CN108304774B - Method for evaluating influence of characteristics of trunk green belt on vision of driver - Google Patents
Method for evaluating influence of characteristics of trunk green belt on vision of driver Download PDFInfo
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Abstract
The invention relates to a method for evaluating the influence of the characteristics of a main road green belt on the vision of a driver, which comprises the following steps: driving a vehicle to run by wearing an eye tracker by a driver, recording a video by the eye tracker and recording the eye movement condition of the driver; importing the video into eye tracker analysis software, dividing a tree visibility interest area, a crown interest area, a tree height interest area, a tree shape interest area, a sky proportion interest area and the like, analyzing and exporting the number of fixation points of each interest area, and taking the proportion of the number of the fixation points of the interest area corresponding to a specific green belt index in all index fixation points as a characteristic value; establishing the relationship between the greenbelt index influence value y and each index value of the greenbelt
x1Is a height value, x2Is a tree index value, x3Is the ratio of the crown width to the road width, x4Is the sky scale, x5Is a tree trunk visibility index value, x6The value of the plant spacing is; inputting an independent variable, namely a green belt index value x into SPSS softwareiCorresponding characteristic value yiAnd solving undetermined constants a, b, c, d, e, f and g.
Description
Technical Field
The invention relates to a method for evaluating the influence of a main road green belt on the vision of a driver, and belongs to the technical field of road traffic safety.
Background
The investigation shows that the number of urban traffic accidents is on the rise in recent years, statistics shows that the traffic accident rate is about 1/3 due to comprehensive factors of drivers and environments, 90% of driving information of the drivers in the driving process is obtained through vision, the area of urban road green belts in the first visual angle area of driving occupies a large proportion, and urban main road green belts are often ignored as a recessive influence factor of urban road traffic safety. For a long time, the research on urban green belts relating to road traffic safety mainly focuses on analyzing aspects such as color and monotonicity, and the evaluation from the aspect of visual effect of drivers is lacked. Therefore, a related technology and a related method which have comprehensive functions and can evaluate the visual influence of the green belts on the driver are needed to be developed so as to reduce the negative influence of the road landscape environment on the driver and improve the driving safety of the vehicle.
Disclosure of Invention
The invention aims to provide a method for evaluating the influence of a plurality of index values of a green belt on the vision of a driver, which is objective and accurate, visually reflects the influence of the green belt on the vision of the driver in a quantitative mode, and can provide a scientific quantitative reference standard for building and trimming the green belt.
The invention relates to a method for evaluating the influence of characteristics of a main road green belt on the vision of a driver, which comprises the following steps:
1) the method comprises the following steps that a driver wears an eye tracker to drive a vehicle to run at a constant speed on a road, the eye tracker is used for recording the front visual field of the driver, and the eye movement condition of the driver is recorded;
2) importing the video recorded by the eye tracker into eye tracker analysis software, and dividing the following interest areas: the tree trunk visibility interest area is an area where a driver can clearly distinguish a tree trunk at a first visual angle and leaves are not shielded; the interest area of the crown breadth is divided into an area from the tree trunk visibility interest area to the outermost tree body extension diameter; the interest area of the tree height is divided into an area from the crown interest area to the highest position of the tree body; the tree-shaped interest area is divided into a tree body outer contour extending to a road section area; the sky proportion interest area is divided into a sky vegetation-free area above a first visual angle of a driver; the interest areas can not be overlapped and all visible parts of the road green belt need to be completely covered;
3) analyzing and deriving the number of the fixation points of each interest area by using eye tracker analysis software; the ratio of the number of the interest area fixation points corresponding to a specific green belt index of the road to the number of all the index fixation points is taken as a characteristic value. The number of all the index gazing points is the sum of the number of all the gazing points of the interest area. If the number of the interest areas corresponding to a specific green belt index is one, the number of the fixation points of the interest area is the number of the fixation points of the interest area corresponding to the specific green belt index. If there are two interest areas corresponding to a specific green belt index, the sum of the number of the fixation points of the two interest areas is the number of the fixation points of the interest area corresponding to the specific green belt index.
