Dynamic Optimization Method for Tunnel Lighting Based on Driver's Visual Adaptation Characteristics

Abstract

The present invention belongs to the field of control of lighting, and relates to a dynamic optimization method for tunnel lighting based on the visual adaptation characteristics of drivers. The method establishes a relationship model between the pupil area of a driver and the ambient illuminance in a tunnel, calculates the change rate of the pupil area according to the pupil area of the driver; establishes the corresponding relationship between the average saccade speed, the change rate of the pupil area and the ambient light in the tunnel based on historical data; constructs a calculation model for the total average illuminance of the lamps on the tunnel road surface based on the natural light outside the tunnel and the average horizontal illuminance of the lamps on the road surface; establishes an objective function for energy consumption and light pollution and an objective function for the psychological load of the driver based on parameters such as the total average illuminance of the lamps on the tunnel road surface, the length from the entrance or exit, etc. By deeply integrating multi-source data such as illuminance, brightness, lamp power, pupil change rate, and saccade speed, multi-dimensional collaborative optimization of "human-vehicle-environment" is realized, taking into account energy consumption while improving driving safety and driver visual comfort.

Description

Technical Field

[0001] The present invention belongs to the field of control of lighting lamps, relates to the control of lighting lamps in tunnels, and particularly relates to a dynamic optimization method for tunnel lighting based on the visual adaptation characteristics of drivers. Background Art

[0002] With the continuous expansion of China's highway network, the construction of highway tunnels has also seen a significant increase. In the complex terrain of mountainous areas, although tunnels effectively shorten the driving distance, their operation safety and energy consumption problems have gradually become the focus of attention. On the one hand, due to the visual effects caused by sudden changes in brightness at the tunnel entrances and exits, drivers need a long time to adapt, which leads to a significantly higher accident rate in this area than on ordinary sections, and the accident handling time is prolonged, increasing the risk of secondary accidents. On the other hand, the existing lighting systems have high energy consumption. In order to save costs, some operators adopt intermittent lighting, resulting in large fluctuations in the illuminance inside the tunnels, which not only exacerbates drivers' visual fatigue but also increases potential safety hazards.

[0003] Chinese Patent (Application No. 202011494429.2) discloses a tunnel lighting adjustment system and method based on the pupil change characteristics of drivers, which regulates the power supply voltage of tunnel lights according to the obtained pupil area data, thereby adjusting the lighting brightness of the lights at the tunnel entrances and exits, solving the "black hole" effect suffered by drivers when entering the tunnels, and reducing the occurrence of traffic accidents. Chinese Patent (Application No. 202310209767.4) discloses a device and method for improving driving comfort based on illuminance and pupil monitoring; Chinese Patent (Application No. 201910557709.4) discloses a method for selecting lighting light sources for the tunnel entrance section based on drivers' visual adaptation, which selects the light color of LED light sources suitable for tunnel entrance lighting according to the light color data of the sun at different times outside the tunnel, reducing the dark adaptation time of drivers. However, the existing methods mainly focus on optimizing the lighting of the entrance and exit sections, rarely study the lighting of the transition section inside the tunnel, and do not consider the energy consumption problem. Therefore, there is an urgent need to develop a tunnel lighting optimization method that can simultaneously optimize visual adaptation and regulate energy consumption. Summary of the Invention

[0004] In view of the shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide a dynamic optimization method for tunnel lighting based on the visual adaptation characteristics of drivers. This method establishes an objective function for energy consumption and light pollution and an objective function for drivers' mental workload, and realizes multi-source data deep fusion of engineering parameters (illuminance, brightness, lamp power) and mental parameters (pupil change rate, saccade speed), achieving multi-dimensional collaborative optimization of "human-vehicle-environment", while taking into account energy consumption while improving driving safety and drivers' visual comfort.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A dynamic optimization method for tunnel lighting based on the visual adaptation characteristics of drivers, comprising the following steps:

[0007] Step 1. Establish a relationship model between the pupil area Q of the driver and the ambient illuminance E in the entrance section, transition section, and exit section of the tunnel, and calculate the pupil area change rate AQ based on the pupil area of the driver;

[0008] Step 2. Establish the corresponding relationship between the average saccade speed and the ambient illuminance in the tunnel, and the corresponding relationship between the pupil area change rate and the ambient illuminance in the tunnel;

[0009] Step 3. Based on the natural light L outside the tunnel 外 and the average horizontal illuminance E of the lamps on the road surface av construct a calculation model for the total average illuminance E i of the i-th lamp near the entrance or exit on the tunnel road surface;

