Intelligent Electric Vehicle Anti-Motion Sickness Cruise Planning Method Based on Comprehensive Subjective and Objective Indicators

Abstract

The present invention discloses an intelligent electric vehicle anti-motion sickness cruise planning method based on subjective and objective comprehensive indicators, comprising the following steps: collecting vehicle motion trajectory data and subjective and objective data of human physiological signals; calculating the motion sickness index under frequency-domain weighting and the evaluation index in the time domain for the vehicle motion trajectory data through Parseval's theorem and a band-pass filter, and using a state-space equation to fuse the motion sickness index and the evaluation index to form a time-frequency domain objective motion sickness index under vehicle variable-speed motion, and obtaining a subjective and objective comprehensive motion sickness index by combining the subjective and objective data of human physiological signals; using the obtained subjective and objective comprehensive motion sickness index and an adaptive weighting factor set to construct a cruise planning model, and jointly solving the optimal anti-motion sickness cruise parameters of the model by the dynamic programming method, so as to minimize the subjective and objective comprehensive motion sickness index and the travel time during vehicle driving. The present invention aims to improve the comfort of passengers and drivers under the cruise condition of intelligent electric vehicles.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent vehicle cruise, and particularly relates to an anti-motion sickness cruise planning method for intelligent electric vehicles based on subjective and objective comprehensive indicators. Background Art

[0002] With the rapid development of intelligent electric vehicle technology, people are liberated from driving tasks, and the role of the driver has changed to a "free-moving" passenger who can read, hold meetings, or rest, etc., and more stringent ride comfort requirements are needed. At the same time, the kinetic energy recovery system and faster power response speed make the speed of electric vehicles change violently during acceleration and braking, especially in crowded urban traffic, and electric vehicles are more likely to induce motion sickness in passengers. When intelligent electric vehicles are used for transportation and passenger service, comfort is one of the main concerns. Therefore, it is urgent to further improve the ride comfort of intelligent electric vehicles and reduce motion sickness in passengers to ensure the social benefits and social acceptance brought by electric vehicles and intelligent driving.

[0003] In terms of reducing vibration stimulation and improving ride comfort, the main methods at the present stage are passive methods such as optimizing the cockpit environment layout and optimizing the user interface. For example, by adjusting the seat position to obtain a wider external road view, reducing the temperature inside the vehicle, and optimizing the seat structure. The essence of such passive methods is that after the uncomfortable vibration excitation occurs, various methods are used to reduce its impact on the human body, and the excitation source cannot be actively and directly eliminated. Another type of method is to optimize vehicle behavior through motion planning control to directly eliminate the vibration excitation source to improve passenger comfort. The motion planning and control of current high-level intelligent vehicle adaptive cruise technology are completely automated, so this method is feasible in practical applications. The core of this improvement method lies in constructing a comprehensive and accurate motion sickness evaluation and quantification index to cover various comfort influencing factors. Due to the advantages of low cost, convenient implementation, and high effectiveness of the subjective evaluation method, it has been widely used in early motion sickness research or treatment. However, the questionnaire can only record the severity of motion sickness of the research object before and after the start of the experiment, and cannot present the possible non-linear changes in its motion sickness level over time during driving, and it is difficult to be directly used in the anti-motion sickness planning strategy. Therefore, objective data that can be collected in real time should be used, and the mapping relationship between it and the subjective feelings of the human body should be mined. On this basis, a subjective and objective comprehensive index of motion sickness is constructed to calculate the current motion sickness level of the occupant in real time, and it is used as the objective function of the planning model to minimize the motion sickness discomfort of the occupant.

[0004] Based on the motion sickness index, constructing a reasonable multi-objective optimization model and incorporating it into the planning control process is a more novel, effective, and suitable comfort improvement strategy for intelligent driving vehicles. Kamiji et al. obtained a more comprehensive six-degree-of-freedom evaluation model by expanding the traditional SVC model (Subjective vertical conflict model), and developed it from a qualitative model to a multi-degree-of-freedom quantitative model. Subsequently, based on this, researchers proposed various optimization methods for intelligent driving vehicles to improve vibration comfort. When using adaptive cruise control technology to assist driving in congested traffic conditions, the driving behavior of the leading vehicle has a strong impact on the motion of the host vehicle. To maintain a reasonable time headway, some studies use the acceleration change rate, i.e., jerk, to analyze vehicle vibration in real time and optimize speed and path. This method that only considers longitudinal jerk reduces the vehicle speed to too low a level in some scenarios, affecting travel time. Although it can improve comfort well, it is not comprehensive for improving the driving experience (Research on the impact of lane-changing operations on the comfort of intelligent vehicle passengers, Guo Yingshi, Su Yanqi, Fu Rui, Yuan Wei, School of Automotive Engineering, Chang'an University, Xi'an 710064). Therefore, the anti-motion sickness planning strategy for intelligent vehicles should fully consider human physiological characteristics while balancing comfort, risk, and travel time, and adaptively adjust the primary criterion target during driving to effectively respond to various driving scenarios. Summary of the Invention

[0005] The object of the present invention is to provide an anti-motion sickness cruise planning method for intelligent electric vehicles based on subjective and objective comprehensive indicators.

[0006] The present invention is achieved by at least one of the following technical solutions.

[0007] An anti-motion sickness cruise planning method for intelligent electric vehicles based on subjective and objective comprehensive indicators, comprising the following steps:

[0008] (1) Collect vehicle motion trajectory data and subjective and objective data of human physiological signals;

[0009] (2) Calculate the motion sickness index under frequency-domain weighting and the evaluation index in the time domain from the collected vehicle motion trajectory data through Parseval's theorem and a band-pass filter, and fuse the motion sickness index under frequency-domain weighting and the evaluation index in the time domain using a state-space equation to form an objective motion sickness index in the time-frequency domain under vehicle variable-speed motion, and obtain a subjective and objective comprehensive motion sickness index in combination with the subjective and objective data of human physiological signals;

[0010] (3) Use the obtained subjective and objective comprehensive motion sickness index and an adaptive weighting factor set to construct a cruise planning model, and jointly solve the optimal anti-motion sickness cruise parameters of the cruise planning model using dynamic programming to minimize the subjective and objective comprehensive motion sickness index and travel time during vehicle driving.

