CN108304774A - A method of evaluation main line greenbelt characteristic influences driver's vision - Google Patents

Abstract

The present invention is a kind of method that evaluation main line greenbelt characteristic influences driver's vision, step：Eye tracker is worn with driver and drives vehicle traveling, is recorded a video with eye tracker and records driver's eye movement situation；Video guide is entered into eye tracker analysis software, carry out the divisions such as trunk visibility region of interest, hat width region of interest, the high region of interest of tree, tree-like region of interest, sky ratio region of interest, each region of interest blinkpunkt number is analyzed and exports, the value characterized by the corresponding region of interest blinkpunkt number of a certain specific greenbelt index accounts for the ratio of all index blinkpunkt numbers；Build the relationship between greenbelt Index Influence angle value y and each index value of greenbeltx₁For height value, x₂For tree-like index value, x₃For hat width and the ratio between have a lot of social connections, x₄For sky ratio, x₅For trunk visibility index value, x₆For spacing in the rows value；Independent variable, that is, greenbelt index value x is inputted in SPSS softwares_iWith corresponding characteristic value y_i, solve undetermined constant a, b, c, d, e, f, g.

Description

A Method for Evaluating the Effects of Green Belt Characteristics on Main Roads on Drivers' Vision

technical field

The invention relates to a method for evaluating the visual impact of a trunk road green belt on a driver, and belongs to the technical field of road traffic safety.

Background technique

According to the survey, the number of urban traffic accidents has been on the rise in recent years. Statistics show that the traffic accident rate caused by the comprehensive factors of the driver and the environment accounts for about 1/3, and 90% of the driving information of the driver is obtained through vision. , while the area of green belts on urban roads accounts for a large proportion in the first perspective of driving, the green belts on urban arterial roads, as a hidden factor affecting urban road traffic safety, are often ignored. For a long time, the research on urban green belts related to road traffic safety has mainly focused on the analysis of color and monotony, and lacks the evaluation of the driver's visual effect. Therefore, it is necessary to develop a related technology and method with comprehensive functions that can evaluate the visual impact of green belts on drivers, so as to reduce the negative impact of road landscape environment on drivers and improve the safety of vehicles.

Contents of the invention

The purpose of the present invention is to provide a method for evaluating the influence of multiple index values of the green belt on the driver's vision. , Repair the green belt to provide a more scientific quantitative reference standard.

The method for evaluating the characteristics of trunk road green belts of the present invention on the driver's visual impact, it may further comprise the steps:

1) The driver wears an eye tracker to drive the vehicle at a constant speed on the road, and uses the eye tracker to record the driver's front vision and record the driver's eye movement at the same time;

2) Import the video recorded by the eye tracker into the eye tracker analysis software, and divide the interest areas as follows: the tree trunk visibility interest area is the area where the driver can clearly distinguish the trunk of the tree from the first perspective, and the leaves are not blocked; The area of interest of the tree height is divided into the area above the trunk visibility area of interest to the outermost part of the tree extension diameter; the area of interest of the tree height is divided into the area above the crown area of interest to the highest part of the tree body; the area of interest of the tree shape is divided into the area outside the tree body The outline extends to the road section area; the sky scale interest area is divided into the area above the driver's first perspective without vegetation in the sky; each interest area cannot overlap and must completely cover all visible parts of the road green belt;

3) Use the eye tracker analysis software to analyze and derive the number of fixation points in each area of interest; the characteristic value is the ratio of the number of fixation points in the interest area corresponding to a specific green belt index of the road to the number of fixation points in all indicators. The number of fixation points of all indicators is the sum of the number of fixation points of all regions of interest. If there is one interest area corresponding to a specific green belt index, the number of gaze points in this interest area is the number of gaze points in the interest area corresponding to the specific green belt index. If there are two AOIs corresponding to a specific green belt index, the sum of the fixation points of the two interest areas is the number of fixation points in the interest area corresponding to the specific green belt indicator.

