CN111160762B - Driving suitability evaluation method and system based on physiological data of driver - Google Patents
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Abstract
The invention discloses a driving suitability evaluation method based on physiological data of a driver, which comprises the following steps of: the method comprises the steps that an angle measuring sensor is arranged on a driver, a pressure sensor is arranged on a driving seat, and a distance sensor is arranged in a vehicle; acquiring human body data of a driver to be evaluated, and adjusting the position of an automobile seat according to the sign of the driver; adjusting the sitting posture of the driver; collecting data of a pressure sensor, a distance sensor and an angle sensor, and transmitting the data into an analysis system; the analysis system calculates the membership degree and the grade association degree of the index according to a preset program; early warning is carried out on the index with the suitability lower than 'qualification'; the driver's suitability level is output. The invention provides the method which can screen the index with lower suitability, replace subjective evaluation according to objective classification of a large amount of measured data, further obtain objective evaluation grade, and provide theoretical basis for pointedly improving the suitability of drivers in the future automobile seat detection technology.
Description
Technical Field
The invention belongs to the technical field of detection of automobile seats, and particularly relates to a driving suitability evaluation method and system based on physiological data of a driver.
Background
Along with the continuous development of economy, the automobile has become the essential part of travel demand, and in long-time driving process, comfortable driving environment can promote driving experience effectively, slows down driving fatigue, promotes driving factor of safety. If the automobile seat is improperly designed, a driver cannot obtain a reasonable and comfortable sitting posture, the suitability of human-vehicle interaction is poor, and the discomfort of the driver is easily increased, so that a malignant traffic accident is caused. Because the number of quantitative indexes for evaluating the driving suitability is large, the relation between the indexes and the evaluation standard of the driving space suitability of a driver is quite complex, and the comfort feeling of the driver is also fuzzy uncertainty, the passenger suitability degree is difficult to directly obtain from visual data. Therefore, the method for detecting the interaction between the driver and the vehicle space under the influence of multiple indexes is an important way for improving the driving safety of the automobile, reducing fatigue driving and reducing traffic accidents.
The seat pressure distribution, the joint angle of the driver and the physical sign have important influence on the suitability of the driver. After entering a driving space, the pressure distribution of the contact surface of the driver and the seat influences the blood flow of the driver, and is an objective factor for directly reflecting the riding comfort relatively; the angle of the arm length, sitting height and other self-body signs of the driver and the elbow and knee joint is related to the high efficiency of the operation in the driving process, and the method has important significance for the driving safety and driving experience feeling of the automobile.
In the related method, for the evaluation of the driving suitability, an evaluation expert is generally employed to subjectively evaluate the driving suitability. The evaluation result is greatly influenced by personal experience of subjective evaluation, and the objectivity and reliability of the evaluation are not high.
Disclosure of Invention
The invention provides a driving suitability evaluation method and system based on physiological data of a driver, which aims to solve the problem of low objectivity and reliability in the existing driving suitability evaluation.
The invention discloses a driving suitability evaluation method based on physiological data of a driver, which comprises the following steps of:
step one, an angle measurement sensor is arranged on a driver, a pressure sensor is arranged on a driving seat, and a distance sensor is arranged in a vehicle; acquiring human body data of a driver to be evaluated, and adjusting the position of an automobile seat according to the sign of the driver; adjusting the sitting posture of the driver, and collecting joint angle data and body pressure distribution data of each region when the driver keeps stable sitting posture under the condition of keeping the optimal driving mode;
step two, dividing 5 evaluation grades according to a driving comfort evaluation test carried out by professionals in the early stage, inputting various parameter data of drivers in different states under different evaluation grades into an evaluation database, constructing an analysis platform, and determining standard values and membership functions of the 5 evaluation grades;
step three, parameter data acquired by the distance sensor, the angle measurement sensor and the pressure sensor are transmitted to an analysis platform, membership of corresponding grades of each index in the data is calculated by using a membership function, a membership matrix A is constructed, membership of corresponding evaluation grades of each parameter data is obtained, and therefore main factors affecting driving suitability are determined;
step four, using a gray correlation method for each parameter data of a driver to construct a correlation matrix B, so as to obtain correlation between 5 evaluation grades and each corresponding parameter data;
and fifthly, determining weight values of all the parameters by using an entropy weight method, calculating the comprehensive association indexes of each of the 5 evaluation grades by combining the parameter association degrees, and taking the grade corresponding to the maximum value of the comprehensive association degrees as the suitability evaluation grade of the driver and the vehicle.