[0011] Further, in step (2), the calculation of the motion sickness index under frequency-domain weighting through the Parseval theorem and the band-pass filter includes: converting the vehicle acceleration in step (1) into the frequency-domain form by using the Parseval theorem, inputting the frequency-domain form into the band-pass filter to extract the data within the human-sensitive frequency band, and simultaneously adding the weighting function specified by the standard ISO2631 to the data within the extracted human-sensitive frequency band, so as to obtain the frequency-domain weighted acceleration, and then integrating the frequency-domain weighted acceleration to obtain the frequency-domain weighted motion sickness measurement value.

[0012] Further, in step (2), the calculation of the evaluation index in the time domain is as follows:

[0013] Based on the frequency-domain weighted acceleration, the time-domain weighted motion sickness measurement value is constructed by combining the inverse Fourier transform to consider the influence of vibration persistence. After the frequency-domain weighted acceleration is transformed, the real part of the transformation result is taken to obtain the time-domain weighted acceleration, and then the time-domain weighted motion sickness measurement value is calculated by using the time-domain weighted acceleration;

[0014] Two additional time-related random perturbations are added to generate perturbation effects. The combination of the time-domain weighted motion sickness measurement value and the perturbation effects constitutes the evaluation index in the time domain.

[0015] Further, in step (2), the band-pass filter is converted into the continuous-time form and discretized. The input quantity of the band-pass filter within the discrete time step is a constant value. When the vehicle travels between the planned points, the matrix exponential in the discretized equivalent expression is different at each time step; the matrix exponential is transformed based on the diagonalization method, and the dynamic characteristic coefficient matrix is diagonalized through the eigenvector matrix. The dynamic characteristic coefficient matrix corresponds to the time constant of the expected cut-off frequency of the band-pass filter; the eigenvector matrix used in the diagonalization process is an invariant of the time step, and before the planning, the value of the eigenvector matrix is predefined according to the actual motion parameters of the vehicle.

[0016] Further, in step (2), obtaining the subjective and objective comprehensive motion sickness index by combining the subjective and objective data of the human physiological signals specifically includes: performing time synchronization processing on the subjective and objective data of the human physiological signals, and performing label annotation on the physiological signal acquisition data according to the subjective feeling quantification data; matching each physiological signal data of the occupant with the reference range to obtain the first parameter deviation of each signal, which is used to characterize whether each physiological signal deviates from the reference range. At the same time, calculate the second parameter deviation according to the deviation between each physiological signal data and the reference range, which is used to characterize the deviation degree of each physiological signal, and assign weights to the physiological signal acquisition data according to the decreasing principle. Subsequently, match the total first parameter deviation and the total second parameter deviation with the objective motion sickness index in the time-frequency domain under vehicle variable-speed motion to obtain the subjective and objective comprehensive motion sickness index.

[0017] Further, in step (3), the cruise planning model takes the subjective and objective comprehensive motion sickness index and the minimum travel time as the objective function, and controls the weights of each objective value in the objective function through the adaptive weighting factor set , and the weighting method. The cruise planning model is as follows:

[0018]

[0019] In the formula and are the motion sickness index weight and the travel time weight, is the current state of the vehicle system, is the objective function value in the current state, is the vehicle system state at the initial moment, is the total planning duration, is the vehicle system state at the planning domain cut-off time point , is the state vector at the initial moment, is obtained according to the target position and speed the state vector at the end moment, represents the vehicle pitch angle is a parameter related to the vehicle longitudinal acceleration , that is is the current vehicle longitudinal acceleration causing the vehicle pitch angle, is expressed as the vehicle driving risk index caused by the current vehicle longitudinal acceleration , , represents the heterogeneous vehicle type threshold; among them, the safety constraint based on the vehicle driving risk index and the comfort constraint of the vehicle pitch angle are used to ensure the most basic safety criterion and comfort criterion for vehicle driving.

[0020] Further, the vehicle driving risk index quantifies the risk heterogeneity of different vehicle models during longitudinal cruise, uses the POT extreme value method to identify the heterogeneous vehicle thresholds of different vehicle models respectively, and obtains the heterogeneous vehicle thresholds , representing different vehicle models. When the current vehicle dynamically adjusts its driving actions, the heterogeneous vehicle thresholds are used to determine whether there is a potential collision risk, that is, to judge the vehicle driving risk index and . When the vehicle driving risk index is greater than the heterogeneous vehicle threshold, there is no potential collision risk between vehicles. When the vehicle driving risk index is less than the heterogeneous vehicle threshold, there is a potential collision risk between vehicles, and the time headway of the host vehicle needs to be constrained outside the heterogeneous vehicle threshold to ensure meeting the basic criteria for driving safety.

[0021] Further, the comfort constraint of the vehicle pitch angle is to form a comfort boundary constraint based on the pitch and vertical dynamics, based on the vehicle body pitch control range and the single-rail pitch dynamics model of the vehicle, and the vehicle body pitch angle needs to be constrained within the range;

[0022] The single-rail pitch dynamics model of the vehicle is a single-rail pitch dynamics model of the vehicle including the suspension and the sprung mass, where the suspension is equivalently expressed by a spring and a damper.

[0023] Further, in step (3), the optimal anti-motion sickness cruise parameters for solving the cruise planning model by the joint dynamic programming method include: using the dynamic programming method to find the optimal solution for the cruise planning model;

[0024] The dynamic programming method discretizes the optimization problem in a distance-segmented manner, solves an optimal control solution in each distance domain by using the dynamic programming method, and applies the optimal control solution to the cruise control system; the dynamic programming method includes a loss function, an action network, and an evaluation network.

[0025] A computer device of the present invention includes: a memory, a processor, and a computer program stored on the memory. When the computer program is executed on the processor, the intelligent electric vehicle anti-motion sickness cruise planning method based on the subjective and objective comprehensive indicators is implemented.

