Method for testing and evaluating anti-sickness performance of electric vehicle based on passenger sickness degree representation

Abstract

The present application relates to the technical field of intelligent automobile testing, and particularly relates to a method for testing and evaluating anti-sickness performance of an electric vehicle based on passenger sickness degree representation, which comprises: performing anti-sickness performance pre-testing on a fuel vehicle to obtain a first sickness test result of a first test sample; the first test sample comprises a plurality of test subjects; selecting a second test sample from the first test sample according to the first sickness test result; performing anti-sickness performance formal testing on the electric vehicle based on the second test sample to obtain a second sickness test result of the second test sample; and obtaining the anti-sickness performance of the electric vehicle according to the first sickness test result and the second sickness test result. The anti-sickness performance of the electric vehicle is obtained by using the first sickness test result of the fuel vehicle and the second sickness test result of the electric vehicle, so as to quantify the anti-sickness degree of the electric vehicle, form a corresponding relationship between the electric vehicle and the sickness degree, and provide a reference performance parameter of the anti-sickness degree of the electric vehicle for the sick passengers.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle testing technology, and specifically to a method for testing and evaluating the anti-motion sickness performance of electric vehicles based on the characterization of occupant motion sickness. Background Technology

[0002] One-third of the world's population suffers from motion sickness, and in one of the countries with the highest incidence of motion sickness, approximately 80% of the population has experienced varying degrees of motion sickness. Due to the high speed of movement of a vehicle, the discrepancy between the internal and external sensations experienced by a person sitting inside can easily lead to dizziness because the central nervous system cannot adjust in time. Therefore, some users pay close attention to whether a vehicle is prone to causing motion sickness when choosing a vehicle.

[0003] With the application of autonomous and electric vehicles, electric vehicles are gradually entering the market. Electric vehicles offer users greater convenience through their powerful output and energy recovery systems. The strong power output of electric vehicles results in a much faster torque response than that of a gasoline engine, increasing several times faster. While a gasoline engine requires approximately 0.4-0.6 seconds to increase torque, an electric vehicle can reach its peak torque within 0.2 seconds. Furthermore, the energy recovery system can recover the kinetic energy output by the vehicle during braking and deceleration, thus preventing ineffective energy loss.

[0004] While the powerful output of electric vehicles allows for rapid acceleration, the intense acceleration can cause a sudden spike in dizziness during frequent starts. Furthermore, the strong drag during braking from the regenerative braking system can also contribute to motion sickness. Therefore, the anti-motion sickness performance of electric vehicles is a crucial factor that cannot be overlooked.

[0005] Although electric vehicles are now in mass production, there is no direct correlation between electric vehicles and the degree of motion sickness they cause, leaving people prone to motion sickness without readily available performance parameters to consider when choosing to buy an electric vehicle. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a test and evaluation method for the anti-motion sickness performance of electric vehicles based on the characterization of the degree of motion sickness among occupants. This method aims to solve the technical problem that there is no corresponding relationship between electric vehicles and the degree of motion sickness in existing technologies, resulting in a lack of performance parameters for people with motion sickness to refer to when choosing and purchasing electric vehicles.

[0007] The technical solution adopted in this invention is a method for testing and evaluating the anti-motion sickness performance of electric vehicles based on the characterization of the degree of motion sickness among occupants.

[0008] In the first feasible approach, a method for testing and evaluating the anti-motion sickness performance of electric vehicles based on the characterization of occupant motion sickness levels includes:

[0009] A preliminary test of the anti-motion sickness performance of a gasoline-powered vehicle was conducted to obtain the first motion sickness test results of the first test sample; the first test sample included multiple test subjects.

[0010] The second test sample was selected from the first test sample based on the results of the first motion sickness test;

[0011] Based on the second test sample, a formal anti-motion sickness performance test was conducted on the electric vehicle to obtain the second motion sickness test results of the second test sample.

[0012] The anti-motion sickness performance of electric vehicles was obtained based on the results of the first and second motion sickness tests.

[0013] In the second feasible method, which combines the first feasible method, the anti-nausea performance test of fuel vehicles and electric vehicles is carried out using the controlled variable method. The variable is the test vehicle, and the invariants include the test time, the location of the test sample, the air conditioning temperature in the vehicle, the seat tilt angle, the odor in the vehicle, the test sample, the test route, and the driver.