Further, in the first step, the angle measuring sensor measures joint angles of the elbow, the knee, the waist and the neck; the pressure sensor measurement comprises a cushion pressure sensor and a back cushion pressure sensor, and pressure values of back, waist, thigh, seat height and seat depth are measured; the distance sensor measures arm length, calf length, thigh length, steering wheel and seat height differential, steering wheel and shoulder height differential, line of sight and seat height differential, line of sight and console height differential.
Further, the human body data of the driver includes sex, height, weight, arm length and leg length.
Further, in the second step, the 5 evaluation grades are respectively too bad, poor, qualified, good and excellent.
In the third step, early warning is carried out on indexes under the qualified grade, and reasonable suggestions are given for hidden dangers of the indexes.
Further, the membership of the evaluation level of each parameter data is calculated by adopting a membership function of a normal structure.
Further, the method for determining the membership evaluation matrix A comprises the following steps: the membership evaluation matrix
Wherein r is ij And the membership degree of the ith index to the jth grade in the driver to be evaluated is represented.
Further, matrix in gray correlation method
In xi ki The degree of association of the evaluation index i of the driver to be evaluated with the rank k is represented.
Further, the formula for determining the weight of each index by the entropy weight method is w= [ w ] 1 W 2 … w i ] T 。
Wherein P is ij For driver j corresponds toSample data of index i at each level, Q ij The result is normalized for data min-max; m is the number of evaluation sample indexes, n is the total number of tested drivers, E i Information entropy of the ith index; w (w) i Is the weight of index i.
The invention also relates to a driving suitability evaluation system based on the physiological data of the driver, which comprises a data acquisition device, an evaluation database and an analysis platform, wherein the data acquisition device comprises various sensors, the collected parameter data are transmitted to the evaluation database for data storage, and the driving suitability classification is carried out based on the analysis platform.
The invention provides the method which can screen the index with lower suitability, replace subjective evaluation according to objective classification of a large amount of measured data, further obtain objective evaluation grade, and provide theoretical basis for pointedly improving the suitability of drivers in the future automobile seat detection technology. The invention can also improve the driving safety, judge the driving suitability and early warn in time in the dynamic driving process of the vehicle, quickly and efficiently identify the influence factors with low driver-vehicle driving matching degree, and reduce the occurrence of bad driving behaviors from the vehicle.
Drawings
FIG. 1 is a schematic view of the process of evaluating driving suitability according to the present invention;
FIG. 2 is a graph showing the normal distribution membership function of the class (overdifference) of the present invention;
FIG. 3 is a graph of the normal distribution membership function of the class (worse) of the present invention;
FIG. 4 is a graph showing the normal distribution membership function of the class (pass) of the present invention;
FIG. 5 is a graph of the normal distribution membership function of the class (good) of the present invention;
FIG. 6 is a graph showing the normal distribution membership function of the class (excellent) of the present invention;
FIG. 7 is a graphical representation of the membership function for driving fitness evaluation according to the present invention.
Detailed Description
The driving suitability evaluation method based on the physiological data of the driver comprises the following steps:
step one, an angle measurement sensor is arranged on a driver, a pressure sensor is arranged on a driving seat, and a distance sensor is arranged in a vehicle; collecting human body data (the human body data comprises gender, height, weight, arm length and leg length) of a driver to be evaluated, and adjusting the position of the automobile seat according to the sign of the driver; adjusting the sitting posture of the driver, and collecting joint angle data and body pressure distribution data of each region when the driver keeps stable sitting posture under the condition of keeping the optimal driving mode;
the angle measuring sensor measures joint angles of the elbow, the knee, the waist and the neck; the pressure sensor measurement comprises a cushion pressure sensor and a back cushion pressure sensor, and pressure values of back, waist, thigh, seat height and seat depth are measured; the distance sensor measures arm length, calf length, thigh length, steering wheel and seat height differential, steering wheel and shoulder height differential, line of sight and seat height differential, line of sight and console height differential.
Step two, dividing 5 evaluation grades according to a driving comfort evaluation test carried out by a professional in the early stage, inputting various parameter data of drivers in different states under different evaluation grades into an evaluation database, constructing an analysis platform, and determining standard values and membership functions of the 5 evaluation grades (the 5 evaluation grades are respectively poor, qualified, good and excellent);
step three, parameter data acquired by the distance sensor, the angle measurement sensor and the pressure sensor are transmitted to an analysis platform, membership of corresponding grades of each index in the data is calculated by using a membership function, a membership matrix A is constructed, membership of corresponding evaluation grades of each parameter data is obtained, and therefore main factors affecting driving suitability are determined; and early warning is carried out on indexes under the qualification grade, and reasonable suggestions are given for hidden dangers of the indexes. And (3) calculating the membership of the evaluation level of each parameter data, and adopting a membership function of a normal structure.