[0026] Compared with the existing technologies, the beneficial effects of the present invention are:

[0027] First, obtain evaluation data that conforms to the physiological and psychological characteristics of Chinese drivers and passengers. Through conducting real-vehicle experiments on motion sickness under multiple working conditions, collect driving trajectory data, objective data of the physiological signals of passengers, and subjective feelings of motion sickness, identify the correlation mapping relationship between the subjective and objective data, extract the main characteristic quantities to reduce the dimension of the collected parameters, and based on this, construct a complete and concise subjective and objective dataset of motion sickness, laying a data foundation for subsequent proposing subjective and objective evaluation indicators of motion sickness and developing anti-motion sickness technologies that meet the needs of the Chinese market.

[0028] Second, meet the systematicness, accuracy, and subjective-objective consistency of motion sickness evaluation. By introducing methods such as Parseval's theorem and state-space equations, fully consider the three main factors affecting human motion sickness in the time domain and frequency domain. At the same time, by considering the subjective feelings of the human body, add subjective feelings on the basis of objective indicators, and propose a subjective and objective comprehensive evaluation method of motion sickness that combines the amplitude of variable-speed shock, time-domain persistence, frequency-domain sensitivity, and human subjective feelings, further improving the evaluation method of motion sickness for drivers and passengers.

[0029] Third, ensure the optimal balance of multi-objectives of safety, comfort, and efficiency of the adaptive cruise system under the influence of heterogeneous vehicles. By identifying the sensitivity of drivers of heterogeneous vehicles to driving risks in natural trajectory data, propose a safety constraint boundary that is more in line with the driving behavior characteristics. At the same time, by considering different driving goals under normal risk-free working conditions, conflict working conditions, and dangerous working conditions, construct a corresponding set of adaptive adjustment weighting factors, and combine it with the subjective and objective comprehensive index of motion sickness and the two objectives of travel time, which can effectively ensure the first criterion of the main goal under different working conditions. Description of the Drawings

[0030] Figure 1 It is a flowchart of an intelligent electric vehicle anti-motion sickness cruise planning method based on subjective and objective comprehensive indicators for the embodiment.

[0031] Figure 2 It is a schematic diagram of the scenario of the slalom test working condition for the embodiment.

[0032] Figure 3 It is a schematic diagram of the acceleration and deceleration alternating test scenario for the embodiment.

[0033] Figure 4 It is a curve graph of the sensitive frequency domain weighting function for the embodiment.

[0034] Figure 5 It is a comparison chart of accelerations before and after anti-motion sickness planning for the embodiment.

[0035] Figure 6 It is a comparison chart of the comprehensive index of motion sickness before and after anti-motion sickness planning for the embodiment.

[0036] Figure 7Statistical chart of the comprehensive index value of motion sickness for the AD4CHE dataset of the embodiment.

[0037] Figure 8 Comparison chart of travel time before and after the anti-motion sickness plan for the embodiment. Detailed implementation manners

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] As Figure 1 shown, the intelligent electric vehicle anti-motion sickness cruise planning method based on the subjective and objective comprehensive index of this embodiment includes the following steps:

[0040] (1) Through multi-condition tests of motion sickness in-vehicle experiments, collect vehicle motion trajectory data and subjective and objective data of human physiological signals to form a motion sickness subjective and objective dataset, which specifically includes the following steps:

[0041] The first step is to design a multi-condition test scenario for motion sickness in-vehicle experiments.

[0042] To explore the sensitive factors of motion sickness feelings and propose subjective and objective comprehensive evaluation indicators, conduct motion sickness in-vehicle experiments and stimulate the motion sickness state of the tested persons through visual and vestibular information. The reason for choosing in-vehicle experiments is mainly to avoid the test interference generally existing in driving simulators. Compared with the driving experience of real vehicles, driving simulators have deficiencies in creating a sense of speed and bring interference to vestibular input. At the same time, to ensure the safety of the experiment, professional test drivers drive the test vehicle to complete the experiment in a closed test area according to the preset conditions. First, analyze the test objectives to determine the types of signals to be collected and the types of equipment required. The motion sickness state is a stress response, so the organs such as the heart rate, skin, and eyes of the tested persons all show reaction behaviors. It is thus determined that the equipment used includes gyroscopes, three-axis acceleration sensors, galvanic skin response meters, electrocardiographs, and eye movement instruments, and the total number of tested persons reaches about 30. The positions of the tested persons and the equipment are as follows: the tested persons sit on the co-pilot side in the back row; the gyroscope is installed on the co-pilot side in the back row; the three-axis acceleration sensor is installed on the seat cushion and backrest of the seat where the tested persons are located in the back row; the tested persons in the back row paste the integrated galvanic skin and electrocardiogram test equipment and carry the eye movement equipment.

[0043] As a specific embodiment, before each test, the subject needs to rest quietly in the vehicle for 3 minutes. After a single-condition test, the subject needs to rest for at least 30 minutes to restore the calm level. Each subject tests two conditions, and each condition is tested at least four times. Among them, the first test is the control group, which is used to unify the expectations of different subjects for the test and avoid obvious differences in subjective feelings caused by different expectations of different subjects for the test content. Condition 1 is the slalom test, and Condition 2 is the straight-line acceleration and deceleration alternating test. The specific test scenarios of each condition are as Figure 2 and Figure 3 shown. The test is carried out on a straight two-way lane in a closed test area. In the slalom test of Condition 1, a cone is placed every 20 m on the straight test field, and a total of 8 cones are placed. When slaloming and changing lanes, the vehicle speed is maintained at 30 km / h, and each subject conducts 4 round trips. In the straight-line acceleration and deceleration alternating test of Condition 2, the vehicle performs rapid acceleration and deceleration from 0 - 30 km / h - 80 km / h - 30 km / h - 0, and each subject conducts 4 round trips. During the test, objective data is collected by the acquisition device at the sampling frequency of the device, and subjective feelings are manually recorded by the subject at a recording frequency of once every 2 minutes, and once again after completing a single test. If the subject cannot bear to get out of the vehicle during the test, a record also needs to be made, which counts as the end of a test.

[0044] Step 2: Collect the vehicle motion trajectory data and the subjective and objective data of the human physiological signals.