4) Construct the relationship between the green belt index influence value y and the green belt index values:

In the formula, a, b, c, d, e, f, and g are undetermined constants; x ₁ is the height value, that is, the distance from the ground to the top of the tree, in m; x ₂ is the tree index value, The tree shape is the spatial structure of the tree, which is divided into spire shape, spherical shape and umbrella shape. It is determined by the product of the driver's sight distance and the area of the visible part above the trunk at the driver's perspective, and the unit is m ³ ; x ₃ is the crown width and road width The tree canopy is the average width of the trees in the north-south and east-west directions; x ₄ is the sky ratio, that is, the ratio of the effective sky area at the driver's first perspective to the driver's total field of view, dimensionless; x ₅ is the trunk The visibility index value is determined by the product of the driver's sight distance and the visible tree trunk area at the driver's perspective, and the unit is m ³ ; x ₆ is the distance between trees, that is, the distance between two adjacent trees, and the unit is m;

5) Input the independent variable, i.e. the green belt index value x _i , and the dependent variable, i.e. the eigenvalue y _i corresponding to the green belt index value, in the SPSS software to solve the undetermined constant.

Procedures for solving undetermined constants:

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA COLLIN TOL

/CRITERIA=PIN(.05)POUT(.10)

/NOORIGIN

/DEPENDENT influence value

/METHOD=ENTER x ₁ x ₂ x ₃ x ₄ (...)

/SCATTERPLOT=(*ZRESID,*ZPRED)

/RESIDUALS DURBIN HISTOGRAM(ZRESID)NORMPROB(ZRESID).

Evaluation models at different speeds:

As a further improvement to the method for evaluating the characteristics of the main road green belt on the driver's visual impact, the step 4) is: construct the relationship between the green belt index influence value y and each index value of the green belt:

In the formula, i and h are constants to be defined, and v _speed is the road design speed.

As a further improvement to the method for evaluating the characteristics of the main road green belt on the driver's visual impact, in step 3), when using the eye tracker analysis software to analyze and derive the number of fixation points in each interest area, the video capture time is At about 20s, it is necessary to select a road section where the vehicle has not passed the main intersection when performing video capture.

As a further improvement to the method for evaluating the impact of the characteristics of the main road green belt on the driver's vision, in step 1), the vehicle travels at 40km/h, 45km/h, 50km/h, 55km/h, and 60km respectively on the same road section. The test was carried out at a constant speed of /h.

As a further improvement to the method for evaluating the impact of the characteristics of the green belt on the main road on the driver's vision, the eye tracker is a Swedish Tobii Glasses eye tracker, and the eye tracker analysis software is ErogLAB.

As a further improvement to the method for evaluating the impact of the characteristics of the main road green belt on the driver's vision, the degree of influence value y is divided into grades; according to the driver's scoring method and the expert scoring method, the specific intervals for the grade division of the degree of influence y are formulated ; The grade interval is divided into three parts: suitable visual effect, general visual effect, and poor visual effect; based on this, it is judged whether the green belt construction is reasonable.

Beneficial effects of the present invention:

This evaluation system adopts the eye tracker experiment method to carry out real vehicle on-road experiments. The eye tracker captures the position and trajectory of the driver's gaze point on the green belt, so as to scientifically analyze the distribution of the driver's gaze point during driving, and eliminate subjective Factors that affect the experimental results. This method can be used to judge whether the existing green belt will have an excessive impact on the driver's visual effect, and to give the relevant departments a certain quantitative reference standard when constructing and repairing the green belt, so that they can reasonably select the specific level of each index of the green belt, so as to Give the driver a comfortable aesthetic feeling and appropriate vigilance and excitement to ensure driving stability.

Description of drawings

Fig. 1 is a schematic diagram of a spire tree;

Fig. 2 is a schematic diagram of a spherical tree;

Fig. 3 is a schematic diagram of an umbrella tree;

The schematic diagram of the area of interest division in Figure 4;

Figure 5 Normal P-P plot of regression standardized residuals.

Detailed ways

1. Composition of the evaluation system

1.1 Evaluation object

This method is suitable for evaluating the indicators that are visible, measurable (including direct or indirect method measurement or estimation), comparable and quantifiable in the driver's first perspective of the typical arterial green belt, and the indicators need to be evaluated by the driver. Produce some visual stimulation. For example, the height of the green belt, the width of the crown, the visibility of the trunk, the proportion of the sky, the distance between the plants, etc.

1.2 Required instruments and personnel

1) Hardware equipment: camera; laser rangefinder; reflector; meter ruler; color card; eye tracker (TobiiGlasses can be used), etc.

