CN103383746A

CN103383746A - Highway long tunnel exit section night lighting optimizing method

Info

Publication number: CN103383746A
Application number: CN2013102795403A
Authority: CN
Inventors: 赵炜华; 刘浩学; 林淼; 谢陈江; 席海华
Original assignee: Changan University
Current assignee: Changan University
Priority date: 2013-07-04
Filing date: 2013-07-04
Publication date: 2013-11-06

Abstract

The invention discloses a nighttime lighting optimization method for the exit section of a long expressway tunnel. The method collects tunnel lighting parameters, driver's visual adaptation time, and driver's pupil area data, and establishes a driver's pupil area and illuminance model. Distance relationship model and driver's visual adaptation model; use software to make a three-dimensional surface between the driver's pupil area, visual adaptation time, and corresponding environmental illuminance, establish a function model based on the parameters obtained from the surface, and finally calculate the distance from the tunnel exit, Relationship between vehicle speed and optimal illuminance in tunnels. The method can not only eliminate the dark adaptation problem caused by the difference in illuminance when driving in a tunnel environment at night, reduce traffic accidents caused by visual impairment, but also greatly reduce the power consumption of tunnel lighting facilities.

Description

Optimizing method for nighttime lighting at exit section of expressway long tunnel

技术领域technical field

本发明涉及交通安全领域，具体涉及一种高速公路长隧道出口段夜间照明优化方法The invention relates to the field of traffic safety, in particular to a nighttime lighting optimization method for the exit section of a long expressway tunnel

背景技术Background technique

随着我国公路建设逐渐向西部推进，越来越多的公路隧道被建成和投入运营。隧道作为道路上的特殊构造物，具有环境封闭、内外差异大的特点，尤其是照明环境差异极其明显。受结构、环境特点限制，隧道成为道路上事故高发的黑点或段。一旦发生道路交通事故，又存在救援困难、交通组织复杂、损失较重的问题，对道路的运行效益具有极大影响。在人、车、路和环境的系统中，驾驶人是影响交通安全的决定性因素。而驾驶是以视觉为引导的，相关信息循环加工、产生决策的过程。在上述过程中，视知觉是决定性因素。受人眼生理功能限制，在环境照度发生变化时，会产生明、暗适应问题，造成视觉认知功能短时障碍，严重影响行车安全。为提高隧道内外环境一致性，减少上述问题的发生，隧道环境中往往要设置照明设施，以改善视场环境。在我国的隧道通风照明标准中，较为详细地给出隧道照明相关参数和设计方法，但存在诸多问题未能全面考虑，致使存在很大安全隐患。在实际隧道照明中，考虑设施施工技术和运营管理的简易性，照度实际值与标准又存在差异，与驾驶人需求相去甚远，进一步加重环境突变带来的安全隐患。As my country's highway construction gradually advances to the west, more and more highway tunnels have been built and put into operation. As a special structure on the road, the tunnel has the characteristics of closed environment and large differences between inside and outside, especially the difference in lighting environment is extremely obvious. Restricted by the structural and environmental characteristics, the tunnel has become a black spot or section with a high incidence of accidents on the road. Once a road traffic accident occurs, there will be difficulties in rescue, complicated traffic organization, and heavy losses, which will have a great impact on the operational efficiency of the road. In the system of human, vehicle, road and environment, the driver is the decisive factor affecting traffic safety. Driving is a visually-guided process in which relevant information is processed cyclically and decisions are made. In the above process, visual perception is the decisive factor. Limited by the physiological function of the human eye, when the ambient illumination changes, light and dark adaptation problems will occur, resulting in short-term visual cognitive function obstacles and seriously affecting driving safety. In order to improve the consistency of the environment inside and outside the tunnel and reduce the occurrence of the above problems, lighting facilities are often installed in the tunnel environment to improve the field of view environment. In my country's tunnel ventilation and lighting standards, relevant parameters and design methods of tunnel lighting are given in more detail, but there are many problems that have not been fully considered, resulting in great safety hazards. In actual tunnel lighting, considering the simplicity of facility construction technology and operation management, the actual value of illuminance is different from the standard, which is far from the needs of drivers, which further aggravates the safety hazards caused by sudden environmental changes.

与此同时，隧道照明不仅需要配置较多机电设施，而且运营中耗费大量电能，造成运营成本居高不下。按照国家标准规定，长度大于100m的公路隧道应设置照明，每延公里隧道照明负荷应不小于60Kw，照明时间按每日10小时计算，年平均耗电量达21.6万度（折合成标准煤76吨），导致公路隧道照明成为运营单位的沉重负担。节能是长期目标，但是不能以降低隧道运营安全为代价来降低能耗，有些运营单位为了降低隧道运营费用，往往采取开启1/4到1/2左右，有的隧道甚至干脆不开灯，造成隧道内环境照度很低，且亮暗不均匀，很容易导致驾驶人视觉产生疲劳，带来了严重的安全隐患。隧道照明的安全性与节能性存在着此消彼长的矛盾。At the same time, tunnel lighting not only needs to be equipped with more electromechanical facilities, but also consumes a lot of electricity during operation, resulting in high operating costs. According to national standards, road tunnels with a length of more than 100m should be equipped with lighting, and the lighting load of each kilometer of tunnel extension should not be less than 60Kw. The lighting time is calculated as 10 hours per day, and the annual average power consumption reaches 216,000 degrees (equivalent to 76 standard coal. tons), resulting in highway tunnel lighting becoming a heavy burden on operating units. Energy saving is a long-term goal, but energy consumption cannot be reduced at the cost of reducing tunnel operation safety. In order to reduce tunnel operating costs, some operating units often turn on about 1/4 to 1/2, and some tunnels even do not turn on lights at all, resulting in The ambient illumination in the tunnel is very low, and the brightness is uneven, which can easily cause driver visual fatigue and bring serious safety hazards. There is a contradiction between the safety and energy saving of tunnel lighting.

针对隧道照明问题，国内外开展了诸多研究并取得一些成果，但均未能明确解决上述问题。赵炜华，刘浩学等，利用眼动仪研究驾驶人在隧道行车过程中视觉特征参数变化规律，并建立模型进行分析。同济大学利用瞳孔面积变化进行隧道行车安全水平评价，并提出了很多结论。但从研究内容和方法来看，将瞳孔面积变化完全归因于驾驶人心理紧张程度，而未考虑环境照度和暗适应时间的影响。张亚林对高速公路短隧道照明问题进行研究，但短隧道所产生的暗适应问题并不明显。同样，涂耕等研究了短隧道照明参数，但并未涉及长隧道条件下相关数值。近两年的隧道照明问题研究，则更多的集中于LED的使用问题。国外对于隧道照明的研究，多从驾驶人视觉变化以及明暗适应与交通安全关系角度开展，提出夜间隧道入口段照度值。国外隧道照明标准中所提出的各参数，则主要是基于CIE曲线所定制。但由于行车速度和交通条件不同，加上国内外驾驶人生理和心理差异，其相关参数的计算方法与国内需求存在较大区别。但由于行车速度和交通条件差异，其各段长度计算方法与国内需求存在较大差异。Aiming at the problem of tunnel lighting, many studies have been carried out at home and abroad and some achievements have been made, but none of them can clearly solve the above problems. Zhao Weihua, Liu Haoxue, etc. used eye trackers to study the changing rules of visual characteristic parameters of drivers during tunnel driving, and established models for analysis. Tongji University used the change of pupil area to evaluate the safety level of tunnel driving, and put forward many conclusions. However, from the perspective of research content and methods, the change of pupil area is completely attributed to the driver's psychological stress, without considering the influence of environmental illumination and dark adaptation time. Zhang Yalin studied the lighting problem of short expressway tunnels, but the dark adaptation problem caused by short tunnels is not obvious. Similarly, Tu Geng et al. studied the lighting parameters of short tunnels, but did not involve the relevant values under long tunnel conditions. In the past two years, research on tunnel lighting issues has focused more on the use of LEDs. Foreign studies on tunnel lighting are mostly carried out from the perspective of driver's visual changes and the relationship between light and dark adaptation and traffic safety, and the illuminance value of the tunnel entrance section at night is proposed. The parameters proposed in foreign tunnel lighting standards are mainly customized based on the CIE curve. However, due to the different driving speeds and traffic conditions, as well as the physiological and psychological differences of domestic and foreign drivers, the calculation methods of the relevant parameters are quite different from the domestic demand. However, due to differences in driving speed and traffic conditions, the calculation method of the length of each section is quite different from the domestic demand.