[0045] As an embodiment, the subjective data recording uses the pain scale widely used in motion sickness tests or treatments, as shown in Table 1. This table measures the severity of motion sickness symptoms on an 11-point scale, where 0 points represent no symptoms and 10 points represent vomiting symptoms. The survey subjects rate from 0 to 10 according to whether they have motion sickness symptoms and the severity of the motion sickness symptoms, so as to reflect the degree of tension and discomfort felt by the subjects during the process of the test vehicle taking different actions. In the pain scale, when the survey subjects rate the severity of their motion sickness symptoms as 2 - 5 points, the symptoms that appear are not limited to the part listed in the questionnaire. Any symptoms that are not listed but are considered by the survey subjects to be caused by motion sickness based on their own daily life experiences are also included as the basis for scoring and the change in the motion sickness level of the subjects.

[0046] Table 1 Pain Scale

[0047]

[0048] In terms of collecting objective physiological indicators of respondents, a physiological multi-channel instrument was used to collect ECG and skin electrical signals. The above signals were measured by electrodes attached to the chest and the inside of the palm, and data such as heart rate and skin conductivity level can be obtained from these measurement signals. In order to collect the physiological activities of the respondents' eyes, an eye tracker was used to record eye movement information, including blinking frequency, pupil diameter, line of sight focus, eyelid opening and closing, and other data. In addition, a gyroscope and a three-way acceleration sensor were used to synchronously record vehicle motion parameters and posture data. Since the test equipment came from different manufacturers, the data was collected and recorded independently during the test, and the world time was used to synchronize the time between different data when analyzing the results. At the same time, the Z-score normalization method was used to map the data within a certain range, and the calculation formula is shown as follows:

[0049]

[0050] In the formula is the original data, is the average value, is the standard deviation, is the standardized dimensionless value after calculation. Standardization solves the problem of large differences in the order of magnitude of each objective indicator and individual differences to ensure the reliability of the results. In addition, due to the inconsistent sampling frequency of the equipment and the excessive amount of data, in order to better obtain the objective data group corresponding to the subjective, the data is downsampled and segmented so that one set of data corresponds to one degree of motion sickness.

[0051] The third step is to construct a subjective and objective dataset of motion sickness based on the correlation of perceptions.

[0052] After completing all the test conditions, the number of obtained parameter items is as high as 18. To facilitate the subsequent work, the parameter items most relevant to human feelings are first extracted based on the task objectives and correlation. To facilitate the subsequent work, the parameter items most relevant to human feelings are first extracted based on the task objectives and correlation, so as to achieve effective data dimension reduction and form a streamlined subjective and objective data set for motion sickness. Motion sickness can be regarded as a stress response, and some physiological parameters of the subjects change significantly. By solving the correlation coefficient To determine the correlation between parameter items, the solution process is a two-by-two combination solution, that is, after selecting a parameter item, the correlation coefficient between the parameter item and each other parameter item is calculated. When calculating the correlation coefficient between the first parameter item and the second parameter item It is recorded as , when finding the correlation coefficient between the first parameter term and the third parameter term It is recorded as , and so on when solving each parameter item pairwise. and are the serial numbers of the two parameter items respectively. Correlation coefficient The formula is as follows:

[0053]

[0054] Where:

[0055]

[0056] In the formula is the total number of samples, and are the standard deviations of two parameter terms involved in calculating the correlation coefficient, and are the standardized dimensionless values of two parameter terms involved in calculating the correlation coefficient, and are the means of two parameter terms involved in calculating the correlation coefficient. Based on the correlation coefficient matrix the eigenvalues of the correlation coefficient matrix are calculated using the standard characteristic equation and it is defined that i.e., after solving, the eigenvalues are sorted in descending order, the largest eigenvalue is denoted as the next is denoted as and so on, the smallest eigenvalue is denoted as Randomly extract 30 groups of data to identify the relationship between each collected physiological parameter and vehicle motion parameter and the motion sickness feeling. Use the proportion of eigenvalues to obtain the characteristic quantities with the cumulative variance contribution rate of the common factor greater than 80%, so as to achieve the purpose of including most of the information of each parameter term and reducing the dimension and streamlining the original collected data set.

[0057] Through hypothesis testing to test the significance of parameter changes, the indicators in the frequency domain analysis of heart rate variability and variability can be included in the subjective and objective data set of motion sickness. Among them, low-frequency power can represent the combined effect of the sympathetic nerve and the vagus nerve, high-frequency power can reflect the activity of the vagus nerve, and the total power represents the total variability of the signal. When the human body is in an uncomfortable state, measuring skin electricity is an important parameter in the field of physiological psychology and motion sickness diagnosis and treatment. Generally speaking, the skin conductance level can reflect the degree of awakening, alertness, mental stress, mental workload and other information of the human body. After testing, the skin conductance level of the subject is significant and can be included in the subjective and objective data set of motion sickness. In addition, the human eye is a visual perception organ, and various indicators of this organ are believed to be able to represent cognitive load, stress level, fatigue level, emotional state, attention and other information. Among them, the pupil diameter increases with the increase of task difficulty, psychological load, tension and so on. Therefore, the pupil diameter among the various indicators collected by the eye tracker is included in the subjective and objective data set of motion sickness. During vehicle driving, road stimulation and the operation of the driver or automatic driving system both affect ride comfort. Therefore, the rest of the motion sickness subjective and objective dataset consists of vehicle motion parameters. This example focuses on the anti-motion sickness effect of longitudinal cruising, so the vehicle motion parameters are selected as the vehicle longitudinal acceleration and pitch angle.

[0058] (2) Design a bandpass filter by combining the transfer function and the zero-order hold hypothesis, introduce the Parseval theorem to calculate the frequency-domain weighted motion sickness measurement value, establish the objective motion sickness index in the time-frequency domain based on the state-space equation, and propose a subjective and objective comprehensive motion sickness index by integrating the subjective and objective motion sickness data sets, including the following steps:

[0059] (21) A bandpass filter is designed by combining the transfer function with the zero-order hold assumption. The vehicle acceleration in step (1) is converted into frequency domain form using Parseval’s theorem and then input into the bandpass filter to extract data within the human body sensitive frequency band.