4) Constructing a relation between the greenbelt index influence value y and each index value of the greenbelt:
in the formula, a, b, c, d, e, f and g are undetermined constants; x is the number of1Is a height value, i.e. the distance of the tree from the ground up to the top of the tree, in m; x is the number of2The tree form index value is a tree form, namely a space structure of the tree, is divided into a pyramidal shape, a spherical shape and an umbrella shape, is determined by the product of the sight distance of a driver and the area of a tangible part above a trunk at the sight angle of the driver, and the unit is m3；x3The crown width is the ratio of the crown width to the road width, and the crown width of the tree is the average value of the widths of the trees in the north-south direction and the east-west direction; x is the number of4The scale of sky is the ratio of the effective sky area at the first visual angle of the driver to the total area of the visual field of the driver, and is dimensionless; x is the number of5The tree trunk visibility index value is determined by the product of the driver sight distance and the driver sight angle visible tree trunk area, and the unit is m3；x6The plant spacing value is the spacing distance between two adjacent trees, and the unit is m;
5) inputting an independent variable, namely a green belt index value x into SPSS softwareiDependent variable, i.e. characteristic value y corresponding to greenbelt index valueiAnd solving the undetermined constant.
Procedure for solving the undetermined constants:
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA＝PIN(.05)POUT(.10)
/NOORIGIN
influence value of/DePENDENT
/METHOD＝ENTER x1x2x3x4(...)
/SCATTERPLOT＝(*ZRESID,*ZPRED)
/RESIDUALS DURBIN HISTOGRAM(ZRESID)NORMPROB(ZRESID).
Evaluation models at different speeds:
as a further improvement to the method for evaluating the influence of the characteristics of the main road green belt on the vision of the driver, the step 4) is: constructing a relation between the greenbelt index influence value y and each index value of the greenbelt:
wherein i and h are constants to be defined, vSpeed measuring deviceAnd designing the speed of the vehicle for the road.
As a further improvement of the method for evaluating the influence of the characteristics of the main road green belt on the vision of the driver, in the step 3), when the number of the fixation points of each interest area is analyzed and derived by using eye tracker analysis software, the video capturing time is about 20s, and a road section where the vehicle does not pass through the main intersection needs to be selected when the video capturing is performed.
As a further improvement to the described method for evaluating the visual impact of the characteristics of the main road green belt on the driver, in step 1) the vehicle is tested at constant speeds of 40km/h, 45km/h, 50km/h, 55km/h, 60km/h, respectively, on the same road section.
As a further improvement of the method for evaluating the influence of the characteristics of the main road green belt on the vision of the driver, the eye tracker is a Tobii Glasses eye tracker in Sweden, and the analysis software of the eye tracker is ErogLAB.
As a further improvement of the method for evaluating the influence of the characteristics of the main road green belt on the vision of the driver, the grade of the influence degree value y is divided; according to a driver grading method and an expert grading method, a specific interval of grading the influence degree y is made; the grade interval is divided into 3 parts with proper visual effect, general visual effect and poor visual effect; and judging whether the green belt construction is reasonable or not according to the judgment result.
The invention has the beneficial effects that:
the evaluation system adopts an eye tracker experiment method to carry out an on-road experiment of a real vehicle, and captures the position and the track of a fixation point of a driver on a green belt through the eye tracker, so that the distribution of the fixation point of the driver in the driving process is scientifically analyzed, and the influence of subjective factors on an experiment result is eliminated. The method can be used for judging whether the existing green belt has excessive influence on the visual effect of the driver, and gives a certain quantitative reference standard to related departments during construction and trimming of the green belt, so that the specific levels of all indexes of the green belt are reasonably selected, comfortable aesthetic feeling and proper alertness and excitation are provided for the driver, and the driving stability is ensured.