Membership evaluation matrix
Wherein r is ij And the membership degree of the ith index to the jth grade in the driver to be evaluated is represented.
Step four, using a gray correlation method for each parameter data of a driver to construct a correlation matrix B, so as to obtain correlation between 5 evaluation grades and each corresponding parameter data;
matrix in gray correlation method
In xi ki The degree of association of the evaluation index i of the driver to be evaluated with the rank k is represented.
And fifthly, determining weight values of all the parameters by using an entropy weight method, calculating the comprehensive association indexes of each of the 5 evaluation grades by combining the parameter association degrees, and taking the grade corresponding to the maximum value of the comprehensive association degrees as the suitability evaluation grade of the driver and the vehicle.
The formula for determining the weight of each index by the entropy weight method is w= [ w ] 1 W 2 … w i ] T 。
Wherein P is ij For driver j to correspond to sample data of index i at each level, Q ij The result is normalized for data min-max; m is the number of evaluation sample indexes, n is the total number of tested drivers, E i Information entropy of the ith index; w (w) i Is the weight of index i.
The first embodiment is as follows: to be evaluatedThe driver's suitability is classified into five classes (d1=overdifference, d2=worse, d3=pass, d4=good, d5=excellent), the class level value corresponding to each evaluation class is determined by the pre-professional test sample data, and a is set 1 ，a 2 ，a 3 ，a 4 ，a 5 N groups of samples were measured for the respective rank level values D1, D2, D3, D4, D5. The normal range of the driver's flexibility based on the neutral position shown in Table 1 corresponds to the difference (a 1 ) And excellent (a) 5 ) And (d) 3 ) Average value of sample data, d 2 And a 4 Sample levels of 20% and 80% were taken, respectively, i.e., 20% and 80% of the samples had values below this data.
The early driving suitability rating includes 16 indices (table 1) related to pressure measurement, angle measurement and distance measurement, and the practitioner calculates different rating level values of each index from the measured sample data.
Table 1 sensor meter
The second embodiment is as follows: in this embodiment, the main index affecting the difference between the driver and the vehicle is obtained to adjust the posture and behavior of the driver in a targeted manner.
The method adopts normal distribution as the basis for dividing the membership degree of each parameter level of the driver to be evaluated, and comprises the following specific processes: membership functions corresponding to the five classes all meet the conditions of the interval end point (end point value a 1 ，a 2 ，a 3 ，a 4 ，a 5 Known) is 95%, and the area in the horizontal axis interval (μ -1.96 σ ) is 95.449974% under each interval normal curve, so that the σ value in each membership function can be obtained, and the following 5 membership functions are obtained:
membership function of certain index to grade (overdifference):
membership function of certain index to grade (poor):
membership function of certain index to class (pass):
membership function of certain index to grade (good):
membership function of certain index to class (excellent):
wherein x is a detection value of a certain index of a driver to be evaluated; a, a 1 ，a 2 ，a 3 ，a 4 ，a 5 The level values corresponding to the five levels for each index (index is different, level value is different).
Judging the membership degree of each index to each level in an evaluation set according to specific data of a certain index of a driver to be evaluated, and determining the membership degree evaluation matrix, wherein the membership degree evaluation matrix is as follows:
wherein r is ij The index i representing the driver to be evaluated is a membership of the class j.
Therefore, the aspect that the driver to be evaluated has better suitability can be obtained according to the grade membership of each index, and the indexes which show poor or poor suitability are adjusted in time, so that serious consequences caused by the conditions of improper seat adjustment, insufficient sight and the like are avoided. For the grade membership fluctuation presented in the dynamic driving process, whether basic stability can be ensured or not needs to be observed, larger fluctuation does not occur, if grade jump (generally grade reduction) occurs, the situation that the driver encounters traffic condition change or bad driving behavior is greatly illustrated, and corresponding adjustment comments or early warning prompts are provided for the index of the driver.
And a third specific embodiment: in the present embodiment, the degree of tightness of a plurality of evaluation indexes related to a driver to each driving suitability level is studied to determine the degree of association between different indexes and levels, and the degree of association is expressed using gray.