[0060] The most sensitive frequency band for motion sickness in humans is 0.0315~0.25Hz. To minimize the stimulation level in this frequency band, a bandpass filter is used to extract the acceleration in this frequency band. The conventional bandpass filter is shown below:

[0061]

[0062] In the formula is the bandpass filter system transfer function, is the system output value, Enter a value for the system. is a complex frequency variable, representing the frequency component of the signal, and is the time constant corresponding to the desired cut-off frequency of the band-pass filter. The band-pass filter is converted to a continuous-time form and discretized. The discretization method is based on the zero-order hold assumption theorem, that is, the input acceleration remains constant within the discrete time step. The discretized equivalent expression is as follows:

[0063]

[0064]

[0065] where is the discrete state vector at time , and are the system input and output at time respectively, is the discretized state transition matrix, is the discretized input matrix, is the discretized output matrix, , and The expressions of are as follows:

[0066] ;

[0067]

[0068] ;

[0069] where is the identity matrix, and the dynamic characteristic coefficient matrix and the discrete state coefficient matrix The expressions are as follows:

[0070] ;

[0071]

[0072] When the vehicle is driving between the planned points, since the time step varies with the time point, the matrix exponential is different at each time step. Based on the diagonalization method to simplify the calculation of the matrix exponential, the matrix is diagonalized at each step:

[0073]

[0074] where is the diagonalization matrix of the matrix , is used to diagonalize the matrix The eigenvector matrix subjected to diagonalization, then the matrix exponential is equal to:

[0075]

[0076] where the diagonal matrix exponential is expressed as:

[0077]

[0078] In the formula and are the diagonal elements of the diagonalization matrix ; and are and exponentiated, representing the dynamic evolution at the time step . Since the eigenvector matrix is an invariant of , the eigenvector matrix can be predefined according to the actual motion parameters of the vehicle in advance, without the need to repeat the calculation at each time step. Based on the predefined method, the computational complexity can be reduced from matrix exponential calculation to calculation based on the diagonalization matrix, effectively improving the computational efficiency.

[0079] (22) Calculate the frequency-domain weighted motion sickness metric using Parseval's theorem.

[0080] Accelerations in different frequency bands have different stimulations on passenger comfort, that is, acceleration shocks in some frequency bands are more likely to induce motion sickness in passengers. Therefore, on the basis of ensuring vehicle maneuverability, safety, and stability, reshape the acceleration in the frequency domain to minimize the stimulation to comfort and the incidence of motion sickness. Define the acceleration frequency-domain form using Parseval's theorem:

[0081]

[0082] In the formula is the cut-off time point of the planning domain, is the Fourier transform of the vehicle's longitudinal acceleration at time is the conjugate complex number of is the imaginary unit, is the frequency variable in the Fourier transform. Input into the band-pass filter, extract the data within the human-sensitive frequency band, and at the same time add the weighting function to the extracted data within the human-sensitive frequency band according to the standard ISO2631. The weighting function curve is as shown in Figure 4 , then the frequency-domain weighted acceleration is obtained As shown below:

[0083]

[0084] Then, the frequency domain weighted acceleration Integrate to obtain the frequency-domain weighted motion sickness measure , as shown below:

[0085]

[0086] In the formula for The complex conjugate of .

[0087] (23) Based on the state-space equation, an objective motion sickness index in the time-frequency domain is proposed.

[0088] In addition to the sensitive frequency factor, the international standard ISO2631 points out that vibration duration also has an important impact on human motion sickness. Therefore, in order to more comprehensively evaluate the vehicle motion comfort and reduce the motion sickness induced by vehicle speed change behavior, the frequency domain weighted acceleration , combined with The time domain index is constructed by the method to consider the influence of vibration persistence. In the calculation process, the frequency domain weighted acceleration Take the conjugate complex number, negate the imaginary part and perform a secondary transform. The result of the transformation is a complex number, but the imaginary part of this complex number is small, so we only need to take the real part of the complex number to get the time-domain weighted acceleration. , and then calculate the time-domain weighted motion sickness measurement value by the following formula :

[0089]

[0090] In the formula is the time-domain weighted motion sickness measurement value, is the planning domain cutoff time point, i.e., the total planning duration. In addition, in order to maximize the stability of the input, two additional time-related random perturbations are added: and The disturbance effect ,Right now:

[0091]

[0092] and The combination constitutes the evaluation index in the time domain, further improving the comfort evaluation method. On this basis, the state space equation is introduced to integrate the disturbance effect , time-domain weighted motion sickness measurement value and frequency-domain weighted motion sickness measure , a time-frequency domain objective motion sickness index TFI (Time & Frequency Index) under the variable-speed motion of the vehicle is formed, as shown in the following formula:

[0093]

[0094] Where:

[0095]

[0096] In the formula - are the weight coefficients of each index component. In the actual planning process, the acceleration-sensitive frequency and persistence have the greatest impact on comfort. Therefore : : The weight ratio between them is 0.4:0.4:0.2.

[0097] (24) Propose a subjective and objective comprehensive index of motion sickness by integrating human feelings.

[0098] Based on the in-vehicle test of motion sickness, a plurality of physiological signal acquisition data and subjective feeling quantification data of the occupants are obtained, and a subjective and objective data set directly describing human feelings is formed. The physiological signal acquisition data includes electrocardiogram signal ECG (Electrocardiogram), electrodermal activity signal EDA (Electrodermal Activity), and pupil diameter PD (Pupil Diameter). The subjective feeling quantification data includes multiple recording times recorded by the occupants and the human feelings corresponding to each recording time (the subjective feeling quantification data in this example is a score value selected according to the pain scale). Perform time synchronization processing on a plurality of physiological signal acquisition data, and segment each segment of physiological signal data. Label the segmented physiological signal acquisition data according to the subjective data to extract the human feelings and quantification ranges in the normal or carsick state. Match the data of each state of the occupants with the reference range to obtain the first parameter deviation ( ) is used to characterize whether the physiological signal deviates from the reference range. At the same time, calculate the second parameter deviation using the deviation between the state data and the reference range to characterize the deviation degree of the physiological signal. Assign weights to each physiological signal acquisition data according to the decreasing principle as > > , so the total first parameter deviation and the total second parameter deviation calculation formulas are as shown in formula (17):

[0099]

[0100] In the formula , , respectively represent the first parameter deviation amounts of the electrocardiogram signal ECG, the electrodermal activity signal EDA, and the pupil diameter PD, , , respectively represent the second parameter deviation amounts of the electrocardiogram signal ECG, the electrodermal activity signal EDA, and the pupil diameter PD, where:

[0101]

[0102]

[0103] In the formula is the actually measured electrocardiogram signal ECG, electrodermal activity signal EDA, and pupil diameter PD, is the reference range of the state data. Subsequently, the total first parameter deviation and the total second parameter deviation are matched with the time-frequency domain objective motion sickness index under vehicle variable-speed motion to obtain the comprehensive subjective and objective motion sickness index (Motion Sickness Subjective-Objective Index, ), and the calculation formula is as shown in formula (20):

[0104]

[0105] In the formula is the time-frequency domain objective motion sickness index. In formula (20), the parameter has a discriminant function. When the various states of the human body are within the reference range, is 0, that is, is 0, and the occupant's state is normal without the feeling of motion sickness discomfort. is 1, the occupant's state is abnormal, and as the second calculated value increases, it is expressed as the occupant's motion sickness feeling becoming more intense.