Drawings
FIG. 1 is a schematic representation of a pyramidal tree;
FIG. 2 is a spherical tree diagram;
FIG. 3 is a schematic diagram of an umbrella tree;
FIG. 4 is a schematic view of a region of interest partition;
FIG. 5 is a normal P-P plot of the regression normalized residuals.
Detailed Description
1. Evaluation of System composition
1.1 evaluation object
The method is suitable for evaluating the indexes which are visible, measurable (including direct or indirect measurement or estimation), comparable and quantifiable at the first visual angle of the driver of the typical main road green belt, and the indexes need to generate certain visual stimulation to the driver. Such as height of green belt, crown width, tree trunk visibility, sky proportion, plant spacing, etc.
1.2 required instruments and personnel
1) Hardware equipment: a camera; a laser range finder; a reflector; a meter ruler; a color comparison card; eye tracker (Tobii Glasses can be used), and the like.
2) Analysis software: SPSS; ErogLAB (note: the product is software for eye movement instrument of Tobii Glasses, Sweden); EXCEL, and the like.
3) The required personnel: experienced drivers are more than one, and need to comprehensively consider the driving age, sex and the like.
2. Evaluation procedure
2.1 Delphi method for Green Belt index screening
Because the green belt index coverage is large, if each index is subjected to quantitative analysis, the workload and complexity of the experiment are increased. The degree of visual influence of the green belt vegetation on the driver is different for different planting forms and types, so that the specific road can be analyzed in a targeted manner. The evaluation system adopts a Delphi method to screen the indexes of the green belts, and the method is mainly characterized in that a questionnaire is drawn up by an investigator and inquires the members of the expert group respectively in a letter mode according to a set program; and the expert group members submit opinions in an anonymous manner (mail). After several times of repeated inquiry and feedback, the opinions of the expert group members gradually tend to be concentrated, and finally, a collective judgment result with high accuracy is obtained. The formulation of the expert scoring table needs to include specific explanation of evaluation indexes, the template is shown in table 1, experts with relevant work and research experience are invited to evaluate all indexes of green belts of the selected roads, and specific numerical value scoring is performed on the indexes provided by all green belts by combining the obvious index characteristic differences of different tree species, driving visual angles, seasonal alternation and other influence factors. The scoring criteria were: the influence was greatly 100 points, the influence was greatly 75 points, the influence was generally 50 points, the influence was less 25 points, and almost no influence was 0 points.
TABLE 1 Delphi-method expert scoring table of visual impact of green belt indexes on drivers
And after the return receipt of the rating table is finished, comprehensively analyzing the expert opinions, and calculating the concentration degree and the variation coefficient of the indexes. Finally, the indexes with larger concentration degree and smaller coefficient of variation are selected.
1) Degree of concentration
In the formula, MjThe concentration degree of expert opinions of the ith index is determined by the size of the expert opinions of the ith index; m isjA score value representing participation in the jth index; cijIndicating the value of the i-th expert's score for the j-th index.
2) Coefficient of variation
In the formula, σiIs a standard deviation representing expert to j; vjThe coefficient of variation is represented, and the coefficient of variation mainly reflects the degree of coordination of the expert opinions (i.e. convergence of the expert opinions), and is an important index representing the relative fluctuation magnitude of the evaluation. The smaller the coefficient of variation, the more focused the opinion representing the expert.
2.2 quantitative analysis of greenbelt indexes
After the indexes are screened, on-site data acquisition needs to be carried out on the indexes of the experimental roads, and quantitative analysis needs to be carried out on the indexes by using related tools and instruments.
The height index of the green belt can be carried out by a laser range finder, the height of the tree body is measured by utilizing a triangular strand hooking principle (depression, elevation angle and horizontal distance), and the measurement result is in meters.