[0014] In conjunction with the first feasible method, in the third feasible method, a pre-test of the anti-motion sickness performance of a fuel-powered vehicle is conducted to obtain the first motion sickness test results of the first test sample, including:

[0015] Multiple subjects were selected as the first test sample;

[0016] After determining the preset positions of each subject in the first test sample within the vehicle, the first resting data of the first test sample with the vehicle stationary was collected.

[0017] Multiple dynamic data points of the first test sample are collected in real time during the vehicle's driving process;

[0018] Simultaneously, the subjective feelings of each participant in the first test sample during the pre-test process were recorded;

[0019] The first resting data and each first dynamic data point were labeled according to the subjective feelings of each participant; the labels included the normal state label corresponding to the first resting data, the motion sickness label corresponding to the first dynamic data, and the non-motion sickness label corresponding to the first dynamic data.

[0020] In conjunction with the third feasible method, the fourth feasible method involves selecting a second test sample from the first test sample based on the results of the first motion sickness test, including:

[0021] The motion sickness level of the first test sample is obtained based on the first resting data and the first dynamic data; the motion sickness levels include level 0, level 1, level 2 and level 3;

[0022] Subjects were selected from the first test sample as the second test sample based on the first and second screening principles.

[0023] The first screening principle is: subjects with motion sickness level 3 are not included; the second screening principle is: the ratio of subjects with motion sickness levels 0, 1, and 2 is 1:1:1.

[0024] In conjunction with the fourth feasible method, in the fifth feasible method, the motion sickness level of the first test sample is obtained based on the first resting data and the first dynamic data, including:

[0025] The labeled first resting data and labeled first dynamic data were input into the car motion sickness classification and evaluation model to obtain the motion sickness level. The first resting data included the subject's blink frequency, pupil diameter, eyelid opening and closing degree and skin conductance information when the vehicle was stationary. The first dynamic data included the subject's blink frequency, pupil diameter, eyelid opening and closing degree and skin conductance information when the vehicle was moving.

[0026] In conjunction with the first feasible method, in the sixth feasible method, the car occupant motion sickness classification and evaluation model obtains the motion sickness level in the following way: Time synchronization processing is performed on the first labeled resting data and the first labeled dynamic data to obtain occupant normal state data and occupant motion sickness state data; a health status reference range is obtained based on the occupant normal state data; the occupant motion sickness state data is compared and analyzed with the health status reference range to obtain the parameter deviation; and the motion sickness level is obtained based on the parameter deviation.

[0027] In the seventh possible implementation method, combining the first feasible method, the anti-motion sickness performance of the electric vehicle is obtained based on the results of the first and second motion sickness tests, including:

[0028] Based on the results of the second motion sickness test, obtain the second motion sickness level of each subject in the second test sample during the formal test;

[0029] Determine the first level of motion sickness for each participant in the second test sample during the pre-test;

[0030] Subjects were selected from the second test sample based on the first and second motion sickness levels as the calculation sample;

[0031] The deviation of each motion sickness level is obtained based on the first and second motion sickness levels of each subject in the calculation sample.

[0032] The anti-motion sickness performance score of electric vehicles is obtained based on the deviation of each motion sickness level.

[0033] In conjunction with the seventh feasible method, the eighth feasible method involves selecting participants from the second test sample as the calculation sample based on the first and second motion sickness levels, including:

[0034] Acquire subjects whose first motion sickness level was greater than the second motion sickness level;

[0035] All subjects to be processed in the second test sample were removed to obtain the calculation sample.

[0036] In conjunction with the seventh feasible method, the ninth feasible method obtains the deviation of each motion sickness level based on the first and second motion sickness levels of each subject in the calculation sample, including:

[0037]

[0038] In the above formula, Δ k n is the deviation of the k-level motion sickness rating. k L represents the number of subjects with a motion sickness level of k. ki0 L represents the first motion sickness level of the i-th subject in the k-level motion sickness scale. ki1 The second motion sickness level is the i-th subject's motion sickness level in the k-level motion sickness rating system.