[0010] Step 4. Establish an energy consumption and light pollution objective function F1 and a driver mental workload objective function F2;

[0011] ;

[0012] ;

[0013] where d i is the length of the cross-section where the i-th lamp near the entrance or exit is located from the entrance or exit, in m; E i represents the total average illuminance of the i-th lamp near the entrance or exit on the tunnel road surface, in lx; a and r represent adjustment coefficients; E i0 represents the ideal illuminance threshold of the i-th lamp near the entrance or exit in the tunnel, in lx; is the adjustment coefficient, is the average saccade speed, is the ideal saccade speed value;

[0014] Step 5. Normalize the energy consumption and light pollution objective function F1 and the driver mental workload objective function F2;

[0015] ;

[0016] ;

[0017] where , are the minimum values of the objective functions F1 and F2 respectively, , are the maximum values of the objective functions F1 and F2 respectively, , are the values of the objective functions F1 and F2 after standardization respectively;

[0018] Step 6. Establish a comprehensive objective function F, and determine the lighting scheme by solving the Pareto front optimal solution of the comprehensive objective function F;

[0019] ;

[0020] The constraint conditions are: ;

[0021] In the formula, θ is the weight coefficient, represents the optimal brightness of the cross-section where the i-th luminaire near the entrance or exit is located, with the unit of cd·m −2 ; and represent the maximum and minimum brightness values that meet the driver's visual comfort requirements; is the standard brightness value specified in the specification; represents taking the maximum value in; P i is the power of the i-th luminaire near the entrance or exit, P max is the maximum power of a single lamp; S i is the distance between the i-th luminaire and the (i + 1)-th luminaire near the entrance or exit, with the unit of m, f min and f max are the minimum and maximum values of the flicker frequency, v t represents the tunnel design speed, with the unit of km / h; represents the change rate of the driver's pupil area at the i-th luminaire near the entrance or exit; d total represents the actual total length of the tunnel, with the unit of m.

[0022] As a preference of the present invention, the relationship model between the driver's pupil area and the ambient illuminance in the entrance section, transition section, and exit section of the tunnel is:

[0023] Q = e 8.509 ×E -0.101；

[0024] In the formula: Q is the driver's pupil area, with the unit of px; E is the ambient illuminance in the tunnel, with the unit of lx.

[0025] As a preference of the present invention, the calculation formula for the change rate of pupil area is:

[0026] ;

[0027] In the formula: represents the change rate of the driver's pupil area at the (i + 1)-th lamp near the tunnel entrance or exit; represents the driver's pupil area at the i-th lamp near the tunnel entrance or exit; represents the driver's pupil area at the (i + 1)-th lamp near the tunnel entrance or exit.

[0028] As a preference of the present invention, the average saccade speed has the following expression:

[0029] ;

[0030] In the formula: represents the average saccade speed, with the unit of ° / s; represents the amplitude of the -th saccade, with the unit of °; represents the time of the -th saccade, with the unit of s; U j represents the total number of saccades from the j-th lamp to the (j + 1)-th lamp.

[0031] As a preference of the present invention, the calculation formula for the total average illuminance of the i-th lamp near the entrance or exit on the tunnel road surface is:

[0032] ;

[0033] In the formula: E 外i represents the actual illuminance of the external natural light on the i-th lamp near the tunnel entrance or exit when there is no lamp lighting in the tunnel; ; where, d i represents the length of the cross-section where the i-th lamp near the tunnel entrance or exit is located from the entrance or exit; L 外 represents the external natural light of the tunnel; k is a coefficient, and its value range is 0.05 m -1 0.5 m -1 ;

[0034] The average horizontal illuminance E av of the lamp on the road surface has the following expression:

[0035] ;

[0036] In the formula: is the lamp layout coefficient, is the utilization factor, W is the width of the tunnel road surface, with the unit of m; S is the distance between adjacent two lamps, with the unit of m; is the rated luminous flux of the lamp, with the unit of lm.

[0037] As a preference of the present invention, when d iWhen in the entrance section TH1, =L th1 ; When d i When in the entrance section TH2, =L th2 ; When d i When in the transition section TR1, =L tr1 ; When d i When in the transition section TR2, =L tr2 ; When d i When in the transition section TR3, =L tr3 ; When d i When in the exit section EX1, =L ex1 ; When d i When in the exit section EX2, =L ex2 ; 、 represent the brightness of the entrance sections TH1 and TH2, 、 、 represent the brightness of the transition sections TR1, TR2, and TR3, 、 represent the brightness of the exit sections EX1 and EX2, calculated according to the "Code for Highway Tunnel Lighting Design" in China;

[0038] For the entrance section, , ; For the transition section, , ; For the exit section, , 。

[0039] As an optimization of the present invention, a genetic algorithm is used to solve the comprehensive objective function F to generate the optimal Pareto front solution.