[0106] (3). Define the safety constraint according to the driver's heterogeneous sensitivity, and construct the adaptive weighted factor set and the cruise planning model through the body pitch dynamics characteristics. Solve the optimal anti-motion sickness cruise parameters by combining the dynamic programming method, including the following steps:

[0107] (31). Define the safety constraint according to the driver's heterogeneous sensitivity.

[0108] For the following vehicle behavior, the acceleration of the leading vehicle is the disturbance signal, and the driver controls the longitudinal acceleration , achieving stable and safe following. By statistically analyzing natural driving data, it is found that when adjusting the vehicle speed in response to the vehicle ahead, the reaction behaviors of drivers of different vehicle types are different, that is, there is a phenomenon of vehicle type heterogeneity in the sensitivity to driving risks. Introduce a risk index MTTC (Modified Time to Collision) to quantify the risk heterogeneity of different vehicle types. The index for measuring longitudinal traffic conflicts MTTC makes up for the shortcomings of traditional TTC indexes. It can consider the relative speed and acceleration of the vehicle ahead and the following vehicle, and is widely used in traffic conflict research. Its calculation formula is shown in (21):

[0109]

[0110] In the formula is the root of the quadratic equation, , , are:

[0111]

[0112] In the formula 、 、 、 are respectively the length, position, speed, and acceleration of the vehicle ahead, 、 、 are respectively the position, speed, and acceleration of the following vehicle.

[0113]

[0114] In the formula, the risk index value is equal to the smallest positive root. By additionally considering vehicle acceleration and speed, can explain more working conditions outside the collision scenarios covered by , such as the vehicle ahead is faster than the following vehicle, and the following vehicle has a braking behavior. Use the POT extreme value method to identify the heterogeneous thresholds of different vehicle types, and obtain the vehicle type heterogeneous threshold denoted as , represents different vehicle types, including sedans and trucks. When the vehicle ahead dynamically adjusts its driving actions, use the heterogeneous risk threshold to judge whether there is a potential collision risk, that is, judge the size relationship between and . Define the safety constraint boundary according to the heterogeneous sensitivity of drivers:

[0115]

[0116] When is greater than the threshold, there is no potential collision risk in the workshop. When is less than the vehicle type heterogeneity threshold, there is a potential collision risk in the workshop. Therefore, the time headway of the host vehicle needs to be constrained outside the vehicle type heterogeneity threshold to ensure meeting the basic criteria for driving safety.

[0117] (32) Form comfort constraints based on the vehicle body pitch dynamics characteristics.

[0118] The vehicle body pitch angle and pitch rate are mainly affected by the longitudinal acceleration. Therefore, a single-track pitch dynamics model of the vehicle including the suspension and the sprung mass is constructed. In the single-track pitch dynamics model of the vehicle, the suspension adopts a spring and a damper. According to the pitch and vertical dynamics, the calculation formulas for the vertical displacement and rate of the front suspension are shown in Equation (25). Since the front and rear suspensions have opposite symmetry, for the sake of simplicity, only the calculation formulas for the relevant variables of the front suspension are shown here, and the relevant formulas for the rear suspension are similar and will not be elaborated.

[0119]

[0120]

[0121] In the formula is the vertical displacement of the front suspension, is the distance from the front axle to the vehicle center of mass, and are the vehicle body pitch angle and angular velocity respectively, and are the vehicle body vertical displacement and velocity respectively, and its angular velocity can be expressed as:

[0122]

[0123]

[0124] In the formula is the horizontal distance from the front suspension to the rotation center. During normal driving and braking, the vehicle body pitch angle has a small value, and its influence on the horizontal displacement of the front and rear suspensions can be ignored. Then the torques and exerted by the front and rear suspensions on the vehicle body are:

[0125]

[0126]

[0127] In the formula is the distance from the rear axle to the vehicle center of mass, , are the rear suspension rotation angle and angular velocity respectively, is the gravitational acceleration, 、 are the front suspension rotation angle and angular velocity respectively, is the unsprung mass, and are the angular stiffnesses of the front and rear suspensions respectively, and are the rotational damping of the front and rear suspensions respectively, and the vehicle pitch angular acceleration can be expressed as:

[0128]

[0129] In the formula is the height of the vehicle's center of mass, is the moment of inertia of the sprung mass rotating around the axis, is the curb weight of the vehicle, represents the vehicle's longitudinal acceleration. To achieve good ride comfort, the vehicle pitch angle should be controlled within the range of ±1°. Therefore, based on the vehicle pitch control range and the vehicle dynamics model, comfort boundary constraints are set, that is:

[0130]

[0131] In the formula represents the vehicle pitch angle is a parameter related to the vehicle's longitudinal acceleration that is is the current induced vehicle pitch angle.

[0132] (33). Construct an adaptive weighting factor set and a cruise planning model.