[0039] Combining the seventh feasible method, the tenth feasible method obtains the anti-motion sickness performance score of electric vehicles based on the deviation of each motion sickness level, including:

[0040] Δ=ρ0Δ0+ρ1Δ1+ρ2Δ2

[0041] In the above formula, Δ is the anti-motion sickness performance score of electric vehicle, ρ0 is the weight of level 0 motion sickness, Δ0 is the deviation of level 0 motion sickness, ρ1 is the weight of level 1 motion sickness, Δ1 is the deviation of level 1 motion sickness, ρ2 is the weight of level 2 motion sickness, and Δ2 is the deviation of level 2 motion sickness.

[0042] As can be seen from the above technical solution, the beneficial technical effects of the present invention are as follows:

[0043] 1. By conducting preliminary tests on gasoline-powered vehicles, suitable second test samples are selected for electric vehicles. The electric vehicles are then formally tested using these second test samples. The anti-motion sickness performance of the electric vehicles is obtained by comparing the results of the first motion sickness test on the gasoline-powered vehicles with the results of the second motion sickness test on the electric vehicles. This quantifies the degree of motion sickness resistance of the electric vehicles and establishes a correlation between electric vehicles and the degree of motion sickness. This provides a reference performance parameter for motion sickness sufferers when choosing to purchase electric vehicles.

[0044] 2. By using pre-test results based on fuel-powered vehicles, the test sample covers people with varying degrees of motion sickness, thus ensuring the high reliability of the final test evaluation results.

[0045] 3. This solution provides a testing method for the anti-motion sickness performance of electric vehicles. By reasonably quantifying the anti-motion sickness performance of electric vehicles through parameters, it achieves the characterization and testing of the anti-motion sickness performance of electric vehicles, which helps companies improve the comfort of electric vehicles and provides a reference for people who suffer from motion sickness when purchasing electric vehicles. Attached Figure Description

[0046] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0047] Figure 1 This is a schematic diagram of a method for testing and evaluating the anti-motion sickness performance of electric vehicles based on the characterization of passenger motion sickness severity, as provided in this embodiment.

[0048] Figure 2 This embodiment provides a data acquisition process during a test. Detailed Implementation

[0049] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore merely examples, and should not be construed as limiting the scope of protection of the present invention.

[0050] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for implementation of the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. Unless otherwise stated, the term "a plurality of" means two or more. In this disclosure, the character " / " indicates an "or" relationship between the preceding and following objects. For example, A / B means: A or B. The term "and / or" describes an association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B. The term "corresponding" can refer to an association or binding relationship; A corresponding to B means that there is an association or binding relationship between A and B.

[0051] Combination Figure 1 As shown, this embodiment provides a method for testing and evaluating the anti-motion sickness performance of electric vehicles based on the characterization of occupant motion sickness levels, including:

[0052] Step S01: Conduct a pre-test on the anti-motion sickness performance of a fuel-powered vehicle to obtain the first motion sickness test results of the first test sample; the first test sample includes multiple test subjects.

[0053] Step S02: Select the second test sample from the first test sample based on the results of the first motion sickness test;

[0054] Step S03: Conduct a formal anti-motion sickness performance test on the electric vehicle based on the second test sample, and obtain the second motion sickness test results of the second test sample;

[0055] Step S04: Obtain the anti-motion sickness performance of the electric vehicle based on the results of the first motion sickness test and the second motion sickness test.

[0056] Optionally, before conducting a pre-test on the anti-nausea performance of a gasoline-powered vehicle, the test route may also be determined.

[0057] In some embodiments, the test route needs to cover scenarios that are prone to causing motion sickness in actual vehicle operation. The test routes in this solution include urban areas, mountainous areas and highways, covering smooth roads, congested roads, continuous bumpy roads, as well as typical road scenarios such as one-way curves, S-curves, uphill and downhill sections and acceleration and deceleration sections, to ensure that the vehicle can fully experience acceleration, deceleration, turning, braking and bumpy conditions when passing through the test route, so that the test results can fully reflect the vehicle's anti-motion sickness performance.

[0058] Optionally, the anti-nausea performance of fuel vehicles and electric vehicles can be tested using the controlled variable method. The variable is the test vehicle, and the invariants include test time, location of the test sample, air conditioning temperature inside the vehicle, seat tilt angle, odor inside the vehicle, test sample, test route, and driver.

[0059] In some embodiments, the windows of both the gasoline-powered vehicle and the electric vehicle are fully opened before the test to dissipate odors and ensure that there are no other abnormal smells inside the vehicle.