[0040] Advantages and beneficial effects of the present invention:

[0041] 1. The present invention can monitor the natural light intensity outside the tunnel in real time through an illuminance sensor, and combine the light attenuation function related to the driving displacement and the curve of the luminaire utilization factor to accurately calculate and dynamically adjust the average illuminance in the tunnel, realizing real-time dynamic dimming, making the lighting system more flexible and having a stronger ability to respond to emergencies compared with traditional fixed strategies, and being superior to traditional static or segmented dimming schemes.

[0042] 2. The present invention innovatively combines driver physiological indicators such as the change rate of pupil area and average saccade speed to quantitatively evaluate visual comfort and mental load, which is more scientific and comprehensive than the traditional method that only relies on illuminance or pupil area. By deeply integrating multi-source data of engineering parameters (illuminance, brightness, lamp power) and psychological parameters (pupil change rate, saccade speed), multi-dimensional collaborative optimization of "human-vehicle-environment" is achieved, taking into account energy consumption while improving driving safety and driver visual comfort.

[0043] 3. The present invention establishes a natural light attenuation model in the tunnel (E 外 =L 外 ×e -kd ), quantifies the influence of external light on the interior of the tunnel, and improves the prediction ability of dynamic dimming; by controlling the flicker frequency (2.5 - 15 Hz) through the lamp spacing constraint, combined with the optimization of the lamp layout form, the driver's eyes are reduced from frequent saccades, significantly reducing visual fatigue and potential safety hazards.

[0044] 4. The present invention establishes a comprehensive objective function (F1 is the objective of energy consumption and light pollution, F2 is the objective of mental load), dynamically balances energy conservation and safety through the weight coefficient method, collaboratively optimizes energy consumption and safety, and the dynamic weight can adapt to different scenarios. For example, the weight coefficient θ can be dynamically adjusted according to the difference between day and night scenarios. During the day, energy conservation can be emphasized, and safety can be prioritized at night.

[0045] 5. The present invention effectively alleviates the "black hole effect" and "white hole effect" caused by the sudden change of external natural light and the interior light of the tunnel by dynamically adjusting the lighting brightness at the entrance and exit sections and the transition section of the tunnel, significantly shortening the visual adaptation time and reducing the accident risk caused by visual discomfort; based on the relationship model between the driver's pupil area and environmental illuminance (Q = e 8.509 ×E -0.101 ), the system accurately adapts to visual needs. At the same time, taking the pupil area change rate (less than 20% is the comfortable state) as the evaluation index, through the smooth illuminance gradient design (such as the high illuminance gradually changing to the basic illuminance in the middle section at the entrance), visual fatigue and mental tension are reduced, thereby improving the safety and comfort of the driver during tunnel driving.

[0046] 6. The present invention significantly reduces the flicker effect caused by the periodic change of light and dark bands on the tunnel pavement and side walls by optimizing the lamp spacing design (the flicker frequency is controlled below 2.5 Hz or above 15 Hz) and power constraint, thereby reducing the interference with the driver's attention. At the same time, by strictly controlling the lamp luminous intensity within the specified range, the generation of glare is effectively suppressed, reducing the negative impact of light pollution on the driver's vision; this design improves driving comfort while enhancing the safety and stability of tunnel lighting.

[0047] 7. The present invention can adaptively adjust the illuminance of the entrance section to 10%-20% of the external natural light according to external variables such as weather conditions (e.g., 5000-10000 lx on sunny days and 100-1000 lx on cloudy days), time changes, and traffic flow, ensuring the continuity and stability of visual adaptation. This dynamic adjustment mechanism is significantly superior to traditional static lighting systems and can effectively cope with complex environmental changes, improving the applicability and accuracy of lighting solutions.

[0048] 8. The present invention supports parameter adjustment of different design speeds (20-120 km / h), tunnel types (one-way / two-way), design traffic volumes, and weather conditions (sunny / cloudy), and is applicable to various types of tunnels such as mountainous and urban tunnels, with a wide range of adaptable scenarios.

[0049] 9. The present invention uses a genetic algorithm to solve a multi-objective optimization model, generates the Pareto front optimal solution, supports flexible selection of multiple lighting solutions, realizes the automatic generation and dynamic adjustment of lighting solutions, and takes into account both engineering feasibility and intelligent requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be elaborated and described in detail through the following detailed description of the embodiments in conjunction with the accompanying drawings.

