CN111160762A - Driving suitability evaluation method and system based on physiological data of driver - Google Patents
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Abstract
The invention relates to a driving suitability evaluation method based on physiological data of a driver, which comprises the following steps: 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; collecting human body data of a driver to be evaluated, and adjusting the position of an automobile seat according to the physical signs 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 degree lower than qualified; the suitability level of the driver is output. The invention provides a method which can screen indexes with low suitability degree, replaces subjective evaluation according to objective classification of a large amount of test data and further obtains objective evaluation grades, and provides a theoretical basis for pertinently improving the suitability of drivers in the automobile seat detection technology in the future.
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 car has become the indispensable partly of trip demand, drives the in-process for a long time, and comfortable driving environment can promote effectively and drive experience, slows down driving fatigue, promotes and drives 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, the discomfort of the driver is easily increased, and therefore a malignant traffic accident is caused. Because the quantitative indexes for evaluating the driving suitability are more, the relationship between the indexes and the evaluation standard of the driver for the suitability of the driving space is quite complex, the comfort feeling of the driver is fuzzy and uncertain, and the suitability degree of the passenger is difficult to obtain directly from visual data. Therefore, the method for detecting the good interaction relationship 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 distribution of seat pressure, the angle of the joints of the driver and the physical signs of the driver have important influence on the suitability of the driver. After a driver enters a driving space, the pressure distribution of the contact surface of the driver and a seat influences the blood flow of the driver, and is an objective factor which relatively and directly reflects the riding comfort; the physical signs of the driver such as arm length and sitting height and the sizes of the elbow and knee joint angles relate to the high efficiency of the operation in the driving process, and the method has important significance for the driving safety and the driving experience of the automobile.
In the related method, for the evaluation of the driving suitability, an evaluation expert is generally used to subjectively evaluate the driving suitability. Since the evaluation result is greatly influenced by personal experience of subjective evaluation, 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 driver physiological data, aiming at solving the problem that objectivity and reliability are not high in the conventional driving suitability evaluation.
The invention relates to a driving suitability evaluation method based on physiological data of a driver, which comprises the following steps:
step one, a driver is provided with an angle measuring sensor, a driving seat is provided with a pressure sensor, and a distance sensor is arranged in a vehicle; collecting human body data of a driver to be evaluated, and adjusting the position of an automobile seat according to the physical signs of the driver; adjusting the sitting posture of a driver, and acquiring joint angle data and body pressure distribution data of each area when the driver keeps stable sitting posture in the optimal driving mode;
dividing 5 evaluation levels according to driving comfort evaluation tests performed by professionals at an earlier stage, inputting various parameter data of drivers with different postures at different evaluation levels into an evaluation database, constructing an analysis platform, and determining standard values and membership functions of the 5 evaluation levels;
transmitting parameter data acquired by a distance sensor, an angle measurement sensor and a pressure sensor into an analysis platform, calculating the membership degree of each index corresponding grade in the data by using a membership degree function, constructing a membership degree matrix A, and obtaining the membership degree of each evaluation grade corresponding to each parameter data, thereby determining main factors influencing driving suitability;
fourthly, constructing a relevancy matrix B for each parameter data of the driver by using a grey relevancy method to obtain relevancy between 5 evaluation grades and corresponding parameter data;
and step five, determining the weight value of each parameter by using an entropy weight method, calculating to obtain respective comprehensive association indexes of 5 evaluation levels by combining the parameter association degrees, and taking the level corresponding to the maximum value of the comprehensive association degrees as the suitability evaluation level of the driver and the vehicle.
Further, in the first step, the angle measuring sensors measure the joint angles of the elbow, knee, waist and neck; the pressure sensor measurement comprises a cushion pressure sensor and a back cushion pressure sensor, and measures the pressure values of the back, the waist, the thigh, the seat height and the seat depth; the distance sensors measure arm length, shank length, thigh length, height difference between a steering wheel and a seat, height difference between the steering wheel and a shoulder, height difference between a sight line and the seat, and height difference between the sight line and an operation table.
Further, the human body data of the driver comprises sex, height, weight, arm length and leg length.
Further, in step two, the 5 evaluation grades are respectively poor, qualified, good and excellent.