[0060] Optionally, a pre-test of anti-motion sickness performance is conducted on a fuel-powered vehicle to obtain the first motion sickness test results of the first test sample, including: selecting multiple test subjects as the first test sample; after determining the preset positions of each test subject in the first test sample within the vehicle, collecting the first resting data of the first test sample in a stationary state; collecting multiple first dynamic data of the first test sample in real time during vehicle movement; simultaneously recording the subjective feelings of each test subject in the first test sample during the pre-test; and labeling the first resting data and each first dynamic data according to the subjective feelings of each test subject; the labels include a normal state label corresponding to the first resting data, a motion sickness label corresponding to the first dynamic data, and a non-motion sickness label corresponding to the first dynamic data.

[0061] In some embodiments, during the pre-testing of gasoline-powered vehicles and the formal testing of electric vehicles, all testing conditions remain consistent except for the test vehicles themselves. During both pre-testing and formal testing, test subjects, guided by staff, don test equipment, undergo calibration, and then enter the test vehicle, sit in designated positions, and fasten their seatbelts. The vehicle is initially stationary for a certain period, during which the test equipment collects the subject's eye movement and skin conductance information. Then, the vehicle travels along a predetermined test route. During this travel, the test equipment worn by the subject synchronously records eye movement and skin conductance information in real time. Eye movement information includes, but is not limited to, blink frequency, pupil diameter, and eyelid opening and closing. During and after the driving experience, the subject cooperates with staff to provide a subjective evaluation of the driving comfort, obtaining subjective experience data for each subject.

[0062] Combination Figure 2 As shown, the testing equipment includes an eye-tracking testing device and a contact device. The eye-tracking testing device is used to detect the subject's eye movement information, and the contact device is used to detect the subject's skin conductance information. Subjects recorded their subjective feelings during and after the test by completing a pre-set questionnaire. Subjective feelings included whether the vehicle was moving and whether they felt motion sickness.

[0063] Optionally, the first resting data and each first dynamic data point are labeled based on the subjective feelings of each participant. This includes: participants recording their subjective feelings at multiple moments during the test by completing a pre-set questionnaire, including whether the vehicle was moving and whether they experienced motion sickness. When the subjective feeling was that the vehicle was not moving, the data collected at the corresponding time is labeled as "normal state". When the subjective feeling was that the vehicle was moving and there was no motion sickness, the data collected at the corresponding time is labeled as "no motion sickness". When the subjective feeling was that the vehicle was moving and there was motion sickness, the data collected at the corresponding time is labeled as "motion sickness".

[0064] Optionally, since motion sickness is a cumulative process, the data from the initial "not moving" label represents the normal state data, while the data from the "not moving" label after a period of driving cannot represent the normal state data.

[0065] Optionally, the first resting data is the subject's identity information, eye movement information, and skin conductance information collected when the fuel-powered vehicle is not in motion; the first dynamic data is the subject's identity information, eye movement information, and skin conductance information collected when the fuel-powered vehicle is in motion.

[0066] Optionally, a second test sample is selected from the first test sample based on the results of the first motion sickness test, including: obtaining the motion sickness level of the first test sample based on the first resting data and the first dynamic data; the motion sickness level includes level 0, level 1, level 2 and level 3; selecting subjects as the second test sample from the first test sample according to the first selection principle and the second selection principle; the first selection principle is: subjects with a motion sickness level of level 3 are not included; the second selection principle is: the ratio of subjects with motion sickness levels of level 0, level 1 and level 2 is 1:1:1.

[0067] In some embodiments, motion sickness levels 0, 1, 2, and 3 represent no motion sickness, mild motion sickness, moderate motion sickness, and severe motion sickness, respectively.

[0068] In some embodiments, the second test sample is screened according to the first screening principle and the second screening principle. Subjects with motion sickness level 3 who are unsuitable for testing are removed, and the same number of subjects with the same level of motion sickness are selected from the remaining subjects. Finally, a second test sample with a suitable testing ratio and suitable test subjects is obtained, which helps to obtain more effective test data and thus obtain more accurate test results.