The crown width index of the green belt can be measured by means of a crown projection measurement method, assuming that the projection of the crown on the ground is an ellipse, measuring the long axis and the short axis of the ellipse, taking the mean value, and taking the meter as the unit of the measurement result.
The greenbelt plant spacing index can be measured by means of a laser range finder and a matched reflector, the distance between two adjacent trees is the plant spacing, and the measurement result is in meters.
The sky proportion index of the green belt can be recorded at a first visual angle of a driver by means of a camera, and the ratio of the sky area to the whole picture area of each frame of picture is measured by a graticule method when the picture is played on a screen after the video recording is finished, so that the average value is obtained.
The visibility index of the trunk of the green belt is measured, and the visibility of the trunk is different from the visual perception of a driver due to the different distance between the driver and the tree body. The closer the distance, the larger the tree will appear, and vice versa, the smaller it will appear. Thus, the trunk visibility is determined as the product of the driver's apparent distance and the driver's apparent angle visible trunk area.
The measurement of the tree form index of the green belt is determined according to the outline shape of the tree body, and the tree form of the conventional trunk road pavement tree can be roughly divided into a pointed tower shape (figure 1), a spherical shape (figure 2) and an umbrella shape (figure 3). The principle of the visibility measurement of the trunk is the same, and the visibility measurement principle is determined by the product of the sight distance of the driver and the area of a tangible part above the trunk at the sight angle of the driver.
2.3 eye-tracker real vehicle test determination of index characteristic value
The on-road experiment of the real vehicle is carried out on the premise of safe driving, an experimental object wears an eye tracker to make a series of preparations such as visual point alignment, the driving vehicle drives to the road to be evaluated, an observer sits at a copilot position to be responsible for starting experiment software, and when the target road is reached, the software is opened to record a video and record the visual change condition of the driver; and when the target road is driven out, checking whether the software data record is completely stored or not and closing the eye tracker. In order to analyze the visual influence of the green belts on the driver at different speeds, the driver needs to change the speed after the same road finishes driving and continue the test. According to the urban road design specification (CJJ37-2016), the driving speed of the urban arterial road is designed to be 40-60km/h, and drivers change the driving speed and respectively test the speed at 40km/h, 45km/h, 50km/h, 55km/h and 60 km/h.
After the experiment is finished, videos recorded by the eye tracker are guided into ErogLAB software to divide interest areas, the video capturing time is preferably about 20s, road sections which do not pass through a main intersection and have little road traffic state change need to be selected during video capturing, the road section part with abnormal traffic is abandoned, and the videos need to be captured separately at the positions of the main intersection, a pedestrian crossing, a large public arrangement entrance and the like. Now, the interest area division is described by taking a main road in a certain city as an example (fig. 4):
tree height interest areas 1, 7; coronary interest areas 2 and 8; tree trunk visibility interest areas 3 and 9; tree interest areas 4, 6; sky proportion region of interest 5. The interest areas 3 and 9 of the tree trunk visibility in the figure are divided into areas which can clearly distinguish the tree trunk from the driver at the first visual angle and are not blocked by the leaves. The interest areas 2 and 8 of the crown breadth are divided into areas from the tree trunk visibility interest area to the outermost part of the tree body extension diameter. Interest areas 1 and 7 of the tree height are divided into areas from the crown interest area to the highest position of the tree body. The tree-shaped interest areas 4 and 6 are divided into tree body outer contours extending to road section areas. The sky proportional interest zone 5 is divided into regions where no vegetation is sheltered from the sky above the first view angle of the driver. Other indexes can be divided according to actual road conditions and green belt characteristics, but the interest areas are required to be non-overlapped and all visible parts of the road green belt need to be completely covered. For example, the interested area of the green belt plant spacing index is a gap part between two adjacent trees, but the gap part cannot be expanded into a non-motor vehicle lane or a sidewalk area.