[0133] The core of the anti-motion sickness planning strategy is to determine appropriate vehicle control inputs to minimize the subjective and objective comprehensive index of motion sickness and the travel time . By setting the objective function, a set of equality and inequality constraints, the anti-motion sickness planning problem is solved as a multi-objective optimal control problem, and the subjective and objective comprehensive index of motion sickness and the travel time together form the overall objective function. Through the weighting factor set , and the weighting method to control the weights of each objective value in the overall objective function as shown in Equation (30):

[0134]

[0135] In the formula and are the weights of the motion sickness index and the travel time, defined as:

[0136]

[0137]

[0138] The essence of the adaptive weighting factor set is that during the vehicle driving process, when , that is, when the vehicle is within the safety boundary and there is no potential collision risk, the vehicle takes comfort as the main goal, and the model outputs a safe and comfortable motion plan; but when decreases and gradually approaches the vehicle type heterogeneity threshold , the driving risk in such working conditions becomes more and more urgent. To avoid accidents, the weight of the motion sickness index should be accelerated and reduced, and the ride comfort is no longer concerned, but safety is taken as the main criterion. By applying the weighting factor set, the proposed cruise planning model can adaptively adjust the driving criterion goal according to whether there is a collision risk in the current working condition, greatly improving the anthropomorphic degree of the cruise strategy. Finally, based on the above objective function and constraint conditions, a cruise planning model is established as follows:

[0139]

[0140] In the formula, s.t. is the constraint, is the current state of the vehicle system, is the overall objective function value in the current state, is the state of the vehicle system at the initial time t = 0, is the cut-off time point of the planning domain, that is, the total planning duration, is t = the state of the vehicle system at the planning end time, is the state vector at the initial time t = 0, is the state vector at t = the end time obtained according to the target position and speed. The equality constraints include the initial point constraint and the end point constraint. represents the pitch angle of the vehicle is a parameter related to the longitudinal acceleration of the vehicle, that is is the current longitudinal acceleration of the vehicle that causes the pitch angle, Similarly, it is specifically expressed as the current longitudinal acceleration of the vehicle that causes the vehicle driving risk index MTTC . Based on the safety constraint of the vehicle driving risk index and the comfort constraint of the vehicle pitch angle, the most basic safety criterion and comfort criterion of vehicle driving can be guaranteed.

[0141] (34) Solve the optimal anti-motion sickness cruise parameters by the combined dynamic programming method.

[0142] In order to obtain the desired motion parameters of anti-motion sickness cruise in real time, the dynamic programming method is used to solve the optimal solution of the cruise planning model. The basic idea of this method is to discretize the optimization problem in a distance-segmented manner, solve an optimal control solution by the dynamic programming method in each distance domain, and apply this control solution to the cruise control system. The dynamic programming method includes a loss function, an action network, and an evaluation network. Here, the loss function is the objective function in the cruise planning model. The role of the action network is to calculate and output the optimal control solution by inputting the vehicle state, that is, the desired motion parameters that can be achieved based on the current vehicle state, and it is a control quantity that can be directly used in the vehicle control system. The structure of this action network is expressed as:

[0143]

[0144] In the formula is the vehicle driving state input during the step of planning, , (·) is the activation function, is the weight matrix of the action network during the step of planning. However, the action network cannot output the optimal control solution through one iteration calculation. Therefore, it is necessary to calculate the error between the current output and the numerical optimal solution , and update the action network weight matrix by minimizing the error, so as to make the output of the action network approach the optimal solution. The calculation formula of the error is as follows:

[0145]

[0146] The iterative formula for updating the action network weight matrix by minimizing the error is:

[0147]

[0148] In the formula is the learning rate of the action network, is the gradient scaling normalization factor. The numerical optimal solution used when calculating the errorIt is calculated by using an evaluation network and a loss function. By applying numerical optimization theory in the evaluation network to minimize the loss function, a numerically optimal solution can be obtained. However, this optimal solution only conforms to mathematical constraints, and there may be cases where it cannot meet the vehicle control system or cannot be achieved through the current vehicle state. Therefore, this numerically optimal solution is applied to the action network, enabling the action network to output an optimal control solution that is closest to the optimal solution and can be used in the control system based on the current vehicle state. Additionally, the updated weight matrix calculated by the action network can be inherited in each distance domain. This method is more applicable to the real world because it reduces the complexity of the optimization problem to be solved in each iteration and does not require updating the weight matrix each time, further improving the real-time performance.

[0149] To verify the effectiveness of the proposed anti-motion sickness driving strategy, vehicles with severe speed change behaviors were extracted in the AD4CHE dataset scenario. Vehicle No. ID1407 had sudden deceleration and acceleration behaviors due to the vehicle in front cutting in, resulting in a relatively high comprehensive index value of motion sickness during the movement process, that is, the comfort was severely insufficient. The proposed anti-motion sickness planning method was used to re-plan its movement, and the results are as Figure 5 and Figure 6 shown. It can be seen that the acceleration curve after planning is smoother, and the comprehensive subjective and objective index value of motion sickness has decreased by 58.92%, and the comfort has been effectively improved. Figure 7 The statistical description of the comprehensive subjective and objective index value of motion sickness in the AD4CHE dataset is shown as follows. It can be found that the vehicles with the comprehensive subjective and objective index value of motion sickness in the range of [0.5, 0.6] are the most, that is, this range is a relatively comfortable range for human perception. The proposed anti-motion sickness planning method reduces the comprehensive subjective and objective index value of motion sickness to 0.56, which is within the comfort range, verifying the effectiveness of the proposed anti-motion sickness cruise planning method. The passing times before and after anti-motion sickness planning were compared within a fixed driving distance (190m), and the results are as Figure 8 shown. The passing times before and after planning are 27.08s and 26.92s respectively, and the difference between them is only 0.6%. This shows that the comprehensive subjective and objective index value of motion sickness has decreased and the comfort has been improved before and after planning, and it has no impact on the passing time, verifying the timeliness of the proposed anti-motion sickness cruise planning method.

[0150] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention.