[0051] Figure 1 is a flowchart of the dynamic optimization method for tunnel lighting based on the visual adaptation characteristics of drivers provided by the present invention;

[0052] Figure 2 is a schematic diagram of the usual lighting fixture layout form; where a) is the centerline layout; b) is the staggered or symmetric layout on both sides; c) is the centerline side-offset layout;

[0053] Figure 3 is an example diagram of the lamp spacing under different lighting fixture layout forms;

[0054] Figure 4 is a sectional view of the lighting system for a one-way traffic highway tunnel;

[0055] Figure 5 is a sectional view of the lighting system for a two-way traffic highway tunnel. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following further describes the content of the present invention in detail in conjunction with examples and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0057] As Figure 1 shown, the present invention provides a dynamic optimization method for tunnel lighting based on the visual adaptation characteristics of drivers, and the method includes the following steps:

[0058] Step 1. Establish a relationship model between the pupil area Q of the driver and the ambient illuminance E in the entrance section, transition section, and exit section of the tunnel, and calculate the pupil area change rate AQ based on the driver's pupil area.

[0059] Step 2. Establish the corresponding relationship between the average saccade speed and the tunnel ambient illuminance, and the corresponding relationship between the pupil area change rate and the tunnel ambient illuminance, based on historical data.

[0060] Step 3. Based on the natural light L outside the tunnel 外 and the average horizontal illuminance E of the luminaires on the road surface, av construct a calculation model for the total average illuminance E on the tunnel road surface of the i-th luminaire near the entrance or exit. i

[0061] Step 4. Establish the energy consumption and light pollution objective function F1 and the driver mental workload objective function F2.

[0062] Step 5. Normalize the energy consumption and light pollution objective function F1 and the driver mental workload objective function F2:

[0063] Step 6. Establish the comprehensive objective function F, and determine the lighting scheme by solving the Pareto front optimal solution of the comprehensive objective function F.

[0064] Specifically, the present invention uses an eye tracker to collect the pupil area of the driver, and records the natural light outside the tunnel and the actual illuminance on the tunnel road surface at the corresponding time and location.

[0065] To reduce the influence of demographic characteristics such as gender and driving experience on the driver's physiological characteristics, the present invention selects the pupil area data samples of multiple drivers during tunnel driving, and conducts a mean analysis based on these data. Since the overall data distribution is unknown, a non-parametric test method (non-parametric tests are flexible and widely applicable, especially suitable for cases where the overall distribution is unclear or skewed) is used for inference and comparison.

[0066] First, the one-sample Kolmogorov-Smirnov test method is used. This method is applicable to any type of data and does not require assumptions about the overall distribution. The test results show that both the Kolmogorov-Smirnov test statistic D and the two-sided asymptotic significance value P of the mean of the driver's pupil area are higher than the average significance level value, and the null hypothesis cannot be rejected. Therefore, the mean value of the driver's pupil area follows a normal distribution, and this conclusion provides an important reference for subsequent research.

[0067] ​Since the Kolmogorov-Smirnov test found no significant differences, the non-parametric test for multiple paired samples was further used for verification. The non-parametric test for multiple paired samples is mainly used to compare the distribution patterns of two or more related samples. Common methods include the signed rank test, Friedman test, and Page trend test, etc. Taking the Friedman test as an example, if the calculated probability value is lower than the significance level, the original hypothesis is rejected, indicating that there are obvious differences in the ranks of each group of samples. Through the Friedman test, it was found that there are significant differences in the mean distribution of the pupil areas of drivers on different road sections.

[0068] The Wilcoxon signed rank test method in the non-parametric test for two paired samples was continued to be used (assuming α = 0.05) to test the pairwise differences among the four road sections. The Wilcoxon signed rank test is a hypothesis testing method based on the rank sum test criterion. By analyzing two paired samples, it is speculated whether the distributions of the samples from two populations are different. The test results show that during driving, the size of the driver's pupils will change with the change of road sections. In the non-tunnel section, the average pupil area of the driver is smaller, probably because the light is stronger and the pupil size needs to be adjusted to adapt to the light. While in the tunnel, due to the weaker light, the driver's pupils will naturally dilate to improve visual sensitivity.

[0069] In addition, there are obvious differences in the average pupil areas among the entrance section, exit section, and transition section, which is related to the different light intensities and visual field ranges of these road sections. As the first lighting section entering the tunnel, the entrance section needs to enable the driver to quickly adapt from the high-brightness environment outside the tunnel to the low-brightness environment inside the tunnel and eliminate the "black hole" phenomenon; the transition section is between the entrance section and the middle section. Due to the large brightness difference between the two, the transition section plays a role in enabling the driver to fully adapt visually; the exit section helps the driver to transition from the low-brightness level inside the tunnel to the high-brightness level and avoid the glare effect caused by the natural light entering.

