threshold time interval; the sensor data at least includes the test dummy's head acceleration, head tilt angle, chest acceleration, chest tilt angle and vehicle speed;

[0009] The data processing module is used to calculate the basic field sickness values of the head and chest along the x-axis, y-axis, and z-axis, respectively. The field sickness value of the head or chest along a single axis is equal to the product of the vehicle speed, the acceleration component of the part along the axis, the acceleration of gravity, and the cosine value of the inclination angle of the part along the axis. The data processing module is also used to independently divide each axis into a number of field sickness value intervals and assign a corresponding field sickness coefficient to each interval, wherein the value of the field sickness value interval is negatively correlated with the field sickness coefficient.

[0010] The data processing module counts the number of data points in each halo value interval on each axis within a preset detection time period, and obtains the score rate on each axis of the head and chest by multiplying the sum of the products of the number of data points in each interval and the corresponding halo coefficient by the sum of the products of the number of data points in all intervals and the first-level halo coefficient.

[0011] The score rates on the same axis of the head and chest are weighted and summed to generate the total score rate on a single axis; then the total score rates of the x-axis, y-axis, and z-axis are weighted and summed to generate a three-axis comprehensive motion sickness score rate. The obtained three-axis comprehensive motion sickness score rate is matched with the preset score rate and score matching table to obtain the vehicle's instantaneous three-axis motion sickness evaluation score.

[0012] The basic principle and beneficial effects of this solution are: the test dummy is equipped with sensors to collect the three-axis acceleration, tilt angle and vehicle speed data of the head and chest in real time, and the basic field sickness value of each part in the x / y / z axis is calculated based on the formula The motion sickness value interval is divided independently for each axis and a negative correlation coefficient is assigned. After counting the number of data points in each interval within the preset time period, a score rate is generated through weighted summation and normalization. Finally, the score rates of the head and chest and the weights of the three axes are integrated to output a comprehensive motion sickness evaluation score for the three axes, realizing a closed-loop analysis from data collection to quantitative scoring.

[0013] This solution uses a test dummy to simulate the occupant's posture, and uses sensors on the head and chest to collect three-axis acceleration (x / y / z axes), tilt angle (reflecting posture stability), and vehicle speed, covering the full range of motion state perception. Multi-source data (acceleration, posture, speed) fusion and independent axial classification accurately quantify the impact of different movement directions on motion sickness; the head reflects the conflict between the visual-vestibular system, and the chest reflects the perception of visceral displacement. The weighted fusion of dual-site data is closer to the real motion sickness experience. The collaborative analysis of the head and chest and the three-axis dynamic weighting are closer to the real perception mechanism of the human body, solving the limitations of traditional methods that rely on subjective feedback or a single data source, and providing an objective and reusable engineering evaluation tool for vehicle comfort optimization.

[0014] This solution has direct application value in vehicle research and development, intelligent cockpit optimization and other fields, and is suitable for comfort evaluation scenarios of autonomous driving vehicles.

[0015] This solution simplifies the motion sickness testing process and reduces the need for test setup. Unlike real-person testing, this solution only requires placing a sensor-equipped test dummy on the car seat. Once the car starts moving, sensor data is collected for later analysis. This reduces the need for various life detection instruments to be placed on the person, and all sensors in this solution are integrated into the test dummy, making it easier to operate. Furthermore, during real-person testing, changes in vital signs can occur not only due to motion sickness but also due to the tester's nervousness, which can lead to data changes and exacerbate motion sickness. This can lead to significant discrepancies between test results and actual results. Using a test dummy allows for more accurate data detection, minimizes external influences, and provides a more realistic reflection of the ride experience.

[0016] Furthermore, it also includes a laser radar module, which is used to measure the viewing distance of the test dummy in real time; the data processing module is also used to obtain the average viewing distance of all data points in a preset detection time period, match the obtained average viewing distance with a preset viewing distance and score matching table to obtain a viewing distance evaluation score, and then perform weighted calculation on the vehicle's three-axis motion sickness evaluation score and the viewing distance evaluation score to obtain a viewing distance-three-axis comprehensive evaluation score.