After the interest areas are divided, a driver fixation point distribution map and a heat point map can be automatically generated. The number of the fixation points of each interest area can be derived by using a software self-static tool. After the fixation point analysis of all experimental roads is finished, the ratio of the number of the interest area fixation points corresponding to a specific green belt index of the road to the number of all index fixation points (namely the sum of the number of the fixation points of all interest areas) is used as a characteristic value.
2.4 establishment of evaluation model
The process of establishing the model can be divided into the following three steps:
1) analyzing the experimental data and eliminating abnormal data
The evaluation model is established on the basis that plant index values are used as independent variables and characteristic values are used as dependent variables. In order to enhance the usability and accuracy of the evaluation result, a Grubbs test method is used for removing abnormal data. Is calculated inAverage of characteristic values of the same index for different drivers
And a standard deviation S. The value of G was calculated and the Grubbs inspection table (Table 2) was consulted based on the number of measurements and confidence requirements. Comparison GComputingAnd GWatch (A)If G isComputing＞GWatch (A)Discard, otherwise, keep.
Wherein S is a standard deviation value; n total number of drivers.
In the formula, XnIs the characteristic value of the nth driver.
TABLE 2 Grubbbs inspection chart
2) Reasonable hypothesis is put forward, and the intrinsic quantitative relation is analyzed
The height of different green belts has comparatively obvious difference, and the driver is in the driving process, along with the increase of the speed of a motor vehicle, and the eyesight can reduce. Therefore, when the height of the tree body is within the dynamic visual field range of the driver, the visual effect on the driver is increased along with the increase of the height, but the visual effect is more gentle along with the increase of the height beyond the visual field range. The height indicator should thus exhibit a certain logarithmic relationship with the gaze characteristic value of the driver.
Different tree forms can directly influence the degree that the driver gazed at to the crown width of cloth, through the research, when the tree form is spherical, can give the driver homogeneous global sense, and when the turriform then top-down tree body radius is constantly changing, therefore can be higher than the sphere to driver's the attraction degree of gazing. Therefore, it can be inferred that the tree-shaped feature and the influence value have a certain linear relationship
The crown width index of the green belt is considered in combination with the road width, and the influence of the width on the crown width is embodied in that when the road width is larger, the size of the crown width is characterized by appearing in the field of vision of the driver in a small proportion, and when the road width is smaller, the size of the crown width is characterized by appearing in the field of vision of the driver in a large proportion.
Due to the different distances between the driver and the tree body, the visibility of the tree trunk is different from the visual perception of the driver. The closer the distance, the larger the tree will appear, and vice versa, the smaller it will appear. The tree trunk visibility index is assumed to be linear, and the SPSS output result is used to judge whether the fitting degree and the significance meet the regulations.
The sky proportion index of the green belt is generally between 1% and 50%, the quantitative fluctuation of the index is small, and the relationship between the index and the visual influence of a driver can be described by directly adopting a simple unitary linear relationship.
When the plant spacing of the green belt is relatively small, a driver can feel slight dazzling, when the plant spacing is relatively large, the driver looks too spacious, and the driver can watch the scenery between the two plants too much. Therefore, the relationship between the plant spacing index and the visual influence can adopt a quadratic parabola form. The optimal planting distance is found, and the dazzling feeling of the individual drivers is avoided, and the spacious visual feeling of the drivers is also avoided.
In summary, the assumption is that the relationship between the greenbelt index influence value and the index value thereof is
In the formula, a, b, c, d, e, f and g are undetermined constants; x is the number of1Is a height value, i.e. the distance of the tree from the ground up to the top of the tree, in m; x is the number of2The tree form index value is a tree form, namely a space structure of the tree, is divided into a pyramidal shape, a spherical shape and an umbrella shape, is determined by the product of the sight distance of a driver and the area of a tangible part above a trunk at the sight angle of the driver, and the unit is m3；x3The crown width and road widthIn comparison, the crown width of the tree is the average value of the widths of the tree in the north-south direction and the east-west direction; x is the number of4The scale of sky is the ratio of the effective sky area at the first visual angle of the driver to the total area of the visual field of the driver, and is dimensionless; x is the number of5The tree trunk visibility index value is determined by the product of the driver sight distance and the driver sight angle visible tree trunk area, and the unit is m3；x6The plant spacing value is the spacing distance between two adjacent trees and is expressed in m.