Claims

1. An intelligent electric vehicle anti-motion sickness cruise planning method based on a comprehensive subjective and objective index, characterized in that, It includes the following steps: (1) Collect vehicle motion trajectory data and subjective and objective data of human physiological signals; (2) The collected vehicle motion trajectory data calculates the motion sickness index under frequency-domain weighting and the evaluation index in the time domain through Parseval's theorem and a band-pass filter. The state space equation is used to fuse the motion sickness index under frequency-domain weighting and the evaluation index in the time domain to form the time-frequency domain objective motion sickness index under vehicle variable-speed motion, and the subjective and objective comprehensive index of motion sickness is obtained by combining the subjective and objective data of human physiological signals; The obtaining of the subjective and objective comprehensive index of motion sickness by combining the subjective and objective data of human physiological signals specifically includes: performing time synchronization processing on the subjective and objective data of human physiological signals, and labeling the physiological signal acquisition data according to the subjective feeling quantification data; matching each physiological signal data of the occupant with the reference range to obtain the first parameter deviation of each signal, which is used to characterize whether each physiological signal deviates from the reference range. At the same time, the second parameter deviation is calculated based on the deviation between each physiological signal data and the reference range, which is used to characterize the deviation degree of each physiological signal, and weights are assigned to each physiological signal acquisition data according to the decreasing principle. Subsequently, the total first parameter deviation and the total second parameter deviation are matched with the time-frequency domain objective motion sickness index under vehicle variable-speed motion to obtain the subjective and objective comprehensive index of motion sickness; (3)Use the obtained comprehensive subjective and objective motion sickness indicators and the adaptive weighting factor set to construct a cruise planning model, and jointly use the dynamic programming method to solve the optimal anti-motion sickness cruise parameters of the cruise planning model, so that during the vehicle driving process, the comprehensive subjective and objective motion sickness indicators and the travel time are minimized; the cruise planning model takes the minimization of the comprehensive subjective and objective motion sickness indicators and the travel time as the objective function, and controls the weights of each objective value in the objective function through the adaptive weighting factor set , and the weighting method. The cruise planning model is shown as follows: where and are the weights of the motion sickness index and the travel time, is the current state of the vehicle system, is the objective function value in the current state, is the state of the vehicle system at the initial moment, is the total planning duration, is the cut-off time point of the planning domain and the state of the vehicle system at this time, is the state vector at the initial moment, is obtained based on the target position and speed and the state vector at the end moment, represents the pitch angle of the vehicle is a parameter related to the longitudinal acceleration of the vehicle i.e., is the current longitudinal acceleration of the vehicle causing the pitch angle of the vehicle, is expressed as the vehicle driving risk index caused by the current longitudinal acceleration of the vehicle , represents the threshold of heterogeneous vehicle types; among them, the safety constraint based on the vehicle driving risk index and the comfort constraint of the vehicle pitch angle are used to ensure the most basic safety criteria and comfort criteria for vehicle driving.

2. The intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators according to claim 1, wherein, In step (2), the calculation of the motion sickness index under frequency-domain weighting through Parseval's theorem and a band-pass filter includes: converting the vehicle acceleration in step (1) into a frequency-domain form by using Parseval's theorem, inputting the frequency-domain form into the band-pass filter to extract the data within the human-sensitive frequency band, and simultaneously adding the weighting function specified by the standard ISO2631 to the extracted data within the human-sensitive frequency band, then the frequency-domain weighted acceleration is obtained, and further integrating the frequency-domain weighted acceleration to obtain the frequency-domain weighted motion sickness measurement value.

3. The intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators according to claim 2, wherein In step (2), the calculation of the evaluation index in the time domain is as follows: On the basis of the frequency-domain weighted acceleration, the time-domain weighted motion sickness measurement value is constructed by combining the inverse Fourier transform to consider the influence of vibration persistence. After the frequency-domain weighted acceleration is transformed, the real part of the transformation result can be taken to obtain the time-domain weighted acceleration, and then the time-domain weighted motion sickness measurement value is calculated by using the time-domain weighted acceleration; Two additional random perturbations related to time are added to generate perturbation effects. The combination of the time-domain weighted motion sickness measurement value and the perturbation effects constitutes the evaluation index in the time domain.

4. The intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators according to claim 2, wherein In step (2), the band-pass filter is converted into a continuous-time form and discretized. The input quantity of the band-pass filter is a constant value within the discrete time step. When the vehicle travels between the planned points, the matrix exponential in the discretized equivalent expression is different at each time step; based on the diagonalization method, the matrix exponential is transformed, and the dynamic characteristic coefficient matrix is diagonalized through the eigenvector matrix. The dynamic characteristic coefficient matrix corresponds to the time constant of the expected cut-off frequency of the band-pass filter; the eigenvector matrix used in the diagonalization process is an invariant of the time step, and before the planning, the value of the eigenvector matrix is predefined according to the actual motion parameters of the vehicle.

5. The intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators according to claim 1, wherein, The vehicle driving risk index is to quantify the risk heterogeneity of different vehicle models during longitudinal cruising. The POT extreme value method is used to identify the heterogeneous vehicle thresholds of different vehicle models respectively to obtain the heterogeneous vehicle thresholds , represents different vehicle models. When the current vehicle dynamically adjusts its driving actions, the heterogeneous vehicle threshold is used to determine whether there is a potential collision risk, that is, to judge the vehicle driving risk index and The size relationship of. When the vehicle driving risk index is greater than the heterogeneous vehicle threshold, there is no potential collision risk between vehicles. When the vehicle driving risk index is less than the heterogeneous vehicle threshold, there is a potential collision risk between vehicles. The time headway of the host vehicle needs to be constrained outside the heterogeneous vehicle threshold to ensure meeting the basic criteria for driving safety.

6. The intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators according to claim 1, characterized in that The comfort constraint of the vehicle pitch angle is a comfort boundary constraint formed based on the pitch and vertical dynamics, based on the vehicle body pitch control range and the single-rail pitch dynamics model of the vehicle, and the vehicle body pitch angle needs to be constrained within the range; The vehicle single-track pitch dynamics model is a vehicle single-track pitch dynamics model including a suspension and the sprung mass, where the suspension is equivalently expressed by a spring and a damper.

7. The intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators according to claim 1, characterized in that, In step (3), the joint dynamic programming method for solving the optimal anti-motion sickness cruise parameters of the cruise planning model includes: using the dynamic programming method to find the optimal solution of the cruise planning model; The dynamic programming method discretizes the optimization problem in a distance-segmented manner, solves an optimal control solution using the dynamic programming method within each distance domain, and applies the optimal control solution to the cruise control system. The dynamic programming method includes a loss function, an action network, and an evaluation network.

8. A computer device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory. When the computer program is executed on the processor, it implements the intelligent electric vehicle anti-motion sickness cruise planning method based on the comprehensive subjective and objective indicators as described in any one of claims 1 to 7.

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