[0017] Beneficial effects: When viewing objects outside / inside the vehicle at a close distance, the eyes focus at a short distance, the range of the scene seen is limited, and it is difficult to obtain comprehensive information about the vehicle's movement. At this time, the eyes will judge the surrounding environment as relatively still, while the vestibular system of the ear can feel the actual movement of the vehicle. This inconsistency between visual and vestibular perception will cause the brain to receive confusing signals, thereby inducing motion sickness. When viewing natural scenes from a distance, visual information is relatively scattered, and the eyes can browse and obtain information more naturally, reducing the pressure on the visual system and helping to alleviate motion sickness. In other words, it can be understood that a long viewing distance is easier to resist motion sickness than a short viewing distance. This solution uses viewing distance as one of the vehicle's motion sickness scoring indicators. The final viewing distance-three-axis comprehensive evaluation score can more truly reflect the actual motion sickness situation.

[0018] Furthermore, the preset detection time period is 10 minutes, and the interval threshold time between two adjacent data points is 100ms.

[0019] Furthermore, it also includes a viewing distance score correction module, which divides the viewing distance into at least two viewing distance ranges from far to near. The viewing distance score correction module is used to obtain the viewing distance value of each data point. If the viewing distance values of at least 10 consecutive data points fall within the same viewing distance range, then this section of consecutive data points is named as far viewing distance and near viewing distance according to the viewing distance range they fall into; within a preset calculation cycle, if the ratio of the number of far viewing distances to the number of near viewing distances reaches a rated value, and the number of far viewing distances and near viewing distances within the calculation cycle is greater than 3, a correction signal is generated, and the viewing distance score correction module confirms the viewing distance within the preset calculation cycle as 0.

[0020] Beneficial effect: This solution detects changes in viewing distance through a viewing distance score correction module. If the viewing distance changes multiple times within a fixed preset calculation cycle, that is, switches back and forth between near and far, when the vehicle is driving, when the eyes look at objects at close range, the visual system receives a relatively static image, while the vestibular system can feel the actual movement of the vehicle. This inconsistency between visual and vestibular perception will cause the brain to receive confusing signals, thereby causing motion sickness. When the viewing distance switches back and forth between near and far, this conflict will become more frequent and obvious, further aggravating the symptoms of motion sickness. Therefore, this solution detects the switching of near and far viewing distances within a preset calculation cycle. If the ratio of near viewing distance to far viewing distance reaches the rated value, and the duration of any far viewing distance and near viewing distance does not exceed 2 / 5 of the preset calculation cycle, the viewing distance will be re-corrected and assigned, and all viewing distances within the preset calculation cycle will be confirmed as 0 and included in the subsequent viewing distance score calculation.

[0021] Furthermore, there are 4 halo value intervals from small to large that are independently divided along each axis, and the corresponding halo coefficients are 0.4, 0.3, 0.2 and 0.1.

[0022] Beneficial effects: Suppressing occasional large-scale motion interference: High motion sickness value ranges (such as severe turbulence) are assigned a low coefficient (0.1) to reduce their excessive impact on the total score and avoid score distortion caused by occasional extreme data; Strengthening the cumulative effect of persistent motion sickness: Low motion sickness value ranges (such as frequent small vibrations) correspond to a high coefficient (0.4), which amplifies the cumulative contribution of persistent motion and is closer to the human body's sensitive perception of long-term motion sickness; Standardized implementation and scalability: The specific numerical definition simplifies the system parameter calibration process.