2) Analysis software: SPSS; ErogLAB (note: this product is the supporting software for Tobii Glasses eye tracker in Sweden); EXCEL, etc.

3) Required personnel: There are several experienced drivers, and it is required to comprehensively consider their driving experience, age, gender and other aspects.

2. Evaluation steps

2.1 Delphi method for screening green belt indicators

Due to the large coverage of the green belt indicators, if each indicator is quantitatively analyzed, the workload and complexity of the experiment will be increased. Different planting forms and types of green belt vegetation have different degrees of visual impact on drivers, so specific roads can be analyzed in a targeted manner. This evaluation system adopts the Delphi method to screen the green belt indicators. In this method, the investigator draws up the questionnaire and consults the members of the expert group by letter according to the established procedures; and the members of the expert group are anonymous. (Letter) Submit comments. After several repeated consultations and feedbacks, the opinions of the members of the expert group gradually tended to be concentrated, and finally a collective judgment result with a high accuracy rate was obtained. The formulation of the expert scoring table must include specific explanations of the evaluation indicators. The template is shown in Table 1. Experts with relevant work and research experience are invited to evaluate the indicators of the green belt of the selected road, combined with the more obvious indicator characteristics of different tree species Influencing factors such as differences, driving angles, and seasonal alternation are used to score the indicators provided by each green belt with specific values. The scoring criteria are: 100 points for great impact, 75 points for large impact, 50 points for general impact, 25 points for small impact, and 0 points for almost no impact.

Table 1. Delphi expert scoring table for the impact of green belt indicators on the driver's vision

After the return receipt of the scoring form is completed, the experts' opinions will be comprehensively analyzed to calculate the degree of concentration and coefficient of variation of the indicators. In the end, select an index with a large degree of concentration and a small coefficient of variation.

1) Degree of concentration

In the formula, M _j is the degree of concentration of expert opinions on the i-th index, and its size determines the importance of the index; m _j represents the scoring value of participating in the j-th index; C _ij represents the i-th expert’s opinion on the j-th index rating value.

2) Coefficient of variation

In the formula, σ _i represents the standard deviation of the experts for the jth; V _j represents the coefficient of variation, which mainly reflects the degree of coordination of expert opinions (that is, the convergence of expert opinions), and is an important index representing the evaluation of relative fluctuations . The smaller the coefficient of variation, the more concentrated the opinions of experts.

2.2 Quantitative analysis of green belt indicators

After the indicators are screened, it is necessary to collect field data on the indicators of the experimental road, and use relevant tools and instruments for quantitative analysis.

The height indicator of the green belt can be carried out with the help of a laser rangefinder, and the triangular Pythagorean principle (pitch, elevation angle and horizontal distance) is used to measure the height of the tree body, and the measurement result is in meters.

The canopy index of the green belt can be measured with the help of tree crown projection, assuming that the projection of the tree crown on the ground is an ellipse, measure the major axis and minor axis of the ellipse, and take the average value, and the measurement result is in meters.

The plant-to-plant distance index of the green belt can be measured with the help of a laser rangefinder and a matching reflector. The distance between two adjacent trees is the plant-to-plant distance, and the measurement result is in meters.

The green belt sky ratio index can be recorded by the camera at the first perspective of the driver. After the video is played on the screen, the ratio of the sky area to the entire screen area is measured for each frame by the grid method, and the average value is taken.

In the determination of the tree trunk visibility index in the green belt, because the distance between the driver and the tree is different, the tree trunk visibility is also different from the driver's visual perception. The closer the distance, the larger the tree will appear, and vice versa, the smaller it will be. Therefore, the tree trunk visibility is determined by the product of the driver's sight distance and the visible tree trunk area at the driver's perspective.

The determination of the tree shape index of the green belt is determined according to the outer contour shape of the tree body. The tree shape of conventional arterial roads can be roughly divided into steeple shape (Figure 1), spherical shape (Figure 2), and umbrella shape (Figure 3). The principle is the same as the trunk visibility measurement, which is determined by the product of the driver's sight distance and the area of the visible part above the trunk at the driver's perspective.