虽然上述研究取得了一些成果，但均未能明确解决上述相关问题。因此，基于现有隧道照明研究和实践中的问题，从驾驶人视觉认知和变化规律入手，以隧道环境照度为研究对象，提出了一种高速公路长隧道出口段夜间照明优化方法。Although the above studies have achieved some results, none of them can clearly solve the above-mentioned related problems. Therefore, based on the problems in the existing research and practice of tunnel lighting, starting from the driver's visual cognition and changing rules, and taking the tunnel environment illumination as the research object, a nighttime lighting optimization method for the exit section of a long expressway tunnel is proposed.

（《公路隧道设计规范》规定，隧道长度在1000-3000米之间，称为长隧道，大于3000m称为特长隧道。在我们的发明适用的范围实际上是包括了特长隧道的。）("Highway Tunnel Design Specifications" stipulates that a tunnel length between 1000-3000 meters is called a long tunnel, and a tunnel longer than 3000 meters is called an extra-long tunnel. The scope of application of our invention actually includes extra-long tunnels.)

发明内容Contents of the invention

本发明的目的在于，提供一种高速公路长隧道出口段夜间照明优化方法。The object of the present invention is to provide a method for optimizing nighttime lighting at the exit section of a long expressway tunnel.

为了实现上述任务，本发明采取如下的技术解决方案：In order to realize above-mentioned task, the present invention takes following technical solution:

一种高速公路长隧道出口段夜间照明优化方法，该方法包括以下步骤：A method for optimizing night lighting at the exit section of a long expressway tunnel, the method comprising the following steps:

步骤一，采集驾驶人瞳孔面积、隧道环境照度、驾驶人视觉适应时间数据；Step 1: Collect the driver’s pupil area, tunnel ambient illumination, and driver’s visual adaptation time data;

步骤二，根据步骤一采集的驾驶人瞳孔面积、环境照度和驾驶人视觉适应时间数据分别建立驾驶人瞳孔面积与照度模型，照度与距隧道出口距离关系模型和驾驶人视觉适应模型；Step 2, according to the driver's pupil area, environmental illuminance and driver's visual adaptation time data collected in step 1, establish the driver's pupil area and illuminance model, the relationship model between illuminance and the distance from the tunnel exit, and the driver's visual adaptation model;

其中驾驶人瞳孔面积与照度模型为：The driver's pupil area and illuminance model are:

取驾驶人瞳孔面积为Q、隧道环境照度为l，对夜间高速公路隧道出口段数据样本建立ln(Q×l)与ln(l)的关系图并进行回归分析，驾驶人瞳孔面积与环境照度存在如式(1)的关系：Taking the driver's pupil area as Q and the tunnel ambient illuminance as l, the relationship between ln(Q×l) and ln(l) is established for the data samples of the exit section of the expressway tunnel at night and the regression analysis is carried out. The driver's pupil area and the environmental illuminance There is a relationship like formula (1):

ln(Q×l)＝0.568ln(l)+9.252 (1)ln(Q×l)＝0.568ln(l)+9.252 (1)

将式(1)写成

形式，整理得到驾驶人瞳孔面积Q与隧道环境照度l的关系函数，如式(2)所示：Write formula (1) as

Form, sorting out the relationship function between the driver's pupil area Q and the tunnel environment illuminance l, as shown in formula (2):

Q=e^9.252×l^-0.432 (2)Q=e ^9.252 × l ^-0.432 (2)

照度与距隧道出口距离关系模型：Model of relationship between illuminance and distance from tunnel exit:

隧道环境照度l与距出口的距离d是乘幂关系，拟合的关系式为：The tunnel ambient illuminance l and the distance d from the exit are in a power relationship, and the fitting relationship is:

l＝0.824d^0.972 (3)l=0.824d ^0.972 (3)

驾驶人视觉适应模型：Driver visual adaptation model:

构建驾驶人瞳孔面积Q驾驶人在隧道中视觉适应时间t和相应隧道环境照度l的二元四次函数：Construct the bivariate quartic function of the driver's pupil area Q and the driver's visual adaptation time t in the tunnel and the corresponding tunnel environment illuminance l:

Q＝x₀+x₁t⁴+x₂t³+x₃t²+x₄t+x₅l⁴+x₆l³+x₇l²+x₈l+x₉tl³+x₁₀tl²+x₁₁tl+x₁₂t²l²+ (4)x₁₃t²l+x₁₄t³l+εQ＝x ₀ +x ₁ t ⁴ +x ₂ t ³ +x ₃ t ² +x ₄ t+x ₅ l ⁴ +x ₆ l ³ +x ₇ l ² +x ₈ l+x ₉ tl ³ +x ₁₀ tl ² +x ₁₁ tl+x ₁₂ t ² l ² + (4)x ₁₃ t ² l+x ₁₄ t ³ l+ε

式中：ε～N(0，σ²),表示随机误差的变量；In the formula: ε～N(0, σ ² ), represents the variable of random error;

通过对驾驶人瞳孔面积Q与驾驶人在隧道中视觉适应时间t和相应隧道环境照度l多次测量统计，取得i组数值：Through multiple measurements and statistics of the driver's pupil area Q, the driver's visual adaptation time t in the tunnel, and the corresponding tunnel ambient illuminance l, the i group of values is obtained:

(t_i ⁴,t_i ³,t_i ²,t_i ¹,l_i ⁴,l_i ³,l_i ²,l_i ¹,t_il_i ³,t_il_i ²,t_il_i,t_i ²l_i ²,t_i ²l_i,t_i ³l_i,y_i),i＝1,2,…,n. (5)构成方程组如下：(t _i ⁴ ,t _i ³ ,t _i ² ,t _i ¹ ,l _i ⁴ ,l _i ³ ,l _i ² ,l _i ¹ ,t _i l _i ³ ,t _i l _i ² ,t _i l _i , t _i ² l _i ² ,t _i ² l _i ,t _i ³ l _i ,y _i ), i=1,2,…,n. (5) The equations are formed as follows:

$\{\begin{matrix} {y the y}_{11} = = {x x}_{00} + + {x x}_{11} {t t}_{11}^{44} + + \cdot \cdot \cdot \cdot \cdot \cdot + + {x x}_{1414} {t t}_{11}^{33} {l l}_{11} + + {ϵ ϵ}_{11} \\ {y the y}_{22} = = {x x}_{00} + + {x x}_{11} {t t}_{22}^{44} + + \cdot \cdot \cdot &Center Dot; \cdot \cdot + + {x x}_{1414} {t t}_{22}^{33} {l l}_{22} + + {ϵ ϵ}_{22} \\ {. .}_{. .}^{. .} \\ {y the y}_{n no} = = {x x}_{00} + + {x x}_{11} {t t}_{n no}^{44} + + {\cdot \cdot \cdot \cdot \cdot \cdot + + x x}_{1414} {t t}_{n no}^{33} {l l}_{n no} + + {ϵ ϵ}_{n no} \end{matrix} - - - - - - ((66))$