[0023] A motion sickness evaluation method based on instantaneous motion sickness value comprises the following steps:

[0024] S1: Obtain motion data of the test dummy at every threshold time, the motion data including:

[0025] Acceleration components of the head on the x / y / z axis:

[0026] The acceleration components of the chest on the x / y / z axis are:

[0027] Vehicle speed: v;

[0028] The tilt angle of the head in the x / y / z axis plane

[0029] The inclination angle of the chest in the x / y / z axis plane Where k∈(x,y,z);

[0030] S2: Calculate the field halo values of the head and chest on the x, y, and z axes respectively;

[0031] (part∈{head,chest})

[0032] S3: Set four levels of halo intervals independently for x / y / z axis;

[0033] [L k1 ,L k2 ),[L k2 ,L k3 ),[L k3 ,L k4 ),[L k4 ,∞);

[0034] And assign a halo coefficient to each level interval: satisfy

[0035] S4: Grading statistics and calculation of score rate:

[0036] Count the number of data points in the four-level intervals of each axis within the preset detection time period:

[0037] Calculate the score rate of each axis of the head and chest:

[0038]

[0039] S5: weighting the score rate of each axis of the head and chest;

[0040]

[0041] S6: Weight the three-dimensional axial scores to generate a three-axis comprehensive motion sickness score;

[0042]

[0043] S7: Match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the vehicle instantaneous three-axis motion sickness evaluation score M xyz .

[0044] Furthermore, it also includes:

[0045] S61: Calculate the total acceleration of each data point:

[0046] S62: Count the number of a within the preset detection time period total >1.2

[0047] S63: Calculate acceleration frequency

[0048] S64: Match the obtained acceleration frequency with the preset accelerometer score matching table to obtain the vehicle acceleration motion sickness evaluation score M a ;

[0049] The step S7 further includes: performing weighted calculation based on the vehicle acceleration motion sickness evaluation score and the vehicle three-axis motion sickness evaluation score to obtain an instantaneous motion sickness evaluation score M total =ω xyz M xyz +w a M a ,ω xyz +ω a =1.

[0050] Furthermore, the four-level halo interval of each axis in step S3 is:

[0051] x-axis: [L x1 ,L x2 ),[L x2 ,L x3 ),[L x3 ,L x4 ),[L x4 ,∞)=[0,24),[24,48),[48,78),[78,∞);

[0052] y-axis: [L y1 ,L y2 ),[L y2 ,L y3 ),[L y3 ,L y4 ),[L y4 ,∞)=[0,140),[140,320),[320,560),[560,∞);

[0053] z-axis: [L z1 ,L z2 ),[L z2 ,L z3 ),[L z3 ,L z4 ),[L z4 ,∞)=[0,250),[250,450),[450,760),[760,∞).

[0054] Furthermore, the motion sickness evaluation method based on instantaneous field sickness value according to claim 7 is characterized in that: a =0.15~0.25,ω xyz =0.75~0.85. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1Schematic diagram of a motion sickness evaluation system based on instantaneous motion sickness value;

[0056] Figure 2 Schematic diagram of the steps of a motion sickness evaluation method based on instantaneous motion sickness value. DETAILED DESCRIPTION

[0057] The following is further described in detail through specific implementation methods:

[0058] Example 1 is as shown in the attached Figure 1 As shown,

[0059] A motion sickness evaluation system based on instantaneous motion sickness value, comprising:

[0060] Test dummy: This dummy is used to replace the occupant in the vehicle seat, with several sensors installed on its head and chest. These sensors can be vibration sensors and attitude sensors, respectively, to measure the dummy's real-time acceleration and tilt angle. Alternatively, an IMU (Inertial Measurement Unit) can be used to obtain the following characteristics to meet the requirements: Acceleration Measurement: The IMU has a built-in three-axis accelerometer that directly obtains acceleration components on the x / y / z axes. Tilt Angle Calculation: By fusing accelerometer and gyroscope data (such as the extended Kalman filter), attitude angles (roll, pitch, etc.) can be calculated in real time to meet tilt angle measurement requirements. An IMU (such as the Epson M-G370) directly outputs attitude angles without the need for additional algorithmic processing. For this solution, consider the Epson M-G370, MicroStrain 3DM-GX5-AHRS, Honeywell TARS-IMU, or Epson M-G570PR, depending on actual use.