[0070] The driver's pupil area changes greatly in these sections, indicating that the driver's visual comfort is poor and may be in a relatively tense mental state. These conditions pose relatively large potential safety hazards. Therefore, the present invention optimizes the lighting of the entrance section, exit section, and transition section inside the tunnel.

[0071] Taking the collected driver data, scatter plots of ln(Q / E) and ln(E) of each driver were constructed respectively. From the scatter plots, it was guessed that there is a linear relationship between the two. Linear regression was performed, and significance testing and homoscedasticity testing were carried out on the obtained model. It was found that the model has a good fitting effect on the sample data points, and the correlation between the two variables ln(Q / E) and ln(E) is very significant. Generally speaking, the fitting effect of this model is good and has statistical significance.

[0072] The t - test analysis results of the model coefficients, where the Sig. values of the coefficients are all less than 0.05, indicating that the coefficients in the model are significantly meaningful and can be used in the model. This result further proves the reliability and effectiveness of the model.

[0073] This invention comprehensively analyzes the relationship model between ln(Q / E) and ln(E) of each driver, and through calculation, obtains the relationship model suitable for the pupil area of the vast majority of drivers and the ambient illuminance in the entrance section, transition section, and exit section of the tunnel:

[0074] Q = e 8.509 ×E -0.101 ;

[0075] In the formula: Q is the pupil area of the driver (px); E is the ambient illuminance in the tunnel (lx).

[0076] The change rate of pupil area can reflect the driver's tension level. The change rate of pupil area is selected as the evaluation index to analyze the psychological state of the subjects when the drivers are affected by the side - wall effect under different illuminances and different linear induction schemes. When the change rate of pupil area is less than 20%, the driver is in a comfortable state; when the pupil area is greater than 20%, the driver is in a tense state. Due to the high acquisition frequency of the eye tracker and numerous data points, the average value of the pupil area of the subject (driver) within every 0.5 s is selected as the pupil area at that moment.

[0077] The calculation formula for the change rate of pupil area is:

[0078] ;

[0079] In the formula: represents the change rate of the pupil area of the driver at the (d i+1 ) i + 1 - th lamp near the tunnel entrance or exit; represents the pupil area of the driver at the (d i ) i - th lamp near the tunnel entrance or exit; represents the pupil area of the driver at the (d i+1 ) i + 1 - th lamp near the tunnel entrance or exit.

[0080] In the tunnel, if the brightness or spacing of the lamps is not designed reasonably, periodic bright and dark stripes will appear on the road surface and side walls. This "flicker effect" will make the driver feel uncomfortable. To adapt to this light change, the driver's eyes will rotate frequently, trying to capture a clear vision. This rapid eye movement behavior is called "saccade". The more severe the flicker effect is, the faster the driver's eyes rotate, and the stronger the visual discomfort is. Therefore, it is possible to judge whether the driver feels comfortable by measuring the saccade speed of the driver's eye rotation.

[0081] Specifically, the saccade speed is obtained by recording the eye movements of the driver over a period of time using an eye tracker. The speed of each saccade can be calculated by dividing the angle of eye rotation (referred to as the saccade amplitude ) by the time taken for this rotation ( ) to obtain a speed value . However, due to the complex light conditions in the tunnel, the saccade speeds of the driver at different positions may vary greatly. If only looking at the data of a single saccade, it may be interfered by accidental factors and unable to reflect the overall situation. Therefore, in the present invention, the speeds of all saccades over a period of time are added up and averaged, which is defined as the average saccade speed , and the formula is as follows:

[0082] ;

[0083] In the formula: represents the average saccade speed, with the unit of ° / s; represents the amplitude of the -th saccade, with the unit of °; represents the time of the -th saccade, with the unit of s; U j represents the total number of saccades from the j-th lamp to the (j + 1)-th lamp.

[0084] In this embodiment, the relationship between the average saccade speed and the saccade time is established. The saccade time is related to the light. By using the existing method to fit the historical data, the relationship between the saccade time and the ambient light in the tunnel can be determined, and thus the corresponding relationship between the average saccade speed and the ambient light in the tunnel can be determined.