The influence degree under different speeds can be obtained by changing the driving speed of the driver on the same road environment and analyzing the fixation point characteristics of the driver under different speeds. Along with the increase of the vehicle speed, the dynamic vision range of the driver is reduced, and along with the increase of the vehicle speed, the driver can concentrate more on the road traffic environment instead of the road landscape environment, so that the vehicle speed and the green belt influence value are in negative correlation. And obtaining greenbelt influence models at different design speeds.
Wherein i and h are constants to be defined, vSpeed measuring deviceAnd designing the speed of the vehicle for the road.
3) Solving undetermined constants
Inputting plant index value independent variable x in SPSS softwareiDependent variable y of eigenvalueiAnd solving the undetermined constant.
The five index languages of the specific trunk road, the height, the crown width, the tree visibility, the sky proportion and the tree form are coded as follows (if other index variables are needed, other variables are needed to be input after the sixth/METHOD is coded as ENTER):
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA＝PIN(.05)POUT(.10)
/NOORIGIN
influence value of/DePENDENT
Tree-shaped sky proportion trunk visibility of ratio of height, crown, breadth and width of ENTER
/SCATTERPLOT＝(*ZRESID,*ZPRED)
/RESIDUALS DURBIN HISTOGRAM(ZRESID)NORMPROB(ZRESID).
After running the program, the input results show the following (tables 3, 4, 5, fig. 5):
TABLE 3 variables input/removeda
a. Dependent variable: influence value
b. All variables requested have been entered.
Table 4 abstract of modelb
a. Prediction variables: constant, tree form, tree visibility, height, crown width to road ratio, sky ratio
b. Dependent variable: influence value
TABLE 5 coefficientsa
a. Dependent variable: influence value
Wherein: in Table 4, R-side represents the degree of fitting, and R-side.gtoreq.0.7 indicates that the degree of fitting is good. A debin-watson value close to 2 indicates that there is no sequence correlation and the regression is not a pseudo regression. In table 5, the non-normalized coefficients correspond to the coefficients of the variables. And the significance is less than or equal to 0.05, which indicates that the independent variable has significant influence on the dependent variable. VIF ≦ 5 indicates that no co-linearity exists between the respective variables.
According to the requirements, the results are checked, and the results are found to meet the requirements. Thereby establishing a multivariate function modeling model for obtaining five indexes of greenbelt height, crown width, tree visibility, sky proportion and tree shape
y＝1.4lnx1+3.1x2+1.8x3-0.83x4+x5+3.2 (8)
Similarly, after a speed variable is introduced into the SPSS, a multivariate function model based on five indexes of evaluation of road green belt height, crown width, tree visibility, sky proportion and tree shape at different speeds can be obtained:
y＝1.4lnx1+3.1x2+1.8x3-0.83x4+x5-0.17vspeed measuring device+h (9)
In the formula, vSpeed measuring deviceA design vehicle speed for the road; the values for h are given in Table 3 below.
TABLE 6 h value suggestion Table
| vSpeed measuring device(km/h) | h |
| 40 | 10.00 |
| 45 | 10.85 |
| 50 | 11.70 |
| 55 | 12.55 |
| 60 | 13.40 |
If other road indexes are still needed, the steps are carried out according to the steps, and then the evaluation model of the specific road green belt indexes can be obtained.
2.5 rating of the impact value y
In the process, the influence value of the green belt to be evaluated can be obtained by substituting each index value of the green belt into the evaluation model. However, whether the green belt is reasonably constructed or not is judged, and a certain evaluation grade needs to be established, namely, the visual influence of the green belt on a driver is different in different influence value intervals, and the evaluation grade can be divided into large influence, common influence, small influence and almost no influence.