And further, in the third step, early warning is carried out on the indexes which belong to the qualified grade or below, and reasonable suggestions are given to the hidden dangers of the indexes.
Further, calculating the membership degree of the evaluation grade of each parameter data by adopting a membership degree function of a normal structure.
Further, the method for determining the membership evaluation matrix A comprises the following steps: the membership degree evaluation matrix
In the formula rijAnd the evaluation result represents the membership degree of the ith index in the driver to be evaluated to the jth grade.
Further, the matrix in the gray correlation method
Formula (III) ξkiAnd representing the degree of association between the evaluation index i of the driver to be evaluated and the level k.
Further, the formula for determining the weight of each index by the entropy weight method is w ═ w1w2… wi]T。
Wherein, PijSample data, Q, for driver j corresponding to index i at each levelijThe standardized result is the min-max data; m is the number of evaluation sample indexes, n is the total number of test drivers, EiInformation entropy of the ith index; w is aiIs the weight of the 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 data acquisition device transmits the collected parameter data to the evaluation database for data storage, and the driving suitability grade is divided based on the analysis platform.
The invention provides a method which can screen indexes with low suitability degree, replaces subjective evaluation according to objective classification of a large amount of test data and further obtains objective evaluation grades, and provides a theoretical basis for pertinently improving the suitability of drivers in the automobile seat detection technology in the future. The invention can also improve the driving safety, can judge the driving suitability degree in the dynamic driving process of the vehicle and give an early warning in time, 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 diagram of a process for evaluating driving suitability according to the present invention;
FIG. 2 is a graph of the normal distribution membership function of the inventive class (over-differential);
FIG. 3 is a graphical illustration of the (worse) normal distribution membership function curve of the present invention;
FIG. 4 is a graph of the normal distribution membership function of the inventive rank (and lattice);
FIG. 5 is a graphical illustration of the inventive graded (good) normal distribution membership function curve;
FIG. 6 is a graph of the normal distribution membership function of the inventive class (excellent);
FIG. 7 is a graph illustrating a driving suitability evaluation membership function 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, a driver is provided with an angle measuring sensor, a driving seat is provided with a pressure sensor, and a distance sensor is arranged in a vehicle; collecting human body data (the human body data comprises sex, height, weight, arm length and leg length) of a driver to be evaluated, and adjusting the position of an automobile seat according to the physical signs of the driver; adjusting the sitting posture of a driver, and acquiring joint angle data and body pressure distribution data of each area when the driver keeps stable sitting posture in the optimal driving mode;
the angle measuring sensor measures the joint angles of the elbow, knee, waist and neck; the pressure sensor measurement comprises a cushion pressure sensor and a back cushion pressure sensor, and measures the pressure values of the back, the waist, the thigh, the seat height and the seat depth; the distance sensors measure arm length, shank length, thigh length, height difference between a steering wheel and a seat, height difference between the steering wheel and a shoulder, height difference between a sight line and the seat, and height difference between the sight line and an operation table.
Dividing 5 evaluation levels according to driving comfort evaluation tests performed by professionals at an earlier stage, inputting various parameter data of drivers with different postures at different evaluation levels into an evaluation database, constructing an analysis platform, and determining standard values and membership functions of the 5 evaluation levels (the 5 evaluation levels are respectively poor, qualified, good and excellent);
transmitting parameter data acquired by a distance sensor, an angle measurement sensor and a pressure sensor into an analysis platform, calculating the membership degree of each index corresponding grade in the data by using a membership degree function, constructing a membership degree matrix A, and obtaining the membership degree of each evaluation grade corresponding to each parameter data, thereby determining main factors influencing driving suitability; and (4) carrying out early warning on indexes which belong to the qualified grade or below, and giving reasonable suggestions to hidden dangers of the indexes. And calculating the membership degree of the evaluation grade of each parameter data by adopting a membership degree function of a normal structure.
Membership degree evaluation matrix
In the formula rijAnd the evaluation result represents the membership degree of the ith index in the driver to be evaluated to the jth grade.