[0069] Optionally, the motion sickness level of the first test sample is obtained based on the first resting data and the first dynamic data, including: inputting the labeled first resting data and the labeled first dynamic data into a car motion sickness classification and evaluation model to obtain the motion sickness level; the first resting data includes the blinking frequency, pupil diameter, eyelid opening and closing degree and skin conductance information of the test subject when the vehicle is stationary; the first dynamic data includes the blinking frequency, pupil diameter, eyelid opening and closing degree and skin conductance information of the test subject when the vehicle is moving.

[0070] The car occupant motion sickness classification and evaluation model obtains the motion sickness level as follows: Time synchronization processing is performed on the first labeled resting data and the first labeled dynamic data to obtain the occupant's normal state data and motion sickness state data; a health status reference range is obtained based on the occupant's normal state data; the occupant's motion sickness state data is compared and analyzed with the health status reference range to obtain the parameter deviation; and the motion sickness level is obtained based on the parameter deviation.

[0071] Optionally, the car occupant motion sickness classification assessment model also includes: validating the validity and reliability of the subjective scale data before performing time synchronization processing on the input data, and filtering out unusable data.

[0072] Optionally, the validity and reliability of subjective evaluations in the subjective scale data can be verified using multidimensional objective test data. If the subjective and objective evaluation data are strongly correlated, the subjective evaluation is reliable; if the subjective and objective evaluation data are weakly correlated, the subjective evaluation is relatively reliable but the results should be discounted; if the subjective and objective evaluation data are not correlated, the subjective evaluation is unreliable and the subjective evaluation data cannot be used.

[0073] In some embodiments, a unified host first provides the same reference time to each data acquisition device, synchronizing the data collected by each device to a unified timestamp. Then, the minimum sampling frequency of each data acquisition device is determined, and this minimum sampling frequency is used as the reference sampling frequency. Data with higher sampling frequencies is matched to this reference sampling frequency, achieving soft synchronization of the physiological signal data collected in the first resting data and the first dynamic data. Since each data acquisition instrument is independently packaged and has its own time reference, and its sampling frequency is different, it is difficult to achieve time synchronization through hard synchronization. This solution achieves soft synchronization by unifying the time and sampling frequency of each data acquisition device, ensuring the time synchronization of the physiological signal data collected in the first resting data and the first dynamic data.

[0074] Optionally, a health status reference range can be obtained based on the occupant's normal status data, including: obtaining the maximum, minimum, mean, median, and standard deviation of the occupant's normal status data; and obtaining the health status reference range based on the maximum, minimum, mean, median, and standard deviation.

[0075] In some embodiments, the range of the maximum, minimum, mean, median, and standard deviation is the reference range for health status.

[0076] In some embodiments, if the skin conductance information A is within the health status reference range Eα corresponding to the skin conductance information, then the first parameter deviation N1 corresponding to the skin conductance information A is 1; otherwise, N1 is 0. The formula is: N1 = A ∈ Eα. The second parameter deviation D1 corresponding to the skin conductance information A is calculated using the formula D1 = |A - Eα| / |Eα|, where |Eα| is the health status reference width value corresponding to the skin conductance information.

[0077] In some embodiments, if the pupil diameter B is within the health status reference range Eβ corresponding to the pupil diameter, then the first parameter deviation N2 corresponding to the pupil diameter B is 1; otherwise, N2 is 0. The formula is: N2 = B ∈ Eβ. The second parameter deviation D2 corresponding to the pupil diameter B is calculated using the formula D2 = |B - Eβ| / |Eβ|, where |Eβ| is the health status reference width value corresponding to the pupil diameter.

[0078] In some embodiments, if the blink frequency Γ falls within the health status reference range Eγ corresponding to the blink frequency, then the first parameter deviation N3 corresponding to the blink frequency Γ is 1; otherwise, N3 is 0. The formula is: N3 = Γ ∈ Eγ. The second parameter deviation D3 corresponding to the blink frequency Γ is calculated using the formula D3 = |Γ - Eγ| / |Eγ|, where |Eγ| is the health status reference width value corresponding to the blink frequency.

[0079] In some embodiments, if the eyelid opening degree Δ is within the health status reference range Eδ corresponding to the eyelid opening degree, then the first parameter deviation N4 corresponding to the eyelid opening degree Δ is 1; otherwise, N4 is 0. The formula is: N4 = Δ ∈ Eδ. The second parameter deviation D4 corresponding to the eyelid opening degree Δ is calculated using the formula D4 = |Δ - Eδ| / |Eδ|, where |Eδ| is the health status reference width value corresponding to the eyelid opening degree.