[0085] The average saccade speed can be directly measured by an eye tracker. By , the visual experience of the driver can be understood more accurately. For example, if is relatively low (such as 5 - 10 ° / s), it indicates that the driver's eye movements are stable, the light environment is comfortable, and the attention is concentrated; if is relatively high (such as exceeding 15 ° / s), it may indicate that the flicker effect is obvious, the driver needs to frequently adjust the line of sight, and feels nervous or fatigued. This way of taking the average value can reduce the data fluctuation, help to better evaluate the effect of tunnel lighting, and provide a basis for optimizing the design.

[0086] The present invention establishes the corresponding relationship between the average saccade speed and the ambient light in the tunnel, and the corresponding relationship between the pupil area change rate and the ambient light in the tunnel; in practical applications, the corresponding average saccade speed and pupil area change rate are selected according to the ambient light in the tunnel.

[0087] The natural light intensity outside the tunnel is affected by factors such as weather and time, usually ranging from 5000 - 10000 lx (sunny day) to 100 - 1000 lx (cloudy day). When there is no lighting by lamps inside the tunnel, it mainly relies on the natural light at the tunnel entrance. Therefore, the external natural light intensity directly affects the illuminance design inside the tunnel.

[0088] The optimization scheme provided by the present invention mainly optimizes the lighting of the tunnel entrance section, transition section and exit section. The lamps are arranged in sequence as 1, 2, 3...i from outside to inside near the entrance, and also arranged in sequence as 1, 2, 3...i from outside to inside near the exit.

[0089] The present invention uses an illuminance sensor to measure the natural light outside the tunnel and the natural illuminance of the road surface when there is no lighting by lamps inside the tunnel, and establishes the actual illuminance function E of the external natural light in the tunnel related to the driving displacement 外 (L 外 ,d).

[0090] ;

[0091] In the formula: E 外i represents the actual illuminance of the external natural light near the i-th lamp at the tunnel entrance or exit when there is no lighting by lamps inside the tunnel, d i represents the length of the section where the i-th lamp is located near the tunnel entrance or exit from the entrance or exit; L 外 represents the external natural light of the tunnel; k represents the coefficient related to the material and shape of the tunnel wall, the area of the tunnel entrance, and the angle between the natural light and the tunnel axis. After calculation, the value range of k is about 0.05 m -1 0.5 m -1 .

[0092] The present invention calculates the average horizontal illuminance E of the lamps on the road surface according to the lamp utilization coefficient curve graph, and the expression is: av The expression is:

[0093] ;

[0094] In the formula: is the lamp layout coefficient, taking 2 for symmetric layout, and taking 1 for staggered, center line and central side deviation single light band layout; is the utilization coefficient, which is obtained from the lamp utilization coefficient curve graph. For the lamp utilization coefficient curve graph, please refer to the "Highway Tunnel Lighting Design Rules"; W is the width of the tunnel road surface (m); S is the distance between adjacent two lamps (m). The lamp spacing under different lighting lamp layout forms is as Figure 3As shown in the figure; M is the maintenance coefficient of the luminaire. Under normal circumstances, the value of the maintenance coefficient M is 0.7. For extra-long luminaires with a longitudinal slope greater than 2% and a large vehicle ratio greater than 50%, the value of the maintenance coefficient M is 0.6; is the rated luminous flux of the luminaire (lm).

[0095] Therefore, the total average illuminance on the tunnel pavement of the i-th luminaire near the entrance or exit .

[0096] According to the formula obtained above, the present invention combines it with multiple factors such as balancing light pollution and energy consumption (energy conservation and environmental protection), and driver psychological load (safety and comfort), etc., and uses the weight coefficient method to construct a comprehensive objective function to achieve multi-objective optimization.

[0097] Define the sub-objective function:

[0098] Energy consumption and light pollution objective function F1:

[0099] ;

[0100] In the formula: E i represents the total average illuminance on the tunnel pavement of the i-th luminaire near the entrance or exit (lx); d i is the length (m) of the cross-section where the i-th luminaire near the entrance or exit is located from the entrance or exit; a and r represent adjustment coefficients, reflecting the sensitivity of illuminance and driving displacement to energy consumption, a ∈ [0.5, 2], r ∈ [1, 3], and the specific values can be adjusted according to field applications; if the model focuses on energy conservation, a can be appropriately increased; if it is necessary to optimize the lighting uniformity of long sections, r can be increased;

[0101] Driver psychological load objective function F2:

[0102] ;

[0103] In the formula: E i0 represents the ideal illuminance threshold (lx) of the i-th luminaire near the entrance or exit in the tunnel determined based on the driver's visual adaptation characteristics in the tunnel lighting system. It represents the illuminance that can keep the change rate of the driver's pupil area within the comfortable range, that is, when AQ = 20%, and at the same time meets the minimum illuminance requirements for safe driving and national standards; E i -E i0 represents the deviation between the actual illuminance and the ideal illuminance threshold of the i-th luminaire in the tunnel. The greater the deviation, the higher the psychological load; is an adjustment coefficient used to balance the relative importance of illuminance deviation and saccade speed deviation, and its value range is (0, 1). The specific value needs to be calibrated through experiments; represents the deviation between the average saccade speed and the ideal saccade speed value, It is usually taken as 10° / s.