[0061] Data acquisition module: used to acquire sensor data at every threshold interval (in this solution, the threshold interval between two adjacent data points is 100ms). The sensor data includes at least the test dummy's head acceleration, head tilt angle, chest acceleration, chest tilt angle, and vehicle speed; that is, the acceleration components of the head on the x / y / z axes: The acceleration components of the chest on the x / y / z axis are: Vehicle speed: ν; tilt angle of the head in the x / y / z axis plane The inclination angle of the chest in the x / y / z axis plane Among them, k∈(x,y,z); the vehicle speed can be directly extracted from the vehicle's own speed sensor. The rest of the data can be extracted from the detection data of the IMU attitude sensor. Data processing module: calculate the basic field halo values of the x-axis, y-axis and z-axis of the head and chest respectively: the field halo value of the head or chest on a single axis is equal to the product of the vehicle speed, the acceleration component of the part on the axis, the acceleration of gravity and the cosine value of the inclination angle of the part on the axis; that is (part∈{head,chest})

[0062] The data processing module is further configured to independently divide each axis into a plurality of halo value intervals and assign a corresponding halo coefficient to each interval, wherein the value of the halo value interval is negatively correlated with the halo coefficient. In this solution, four halo intervals are independently set for the x, y, and z axes, and the first to fourth halo intervals for each axis are respectively:

[0063] x-axis: [L x1 ,L x2 ),[L x2 ,L x3 ),[L x3 ,L x4 ),[L x4 ,∞)=[0,24),[24,48),[48,78),[78,∞);

[0064] y-axis: [L y1 ,L y2 ),[L y2 ,L y3 ),[L y3 ,L y4 ),[L y4 ,∞)=[0,140),[140,320),[320,560),[560,∞);

[0065] z-axis: [L z1 ,L z2 ),[L z2 ,L z3 ),[L z3 ,L z4 ),[L z4 ,∞)=[0,250),[250,450),[450,760),[760,∞).

[0066] And assign a halo coefficient to each halo interval of each axis: That is, the first-level halo field interval in each axis is assigned a halo field coefficient of 0.4, the second-level halo field interval in each axis is assigned a halo field coefficient of 0.3, the third-level halo field interval in each axis is assigned a halo field coefficient of 0.2, and the fourth-level halo field interval in each axis is assigned a halo field coefficient of 0.1.

[0067] The data processing module counts the number of data points in each halo value interval on each axis within a preset detection time period (the preset detection time period is 10 minutes), and obtains the score rate on each axis of the head and chest by multiplying the sum of the products of the number of data points in each interval by the corresponding halo coefficient, divided by the sum of the products of the number of data points in all intervals and the first-level halo coefficient;

[0068] Right now Taking the head data in Table 1 as an example, the score rate on the x-axis within the preset detection time period is:

[0069]

[0070] Table 1

[0071] Grading X-axis basic halo threshold X-axis basic halo coefficient X-axis basic halo points X-axis basic field sickness score Level 1 0≤X<24 0.4 5421 2168.4 Level 2 24≤X<48 0.3 563 168.9 Level 3 48≤X<78 0.2 9 1.8 Level 4 78≤X 0.1 7 0.7

[0072] According to the above calculation method and the test data, the calculation data of the x-axis chest, the y-axis head and chest, and the z-axis head and chest are calculated in sequence.

[0073] The score rates on the same axis of the head and chest are weighted and summed to generate the total score rate on a single axis; that is,

[0074] In this plan,

[0075] ω head =0.7,ω chest =0.3

[0076] Then perform weighted summation on the total score rates of the x-axis, y-axis, and z-axis;

[0077]

[0078] Generate a three-axis comprehensive motion sickness score rate, match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the vehicle instantaneous three-axis motion sickness evaluation score. The preset score rate and score matching table is shown in Table 2.

[0079] Table 2

[0080]

[0081] In Table 2, the corresponding scores are matched by the total score rate of each axis, multiplied by their respective weights, and superimposed to obtain the three-axis motion sickness evaluation score of the car, which is output as the motion sickness evaluation of the car.