2.3 Determining the eigenvalues of the indicators through the real vehicle test of the eye tracker

The real vehicle road test should be carried out under the premise of driving safety. The test subjects wear the eye tracker and make a series of preparations such as point of view alignment, drive the vehicle to the road to be evaluated, and the observer sits in the co-pilot position and is responsible for opening the test software. , when reaching the target road, open the software to record and record the driver's visual changes; when driving out of the target road, check whether the software data records are complete and save them, and close the eye tracker. In order to analyze the visual impact of the green belt on the driver at different speeds, the driver needs to change the speed to continue the test after driving on the same road. According to the "Code for Design of Urban Roads" (CJJ37-2016), the design speed of urban arterial roads is 40-60km/h, and the driving speed of drivers is 40km/h, 45km/h, 50km/h, 55km/h, 60km/h h to test.

After the experiment is over, import the video recorded by the eye tracker into ErogLAB software to divide the interest area. The appropriate time for video capture is about 20s. When performing video capture, it is necessary to select vehicles that have not passed through major intersections and that the road traffic status does not change much. For road sections with abnormal traffic, the section with abnormal traffic is discarded, and the video needs to be intercepted separately at major intersections, pedestrian crosswalks, and entrances and exits of large public facilities. Taking a main road in a certain city as an example to introduce the division of interest areas (Figure 4):

Tree height ROIs 1 and 7; crown width ROIs 2 and 8; trunk visibility ROIs 3 and 9; tree shape ROIs 4 and 6; sky scale ROI 5. The ROIs 3 and 9 of the tree trunk visibility in the figure are divided into areas where the driver can clearly distinguish the tree trunk from the first perspective, and the leaves are not blocked. The ROIs 2 and 8 of the canopy are divided into the area above the trunk visibility ROI to the outermost area of the tree extension diameter. Tree height interest areas 1 and 7 are divided into areas above the canopy interest area to the highest tree body. The tree-shaped interest areas 4 and 6 are divided into areas extending from the outline of the tree to the road section. The sky ratio interest area 5 is divided into the area above the driver's first perspective without vegetation blocking the sky. Other indicators can be divided according to the actual road conditions and the characteristics of the green belt, but the interest areas must not overlap and must completely cover all visible parts of the road green belt. For example, the interest area of the green belt spacing index is the gap between two adjacent trees, but the gap cannot be extended to the non-motor vehicle lane or sidewalk area.

After the area of interest is divided, the driver's gaze point distribution map and heat map can be automatically generated. The number of gaze points of each area of interest can be exported by using the Statistic tool of the software itself. After the fixation points of all experimental roads are analyzed, the ratio of the number of fixation points in the area of interest corresponding to a specific green belt index of the road to the number of fixation points in all indicators (that is, the sum of the number of fixation points in all areas of interest) is Eigenvalues.

2.4 Establishment of evaluation model

The model building process can be divided into the following three steps:

1) Analyze the experimental data and eliminate abnormal data

This evaluation model is established on the basis that the plant index value is the independent variable and the characteristic value is the dependent variable. In order to enhance the applicability and accuracy of the evaluation results, the Grubbs test method was used to eliminate abnormal data. Calculated on the average of the eigenvalues of the same indicator for different drivers and standard deviation S. To calculate the G value, check the Grubbs test table (Table 2) according to the measurement times and confidence requirements. Compare G _calculation and G _table , if G _calculation > G _table , discard, otherwise keep.

In the formula, S is the standard deviation value; the total number of n drivers.

In the formula, X _n is the characteristic value of the nth driver.

Table 2 Grubbbs test table

2) Propose reasonable assumptions and analyze internal quantitative relationships

There are obvious differences in the heights of different green belts. During driving, as the speed of the driver increases, the range of vision will decrease. Therefore, when the height of the tree is within the range of the driver's visual field, the visual impact on the driver should increase as the height increases, but beyond the visual range, the impact should tend to be gentle as the height increases. Therefore, there should be a certain logarithmic relationship between the height index and the driver's gaze feature value.

Different tree shapes will directly affect the driver's gaze on the canopy. Through research, when the tree shape is spherical, it will give the driver a uniform overall feeling, and when it is a steeple shape, the radius of the tree body from top to bottom is Constantly changing and therefore more attractive to the driver's gaze than a spherical shape. Therefore, it can be inferred that the tree-shaped feature has a certain linear relationship with the influence value

The crown width index of the green belt should be considered in combination with the road width. The influence of width on the crown width is reflected in the fact that when the road width is large, the crown width appears in the driver's field of vision in a small proportion. When the road width When it is small, the size of the crown appears in the driver's field of vision with a large-scale feature.