[0104] For range normalization to eliminate the influence of dimension, the sub-objective function is normalized:

[0105] ;

[0106] ;

[0107] In the formula, , are the minimum values of the objective functions F1 and F2 respectively, , are the maximum values of the objective functions F1 and F2 respectively, , are the values of the objective functions F1 and F2 after normalization respectively; after normalization, , ∈[0, 1].

[0108] For the comprehensive objective function F, two sub-objectives are balanced through the weight coefficients θ∈[0, 1], (1 - θ)∈[0, 1]:

[0109] ;

[0110] When θ→1, energy consumption and light pollution are preferentially optimized; when θ→0, the driver's mental load is preferentially reduced.

[0111] In addition, since the optimization problem needs to meet the actual engineering and safety specifications, the constraint conditions are analyzed, and the main constraints are as follows:

[0112] The optimal brightness should be within the range of the driver's visual comfort brightness, and the illuminance of each section of the tunnel should meet the national standards.

[0113] ;

[0114] In the formula: represents the optimal brightness (cd·m −2 ) of the i-th lighting area (the optimal brightness of the cross-section where the i-th lamp is located); and represent the maximum and minimum brightness values (cd·m −2 ) that meet the driver's visual comfort requirements; usually, the , of the entrance section are taken; the , of the transition section; the , of the exit section; is the standard brightness value specified in the specification, cd·m −2 ; Represents taking the maximum value in; when d i is in the entrance section TH1 road section, = L th1 ; when d i is in the entrance section TH2, = L th2 ; when d i is in the transition section TR1, = L tr1 ; when d i is in the transition section TR2, = L tr2 ; when d i is in the transition section TR3, = L tr3 ; when d i is in the exit section EX1, = L ex1 ; when d i is in the exit section EX2, = L ex2 .

[0115] As Figure 4 , Figure 5 shown, the one-way traffic highway tunnel lighting system tunnel is divided into an entrance section, a transition section, a middle section, and an exit section; the two-way traffic highway tunnel lighting system tunnel is divided into an entrance section, a transition section, and a middle section; according to the "Code for Highway Tunnel Lighting Design" (JTG / T D70 / 2 - 01 - 2014) in China, the brightness calculation formulas for the entrance section (entrance area) and the transition section (transition area) are:

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] In the formula: , represent the brightness of the entrance sections TH1 and TH2 (cd· ); , , represent the brightness of the transition sections TR1, TR2, and TR3 (cd· ); represents the brightness of the area outside the tunnel approaching the entrance section (approach section) (cd· ); is the brightness reduction coefficient of the entrance section, and its value can be obtained according to Table 1.

[0122] Table 1 Value table of the brightness reduction coefficient k of the entrance section

[0123]

[0124] Note: When the traffic volume is at its intermediate value, the value is obtained by linear interpolation.

[0125] For example: When the design speed is 120 km / h and the one-way traffic ≥ 1200 or the two-way traffic ≥ 650, k takes 0.070; when the design speed is 20 - 40 km / h and the one-way traffic ≥ 1200 or the two-way traffic ≥ 650, k takes 0.012.

[0126] Brightness calculation formula for the exit section (exit area):

[0127] ;

[0128] ;

[0129] In the formula: represents the brightness of the intermediate section (cd· ), and the value of the brightness of the intermediate section is shown in Table 2; , represent the brightness of the exit sections EX1 and EX2 (cd· ).

[0130] Table 2 Brightness table of the intermediate section L in (cd / m 2 )

[0131]

[0132] Note: When the design speed v t is 120 km / h, the brightness of the intermediate section can be taken as the corresponding brightness at 100 km / h; when the design speed v t is 100 km / h, the brightness of the intermediate section can be taken as the corresponding brightness at 80 km / h; when the design speed v t ≤ 80 km / h, the corresponding brightness in the table can be taken;

[0133] For example: When the design speed is 100 km / h and the one-way traffic ≥ 1200 or the two-way traffic ≥ 650, L in takes 3.5; when the design speed is 120 km / h and the one-way traffic ≥ 1200 or the two-way traffic ≥ 650, L in takes 6.5; when the design speed is 60 km / h and the one-way traffic ≥ 1200 or the two-way traffic ≥ 650, Lin Take 2.