The evaluation database stores 16 indexes including pressure, angle and distance measurement data of multiple parts of the body and data of the data advancing along with the detection time. When a driver sits on the driving seat, the data acquisition card acquires a first group of objective physiological parameter samples related to the driver and the seat; in the process that a driver performs a certain task in the dynamic driving process, the data acquisition card acquires a second group of physiological parameter samples related to the driver and the seat; when the driver finishes the driving task, the data acquisition card acquires a third group of comparison physiological parameter samples about the driver and the seat. In this dynamic driving process, at least 3 pieces of detection data per group are required to be ensured as suitability evaluation indexes for the continuous driving process. And judging the association degree between the index of the driver and the suitability level in each driving stage according to the similarity degree of the change of each index in the continuous process, and giving the comprehensive suitability level of the driver and the vehicle in the dynamic driving process.
And extracting a plurality of index data of a certain driver to be evaluated in the evaluation database, and quantifying the development situation of the dynamic process by using a gray correlation analysis method so as to obtain comprehensive suitability grade discrimination. Let y= { Y (k) |k=1, 2 … n } be the overall fitness of the dynamic driving process, X i ＝{X i (k) I k=1, 2 … m } is several indicators stored in the evaluation database that affect the suitability evaluation, here,n=5, m=16. In consideration of the fact that the driver basically keeps a fixed posture during driving, the state is changed only under the condition of meeting traffic conditions and the amplitude is not large, initial value dimensionless processing is carried out on the original data. And (3) calculating gray correlation coefficients of a plurality of groups acquired by the data acquisition card under 5 grades for a plurality of indexes by using the formula (6). Thereby obtaining a gray correlation matrix:
the degree of association between a certain evaluation level k and an index i is shown.
Wherein i is a certain evaluation index, k is an evaluation grade, and x 0 (k) Taking the standard value of the grade k, x i (k) The measurement data for index i is independent of k.
In order to reflect the role of the parameter indicators in the overall evaluation, the weight of each indicator needs to be determined. In the step 8), the weight set is determined by using an entropy weight method, and the weight is determined according to the index variation from the aspect of information entropy, so that the randomness caused by artificial judgment can be avoided, and the method is objective.
Based on the previous test data sample table 2, index data of the testers are classified according to grades by combining membership functions. Taking the driver suitability grade (overdifference) as an example, firstly, carrying out min-max standardization processing on data to obtain Q ij The information entropy of the index m is calculated according to the formulas (7) and (8), and when the information entropy of a certain index is larger, the degree of variation of the grade is smaller, the effect on the comprehensive suitability evaluation is smaller, and the weight is smaller. Next, each index weight is calculated according to the formula (9)
w＝[w 1 w 2 … w i ] T 。
Wherein P is ij For driver j to correspond to sample data of index i at a level (worse), Q ij The result is normalized for data min-max; m is the number of evaluation sample indexes, n is the total number of tested drivers, E i Information entropy of the ith index; w (w) i Is the weight of index i.
TABLE 2 early test Classification Table
By means of weight values w i Calculating the comprehensive association degree W of the index relative to each grade i =b·w. And according to the maximum principle, taking the grade with the maximum sample association degree as a suitability grade evaluation result of the test sample.
The specific embodiment IV is as follows: the invention also comprises a driving suitability evaluation system based on the physiological data of the driver, which comprises a data acquisition device, an evaluation database and an analysis platform, wherein the data acquisition device comprises various sensors which transmit the collected parameter data to the evaluation database for data storage, and finally, the driving suitability classification is carried out based on the analysis platform.
For pressure measurement, the posture of a person to be evaluated is adjusted, a plurality of pressure distribution measuring pads are adopted to obtain body pressure distribution data of the person to be evaluated in a static sitting posture, the body pressure distribution data comprise cushion pressure sensors and cushion pressure sensors, the measured pressures comprise back pressure distribution data, waist pressure distribution data, thigh pressure distribution data, seat height pressure distribution data and seat depth pressure distribution data, and the pressure distribution data are transmitted to a computer database through the pressure sensors for data storage.
For angle measurements, the driver's elbow, knee, waist, neck joints are measured. The 4 parts are used as vertexes of angle measurement, the elbow joint measures the included angle of a large arm and a small arm of a driver holding the steering wheel, the knee joint measures the joint angle of the thigh and the shank of the driver, the waist joint measures the angle of the spine and the thigh, and the neck joint measures the joint angle of the neck, the shoulder and the trunk of the driver. And placing the marker to acquire the angle change of the bent part of the body, and transmitting the angle change to an evaluation database for storage through an angle sensor.