Because the distance between the driver and the tree is different, the visibility of the trunk is different from the driver's visual perception. The closer the distance, the larger the tree will appear, and vice versa, the smaller it will be. The index of tree trunk visibility can be assumed to be a linear relationship first, and the SPSS output results can be used to judge whether the fitting degree and significance meet the regulations.

The sky ratio index of the green belt is generally between 1% and 50%, and the quantification of the index fluctuates little. A simple linear relationship can be directly used to describe the relationship between the index and the driver's visual impact.

When the plant-to-plant distance of the green belt is relatively small, it will give the driver a slight sense of dizziness, and when the plant-to-plant distance is relatively large, it will appear too empty, and the driver will pay too much attention to the scenery between the two plants. Therefore, the relationship between the distance between plants and the visual impact can be in the form of a quadratic parabola. Find the best plant spacing, which will neither dazzle the driver nor give the driver an empty visual experience.

In summary, the assumptions about the relationship between the green belt index influence value and its index value are as follows:

In the formula, a, b, c, d, e, f, and g are undetermined constants; x ₁ is the height value, that is, the distance from the ground to the top of the tree, in m; x ₂ is the tree index value, The tree shape is the spatial structure of the tree, which is divided into spire shape, spherical shape and umbrella shape. It is determined by the product of the driver's sight distance and the area of the visible part above the trunk at the driver's perspective, and the unit is m ³ ; x ₃ is the crown width and road width The tree canopy is the average width of the trees in the north-south and east-west directions; x ₄ is the sky ratio, that is, the ratio of the effective sky area at the driver's first perspective to the driver's total field of view, dimensionless; x ₅ is the trunk The visibility index value is determined by the product of the driver's sight distance and the visible tree trunk area at the driver's perspective, and the unit is m ³ ; x ₆ is the distance between trees, that is, the distance between two adjacent trees, and the unit is m.

By changing the driving speed of the driver on the same road environment and analyzing the characteristics of the driver's gaze point at different speeds, the degree of influence at different speeds can be obtained. As the vehicle speed increases, the range of dynamic vision of the driver decreases, and as the vehicle speed increases, the driver will focus more on the road traffic environment rather than the road landscape environment, so it can be inferred that the impact of vehicle speed and green belt Degree values are negatively correlated. That is to say, the green belt influence degree model at different design speeds is obtained.

In the formula, i and h are constants to be defined, and v _speed is the road design speed.

3) Solve the undetermined constant

Use the SPSS software to input the independent variable x _i of the plant index value and the dependent variable y _i of the characteristic value to solve the undetermined constant.

For the height, crown width, tree trunk visibility, sky ratio, and tree shape of certain arterial roads, the five index languages are coded as follows (if other index variables are needed, other variables must be entered after coding the sixth article/METHOD=ENTER) :

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA COLLIN TOL

/CRITERIA=PIN(.05)POUT(.10)

/NOORIGIN

/DEPENDENT influence value

/METHOD=ENTER height crown width road width ratio tree sky ratio tree trunk visibility

/SCATTERPLOT=(*ZRESID,*ZPRED)

/RESIDUALS DURBIN HISTOGRAM(ZRESID)NORMPROB(ZRESID).

After running the program, the input result will display the following content (Table 3, 4, 5, Figure 5):

Table 3 Variables entered/removed ^a

a. Dependent variable: influence value

b. All variables requested have been entered.

Table 4 Model ^Summaryb

a. Predictor variables: (constant), tree trunk visibility, height, crown width ratio, sky ratio, tree shape

b. Dependent variable: influence value

Table 5 Coefficient ^a

a. Dependent variable: influence value

Among them: the R square in Table 4 represents the fitting degree, and the R square ≥ 0.7 indicates that the fitting degree is good. A Durbin-Watson value close to 2 indicates that there is no serial correlation, and the regression is not a spurious regression. Table 5 shows the coefficients of each unstandardized coefficient corresponding to each variable. And the significance ≤ 0.05 indicates that the independent variable has a significant impact on the dependent variable. VIF ≤ 5 indicates that there is no collinearity among the respective variables.