[0082] This solution performs weighted calculations using head and chest data. Compared to single head data calculations, this solution integrates data from multiple parts, that is, the dual-part data complements each other, avoiding misjudgments caused by single sensor deviations and improving the comprehensiveness and accuracy of the score.

[0083] Example 2

[0084] A motion sickness evaluation method based on instantaneous motion sickness value comprises the following steps:

[0085] S1: Obtain motion data of the test dummy at every threshold time, the motion data including:

[0086] Acceleration components of the head on the x / y / z axis:

[0087] The acceleration components of the chest on the x / y / z axis are:

[0088] Vehicle speed: ν;

[0089] The tilt angle of the head in the x / y / z axis plane

[0090] The inclination angle of the chest in the x / y / z axis plane Where k∈(x,y,z);

[0091] S2: Calculate the field halo values of the head and chest on the x, y, and z axes respectively;

[0092] (part∈{head,chest})

[0093] S3: Set four levels of halo intervals independently for x / y / z axis;

[0094] [L k1 ,L k2 ),[L k2 ,L k3 ),[L k3 ,L k4 ),[L k4 ,∞);

[0095] And assign a halo coefficient to each level interval: satisfy (j=1,2,3,4);

[0096] x-axis: [L x1 ,L x2 ),[L x2 ,L x3 ),[L x3 ,L x4 ),[L x4 ,∞)=[0,30),[30,50),[50,80),[80,∞)

[0097] y-axis: [L y1 ,L y2 ),[L y2 ,L y3 ),[L y3 ,L y4 ),[L y4 ,∞)=[0,180),[180,360),[360,540),[540,∞)

[0098] z-axis: [L z1 ,L z2 ),[L z2 ,L z3 ),[L z3 ,L z4 ),[L z4 ,∞)=[0,300),[300,500),[500,750),[750,∞).

[0099] Field halo coefficient:

[0100] S4: Grading statistics and calculation of score rate:

[0101] Count the number of data points in the four-level intervals of each axis within the preset detection time period:

[0102] Calculate the score rate of each axis of the head and chest:

[0103]

[0104] S5: weighting the score rate of each axis of the head and chest;

[0105]

[0106] S6: Weight the three-dimensional axial scores to generate a three-axis comprehensive motion sickness score;

[0107]

[0108] S61: Calculate the total acceleration of each data point:

[0109] S62: Count the number of a within the preset detection time period total >1.2

[0110] S63: Calculate acceleration frequency

[0111] S64: Match the obtained acceleration frequency with the preset accelerometer score matching table to obtain the vehicle acceleration motion sickness evaluation score M a ;

[0112] S7: Match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the vehicle instantaneous three-axis motion sickness evaluation score M xyz The instantaneous motion sickness evaluation score is obtained by weighted calculation based on the vehicle acceleration motion sickness evaluation score and the vehicle three-axis motion sickness evaluation score. a=0.15~0.25,ω xyz =0.75~0.85, in this scheme ω xyz =0.8,ω a =0.2.

[0113] Table 3 is a combined table of the three-axis comprehensive motion sickness score rate, the preset score rate and score matching table, and the preset acceleration and score matching table; the total score rates of the x, y, and z axes are calculated to match the scores of the corresponding axes, and then the scores are multiplied by the weights of the axes. The scores of the three axes are added together to obtain the three-axis motion sickness evaluation score (basic field sickness score), and then the frequency of acceleration is calculated and matched to obtain the score. The instantaneous field sickness evaluation score is then obtained by weighted calculation of 0.8*three-axis motion sickness evaluation score+0.2*acceleration frequency score.

[0114] Table 3

[0115]

[0116]

[0117] This solution compensates for the smoothing effect of the three-axis weighted scoring on high-frequency signals through weighted calculation of acceleration frequency, calculation of head data only, and weighted calculation of the head and chest, making the comprehensive evaluation more closely aligned with the occupants' sensitive feedback to sudden movements, especially in the high-torque response scenarios of electric vehicles.