其中：ε₁,ε₂,…,ε_n相互独立，Among them: ε ₁ ,ε ₂ ,…,ε _n are independent of each other,

将方程组转化成矩阵形式如式(7)：Convert the system of equations into a matrix form as formula (7):

Y＝λX+ε (7)Y＝λX+ε

其中： $Y = [\begin{matrix} y_{1} \\ y_{2} \\ ._{.}^{.} \\ y_{n} \end{matrix}],$ $λ = [\begin{matrix} 1 & {t_{1}}^{4} & {t_{1}}^{3} & \cdot \cdot \cdot & {t_{1}}^{3} l_{1} \\ 1 & {t_{2}}^{4} & {t_{2}}^{3} & \cdot \cdot \cdot & {t_{2}}^{3} l_{2} \\ ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} \\ 1 & {t_{n}}^{4} & {t_{n}}^{3} & \cdot \cdot \cdot & {t_{n}}^{3} l_{n} \end{matrix}],$ $X = [\begin{matrix} x_{0} \\ x_{1} \\ \cdot_{\cdot}^{\cdot} \\ x_{14} \end{matrix}]$ in: $Y = [\begin{matrix} {they}_{1} \\ {they}_{2} \\ ._{.}^{.} \\ {the y}_{no} \end{matrix}],$ $λ = [\begin{matrix} 1 & {t_{1}}^{4} & {t_{1}}^{3} & \cdot \cdot \cdot & {t_{1}}^{3} l_{1} \\ 1 & {t_{2}}^{4} & {t_{2}}^{3} & &Center Dot; &Center Dot; &Center Dot; & {t_{2}}^{3} l_{2} \\ ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} \\ 1 & {t_{no}}^{4} & {t_{no}}^{3} & \cdot &Center Dot; \cdot & {t_{no}}^{3} l_{no} \end{matrix}],$ $x = [\begin{matrix} x_{0} \\ x_{1} \\ {&Center Dot;}_{&Center Dot;}^{&Center Dot;} \\ x_{14} \end{matrix}]$

向量Y，λ都是已知的，利用最小二乘法，计算出向量X的估计值，过程如下：The vectors Y and λ are known, and the estimated value of the vector X is calculated by using the least square method. The process is as follows:

根据最小二乘法定义可以得到式(8)：According to the definition of the least square method, formula (8) can be obtained:

$F f (({x x}_{00},, {x x}_{11},, \cdot \cdot \cdot \cdot \cdot \cdot,, {x x}_{1414})) = = {Σ Σ}_{i i = = 11}^{n no} {(({y the y}_{11} - - {x x}_{00} - - {x x}_{11} {t t}_{i i}^{44} - - \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; - - {x x}_{1414} {t t}_{i i}^{33} {l l}_{i i}))}^{22} - - - - - - ((88))$

向量X的最小二乘估计值，应满足F取最小值时的解，对Q求偏导数，可以得到正规方程组如式(9)：The least squares estimated value of the vector X should satisfy the solution when F takes the minimum value, and the partial derivative of Q can be obtained, and the normal equation system can be obtained as formula (9):

$\{\begin{matrix} {nx nx}_{00} + + {x x}_{11} Σ Σ {t t}_{i i}^{44} + + \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; + + {x x}_{1414} Σ Σ {t t}_{i i}^{33} {l l}_{i i} = = Σ Σ {y the y}_{i i} \\ {x x}_{00} Σ Σ {t t}_{i i}^{44} + + {x x}_{11} Σ Σ {t t}_{i i}^{44} {t t}_{i i}^{44} + + \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; + + {x x}_{1414} Σ Σ {t t}_{i i}^{44} {t t}_{i i}^{33} {l l}_{i i} = = Σ Σ {t t}_{i i}^{44} {y the y}_{i i} \\ {. .}_{. .}^{. .} \\ {x x}_{00} Σ Σ {t t}_{i i}^{33} {l l}_{i i} + + {x x}_{11} Σ Σ {t t}_{i i}^{33} {l l}_{i i} {t t}_{i i}^{44} + + \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; + + {x x}_{1414} Σ Σ {t t}_{i i}^{33} {l l}_{i i} {t t}_{i i}^{33} {l l}_{i i} = = Σ Σ {t t}_{i i}^{33} {l l}_{i i} {y the y}_{i i} \end{matrix} - - - - - - ((99))$

相应的矩阵形式为λ^TY＝λ^TλX，λ^Tλ列满秩，故向量X有唯一解，将其代入驾驶人瞳孔面积Q与驾驶人在隧道中视觉适应时间t和相应隧道环境照度l的二元四次函数式(4)中，即获得驾驶人视觉适应函数模型，如式(10)所示：The corresponding matrix form is λ ^T Y = λ ^T λX, λ ^T λ has a full rank, so the vector X has a unique solution, which is substituted into the driver's pupil area Q, the driver's visual adaptation time t in the tunnel and the corresponding tunnel environment illuminance In the binary quartic function formula (4) of l, the driver's visual adaptation function model is obtained, as shown in formula (10):

Q(t,l)＝2007.246+0.046t⁴+0.539tl+0.002t²l²-0.031t³l-0.0000004744l⁴ (10)Q(t,l)＝2007.246+0.046t ⁴ +0.539tl+0.002t ² l ² -0.031t ³ l-0.0000004744l ⁴ (10)

步骤三：夜间长隧道出口段照明优化Step 3: Lighting optimization for the exit section of the long tunnel at night

利用matlab软件做出驾驶人瞳孔面积Q、视觉适应时间t、相应环境照度l之间的三维曲面，驾驶人视觉适应函数模型中，瞳孔面积作因变量，分别对驾驶人在隧道中视觉适应时间t和相应隧道环境照度l求偏导数，建立式（11）方程组：Use Matlab software to make a three-dimensional surface among the driver's pupil area Q, visual adaptation time t, and corresponding environmental illuminance l. In the driver's visual adaptation function model, the pupil area is used as the dependent variable, and the driver's visual adaptation time in the tunnel is affected respectively. Calculate the partial derivative of t and the corresponding tunnel ambient illuminance l, and establish the formula (11) equation group:

$\{\begin{matrix} \frac{&PartialD; &PartialD; Q Q}{&PartialD; &PartialD; t t} = = 00 \\ \frac{&PartialD; &PartialD; Q Q}{&PartialD; &PartialD; l l} = = 00 \end{matrix} - - - - - - ((1111))$

得到一系列数据组(t,l)，即为驾驶人适应隧道环境照度变化视觉要求的控制参数；选取数据组定为视觉适应时间t_n对应的照度值l_n；A series of data sets (t, l) are obtained, which are the control parameters for the driver to adapt to the visual requirements of the tunnel environment illumination change; select the data set as the illuminance value l _n corresponding to the visual adaptation time t _n ;

将驾驶人适应隧道环境照度变化视觉要求的照明控制参数，按照驾驶人视觉适应时间进行相应的回归拟合，其关系式为：The lighting control parameters for the driver to adapt to the visual requirements of the tunnel environment illumination change are regressed according to the driver's visual adaptation time, and the relationship is as follows:

l＝-39.849ln(t)+119.209 (12)l＝-39.849ln(t)+119.209 (12)

依据式(12)建立函数模型求解驾驶人不同视觉适应时间下所需要的环境照度值，直至隧道出口；然后再根据According to formula (12), a function model is established to solve the environmental illumination value required by the driver under different visual adaptation times until the exit of the tunnel; and then according to

$d d = = 250250 - - {&Integral; &Integral;}_{00}^{t t} vtdt vtdt = = 250250 - - \frac{11}{22} {vt vt}^{22} - - - - - - ((1313))$

式中，d为距隧道出口的距离，t为视觉适应时间，v为车辆运行速度，确定距隧道出口距离d与驾驶人视觉适应时间t之间的关系；In the formula, d is the distance from the tunnel exit, t is the visual adaptation time, v is the vehicle running speed, and the relationship between the distance d from the tunnel exit and the driver’s visual adaptation time t is determined;

将式(12)代入式(13)得到公式(14)，即可得到距隧道出口距离d、车速v与隧道优化照度l之间的关系。Substituting Equation (12) into Equation (13) to obtain Equation (14), the relationship between the distance d from the exit of the tunnel, the vehicle speed v, and the optimal illuminance l of the tunnel can be obtained.