[0080] Optionally, a weighted calculation is performed on the parameter deviations to obtain a quantitative value for the degree of motion sickness, including: ranking the physiological signals based on the severity of motion sickness symptoms, with the ranking being skin conductance signal, pupil diameter, blink frequency, and eyelid opening and closing degree; assigning weights to each physiological signal data according to a decreasing principle based on the ranking; multiplying the deviation of the first parameter corresponding to each physiological signal data by the corresponding weight to obtain a first quantitative value for the degree of motion sickness; and multiplying the deviation of the second parameter corresponding to each physiological signal data by the corresponding weight to obtain a second quantitative value for the degree of motion sickness.

[0081] In some embodiments, weights are assigned to each physiological signal acquisition data according to a decreasing principle. The weights of skin conductance signal, pupil diameter, blink frequency and eyelid opening and closing degree are K1, K2, K3 and K4, respectively, where K1 > K2 > K3 > K4.

[0082] Optionally, the first motion sickness severity quantification value N and the second motion sickness severity quantification value D are obtained using the following formula:

[0083] N = K1N1 + K2N2 + K3N3 + K4N4;

[0084] D = K1D1 + K2D2 + K3D3 + K4D4.

[0085] Optionally, the motion sickness level is obtained based on the motion sickness severity quantification value, including: matching the motion sickness severity quantification value with the evaluation criteria corresponding to each motion sickness level to obtain the motion sickness level; the evaluation criteria corresponding to each motion sickness level include: the evaluation criteria for level 0 motion sickness level is: the first motion sickness severity quantification value is 0 and the second motion sickness severity quantification value is 0; the evaluation criteria for level 1 motion sickness level is: the first motion sickness severity quantification value is less than the first threshold and the second motion sickness severity quantification value is less than the first threshold; the evaluation criteria for level 2 motion sickness level is: the first motion sickness severity quantification value is greater than or equal to the first threshold, or the second motion sickness severity quantification value is greater than or equal to the first threshold; the evaluation criteria for level 3 motion sickness level is: the first motion sickness severity quantification value is greater than or equal to the first threshold, and the second motion sickness severity quantification value is greater than or equal to the second threshold.

[0086] Optionally, the evaluation criteria for the four levels of motion sickness—Level 0, Level 1, Level 2, and Level 3—are expressed by the following formula:

[0087] Level 0: N=0 & D=0;

[0088] Level 1: N < 0.5 & D < 0.5;

[0089] Level 2: N≥0.5|D≥0.5;

[0090] Level 3: N≥0.5 & D≥2.

[0091] Optionally, a formal motion sickness test is conducted on the electric vehicle based on the second test sample to obtain the second motion sickness test results of the second test sample, including: using the variable control method, keeping the test time, the position of the test sample, the air conditioning temperature in the vehicle, the seat tilt angle, the odor in the vehicle, the test sample, the test route and the driver unchanged, and using the second test sample to conduct a formal motion sickness test on the electric vehicle to obtain labeled second resting data and labeled second dynamic data.

[0092] Optionally, the second motion sickness test results include labeled second resting data and labeled second dynamic data. The second resting data consists of eye movement and skin conductance information collected from the subjects when the electric vehicle is not moving; the second dynamic data consists of eye movement and skin conductance information collected from the subjects when the electric vehicle is moving.

[0093] Optionally, the anti-motion sickness performance of the electric vehicle is obtained based on the results of the first and second motion sickness tests, including: obtaining the second motion sickness level of each subject in the second test sample during the formal test based on the results of the second motion sickness test; determining the first motion sickness level of each subject in the second test sample during the pre-test; selecting subjects from the second test sample as a calculation sample based on the first and second motion sickness levels; obtaining the deviation of each motion sickness level based on the first and second motion sickness levels of each subject in the calculation sample; and obtaining the anti-motion sickness performance score of the electric vehicle based on the deviation of each motion sickness level.

[0094] Optionally, the labeled second resting data and the labeled second dynamic data are input into the car motion sickness rating model to obtain the second motion sickness level of each subject in the second test sample.