The evaluation grade is established on the basis of the expert scoring method and the driver scoring method, and in order to enable the evaluation standard to have stronger universality and accuracy, a large amount of road landscape data needs to be collected, and drivers covering different driving ages, sexes, heights, eyesight and other characteristics are invited to carry out real-vehicle test scoring. If a large number of real vehicle tests cannot be carried out due to the limitation of capital, time and equipment, a driving simulation cabin experimental method can be adopted on the basis of real vehicle tests with certain quantity and quality to effectively simulate a test scene, and the tests are finished indoors.
The driver scoring method is that a driver scores the influence degree of each index in the real vehicle test process. The scoring standard adopts a 5-point system: the influence is greatly 5 points; the influence is more divided into points; the effect is generally 3 points; the influence is less 2 points; almost no influence (proper layout) was 1 minute. And after the grading is finished, performing sub-classification on the indexes of all the experimental roads. For example: sub-classifications for height may be 0-5m, 5-10m, 10-15m, 15-20m, and the like. The sub-classes of the tree are pyramidal, umbrella, spherical, etc. And (5) bringing different scores into corresponding sub-classifications, and taking a mean value after conclusion. And (3) establishing a hierarchical analysis tree diagram by using hierarchical analysis software YAAHP, and inputting the calculated mean value to obtain the weight value of the parent index.
The expert scoring rule is to invite experts with related work and research experience to evaluate whether each index of the green belt of the selected road is properly laid, and the method adopts 10-point system: the index has proper visual influence degree on the driver, and is reasonably distributed (1-3 min); the index has overlarge influence on the vision of a driver, and trimming is recommended (4-6 points); the index has great influence on the vision of a driver, has potential safety hazard and needs to be repaired (7 points and above).
After the steps are finished, performing mathematical operation (formula 10) on the index weight value and an expert scoring method, and determining the comprehensive evaluation index of the road index.
In the formula: b is a comprehensive evaluation index; x is the weight value of each index; fiScoring a value for a valid expert; n is the total number of experts.
And (3) carrying out numerical value arrangement on the comprehensive evaluation indexes of the road indexes, and selecting roads with proper visual effect (the comprehensive evaluation indexes are lower and generally account for 15% -20% of the total number of the experimental roads), general visual effect (the comprehensive evaluation indexes are medium and generally account for 30% -40% of the total number of the experimental roads) and poor visual effect (the comprehensive evaluation indexes are higher and generally account for 15% -20% of the total number of the experimental roads).
After the visual effect of the roads is calibrated, the index parameter values of the roads are substituted into the evaluation model to obtain evaluation intervals of different influence degrees divided by evaluation levels, and the intervals can be used as evaluation standards to measure whether the construction of the green belt to be evaluated is reasonable or not and whether overlarge visual stimulation is generated to a driver or not. For general or poor visual effect, the specific levels of all factors are reasonably selected according to a scoring interval with proper visual effect, and the green belt scoring is enabled to fall into the interval by combining an evaluation model, so that the drivers are provided with comfortable aesthetic feeling and proper alertness and excitement, and the driving stability is ensured.
Through the experiment of the steps on the green belts of certain city trunk roads, the optimal scoring intervals of five indexes, namely the height of the green belts, the tree shape, the visibility of the trunk, the sky proportion and the crown width, are preliminarily obtained to be [15,20], the general scoring intervals are [12,15 ] and (20,24], and the rest influence degree values which are not in the intervals are roads with poor visual effects, and the specific levels of all factors are required to be reasonably adjusted to enable the green belt scores to fall into [15,20 ]. Due to the limitations of time and experimental equipment, and the influence of seasons on road vegetation, the scoring interval still needs to be further accurate and optimized.