Fourthly, constructing a relevancy matrix B for each parameter data of the driver by using a grey relevancy method to obtain relevancy between 5 evaluation grades and corresponding parameter data;
matrix in grey correlation method
Formula (III) ξkiAnd representing the degree of association between the evaluation index i of the driver to be evaluated and the level k.
And step five, determining the weight value of each parameter by using an entropy weight method, calculating to obtain respective comprehensive association indexes of 5 evaluation levels by combining the parameter association degrees, and taking the level corresponding to the maximum value of the comprehensive association degrees as the suitability evaluation level of the driver and the vehicle.
The formula for determining each index weight by the entropy weight method is w ═ w1w2…wi]T。
Wherein, PijSample data, Q, for driver j corresponding to index i at each levelijThe standardized result is the min-max data; m is the number of evaluation sample indexes, n is the total number of test drivers, EiInformation entropy of the ith index; w is aiIs the weight of the index i.
Detailed description of the inventionThe first method is as follows: dividing the suitability of the driver to be evaluated into five grades (D1 is too poor, D2 is poor, D3 is good, D4 is good, and D5 is excellent), determining the grade level value corresponding to each evaluation grade through the test sample data of professionals at the early stage, and setting a1，a2，a3，a4，a5The grade level values are respectively D1, D2, D3, D4 and D5, and n groups of samples are measured. The normal range in which the driver can flex with the neutral position as a reference listed in table 1 corresponds to 5 levels of gross errors (a)1) And excellence (a)5) The level value of (a), and3) Average of sample data, α2And a4The 20% bit and 80% bit sample levels were taken, respectively, i.e. 20% and 80% of the sample values were below the data.
The evaluation of the driving suitability grade in the early period comprises 16 indexes (table 1) related to pressure measurement, angle measurement and distance measurement, and a professional calculates different grade level values of the indexes according to measured sample data.
TABLE 1 sensor measuring instrument
The second embodiment is as follows: the embodiment is used for obtaining main indexes influencing the suitability difference between a driver and a vehicle so as to carry out targeted adjustment on the posture and the behavior of the driver.
The method adopts normal distribution as a basis for dividing the grade membership degree of each parameter of a driver to be evaluated, and comprises the following specific processes: all the membership function corresponding to the five grades meet the requirement of an interval endpoint (endpoint value a)1，a2，a3，a4，a5Known) and the area in the interval of the horizontal axis (μ -1.96 σ ) under the normal curve of each interval is 95.449974%, thereby obtaining the σ value in each membership function, the following 5 membership functions are obtained:
membership function of some index to grade (over-difference):
membership function of certain index to grade (poor):
membership function of some index to rank (pass):
membership function of certain index to grade (good):
membership function of certain index to grade (excellence):
in the formula, x is a detection value of a certain index of a driver to be evaluated; a is1，a2，a3，a4，a5The index corresponds to the level values of five levels (different indexes and different level values).
According to specific data of a certain index of a driver to be evaluated, judging the membership of each index to each grade in an evaluation set, and determining a membership evaluation matrix, wherein the membership evaluation matrix is as follows:
in the formula rijAnd the index i of the driver to be evaluated represents the membership degree of the grade j.
Therefore, the aspect that the driver to be evaluated has better suitability can be obtained according to the grade membership of each index, indexes which show poor or worse suitability are adjusted in time, and serious consequences caused by the conditions of improper seat debugging, insufficient sight and the like are avoided. For the grade membership degree fluctuation presented in the dynamic driving process, whether basic stability can be ensured or not needs to be observed, and larger fluctuation does not occur, if grade jump (generally grade reduction) occurs, the condition that the driver suffers from the change of traffic conditions or has bad driving behaviors is explained to a great extent, and corresponding adjustment suggestions or early warning prompts are provided for the index of the driver.
The third concrete implementation mode: in the present embodiment, the degree of closeness 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 by using a gray degree of association here.