$l l = = - - 39.849 39.849 \times \times ln ln ((\sqrt{22 ((250250 - - d d)) / / v v} + + 119.209 119.209 - - - - - - ((1414))$

当夜间驾驶人驾车驶离隧道时，将面临暗适应问题。尤其在现有的照明条件下，暗适应问题更加严重，驾驶人根本无法看清前方道路信息，这也是很多夜间隧道出口事故的重要诱因。同时，大量密集的照明灯具所造成的光环境，是以较高的电能消耗为代价，即浪费能源又影响安全。该方法从驾驶人的视觉认知和变化规律入手，较现有的研究方法比，研究结果准确可靠；使用了本方法后不仅能消除夜间隧道环境行车时，因照度差异造成的暗适应问题，减少视觉障碍诱发的交通事故，同时能大幅度降低隧道照明设施电能消耗，对解决目前隧道存在的安全与节能此消彼长的矛盾有重要意义。When the driver drives out of the tunnel at night, he will face the problem of dark adaptation. Especially under the existing lighting conditions, the problem of dark adaptation is even more serious, and the driver cannot see the road information ahead at all, which is also an important cause of many tunnel exit accidents at night. At the same time, the light environment caused by a large number of dense lighting fixtures is at the cost of higher power consumption, which wastes energy and affects safety. This method starts with the driver's visual cognition and change rules. Compared with the existing research methods, the research results are more accurate and reliable. After using this method, it can not only eliminate the dark adaptation problem caused by the difference in illumination when driving in a tunnel environment at night, Reducing traffic accidents caused by visual impairment and greatly reducing the power consumption of tunnel lighting facilities is of great significance to solving the current contradiction between safety and energy saving in tunnels.

附图说明Description of drawings

图1为本发明的流程图；Fig. 1 is a flowchart of the present invention;

图2为数据采集区域内采集点的详细布设图；Fig. 2 is the detailed layout diagram of collection points in the data collection area;

图3为驾驶人瞳孔面积与隧道环境照度散点图；Figure 3 is a scatter diagram of the driver's pupil area and the tunnel ambient illuminance;

图4为隧道环境照度随距隧道出口距离变化散点图；Figure 4 is a scatter diagram of the variation of tunnel ambient illumination with the distance from the tunnel exit;

图5为夜间隧道出口段驾驶人瞳孔面积变化曲面；Figure 5 is the changing surface of the driver's pupil area at the exit section of the tunnel at night;

图6为夜间隧道出口驾驶人对环境照度变化要求的控制参数分布图；Figure 6 is a diagram showing the distribution of control parameters required by drivers at the exit of the tunnel at night for changes in ambient illuminance;

图7为夜间隧道出口段路面照度参考值变化示意图；Figure 7 is a schematic diagram of changes in the reference value of road surface illumination at the exit section of the tunnel at night;

图8为夜间隧道出口段优化前后照明参数对比。Figure 8 is a comparison of lighting parameters before and after optimization of the tunnel exit section at night.

具体实施方式Detailed ways

《公路隧道设计规范》规定，隧道长度在1000-3000米之间，称为长隧道，大于3000m称为特长隧道。本发明方法适用于隧道长度在1000米以上的隧道，因此本发明方案中所述的长隧道，是包含了特长隧道在内的，即隧道长度在1000米以上的隧道。The "Code for Design of Highway Tunnels" stipulates that a tunnel with a length of 1000-3000 meters is called a long tunnel, and a tunnel longer than 3000 meters is called an extra-long tunnel. The method of the present invention is applicable to tunnels with a tunnel length of more than 1000 meters. Therefore, the long tunnel described in the solution of the present invention includes extra-long tunnels, that is, tunnels with a tunnel length of more than 1000 meters.

本发明的高速公路长隧道出口段夜间照明优化方法，主要包括下列步骤：The nighttime lighting optimization method of the expressway long tunnel exit section of the present invention mainly includes the following steps:

步骤一，采集相关数据Step 1, collect relevant data

一、采集准备阶段1. Collection preparation stage

1、仪器1. Instrument

（1）动态视觉测试仪，采用加拿大SR Research公司生产的Eye LinkⅡ型眼动仪。仪器由控制模块、场景摄像头、光学头等部分组成。控制模块包括眼动仪的主试机和被试机，主要负责对实验驾驶人的动态视觉特性参数尤其是眼动资料进行采集、记录、数据处理加工。场景摄像头的作用是将驾驶人视觉范围内的视景采集并显示在主试机显示屏上。光学头包括两个摄像头和其他光学元件，主要作用是将驾驶人在行车过程中的眼动信息输入仪器中。眼动仪利用自备的数据采集软件和数据分析软件来测试和记录驾驶人眼睛视线角度、注视点位置坐标，眼球运动速度、轨迹及瞳孔面积等动态视觉特性参数。采样频率选择500赫兹，瞳孔尺寸分辨率为1%。(1) The dynamic vision tester adopts the Eye Link II eye tracker produced by SR Research, Canada. The instrument is composed of control module, scene camera, optical head and other parts. The control module includes the main test machine and the test machine of the eye tracker, and is mainly responsible for collecting, recording, and data processing the dynamic visual characteristic parameters of the experimental driver, especially the eye movement data. The role of the scene camera is to collect and display the scene within the driver's visual range on the display screen of the main test machine. The optical head includes two cameras and other optical components, and its main function is to input the driver's eye movement information into the instrument during driving. The eye tracker uses its own data collection software and data analysis software to test and record dynamic visual characteristic parameters such as the driver's eye sight angle, fixation point position coordinates, eyeball movement speed, trajectory and pupil area. The sampling frequency was chosen to be 500 Hz, and the pupil size resolution was 1%.

（2）照度计和亮度计实验选用型号为LX1330B的数字照度计、型号为M118660的彩色亮度计，用于测量和记录实车实验过程中夜间隧道出口段环境照度值和亮度值。RS232接口可以和计算机连接，在计算机中进行数据存储、分析、打印。(2) Illuminance meter and luminance meter The digital illuminance meter model LX1330B and the color luminance meter model M118660 are used in the experiment to measure and record the ambient illuminance value and brightness value of the exit section of the tunnel at night during the actual vehicle experiment. The RS232 interface can be connected to a computer for data storage, analysis and printing in the computer.

（3）非接触式五轮仪，由于隧道的封闭性，GPS在隧道内无信号，无法使用，实验采用ISKRA-1D型非接触式五轮仪采集隧道内实验车辆速度和加速度信息，采样频率为10赫兹，误差为±3km/h，保证实验车速在误差允许的范围内。(3) The non-contact five-wheel instrument, due to the closedness of the tunnel, GPS has no signal in the tunnel and cannot be used. The experiment uses the ISKRA-1D non-contact five-wheel instrument to collect the speed and acceleration information of the experimental vehicle in the tunnel, and the sampling frequency It is 10 Hz, and the error is ±3km/h, ensuring that the experimental vehicle speed is within the allowable range of error.