Claims (6)
1. A method for evaluating the influence of the characteristics of a main road green belt on the vision of a driver is characterized by comprising the following steps: it comprises the following steps:
1) the method comprises the following steps that a driver wears an eye tracker to drive a vehicle to run at a constant speed on a road, the eye tracker is used for recording the front visual field of the driver, and the eye movement condition of the driver is recorded;
2) importing the video recorded by the eye tracker into eye tracker analysis software, and dividing the following interest areas: the tree trunk visibility interest area is an area where a driver can clearly distinguish a tree trunk at a first visual angle and leaves are not shielded; the crown interesting area is divided into an area from the tree trunk visibility interesting area to the outermost tree body extending diameter; the tree high interest area is divided into an area from the crown interest area to the highest position of the tree body; the tree-shaped interest area is divided into a tree body outer contour extending to a road section area; the sky proportion interest area is divided into a sky vegetation-free area above a first visual angle of a driver; the interest areas can not be overlapped and all visible parts of the road green belt need to be completely covered;
3) analyzing and deriving the number of the fixation points of each interest area by using eye tracker analysis software; taking the ratio of the number of the interest area fixation points corresponding to a specific green belt index of the road to the number of all the index fixation points as a characteristic value;
4) constructing a relation between the greenbelt index influence value y and each index value of the greenbelt:
in the formula, a, b, c, d, e, f, g, i and h are undetermined constants; x is the number of1Is a height value, i.e. the distance of the tree from the ground up to the top of the tree, in m; x is the number of2Is a tree form index value, i.e. the space structure of the tree, and is divided into a tip towerThe shape, sphere and umbrella shape are determined by the product of the sight distance of the driver and the area of the visible part above the trunk at the sight angle of the driver, and the unit is m3；x3The crown width is the ratio of the crown width to the road width, and the crown width of the tree is the average value of the widths of the trees in the north-south direction and the east-west direction; x is the number of4The scale of sky is the ratio of the effective sky area at the first visual angle of the driver to the total area of the visual field of the driver, and is dimensionless; x is the number of5The tree trunk visibility index value is determined by the product of the driver sight distance and the driver sight angle visible tree trunk area, and the unit is m3；x6The plant spacing value is the spacing distance between two adjacent trees, and the unit is m; v. ofSpeed measuring deviceDesigning the speed of a road in km/h;
5) inputting an independent variable, namely a green belt index value x into SPSS softwareiAnd solving the undetermined constant according to the corresponding influence value y.
2. The method of evaluating the visual impact of the characteristics of a main road green belt on a driver as claimed in claim 1, wherein: in the step 3), when eye tracker analysis software is used for analyzing and deriving the number of the fixation points of each interest area, the video capturing time is 20s, and a road section where the vehicle does not pass through the main intersection needs to be selected during video capturing.
3. The method of evaluating the visual impact of the characteristics of a main road green belt on a driver as claimed in claim 1, wherein: in the step 1), the vehicles are tested at constant speeds of 40km/h, 45km/h, 50km/h, 55km/h and 60km/h respectively on the same road section.
4. The method of evaluating the visual impact of the characteristics of a main road green belt on a driver as claimed in claim 1, wherein: the eye tracker is a Tobii Glasses eye tracker in Sweden, and the analysis software of the eye tracker is ErogLAB.
5. The method of evaluating the visual impact of the characteristics of a main road green belt on a driver as claimed in claim 1, wherein: dividing the grade of the influence degree value y; according to a driver grading method and an expert grading method, a specific interval of grading the influence degree y is made; the grade interval is divided into 3 parts with proper visual effect, general visual effect and poor visual effect; and judging whether the green belt construction is reasonable or not according to the judgment result.
6. The method of evaluating the visual impact of the characteristics of a main road green belt on a driver as claimed in claim 1, wherein: the SPSS software was used to perform regression analysis of the multivariate linear function and the unit nonlinear function.
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