The evaluation database stores 16 indexes including pressure, angle and distance measurement data of a plurality of parts of the body and data of the indexes advancing along with the detection time. When a driver sits on a driving seat, a data acquisition card acquires a first group of objective physiological parameter samples about the driver and the seat; the method comprises the following steps that in the process that a driver executes a certain task in the dynamic driving process, a data acquisition card acquires a second group of physiological parameter samples related to the driver and a seat; and after 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 the dynamic driving process, at least 3 detection data of each group are required to be ensured to be used as suitability evaluation indexes in the continuous driving process. And judging the degree of correlation between the indexes of the driver and the suitability levels in each stage of driving according to the changed similarity degree of each index in the continuous process, and giving the comprehensive suitability levels 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 grey correlation degree analysis method to further obtain comprehensive suitability grade judgment. Let Y ═ Y (k) | k ═ 1, 2 … n } be the overall suitability of the dynamic driving process, Xi＝{Xi(k) I k is 1, 2 … m, where n is 5 and m is 16, which are several indices stored in the evaluation database that affect the evaluation of suitability. Taking into account that the driver remains substantially in a fixed posture during driving, changes shape only in the event of encountering traffic conditionsThe state and the amplitude are not large, so the 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). Thus, a gray correlation matrix is obtained:
indicating the degree of association between a certain evaluation level k and the index i.
Wherein i is a certain evaluation index, k is an evaluation grade, and x0(k) Taking the standard value of the grade k, xi(k) Is the measured data of index i, independent of k.
In order to reflect the role of each parameter index in the comprehensive evaluation, the weight of each index needs to be determined. In the step 8), the weight set is determined by using an entropy weight method, and the weight is determined by using the entropy weight method according to the index variation size from the information entropy point of view, so that randomness caused by artificial judgment can be avoided, and the method is objective.
And dividing the index data of the testers according to grades by combining the membership function on the basis of the early-stage test data sample table 2. Taking the driver suitability level (over-tolerance) as an example, the data is first subjected to min-max normalization to obtain QijThe information entropy of the index m is calculated according to the expressions (7) and (8), and when the information entropy of a certain index is larger, the degree of variation of the index is smaller, the effect on the overall suitability evaluation is smaller, and the weight is smaller. Then, each index weight is calculated according to the formula (9)
w＝[w1w2…wi]T。
Wherein, PijFor sample data for driver j corresponding to index i at level (worse), QijThe standardized result is the min-max data; m is the number of evaluation sample indexes, n is the total number of test drivers, EiInformation entropy of the ith index; w is aiIs the weight of the index i.
TABLE 2 early stage test Classification Table
Using weight values wiCalculating the comprehensive relevance W of the index relative to each gradeiB · w. And according to the maximum principle, the grade with the maximum sample relevance degree is used as the evaluation result of the suitability grade of the test sample.
The fourth concrete implementation mode: 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, the data acquisition device transmits the collected parameter data to the evaluation database for data storage, and finally, the driving suitability grade is divided based on the analysis platform.
Aiming at pressure measurement, the posture of a person to be evaluated is adjusted, body pressure distribution data of the person to be evaluated in a static sitting posture is acquired by adopting a plurality of pressure distribution measuring pads, the body pressure distribution data comprise cushion pressure sensors and back cushion pressure sensors, measured pressure comprises 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 measured pressure is transmitted to a computer database through the pressure sensors to be stored.
For the angle measurement, the elbow joint, knee joint, waist joint, neck joint of the driver were measured. The 4 positions are used as vertexes of angle measurement, the elbow joint measures the included angle between the large arm and the small arm when a driver holds the steering wheel, the knee joint measures the joint angle between the thigh and the calf of the driver, the waist joint measures the angle between the spine and the thigh, and the neck joint measures the joint angle between the neck, the shoulder and the trunk of the driver. And placing a marker to obtain 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 evaluating main physiological parameters in the database, transmitting the main physiological parameters to an analysis platform, calculating the grade membership and the comprehensive association of each parameter according to a membership function of a normal structure, a grey association algorithm and an entropy weight method, and judging the driving suitability grade of the driver.
The evaluation method for the driving suitability grade establishes a relation between objective parameter data in a driver physiological test and a subjective fuzzy evaluation grade, and eliminates errors caused by data dispersion by using a grey correlation theory and an entropy weight method, so that the evaluation method can fairly and accurately reflect the driving suitability degree.