[0095] Optionally, subjects are selected from the second test sample as the calculation sample based on the first motion sickness level and the second motion sickness level, including: obtaining subjects whose first motion sickness level is greater than the second motion sickness level; and removing all subjects in the second test sample to obtain the calculation sample.

[0096] In some embodiments, since electric vehicles are more likely to cause motion sickness in passengers than fuel vehicles, the first motion sickness level in the pre-test is identified as abnormal test data if it is higher than the second motion sickness level in the formal test. Abnormal test data is then removed to obtain a more accurate calculation sample.

[0097] Optionally, the deviation of each motion sickness level is obtained based on the first and second motion sickness levels of each subject in the calculation sample, including:

[0098]

[0099] In the above formula, Δ k n is the deviation of the k-level motion sickness rating. k L represents the number of subjects with a motion sickness level of k. ki0 L represents the first motion sickness level of the i-th subject in the k-level motion sickness scale. ki1 The second motion sickness level is the i-th subject's motion sickness level in the k-level motion sickness rating system.

[0100] Optionally, the deviation from a level 0 motion sickness rating is calculated as follows:

[0101]

[0102] In the above formula, Δ0 is the deviation of the motion sickness level 0, n0 is the number of subjects with the motion sickness level 0, and L 0i0 L represents the first motion sickness level of the i-th subject in the 0-level motion sickness rating system. 0i1The second motion sickness level is the i-th subject's motion sickness level in the 0-level motion sickness level.

[0103] Optionally, the deviation of a Level 1 motion sickness rating is calculated as follows:

[0104]

[0105] In the above formula, Δ1 is the deviation of the level 1 motion sickness rating, n1 is the number of subjects with the level 1 motion sickness rating, and L 1i0 L represents the first motion sickness level of the i-th subject in a Level 1 motion sickness rating system. 1i1 The second motion sickness level is the i-th subject's motion sickness level within the first-level motion sickness rating.

[0106] Optionally, the deviation from a level 2 motion sickness rating is calculated as follows:

[0107]

[0108] In the above formula, Δ2 is the deviation of the level 2 motion sickness rating, n2 is the number of subjects with the level 2 motion sickness rating, and L 2i0 L represents the first motion sickness level of the i-th subject in a 2-level motion sickness rating system. 2i1 The second motion sickness level is the i-th subject's motion sickness level in the 2-level motion sickness rating system.

[0109] Optionally, an anti-motion sickness performance score for the electric vehicle can be obtained based on the deviation from each motion sickness level, including:

[0110] Δ=ρ0Δ0+ρ1Δ1+ρ2Δ2

[0111] In the above formula, Δ is the anti-motion sickness performance score of electric vehicle, ρ0 is the weight of level 0 motion sickness, Δ0 is the deviation of level 0 motion sickness, ρ1 is the weight of level 1 motion sickness, Δ1 is the deviation of level 1 motion sickness, ρ2 is the weight of level 2 motion sickness, and Δ2 is the deviation of level 2 motion sickness.

[0112] Optionally, the weights of motion sickness levels 0, 1, and 2 are determined based on the proportion of people who do not experience motion sickness, those with mild motion sickness, and those with moderate motion sickness in the statistical data.