[0118] Example 3

[0119] The difference between Example 3 and Example 2 is that: it also includes a laser radar module, which is used to measure the viewing distance (visual distance) of the test dummy in real time; the data processing module is also used to obtain the average viewing distance of all data points in a preset detection time period, and match the obtained average viewing distance with the preset viewing distance and score matching table to obtain a viewing distance evaluation score, and then perform weighted calculation on the vehicle's three-axis motion sickness evaluation score and the viewing distance evaluation score to obtain a viewing distance-three-axis comprehensive evaluation score.

[0120] As shown in Table 4, which shows the grading standards and scoring details of visual distance, the visual distance of all data points (sampling frequency is 10 Hz, that is, data points are taken at intervals of 100 ms) within the preset detection period (within 10 minutes) is averaged. The corresponding score is then found in Table 4 and weighted with the evaluation score of transient field of view sickness: transient field of view sickness score * 0.8 + visual space * 0.2. The resulting transient field of view sickness comprehensive performance score is used as the output vehicle evaluation score.

[0121] Table 4

[0122]

[0123] By adding dynamic weighting of the sight distance and three-axis scores (such as sight distance accounting for 20% of the weight), the scores can be made more consistent with the actual perception of the occupants. For example, the far sight distance score can partially offset the negative impact of severe vertical vibration.

[0124] Example 4

[0125] The difference between the fourth embodiment and the third embodiment is that:

[0126] It also includes a sight distance score correction module, which divides the sight distance from far to near into at least two sight distance ranges, as shown in Table 4. In this scheme, there are four sight distance ranges, namely, the first sight distance, the second sight distance, the third sight distance and the fourth sight distance; the first sight distance and the second sight distance are combined and named as the far sight distance, and the third sight distance and the fourth sight distance are combined and named as the near sight distance;

[0127] The viewing distance score correction module is used to obtain the viewing distance value of each data point. If the viewing distance values of no less than 10 consecutive data points fall within the same viewing distance range, that is, in this scheme, more than 1s (data points are sampled at intervals of 100ms, and 10 are 1s) fall within the range of far viewing distance or near viewing distance, then the corresponding far viewing distance or near viewing distance is used for naming; within a preset calculation period (10s is selected in this scheme, and a longer calculation period can be selected according to actual conditions during the actual test process), if the ratio of the number of far viewing distances to the number of near viewing distances reaches the rated value (2:8 or 8:2 is selected in this scheme, that is, the number of far viewing distances or near viewing distances reaches 20-80%), and the number of far viewing distances and near viewing distances in the calculation period is greater than 3, a correction signal is generated, and the viewing distance score correction module confirms the viewing distance within the preset calculation period (10s) as 0. An example of the arrangement of far and near viewing distances in 10s is as follows:

[0128] Example 1: Near sight distance: Far sight distance = 6:4: Near sight distance 1 (2s), Far sight distance 1 (3s), Near sight distance 2 (3s), Far sight distance 2 (1s), Near sight distance 3 (1s). If the discard condition is met, when calculating the average sight distance, the sight distance of the data points within the calculation cycle is corrected to 0.

[0129] Example 2: Near sight distance: Far sight distance = 2:8 Near sight distance 1 (1s), Far sight distance 1 (8s), Near sight distance 2 (1s). If the discard condition is not met, the sight distances of the data points within the calculation period will be included in the calculation according to the original data when calculating the average sight distance.