2、数据采集对象的选取2. Selection of data collection objects

为保证采集过程中驾驶安全和实验结果的可信度，通过抽样随机选取具有不同职业、驾驶经历、合适年龄和适当驾龄的驾驶人作为实验对象。要求实验对象具有良好的驾驶习惯，并且视觉机能没有障碍，视力均在0.8以上，无生理缺陷和重、特大事故经历。In order to ensure driving safety and the credibility of the experimental results during the collection process, drivers with different occupations, driving experience, appropriate age, and appropriate driving experience were randomly selected as experimental objects by sampling. The test subjects are required to have good driving habits, and have no impairment of visual function, with vision above 0.8, and no physical defects and severe or serious accidents.

二、数据采集方案2. Data collection plan

鉴于照明优化表征参数的确定和参数隧道出口段照明优化的需要，采集的数据包括夜间长隧道出口段环境照度值、驾驶人瞳孔面积、驾驶人视觉适应时间。In view of the determination of lighting optimization characterization parameters and the need for parameter tunnel exit section lighting optimization, the collected data includes the ambient illuminance value of the long tunnel exit section at night, the driver's pupil area, and the driver's visual adaptation time.

1、照度亮度数据采集1. Illuminance brightness data collection

本发明中所述的长隧道出口段是指距离隧道出口0至300米范围内的隧道。从距离隧道出口约300m内部开始测量，在纵方向每隔0.5m设置1个采集点，横向每隔0.25m设置1个采集点，一块数据采集区域大小为3m×1m，包括35个采集点。35个采集点照度值的平均值作为此区域的照度值，同时区域内照度的最小值、最大值、纵向均匀度、总均匀度都可以求出。亮度值采集与之相同。数据采集区域内采集点的详细布设情况参见图2所示。The long tunnel exit section mentioned in the present invention refers to the tunnel within the range of 0 to 300 meters from the tunnel exit. Starting from the inside about 300m away from the tunnel exit, a collection point is set every 0.5m in the vertical direction, and a collection point is set every 0.25m in the horizontal direction. The size of a data collection area is 3m×1m, including 35 collection points. The average illuminance value of 35 collection points is used as the illuminance value of this area, and the minimum value, maximum value, longitudinal uniformity, and total uniformity of the illuminance in the area can be calculated at the same time. Luminance value acquisition is the same. The detailed layout of the collection points in the data collection area is shown in Figure 2.

2、驾驶人瞳孔面积、视觉适应时间数据采集2. Data collection of driver's pupil area and visual adaptation time

隧道实验中驾驶人瞳孔面积变化是利用眼动仪监测的，通过眼动仪自备的数据软件系统、导出瞳孔数据。驾驶人视觉适应时间是利用非接触式五轮仪、GPS、秒表及眼动仪视频录像综合确定。在外界环境照度变化不大，且驾驶环境变化不大时，驾驶人的瞳孔面积变化率基本保持在-6mm²/s到4mm²/s之间，而当瞳孔面积的变化率超过这一范围后则表示驾驶人发生了视觉上的明暗适应，超出这个范围的时刻点为视觉适应时间t=0；即瞳孔面积变化率开始波动的时刻，这些数据可以从眼动仪中读出。In the tunnel test, the driver's pupil area change is monitored by the eye tracker, and the pupil data is exported through the eye tracker's own data software system. The driver's visual adaptation time is comprehensively determined by using non-contact five-wheel instrument, GPS, stopwatch and eye tracker video recording. When the ambient illuminance changes little and the driving environment changes little, the driver's pupil area change rate is basically maintained between -6mm ² /s and 4mm ² /s, and when the pupil area change rate exceeds this range Afterwards, it means that the driver has experienced visual light-dark adaptation, and the moment beyond this range is the visual adaptation time t=0; that is, the moment when the pupil area change rate begins to fluctuate, and these data can be read from the eye tracker.

步骤二，根据步骤一采集的隧道照明参数、视觉适应时间、驾驶人瞳孔面积数据分别建立驾驶人瞳孔面积与照度模型，照度与距隧道出口距离关系模型和驾驶人视觉适应模型；Step 2, according to the tunnel lighting parameters, visual adaptation time, and driver's pupil area data collected in step 1, establish the driver's pupil area and illuminance model, the relationship between illuminance and the distance from the tunnel exit, and the driver's visual adaptation model;

1．驾驶人瞳孔面积与照度模型1. Driver's pupil area and illuminance model

取驾驶人瞳孔面积为Q、隧道环境照度为l，对夜间高速公路隧道出口段数据样本建立ln(Q×l)与ln(l)的关系图并进行回归分析，驾驶人瞳孔面积与环境照度存在如式(1)的关系。Taking the driver's pupil area as Q and the tunnel ambient illuminance as l, the relationship between ln(Q×l) and ln(l) is established for the data samples of the exit section of the expressway tunnel at night and the regression analysis is carried out. The driver's pupil area and the environmental illuminance There is a relationship such as formula (1).

ln(Q×l)＝0.568ln(l)+9.252 (1)ln(Q×l)＝0.568ln(l)+9.252 (1)

将式(1)写成e^ln(Q×l)=e^{9.252+0.568ln(l)}形式，整理得到驾驶人瞳孔面积Q与隧道环境照度l的关系函数，如式(2)所示。Formula (1) is written in the form of e ^ln(Q×l) =e ^{9.252+0.568ln(l)} , and the relationship function between the driver's pupil area Q and the tunnel environment illuminance l is obtained, as shown in formula (2).

Q=e^9.252×l^-0432 (2)Q=e ^9.252 × l ^-0432 (2)

用驾驶人瞳孔面积来表征驾驶员的心理反应量，隧道环境照度表征外界刺激量，这与实验心理学中的 Stevens定律一致，即心理反应量与物理刺激量符合幂定律。The driver's pupil area is used to represent the driver's psychological reaction, and the tunnel environment illuminance is used to represent the external stimulus, which is consistent with Stevens' law in experimental psychology, that is, the psychological reaction and physical stimulation conform to the power law.

2．照度与距隧道出口距离关系模型2. Model of relationship between illuminance and distance from tunnel exit

夜间从隧道内部到出口的过程中，由于外界自然光的影响，距离出口越近，隧道环境照度会越高，实验采集距隧道出口不同距离位置对应的环境照度，并根据数据散点图进行回归分析，参见图4所示。可以看出隧道环境照度l与距出口的距离d是乘幂关系，拟合的关系式为During the process from the inside of the tunnel to the exit at night, due to the influence of external natural light, the closer the distance to the exit, the higher the ambient illuminance of the tunnel. The experiment collected the ambient illuminance corresponding to different distances from the tunnel exit, and performed regression analysis according to the data scatter diagram , see Figure 4. It can be seen that the tunnel ambient illuminance l and the distance d from the exit are in a power relationship, and the fitted relationship is

l＝0.824d^0.972 (3)l=0.824d ^0.972 (3)

3．驾驶人视觉适应模型3. Driver Visual Adaptation Model

隧道行车过程中，驾驶人瞳孔面积与环境照度之间符合幂定律，隧道环境照度与距离即驾驶人视觉适应时间存在乘幂关系，建立相关模型，可得出瞳孔面积与适应时间、照度之间的关系，其建立具体过程如下：During tunnel driving, the relationship between the pupil area of the driver and the ambient illuminance conforms to the power law, and there is a power relationship between the tunnel ambient illuminance and the distance, that is, the driver’s visual adaptation time. The specific process of establishing the relationship is as follows:

式中：ε～N(0，σ²)正态分布，表示随机误差的变量。In the formula: ε～N(0, σ ² ) is normally distributed, representing the variable of random error.