[0113] In some embodiments, this scheme uses the motion sickness level of a gasoline-powered vehicle as the base motion sickness level. The deviation amount is used to measure the degree of positive deviation of the electric vehicle's motion sickness level from the base level. A weighted calculation of the deviation amount yields an electric vehicle anti-motion sickness performance score. This score characterizes the anti-motion sickness performance of the electric vehicle; a smaller score indicates better performance, while a larger score indicates poorer performance. This establishes a quantitative correlation between electric vehicles and the degree of motion sickness, providing a reference performance parameter for individuals experiencing motion sickness when choosing an electric vehicle to characterize its anti-motion sickness level.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for testing and evaluating the anti-motion sickness performance of electric vehicles based on the characterization of occupant motion sickness severity, characterized in that, include: Preliminary motion sickness test results were obtained for the first test sample of a gasoline-powered vehicle to assess its anti-motion sickness performance. The first test sample includes multiple subjects; A second test sample is selected from the first test sample based on the results of the first motion sickness test; Based on the second test sample, a formal anti-motion sickness performance test was conducted on the electric vehicle to obtain the second motion sickness test results of the second test sample. The anti-motion sickness performance of the electric vehicle is obtained based on the results of the first motion sickness test and the results of the second motion sickness test; Preliminary motion sickness tests were conducted on gasoline-powered vehicles to obtain the initial motion sickness test results for the first test sample, including: Multiple subjects were selected as the first test sample; After determining the preset positions of each subject in the first test sample within the vehicle, the first resting data of the first test sample with the vehicle stationary is collected. Multiple dynamic data points of the first test sample are collected in real time during the vehicle's driving process; Simultaneously, the subjective feelings of each participant in the first test sample during the pre-test process were recorded; The first resting data and each of the first dynamic data were labeled according to the subjective feelings of each subject; the labels included a normal state label corresponding to the first resting data, a motion sickness label corresponding to the first dynamic data, and a non-motion sickness label corresponding to the first dynamic data. Based on the results of the first motion sickness test, a second test sample is selected from the first test sample, including: The motion sickness level of the first test sample is obtained based on the first resting data and the first dynamic data; the motion sickness level includes level 0, level 1, level 2 and level 3; Subjects were selected from the first test sample as the second test sample based on the first and second screening principles. The first screening principle is: subjects with motion sickness level 3 are not included; the second screening principle is: the ratio of subjects with motion sickness levels 0, 1, and 2 is 1:1:

1. The anti-motion sickness performance of electric vehicles is obtained based on the results of the first and second motion sickness tests, including: Based on the results of the second motion sickness test, obtain the second motion sickness level of each subject in the second test sample during the formal test; Determine the first level of motion sickness for each subject in the second test sample during the pre-test; Subjects were selected from the second test sample as a calculation sample based on the first motion sickness level and the second motion sickness level; The deviation of each motion sickness level is obtained based on the first and second motion sickness levels of each subject in the calculation sample; The anti-motion sickness performance score of electric vehicles is obtained based on the deviation of each motion sickness level; Based on the first motion sickness level and the second motion sickness level, subjects are selected from the second test sample as the calculation sample, including: Acquire subjects whose first motion sickness level was greater than the second motion sickness level; All subjects to be processed in the second test sample were removed to obtain the calculation sample; The deviation of each motion sickness level is obtained based on the first and second motion sickness levels of each subject in the calculation sample, including: In the above formula, This represents the deviation from the k-level motion sickness rating. The number of subjects with a k-level of motion sickness. Let k be the first motion sickness level of the i-th subject in the k-level motion sickness rating system. The second motion sickness level is the i-th subject's motion sickness level in the k-level motion sickness rating system.

2. The method according to claim 1, characterized in that, The anti-nausea performance of fuel vehicles and electric vehicles was tested using the controlled variable method. The variable was the test vehicle, and the invariants included test time, location of the test sample, air conditioning temperature inside the vehicle, seat tilt angle, odor inside the vehicle, test sample, test route, and driver.

3. The method according to claim 1, characterized in that, The motion sickness level of the first test sample was obtained based on the first resting data and the first dynamic data, including: The labeled first resting data and labeled first dynamic data were input into the car motion sickness classification and evaluation model to obtain the motion sickness level. The first resting data included the subject's blink frequency, pupil diameter, eyelid opening and closing degree and skin conductance information when the vehicle was stationary. The first dynamic data included the subject's blink frequency, pupil diameter, eyelid opening and closing degree and skin conductance information when the vehicle was moving.

4. The method according to claim 1, characterized in that, The motion sickness rating model for car occupants obtains the motion sickness level as follows: Time synchronization processing is performed on the first resting data with labels and the first dynamic data with labels to obtain the passenger's normal state data and passenger's motion sickness state data; A health status reference range is obtained based on the occupant's normal status data; The passenger motion sickness data is compared and analyzed with the health status reference range to obtain the parameter deviation. The motion sickness level is determined based on the deviation of the parameters.

5. The method according to claim 1, characterized in that, The anti-motion sickness performance score of electric vehicles is obtained based on the deviation from each motion sickness level, including: In the above formula, Rating the anti-sickness performance of electric vehicles The weighting for a level 0 motion sickness rating. This is the deviation from a level 0 motion sickness rating. The weighting for a Level 1 motion sickness rating is as follows: This is the deviation from the Level 1 motion sickness rating. The weighting for a level 2 motion sickness rating is as follows: This represents the deviation from a level 2 motion sickness rating.

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