[0130] When a vehicle is controlled by automatic driving and traveling at a constant speed on the road (for example, there are three lanes in a row, the test vehicle is traveling at a constant speed in the middle lane, and the test dummy is sitting in the passenger seat), if the middle lane is empty (no other vehicles, and the field of vision is wide), and other vehicles in the adjacent lane (the right lane) are constantly overtaking to the middle lane, and then traveling to the left lane, the field of vision in front of the test vehicle (i.e., visual distance) will switch back and forth between short visual distance and near visual distance in a short period of time. The back-and-forth visual conflict will increase the probability of motion sickness in the passengers. If the average visual distance of the test section is included in the final score weighting according to the previous visual distance, it will not be able to dynamically reflect the actual motion sickness situation during this test process. Because this process involves constant, straight-line driving without turning, acceleration, speed, and bank angle remain constant. This means the only variable at this point is the influence of sight distance. Using the conventional average sight distance for the final weighted total calculation, or weighting each data point's sight distance to its motion sickness value, only numerically demonstrates the impact of sight distance on motion sickness. This approach, however, accounts for the dynamic changes in sight distance and takes into account real-time road conditions. By factoring in frequent switching between near and far sight distances, it also incorporates the metric of frequent overtaking. The resulting score is more accurate and more closely reflects the actual passenger experience. Specifically, when the vehicle is traveling at a constant speed but the visual environment is complex (e.g., frequent overtaking in side lanes), the correction module ensures that the score focuses on the vehicle's own motion characteristics, improving the stability and reliability of the results.

[0131] The comparison of the test results of Examples 1 to 4 with real people is shown in Table 5:

[0132] Table 5

[0133]

[0134] Comparing real-life experiences with the technologies of several examples reveals that, in the basic system of Example 1, only three-axis motion sickness values are used as the evaluation criteria. Example 2 adds acceleration frequency as a criterion to the basic system. Example 3 builds on Example 2 by adding line of sight as a criterion. Example 4 corrects the line of sight in Example 3. As shown in Table 5, the scoring accuracy of Examples 1 through 4 compared to real-life experiences is over 80%. The real-life experience data is derived from real-life measurements, i.e., the motion sickness condition is assessed through real-life perception or vital sign data. Different scores are assigned to vehicles based on different motion sickness conditions, with the more severe the motion sickness, the lower the score. As shown in the above data, by gradually introducing acceleration frequency, line of sight fusion, and line of sight correction modules, Example 4 approaches real-life experience in terms of scoring accuracy, high-frequency vibration sensitivity, and anti-interference capability, providing a highly reliable engineering solution for vehicle motion sickness detection.

[0135] The above are only embodiments of the present invention. The invention is not limited to the fields involved in this implementation case. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field are aware of all common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A motion sickness evaluation system based on instantaneous motion sickness value, characterized in that: include: Test dummy: The test dummy is used to replace the passenger in the car seat, and several sensors are installed on its head and chest; Data acquisition module: used to acquire sensor data at each threshold time interval; the sensor data at least includes the test dummy's head acceleration, head tilt angle, chest acceleration, chest tilt angle and vehicle speed; The data processing module is used to calculate the basic field sickness values of the head and chest along the x-axis, y-axis, and z-axis, respectively. The field sickness value of the head or chest along a single axis is equal to the product of the vehicle speed, the acceleration component of the part along the axis, the acceleration of gravity, and the cosine value of the inclination angle of the part along the axis. The data processing module is also used to independently divide each axis into a number of field sickness value intervals and assign a corresponding field sickness coefficient to each interval, wherein the value of the field sickness value interval is negatively correlated with the field sickness coefficient. The data processing module counts the number of data points in each halo value interval on each axis within a preset detection time period, and obtains the score rate on each axis of the head and chest by multiplying the sum of the products of the number of data points in each interval and the corresponding halo coefficient by the sum of the products of the number of data points in all intervals and the first-level halo coefficient. The score rates on the same axis of the head and chest are weighted and summed to generate the total score rate on a single axis; then the total score rates of the x-axis, y-axis, and z-axis are weighted and summed to generate a three-axis comprehensive motion sickness score rate. The obtained three-axis comprehensive motion sickness score rate is matched with the preset score rate and score matching table to obtain the vehicle's instantaneous three-axis motion sickness evaluation score.

2. The motion sickness assessment system based on instantaneous field sickness value according to claim 1, characterized in that: It also includes a laser radar module, which is used to measure the viewing distance of the test dummy in real time; the data processing module is also used to obtain the average viewing distance of all data points in a preset detection time period, match the obtained average viewing distance with a preset viewing distance and score matching table to obtain a viewing distance evaluation score, and then perform a weighted calculation on the vehicle's three-axis motion sickness evaluation score and the viewing distance evaluation score to obtain a viewing distance-three-axis comprehensive evaluation score.