According to the above requirements, the results were checked and found to meet the above requirements. In order to establish the multivariate function modeling of the five indicators of green belt height, crown width, tree trunk visibility, sky proportion and tree shape

y＝1.4lnx ₁ +3.1x ₂ +1.8x ₃ -0.83x ₄ +x ₅ +3.2 (8)

Similarly, after the speed variable is introduced into SPSS, a multivariate function model based on the five indicators of road green belt height, crown width, tree trunk visibility, sky proportion, and tree shape can be obtained at different speeds:

y＝1.4lnx ₁ +3.1x ₂ +1.8x ₃ -0.83x ₄ +x ₅ -0.17v _speed +h (9)

In the formula, v _speed is the design speed of the road; the value of h is shown in Table 3 below.

Table 6 h value suggestion table

V _speed (km/h) h 40 10.00 45 10.85 50 11.70 55 12.55 60 13.40

If other road indicators are still needed, follow the above steps to obtain the evaluation model of specific road green belt indicators.

2.5 Classification of evaluation influence value y

In the above process, the influence degree 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, to determine whether the green belt is reasonably constructed, a certain evaluation level needs to be established, that is, in different influence value intervals, the green belt has different visual impacts on drivers, and the evaluation level can be divided into great influence, moderate influence Large, average, small, and almost no impact.

The evaluation level is based on the above-mentioned expert scoring method and driver scoring method. In order to make the evaluation standard more versatile and accurate, a large amount of road landscape data needs to be collected, and invitations covering different driving ages, genders, heights, eyesight Drivers with other characteristics are scored in the real vehicle test. If a large number of real vehicle tests cannot be carried out due to limitations of funds, time, and equipment, then on the basis of a certain number and quality of real vehicle tests, the driving simulation cabin experiment method can be used to effectively simulate the test scene and complete the test indoors .

The driver scoring method is to score the driver's influence on various indicators during the actual vehicle test. The scoring standard adopts a 5-point system: 5 points for a large impact; 3 points for a large impact; 3 points for a general impact; 2 points for a small impact; 1 point for almost no impact (suitable layout). After the scoring is completed, the indicators of all experimental roads are sub-classified. For example: subcategories for height may be 0-5m, 5-10m, 10-15m, 15-20m, etc. Trees are subcategorized into Spires, Umbrellas, Spheres, etc. Include different scores into corresponding subcategories, and take the mean after induction. Use the hierarchical analysis software YAAHP to establish a hierarchical analysis tree diagram, and input the calculated mean value to get the weight value of the parent index.

The expert scoring method is to invite experts with relevant work and research experience to evaluate the appropriateness of the layout of the green belts on the selected roads, using a 10-point system: the indicator has a suitable degree of impact on the driver's vision, and the layout is more reasonable (1- 3 points); this indicator has too much impact on the driver's vision, it is recommended to modify it (4-6 points); this indicator has a great impact on the driver's vision, there are potential safety hazards, and it needs to be revised (7 points and above).

After the above steps are completed, perform mathematical operations (Formula 10) on the index weight value and the expert scoring method to determine the comprehensive evaluation index of the road index.

In the formula: B is the comprehensive evaluation index; X is the weight value of each index; F _i is the scoring value of effective experts; n is the total number of experts.

Arrange the comprehensive evaluation index of the road index numerically, and choose suitable visual effect (the comprehensive evaluation index is low, generally accounting for 15%-20% of the total number of experimental roads), and the visual effect is average (the comprehensive evaluation index is medium, generally accounting for the total number of experimental roads). 30%-40% of the number of roads), roads with poor visual effects (the comprehensive evaluation index is relatively high, generally accounting for 15%-20% of the total number of roads in the experiment).

After the visual effect of the road is calibrated, the index parameter values of these roads are substituted into the evaluation model to obtain the evaluation intervals divided by the evaluation grades of different influence degrees. These intervals can be used as evaluation standards to measure whether the construction of the green belt to be evaluated is reasonable. Whether it produces excessive visual stimulation to the driver. As for the reasons for the general or poor visual effect, the specific level of each factor should be reasonably selected according to the appropriate scoring range for the visual effect, combined with the evaluation model, so that the green belt score falls within the range, giving the driver a comfortable aesthetic feeling and appropriate Vigilance and excitement to ensure driving stability.