$\{\begin{matrix} {y the y}_{11} = = {x x}_{00} + + {x x}_{11} {t t}_{11}^{44} + + \cdot \cdot \cdot \cdot \cdot \cdot + + {x x}_{1414} {t t}_{11}^{33} {l l}_{11} + + {ϵ ϵ}_{11} \\ {y the y}_{22} = = {x x}_{00} + + {x x}_{11} {t t}_{22}^{44} + + \cdot \cdot \cdot \cdot \cdot \cdot + + {x x}_{1414} {t t}_{22}^{33} {l l}_{22} + + {ϵ ϵ}_{22} \\ {. .}_{. .}^{. .} \\ {y the y}_{n no} = = {x x}_{00} + + {x x}_{11} {t t}_{n no}^{44} + + {\cdot \cdot \cdot \cdot \cdot \cdot + + x x}_{1414} {t t}_{n no}^{33} {l l}_{n no} + + {ϵ ϵ}_{n no} \end{matrix} - - - - - - ((66))$

其中：ε₁,ε₂,…,ε_n相互独立。Among them: ε ₁ , ε ₂ ,…, ε _n are independent of each other.

Y＝λX+ε (7)Y＝λX+ε (7)

其中： $Y = [\begin{matrix} y_{1} \\ y_{2} \\ ._{.}^{.} \\ y_{n} \end{matrix}],$ $λ = [\begin{matrix} 1 & {t_{1}}^{4} & {t_{1}}^{3} & \cdot \cdot \cdot & {t_{1}}^{3} l_{1} \\ 1 & {t_{2}}^{4} & {t_{2}}^{3} & \cdot \cdot \cdot & {t_{2}}^{3} l_{2} \\ ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} \\ 1 & {t_{n}}^{4} & {t_{n}}^{3} & \cdot \cdot \cdot & {t_{n}}^{3} l_{n} \end{matrix}],$ $X = [\begin{matrix} x_{0} \\ x_{1} \\ ._{.}^{.} \\ x_{14} \end{matrix}]$ in: $Y = [\begin{matrix} {they}_{1} \\ {they}_{2} \\ ._{.}^{.} \\ {the y}_{no} \end{matrix}],$ $λ = [\begin{matrix} 1 & {t_{1}}^{4} & {t_{1}}^{3} & \cdot \cdot \cdot & {t_{1}}^{3} l_{1} \\ 1 & {t_{2}}^{4} & {t_{2}}^{3} & \cdot &Center Dot; &Center Dot; & {t_{2}}^{3} l_{2} \\ ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} \\ 1 & {t_{no}}^{4} & {t_{no}}^{3} & &Center Dot; &Center Dot; &Center Dot; & {t_{no}}^{3} l_{no} \end{matrix}],$ $x = [\begin{matrix} x_{0} \\ x_{1} \\ ._{.}^{.} \\ x_{14} \end{matrix}]$

向量Y，λ都是已知的，利用最小二乘法，计算出向量X的估计值。过程如下：The vectors Y and λ are known, and the estimated value of the vector X is calculated by the method of least squares. The process is as follows:

$F f (({x x}_{00},, {x x}_{11},, \cdot &Center Dot; \cdot \cdot \cdot &Center Dot;,, {x x}_{1414})) = = {Σ Σ}_{i i = = 11}^{n no} {(({y the y}_{11} - - {x x}_{00} - - {x x}_{11} {t t}_{i i}^{44} - - \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; - - {x x}_{1414} {t t}_{i i}^{33} {l l}_{i i}))}^{22} - - - - - - ((88))$

相应的矩阵形式为λ^TY＝λ^TλX，λ^Tλ列满秩，故向量X有唯一解，将其代入驾驶人瞳孔面积Q与驾驶人在隧道中视觉适应时间t和相应隧道环境照度l的二元四次函数，即获得驾驶人视觉适应函数模型。如式(10)所示：The corresponding matrix form is λ ^T Y = λ ^T λX, λ ^T λ has a full rank, so the vector X has a unique solution, which is substituted into the driver's pupil area Q, the driver's visual adaptation time t in the tunnel and the corresponding tunnel environment illuminance The bivariate quartic function of l is to obtain the driver's visual adaptation function model. As shown in formula (10):

步骤三，夜间长隧道出口段照明优化Step 3: Lighting optimization for the exit section of the long tunnel at night

1．瞳孔面积变化速度1. Change speed of pupil area

由夜间隧道出口段驾驶人视觉适应模型可知，主要影响瞳孔面积的两个因素为视觉适应时间和环境照度。瞳孔面积变化速度受视觉适应变化速度和环境照度变化速度的影响。统计分析表明，基于行车安全的驾驶人瞳孔面积随视觉适应时间变化的临界速度在-6mm²/s到4mm²/s之间。在视觉适应过程中，驾驶人瞳孔面积随环境照度变化出现相应的变化，当瞳孔面积随环境照度的变化速度趋于0不再发生剧烈变化时，说明驾驶人视觉基本上适应了照明环境。由以上分析可知，当瞳孔面积变化速度同时满足视觉适应时间和环境照度的要求时，才能保证驾驶人在很小的视觉负荷下顺利安全地通过隧道。According to the driver's visual adaptation model at the tunnel exit section at night, the two main factors affecting the pupil area are visual adaptation time and environmental illuminance. The change speed of pupil area is affected by the change speed of visual adaptation and the change speed of environmental illuminance. Statistical analysis shows that the critical speed of driver's pupil area changing with visual adaptation time based on driving safety is between -6mm ² /s and 4mm ² /s. During the process of visual adaptation, the pupil area of the driver changes accordingly with the change of the ambient illuminance. When the pupil area changes to 0 and no longer changes sharply, it means that the driver's vision basically adapts to the lighting environment. From the above analysis, it can be seen that when the change speed of the pupil area satisfies the requirements of visual adaptation time and environmental illuminance at the same time, the driver can be guaranteed to pass through the tunnel smoothly and safely with a small visual load.

2．照明优化控制参数的确定2. Determination of Lighting Optimal Control Parameters

根据求得的夜间隧道出口段驾驶人视觉适应函数模型，利用Matlab软件绘制三维曲面，做出驾驶人瞳孔面积Q、视觉适应时间t、相应环境照度l之间的三维曲面，参见图5所示。According to the obtained visual adaptation function model of the driver at the exit section of the tunnel at night, use Matlab software to draw a three-dimensional surface, and make a three-dimensional surface between the driver's pupil area Q, visual adaptation time t, and the corresponding environmental illuminance l, as shown in Figure 5 .

寻找三维曲面上时间变化最快的那些点，即为瞳孔面积变化率为0的点，此时瞳孔基本稳定不再发生剧烈变化，视觉基本处于无负荷适应状态，将这些点作为驾驶人适应环境照度变化视觉要求的照明优化控制参数。Find the points with the fastest time change on the three-dimensional surface, that is, the points where the change rate of the pupil area is 0. At this time, the pupil is basically stable and does not change drastically, and the vision is basically in a state of no-load adaptation. These points are used as the driver's adaptation to the environment. Lighting optimization control parameters for visual requirements of illuminance changes.