3. A motion sickness assessment system based on instantaneous field sickness value according to claim 1 or 2, characterized in that: The preset detection time period is 10 minutes, and the interval threshold time between two adjacent data points is 100ms.

4. The motion sickness assessment system based on instantaneous field sickness value according to claim 2, characterized in that: It also includes a sight distance score correction module, which divides the sight distance into at least two sight distance ranges from far to near. The sight distance score correction module is used to obtain the sight distance value of each data point. If the sight distance values of at least 10 consecutive data points fall within the same sight distance range, then this section of consecutive data points is named as far sight distance and near sight distance according to the sight distance range they fall into; within a preset calculation cycle, if the ratio of the number of far sight distances to the number of near sight distances reaches a rated value, and the number of far sight distances and near sight distances within the calculation cycle is greater than 3, a correction signal is generated, and the sight distance score correction module confirms the sight distance within the preset calculation cycle as 0.

5. The motion sickness assessment system based on instantaneous field sickness value according to claim 1, characterized in that: There are four halo value intervals from small to large for each axis, and the corresponding halo coefficients are 0.4, 0.3, 0.2 and 0.

1.

6. A motion sickness evaluation method based on instantaneous motion sickness value, characterized by: The following steps are included: S1: Obtain motion data of the test dummy at every threshold time, the motion data including: Acceleration components of the head on the x / y / z axis: The acceleration components of the chest on the x / y / z axis are: Vehicle speed: v; The tilt angle of the head in the x / y / z axis plane The inclination angle of the chest in the x / y / z axis plane Where k∈(x,y,z); S2: Calculate the field halo values of the head and chest on the x, y, and z axes respectively; S3: Set four levels of halo intervals independently for x / y / z axis; [L k1 ,L k2 ),[L k2 ,L k3 ),[L k3 ,L k4 ),[L k4 ,∞); And assign a halo coefficient to each level interval: satisfy S4: Grading statistics and calculation of score rate: Count the number of data points in the four-level intervals of each axis within the preset detection time period: Calculate the score rate of each axis of the head and chest: S5: weighting the score rate of each axis of the head and chest; S6: Weight the three-dimensional axial scores to generate a three-axis comprehensive motion sickness score; S7: Match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the vehicle instantaneous three-axis motion sickness evaluation score M xyz .

7. The motion sickness evaluation method based on instantaneous field sickness value according to claim 6, characterized in that: Also includes: S61: Calculate the total acceleration of each data point: S62: Count the number of a within the preset detection time period total >1.2 number C atotal＞1.2 ; S63: Calculate acceleration frequency S64: Match the obtained acceleration frequency with the preset accelerometer score matching table to obtain the vehicle acceleration motion sickness evaluation score M a ; The step S7 further includes: performing weighted calculation based on the vehicle acceleration motion sickness evaluation score and the vehicle three-axis motion sickness evaluation score to obtain an instantaneous motion sickness evaluation score M total =ω xyz M xyz +w a M a ,ω xyz +ω a =1.

8. The motion sickness evaluation method based on instantaneous field sickness value according to claim 6, characterized in that: The four-level halo interval of each axis in step S3 is: x-axis: [L x1 ,L x2 ),[L x2 ,L x3 ),[L x3 ,L x4 ),[L x4 ,∞)=[0,24),[24,48),[48,78),[78,∞); y-axis: [L y1 ,L y2 ),[L y2 ,L y3 ),[L y3 ,L y4 ),[L y4 ,∞)=[0,140),[140,320),[320,560),[560,∞); z-axis: [L z1 ,L z2 ),[L z2 ,L z3 ),[L z3 ,L z4 ),[L z4 ,∞)=[0,250),[250,450),[450,760),[760,∞).

9. The motion sickness evaluation method based on instantaneous field sickness value according to claim 7, characterized in that: The ω a =0.15~0.25,ω xyz =0.75~0.85.

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