Through the experiment of the above-mentioned steps on the green belt of a certain city’s main road, the best scoring range for the five indicators of the height of the green belt, tree shape, trunk visibility, sky ratio and crown width has been preliminarily obtained [15,20]. Scoring intervals [12,15) and (20,24], the rest of the influence degree values not in the above intervals are roads with poor visual effects, and the specific levels of each factor need to be adjusted reasonably to make the green belt score 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 refined and optimized.

Claims (7)

1. a kind of method that evaluation main line greenbelt characteristic influences driver's vision, it is characterized in that：It includes the following steps：

1) eye tracker is worn with driver and drives vehicle with constant speed drive on road, with eye tracker to being regarded in front of driver Open country is recorded a video, while recording driver's eye movement situation；

2) video guide that eye tracker records is entered into eye tracker analysis software, and carries out following interest Division：Trunk visibility Region of interest is that driver is clearly discernible tree body trunk, the region that leaf does not block in the first visual angle；The interest zoning of hat width It is divided into trunk visibility region of interest to extend diameter outermost region up to tree body；It sets high region of interest and is divided into hat width region of interest With up to tree body highest point region；Tree-like region of interest is divided into tree body outer profile and extends to section region；Sky ratio region of interest Sky is divided into above the first visual angle of driver without vegetation occlusion area；Each region of interest is non-overlapping and need to completely cover All viewable portions of road greening band；

3) it utilizes eye tracker analysis software and exports the blinkpunkt number of each region of interest；With a certain specific greenbelt of the road The ratio that the corresponding region of interest blinkpunkt number of index accounts for all index blinkpunkt numbers is characterized value；

4) relationship between greenbelt Index Influence angle value y and each index value of greenbelt is built：

In formula, a, b, c, d, e, f, g are undetermined constant；x₁For height value, i.e. trees face upward from ground to the distance of treetop, single Position is m；x₂For tree-like index value, the space structure of tree-like i.e. trees is divided into steeple shape, spherical shape, umbrella shape, with driver's sighting distance with The product of the above tangible part area of trunk determines at driver visual angle, unit m³；x₃For hat width and the ratio between have a lot of social connections, trees hat Width is the average value in trees north and south and east-west direction width；x₄For sky ratio, i.e., effective sky at the first visual angle of driver Area accounts for the ratio of the driver visual field gross area, dimensionless；x₅For trunk visibility index value, with driver's sighting distance and driver The product of visual trunk area determines at visual angle, unit m³；x₆For spacing in the rows value, i.e., the spacing distance of adjacent two trees, unit is m；

5) independent variable, that is, greenbelt index value x is inputted in SPSS softwares_iWith corresponding characteristic value y_i, solve undetermined constant.

2. the method that evaluation main line greenbelt characteristic influences driver's vision as described in claim 1, it is characterized in that：It is described Step 4) is the relationship built between greenbelt Index Influence angle value y and each index value of greenbelt：

Wherein, i, h are undetermined constant, v_SpeedFor highway layout speed.

3. the method that evaluation main line greenbelt characteristic influences driver's vision as described in claim 1, it is characterized in that：Step 3) in, using eye tracker analysis software and when exporting the blinkpunkt number of each region of interest, the video intercepting time is 20s left The right side need to select vehicle without the section of primary cross mouth when carrying out video intercepting.

4. the method that evaluation main line greenbelt characteristic influences driver's vision as described in claim 1, it is characterized in that：Step 1) in, vehicle is surveyed under the constant speed of 40km/h, 45km/h, 50km/h, 55km/h, 60km/h respectively in same a road section Examination.

5. the method that evaluation main line greenbelt characteristic influences driver's vision as described in claim 1, it is characterized in that：It is described Eye tracker is Sweden's Tobii Glasses eye trackers, which is ErogLAB.

6. the method that evaluation main line greenbelt characteristic influences driver's vision as described in claim 1, it is characterized in that：It divides Influence the grade of angle value y；According to driver's point system and expert graded, the specific section of the grade classification of disturbance degree y is formulated； Grade interval is divided into that visual effect is suitable, visual effect is general, bad 3 part of visual effect；Greenbelt is judged on this basis Whether build reasonable.

7. fly the method that evaluation main line greenbelt characteristic influences driver's vision as described in claim 1, it is characterized in that：It utilizes SPSS softwares carry out the regression analysis of Multivariate Linear function and the regression analysis of unit nonlinear function.