And (3) evaluating main physiological parameters in the database, transmitting the main physiological parameters to an analysis platform, calculating the grade membership and comprehensive association degree of each parameter according to the membership function of a normal structure, a gray association algorithm and an entropy weight method, and judging the driving suitability grade of a driver.
The method for evaluating the driving suitability level establishes a connection between objective parameter data in the physiological test of the driver and the subjective fuzzy evaluation level, and eliminates errors caused by data dispersion by using a gray correlation theory and an entropy weight method, so that the evaluation method reflects the driving suitability level fairly and accurately.
The foregoing is merely illustrative of the preferred embodiments of the present invention and is not intended to limit the embodiments of the present invention, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present invention, so the protection scope of the present invention shall be defined by the claims.
Claims (2)
1. The driving suitability evaluation method based on the physiological data of the driver is characterized by comprising the following steps of:
step one, an angle measurement sensor is arranged on a driver, a pressure sensor is arranged on a driving seat, and a distance sensor is arranged in a vehicle; acquiring human body data of a driver to be evaluated, and adjusting the position of an automobile seat according to the sign of the driver; adjusting the sitting posture of the driver, and collecting joint angle data and body pressure distribution data of each region when the driver keeps stable sitting posture under the condition of keeping the optimal driving mode;
step two, dividing 5 evaluation grades according to a driving comfort evaluation test carried out by professionals in the early stage, inputting various parameter data of drivers in different states under different evaluation grades into an evaluation database, constructing an analysis platform, and determining standard values and membership functions of the 5 evaluation grades;
step three, parameter data acquired by the distance sensor, the angle measurement sensor and the pressure sensor are transmitted to an analysis platform, membership of corresponding grades of each index in the data is calculated by using a membership function, a membership matrix A is constructed, membership of corresponding evaluation grades of each parameter data is obtained, and therefore main factors affecting driving suitability are determined;
step four, using a gray correlation method for each parameter data of a driver to construct a correlation matrix B, so as to obtain correlation between 5 evaluation grades and each corresponding parameter data;
step five, determining weight values of all parameters by an entropy weight method, calculating to obtain respective comprehensive association indexes of 5 evaluation grades by combining parameter association degrees, and taking the grade corresponding to the maximum value of the comprehensive association degrees as the suitability evaluation grade of a driver and a vehicle;
in the first step, the angle measuring sensor measures the joint angles of the elbow, the knee, the waist and the neck; the pressure sensor measurement comprises a cushion pressure sensor and a back cushion pressure sensor, and pressure values of back, waist, thigh, seat height and seat depth are measured; the distance sensor measures arm length, shank length, thigh length, steering wheel and seat height difference, steering wheel and shoulder height difference, sight line and seat height difference, sight line and operating table height difference;
the human body data of the driver comprises gender, height, weight, arm length and leg length;
in the second step, the 5 evaluation grades are respectively excessively poor, qualified, good and excellent;
calculating the membership of the evaluation level of each parameter data, and adopting a membership function of a normal structure;
membership function of certain index to level overdifference:
a membership function with poor level of a certain index:
membership function of certain index level and lattice:
membership function with good grade of certain index:
membership function with excellent grade of certain index:
wherein x is a detection value of a certain index of a driver to be evaluated; a, a 1 ，a 2 ，a 3 ，a 4 ，a 5 A level value corresponding to each of the five levels for a certain index;
the method for determining the membership evaluation matrix A comprises the following steps: the membership evaluation matrix
Wherein r is ki Representing the membership degree of the index i to the grade k in the driver to be evaluated;
matrix in gray correlation method
In xi ki Representing the association degree of the evaluation index i of the driver to be evaluated and the grade k;
wherein i is a certain evaluation index, k is an evaluation grade, and x k Taking the level value of the level k of the index i, and x i The measurement data of index i is irrelevant to k; the formula for determining the weight of each index by the entropy weight method is w= [ w ] 1 w 2 … w i ] T ，
Wherein P is ij For driver j to correspond to sample data of index i at each level, Q ij The result is normalized for data min-max; m is the number of evaluation sample indexes, n is the total number of tested drivers, E i Information entropy of the ith index; w (w) i Is the weight of index i.
2. The driving suitability evaluation method based on the physiological data of the driver according to claim 1, wherein in the third step, early warning is performed on the index belonging to the grade below the qualification grade, and reasonable advice is given on hidden danger existing in the index.
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