The above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the embodiments of the present invention, and those skilled in the art can easily make various changes and modifications according to the main concept and spirit of the present invention, so the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A driving suitability evaluation method based on physiological data of a driver based on the physiological data of the driver is characterized by comprising the following steps:
step one, a driver is provided with an angle measuring sensor, a driving seat is provided with a pressure sensor, and a distance sensor is arranged in a vehicle; collecting human body data of a driver to be evaluated, and adjusting the position of an automobile seat according to the physical signs of the driver; adjusting the sitting posture of a driver, and acquiring joint angle data and body pressure distribution data of each area when the driver keeps stable sitting posture in the optimal driving mode;
dividing 5 evaluation levels according to driving comfort evaluation tests performed by professionals at an earlier stage, inputting various parameter data of drivers with different postures at different evaluation levels into an evaluation database, constructing an analysis platform, and determining standard values and membership functions of the 5 evaluation levels;
transmitting parameter data acquired by a distance sensor, an angle measurement sensor and a pressure sensor into an analysis platform, calculating the membership degree of each index corresponding grade in the data by using a membership degree function, constructing a membership degree matrix A, and obtaining the membership degree of each evaluation grade corresponding to each parameter data, thereby determining main factors influencing driving suitability;
fourthly, constructing a relevancy matrix B for each parameter data of the driver by using a grey relevancy method to obtain relevancy between 5 evaluation grades and corresponding parameter data;
and step five, determining the weight value of each parameter by using an entropy weight method, calculating to obtain respective comprehensive association indexes of 5 evaluation levels by combining the parameter association degrees, and taking the level corresponding to the maximum value of the comprehensive association degrees as the suitability evaluation level of the driver and the vehicle.
2. The driving suitability evaluation method based on physiological data of a driver according to claim 1, wherein in step one, the angle measurement sensors measure joint angles of elbows, knees, waist and neck; the pressure sensor measurement comprises a cushion pressure sensor and a back cushion pressure sensor, and measures the pressure values of the back, the waist, the thigh, the seat height and the seat depth; the distance sensors measure arm length, shank length, thigh length, height difference between a steering wheel and a seat, height difference between the steering wheel and a shoulder, height difference between a sight line and the seat, and height difference between the sight line and an operation table.
3. The driving suitability evaluation method based on the physiological data of the driver as claimed in claim 1, wherein the human body data of the driver includes sex, height, weight, arm length and leg length.
4. The method of claim 1, wherein in the second step, the 5 evaluation levels are respectively poor, qualified, good, and excellent.
5. The method for evaluating the driving suitability based on the physiological data of the driver as claimed in claim 1, wherein in the third step, an early warning is given to the index which is under the qualified grade, and a reasonable suggestion is given to the hidden danger of the index.
6. The driving suitability evaluation method based on driver physiological data according to claim 1, wherein the membership degree calculation of the evaluation level of each parameter data employs a membership degree function of a "normal" structure.
7. The driving suitability evaluation method based on physiological data of a driver according to claim 1, wherein the method of determining the membership degree evaluation matrix a includes: the membership degree evaluation matrix
In the formula rijAnd the evaluation result represents the membership degree of the ith index in the driver to be evaluated to the jth grade.
8. The driving suitability evaluation method based on driver physiological data according to claim 1, characterized by matrix in gray correlation method
Formula (III) ξkiAnd representing the degree of association between the evaluation index i of the driver to be evaluated and the level k.
9. The method of claim 1, wherein the formula for determining the weight of each index by the entropy weight method is w ═ w [ w ] w1w2…wi]T。
Wherein, PijSample data, Q, for driver j corresponding to index i at each levelijThe standardized result is the min-max data; m is the number of evaluation sample indexes, n is the total number of test drivers, EiInformation entropy of the ith index; w is aiIs the weight of the index i.
10. A driving suitability evaluation system based on physiological data of a driver is characterized by comprising a data acquisition device, an evaluation database and an analysis platform, wherein the data acquisition device comprises various sensors, the data acquisition device transmits collected parameter data to the evaluation database for data storage, and finally, the driving suitability is graded based on the analysis platform.
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| 冯树民: "考虑攻击程度的城市轨道交通网络抗毁性分析" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114030436A (en) * | 2021-11-24 | 2022-02-11 | 延锋汽车饰件系统有限公司 | Automobile cabin adjusting method |
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