照明优化控制参数的求解方法如下：在驾驶人视觉适应函数模型中，瞳孔面积作因变量，分别对驾驶人在隧道中视觉适应时间t和相应隧道环境照度l求偏导数，建立式（11）方程组：The solution method of lighting optimization control parameters is as follows: In the driver’s visual adaptation function model, the pupil area is used as the dependent variable, and the partial derivatives are calculated for the driver’s visual adaptation time t in the tunnel and the corresponding tunnel environmental illuminance l, and the formula (11) is established equation set:

求解微分方程组，可以得到一系列数据组(t,l)，即为驾驶人适应隧道环境照度变化视觉要求的控制参数。选取数据组中的合适值，定为视觉适应时间t_n对应的照度值l_n。By solving the differential equations, a series of data sets (t, l) can be obtained, which are the control parameters for the driver to adapt to the visual requirements of the tunnel environment illumination changes. Select an appropriate value in the data group and set it as the illuminance value l _n corresponding to the visual adaptation time t _n .

l＝-39.849ln(t)+119.209 (12)l＝-39.849ln(t)+119.209 (12)

通过进一步确定距离隧道出口不同距离位置对应的照度值，实现对隧道出口段照明参数优化，从而确定照明优化方案。依据式(12)建立函数模型求解驾驶人不同视觉适应时间下所需要的环境照度值，直至隧道出口。取车辆经过隧道出口向内约250m处的时刻记作t=0，依据驾驶人视觉适应时间t与车辆行驶速度，确定距隧道出口距离d与驾驶人视觉适应时间t之间的关系。其关系式如下：By further determining the illuminance values corresponding to different distances from the tunnel exit, the optimization of the lighting parameters of the tunnel exit section is realized, thereby determining the lighting optimization scheme. According to formula (12), a function model is established to solve the environmental illumination value required by the driver under different visual adaptation times until the exit of the tunnel. The moment when the vehicle passes through the tunnel exit about 250m inward is recorded as t=0, and the relationship between the distance d from the tunnel exit and the driver’s visual adaptation time t is determined according to the driver’s visual adaptation time t and the vehicle’s driving speed. Its relationship is as follows:

式中，d为距隧道出口的距离，t为视觉适应时间，v为车辆运行速度。In the formula, d is the distance from the tunnel exit, t is the visual adaptation time, and v is the vehicle running speed.

将式(13)代入式(12)可得到距隧道出口距离d、车速v与隧道优化照度l之间的关系，如下式所示：Substituting Equation (13) into Equation (12), the relationship between the distance d from the tunnel exit, the vehicle speed v, and the optimal illuminance l of the tunnel can be obtained, as shown in the following equation:

由此，本发明基于现有隧道照明研究和实践中的问题，从驾驶人视觉认知和变化规律入手，以隧道环境照度为研究对象，我们给出了隧道照度l与距随口出口距离d及车辆速度v的优化模型。以下具体给出该模型的成功应用实施例，进一步说明本发明的整体技术方案。Therefore, the present invention is based on the problems in existing tunnel lighting research and practice, starting from the driver's visual cognition and changing rules, and taking the tunnel environment illuminance as the research object, we have given the tunnel illuminance l and the distance d from the exit of the tunnel and Optimization model for vehicle velocity v. The successful application examples of the model are specifically given below to further illustrate the overall technical solution of the present invention.

实施例Example

南五台隧道高速公路长隧道出口段（夜间）Nanwutai Tunnel Expressway Long Tunnel Exit Section (Night)

1．照明控制参数确定1. Determination of lighting control parameters

运用EyeLinkⅡ在南五台隧道进行眼动数据的采集后，按照上述步骤建立(t，l)的关系式后，选取适宜的视觉适应时间作为夜间隧道出口驾驶人适应环境照度变化视觉要求的照明控制参数如下表所示。After using EyeLink II to collect eye movement data in Nanwutai Tunnel, follow the steps above to establish the relational expression of (t, l), and select the appropriate visual adaptation time as the lighting control for drivers to adapt to the visual requirements of environmental illumination changes at night at the tunnel exit The parameters are shown in the table below.

夜间隧道出口驾驶人对环境照度变化要求的控制参数(t,l)The control parameters (t,l) required by drivers at the tunnel exit at night for changes in ambient illumination

上述计算获得夜间隧道出口驾驶人适应环境照度变化视觉要求的照明控制参数(t,l)是指当驾驶人视觉适应时间为t时，视觉若达到舒适无较大负荷感，对环境照明要求的照度值为l。The lighting control parameters (t,l) obtained from the above calculations for nighttime tunnel exit drivers to adapt to the visual requirements of environmental illuminance changes refer to the requirements for environmental lighting when the driver’s visual adaptation time is t, if the vision is comfortable and there is no large sense of load. The illuminance value is l.

2．照明参数优化2. Lighting parameter optimization

在现有隧道照明条件，以距离隧道出口约200米处对照明参数进行优化设计，依据式(12)建立函数模型求解驾驶人不同视觉适应时间下所需要的环境照度值，直至隧道出口。依据驾驶人视觉适应时间t与车辆行驶速度v，确定距隧道出口距离d。最终确定夜间隧道出口段中距出口不同距离下照度优化参数，进而确定照明优化方案，具体方案如下表所示和照明参数趋势参见图7所示。Under the existing tunnel lighting conditions, the lighting parameters are optimally designed at a distance of about 200 meters from the tunnel exit, and the function model is established according to formula (12) to solve the environmental illuminance values required by the driver under different visual adaptation times until the tunnel exit. According to the driver's visual adaptation time t and the vehicle speed v, the distance d from the tunnel exit is determined. Finally determine the illumination optimization parameters at different distances from the exit of the tunnel at night, and then determine the lighting optimization scheme. The specific scheme is shown in the table below and the lighting parameter trend is shown in Figure 7.

夜间隧道出口段路面照度参考值Reference value of road surface illumination at night tunnel exit section

3．照明优化评估3. Lighting Optimization Assessment

由数据可以得到夜间隧道出口段距隧道出口不同距离位置照度优化参数与实地调研隧道照度参数的变化趋势，参见图8所示，并进行对比分析。From the data, the variation trend of illumination optimization parameters at different distances from the tunnel exit section at night and tunnel illumination parameters in field investigations can be obtained, as shown in Figure 8, and a comparative analysis is performed.

通过优化前后的照明参数对比，发现隧道现有照明参数设计过度，明显高于优化后的照度参数，且距离隧道出口越近，两者差距越大，这不仅造成不必要能源浪费，而且严重影响驾驶人视觉功能，不利于行车安全。优化后隧道照明参数显著降低，对解决目前隧道存在的安全与节能此消彼长的矛盾有重要意义。Through the comparison of lighting parameters before and after optimization, it is found that the existing lighting parameters of the tunnel are over-designed, significantly higher than the optimized illumination parameters, and the closer to the tunnel exit, the greater the gap between the two, which not only causes unnecessary energy waste, but also seriously affects The driver's visual function is not conducive to driving safety. After optimization, the tunnel lighting parameters are significantly reduced, which is of great significance to solve the current conflict between safety and energy saving in tunnels.

Claims

1. A method for nighttime lighting optimization of expressway long tunnel exit section, is characterized in that, the method comprises the following steps:

Step 1: Collect the driver’s pupil area, tunnel ambient illumination, and driver’s visual adaptation time data;

Step 2, according to the driver's pupil area, environmental illuminance and driver's visual adaptation time data collected in step 1, establish the driver's pupil area and illuminance model, the relationship model between illuminance and the distance from the tunnel exit, and the driver's visual adaptation model;

The driver's pupil area and illuminance model are:

Taking the driver's pupil area as Q and the tunnel ambient illuminance as l, the relationship between ln(Q×l) and ln(l) is established for the data samples of the exit section of the expressway tunnel at night and the regression analysis is carried out. The driver's pupil area and the environmental illuminance There is a relationship like formula (1):

ln(Q×l)＝0.568ln(l)+9.252 (1)

Write formula (1) as

Q=e ^9.252× l ^-0.432 (2)

Model of relationship between illuminance and distance from tunnel exit:

The tunnel ambient illuminance l and the distance d from the exit are in a power relationship, and the fitting relationship is:

l=0.824d ^0.972 (3)

Driver visual adaptation model:

Construct the bivariate quartic function of the driver's pupil area Q and the driver's visual adaptation time t in the tunnel and the corresponding tunnel environment illuminance l:

Q＝x ₀ +x ₁ t ⁴ +x ₂ t ³ +x ₃ t ² +x ₄ t+x ₅ l ⁴ +x ₆ l ³ +x ₇ l ² +x ₈ l+x ₉ tl ³ +x ₁₀ tl ² +x ₁₁ tl+x ₁₂ t ² l ² + (4)

x ₁₃ t ² l+x ₁₄ t ³ l+ε

In the formula: ε～N(0, σ ² ), represents the variable of random error;

Through multiple measurements and statistics of the driver's pupil area Q, the driver's visual adaptation time t in the tunnel, and the corresponding tunnel ambient illuminance l, the i group of values is obtained:

(t _i ⁴ ,t _i ³ ,t _i ² ,t _i ¹ ,l _i ⁴ ,l _i ³ ,l _i ² ,l _i ¹ ,t _i l _i ³ ,t _i l _i ² ,t _i l _i , t _i ² l _i ² ,t _i ² l _i ,t _i ³ l _i ,y _i ), i=1,2,…,n. (5) The equations are formed as follows:

\{\begin{matrix} {y the y}_{11} = = {x x}_{00} + + {x x}_{11} {t t}_{11}^{44} + + \cdot \cdot \cdot &Center Dot; \cdot \cdot + + {x x}_{1414} {t t}_{11}^{33} {l l}_{11} + + {ϵ ϵ}_{11} \\ {y the y}_{22} = = {x x}_{00} + + {x x}_{11} {t t}_{22}^{44} + + \cdot \cdot \cdot \cdot \cdot \cdot + + {x x}_{1414} {t t}_{22}^{33} {l l}_{22} + + {ϵ ϵ}_{22} \\ {. .}_{. .}^{. .} \\ {y the y}_{n no} = = {x x}_{00} + + {x x}_{11} {t t}_{n no}^{44} + + {\cdot \cdot \cdot \cdot \cdot \cdot + + x x}_{1414} {t t}_{n no}^{33} {l l}_{n no} + + {ϵ ϵ}_{n no} \end{matrix} - - - - - - ((66))

Among them: ε ₁ ,ε ₂ ,…,ε _n are independent of each other,

Convert the system of equations into a matrix form as formula (7):

Y＝λX+ε

in:

Y = [\begin{matrix} {the y}_{1} \\ {the y}_{2} \\ ._{.}^{.} \\ {the y}_{no} \end{matrix}],

λ = [\begin{matrix} 1 & {t_{1}}^{4} & {t_{1}}^{3} & &Center Dot; &Center Dot; &Center Dot; & {t_{1}}^{3} l_{1} \\ 1 & {t_{2}}^{4} & {t_{2}}^{3} & \cdot \cdot \cdot & {t_{2}}^{3} l_{2} \\ ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} & ._{.}^{.} \\ 1 & {t_{no}}^{4} & {t_{no}}^{3} & &Center Dot; &Center Dot; &Center Dot; & {t_{no}}^{3} l_{no} \end{matrix}],

x = [\begin{matrix} x_{0} \\ x_{1} \\ ._{.}^{.} \\ x_{14} \end{matrix}]

The vectors Y and λ are known, and the estimated value of the vector X is calculated by using the least square method. The process is as follows:

According to the definition of the least square method, formula (8) can be obtained:

F f (({x x}_{00},, {x x}_{11},, \cdot \cdot \cdot &Center Dot; \cdot &Center Dot;,, {x x}_{1414})) = = {Σ Σ}_{i i = = 11}^{n no} {(({y the y}_{11} - - {x x}_{00} - - {x x}_{11} {t t}_{i i}^{44} - - \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; - - {x x}_{1414} {t t}_{i i}^{33} {l l}_{i i}))}^{22} - - - - - - ((88))

The least squares estimated value of the vector X should satisfy the solution when F takes the minimum value, and the partial derivative of Q can be obtained, and the normal equation system can be obtained as formula (9):

\{\begin{matrix} {nx nx}_{00} + + {x x}_{11} Σ Σ {t t}_{i i}^{44} + + \cdot &Center Dot; \cdot \cdot \cdot \cdot + + {x x}_{1414} Σ Σ {t t}_{i i}^{33} {l l}_{i i} = = Σ Σ {y the y}_{i i} \\ {x x}_{00} Σ Σ {t t}_{i i}^{44} + + {x x}_{11} Σ Σ {t t}_{i i}^{44} {t t}_{i i}^{44} + + \cdot \cdot \cdot &Center Dot; \cdot \cdot + + {x x}_{1414} Σ Σ {t t}_{i i}^{44} {t t}_{i i}^{33} {l l}_{i i} = = Σ Σ {t t}_{i i}^{44} {y the y}_{i i} \\ {. .}_{. .}^{. .} \\ {x x}_{00} Σ Σ {t t}_{i i}^{33} {l l}_{i i} + + {x x}_{11} Σ Σ {t t}_{i i}^{33} {l l}_{i i} {t t}_{i i}^{44} + + \cdot \cdot \cdot \cdot \cdot \cdot + + {x x}_{1414} Σ Σ {t t}_{i i}^{33} {l l}_{i i} {t t}_{i i}^{33} {l l}_{i i} = = Σ Σ {t t}_{i i}^{33} {l l}_{i i} {y the y}_{i i} \end{matrix} - - - - - - ((99))

The corresponding matrix form is λ ^T Y = λ ^T λX, λ ^T λ has a full rank, so the vector X has a unique solution, which is substituted into the driver's pupil area Q, the driver's visual adaptation time t in the tunnel and the corresponding tunnel environment illuminance In the binary quartic function formula (4) of l, the driver's visual adaptation function model is obtained, as shown in formula (10):

Q(t,l)＝2007.246+0.046t ⁴ +0.539tl+0.002t ² l ² -0.031t ³ l-0.0000004744l ⁴ (10)

Step 3: Lighting optimization for the exit section of the long tunnel at night

Use Matlab software to make a three-dimensional surface between the driver's pupil area Q, visual adaptation time t, and the corresponding environmental illuminance l. In the driver's visual adaptation function model, the pupil area is used as the dependent variable, and the driver's visual adaptation time in the tunnel is affected respectively. Calculate the partial derivative of t and the corresponding tunnel ambient illuminance l, and establish the formula (11) equation group:

\{\begin{matrix} \frac{&PartialD; &PartialD; Q Q}{&PartialD; &PartialD; t t} = = 00 \\ \frac{&PartialD; &PartialD; Q Q}{&PartialD; &PartialD; l l} = = 00 \end{matrix} - - - - - - ((1111))

A series of data sets (t, l) are obtained, which are the control parameters for the driver to adapt to the visual requirements of the tunnel environment illumination change; select the data set as the illuminance value l _n corresponding to the visual adaptation time t _n ;

The lighting control parameters for the driver to adapt to the visual requirements of the tunnel environment illuminance change are regressed according to the driver's visual adaptation time, and the relationship is as follows:

l＝-39.849ln(t)+119.209 (12)

According to formula (12), a function model is established to solve the environmental illumination value required by the driver under different visual adaptation times until the exit of the tunnel; and then according to

d d = = 250250 - - {&Integral; &Integral;}_{00}^{t t} vtdt vtdt = = 250250 - - \frac{11}{22} {vt vt}^{22} - - - - - - ((1313))

In the formula, d is the distance from the tunnel exit, t is the visual adaptation time, and v is the vehicle running speed, determine the relationship between the distance d from the tunnel exit and the driver’s visual adaptation time t;

Substituting Equation (12) into Equation (13) to obtain Equation (14), the relationship between the distance d from the tunnel exit, the vehicle speed v, and the optimal illuminance l of the tunnel can be obtained:

l l = = - - 39.849 39.849 \times \times ln ln ((\sqrt{22 ((250250 - - d d)) / / v v} + + 119.209 119.209 - - - - - - ((1414))