[0134] Relationship between luminance and illuminance and calculation method:

[0135] In tunnel lighting design, illuminance and luminance are two key parameters; illuminance reflects the intensity of light and can be directly measured by an illuminance sensor or calculated based on the parameters of the lighting fixtures; luminance reflects the light intensity perceived by the human eye and is directly related to the visual comfort of the driver; the relationship between the two is:

[0136] ;

[0137] In the formula: represents luminance ( ); E represents illuminance ( ); represents the reflectivity of the wall or road surface, and the value range is 0 1, rough concrete smooth coating .

[0138] Lamp power and layout constraints:

[0139] Single lamp power limit: ;

[0140] In the formula, P i refers to the power of the i-th lamp near the entrance or exit, and P max refers to the maximum power of a single lamp, represents that the single lamp power limit applies to each lamp numbered i;

[0141] Lamp spacing limit:

[0142] When the driver travels in the tunnel at the design speed for more than 20 s, the spacing of the lighting fixtures should have a flicker frequency lower than 2.5 Hz or higher than 15 Hz.

[0143] Flicker frequency formula: ;

[0144] The lamp spacing can be obtained: ;

[0145] In the formula: v t represents the tunnel design speed (km / h); f min and f max are the minimum and maximum values of the flicker frequency, which are set to 2.5 Hz and 15 Hz respectively; S i is the distance (m) between the i-th lamp and the (i + 1)-th lamp near the entrance or exit.

[0146] Displacement dynamic constraint, the length (travel displacement) d of the cross-section where the i-th lamp is located from the inlet or outlet i It needs to match the actual tunnel length:

[0147] 0 ≤ d i ≤ d total

[0148] In the formula: d total represents the total actual tunnel length (m).

[0149] Thus, the optimization model (comprehensive objective function) of the present invention can be obtained:

[0150] ;

[0151] Constraint conditions:

[0152] ;

[0153] Algorithms such as optimization algorithms based on genetic algorithms can be used to solve this model. This model effectively balances the energy-saving requirements and safety goals in tunnel lighting through the weight coefficient method and range standardization. The constraint conditions cover engineering specifications and dynamic scenarios, providing theoretical support for intelligent lighting systems. In practical applications, the weights can be dynamically adjusted in combination with real-time data to achieve the coordination of optimal energy efficiency and safety.

[0154] The optimization method provided by the present invention can be used for dynamic weight adjustment in practical applications. In the daytime scenario: when the light is sufficient, the driver has strong adaptability, and can be increased to save energy and reduce consumption. For example, can be controlled to be 0.75; in the night scenario: the visual sensitivity is high, and needs to be reduced. For example, can be controlled to be 0.25 to give priority to ensuring the psychological load index. And by solving the Pareto front optimal solution of the comprehensive objective function F, multiple lighting schemes can be provided. This model also has certain engineering feasibility, and the model supports real-time adjustment of lamp parameters (such as dimming systems) to adapt to changes in traffic flow.

[0155] The above uses specific examples to elaborate on the present invention, which is only used to help understand the present invention and does not limit the present invention. For those skilled in the art of the present invention, based on the idea of the present invention, several simple deductions, deformations or substitutions can also be made. Therefore, the protection scope of the present invention should be subject to the protection scope of the claimed rights.

Claims

1. A tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics, characterized in that: The method comprises the following steps: Step 1. Establish a relationship model between the driver's pupil area Q and the ambient light illumination E at the entrance, transition and exit sections of the tunnel, and calculate the pupil area change rate AQ based on the driver's pupil area; Step 2. Establish average scanning speed based on historical data The correspondence between the pupil area change rate and the ambient light in the tunnel; Step 3. Based on the natural light outside the tunnel L 外 And the average level illumination E of the lamp on the road surface av Construct the total average illumination E of the road surface in the tunnel for the ith lamp near the entrance or exit i The computational model of Step 4. Establish energy consumption and light pollution objective function F1 and driver psychological load objective function F2; ； ； Where, d i is the length of the section where the i-th lamp is located near the entrance or exit from the entrance or exit, in meters; E i represents the total average illumination of the road surface in the tunnel for the ith lamp near the entrance or exit, in lx; a and r represent the adjustment coefficients; E i0 Represents the ideal illumination threshold of the ith lamp near the entrance or exit in the tunnel, in lx; is the adjustment coefficient, is the average scanning speed, is the ideal scanning speed value; Step 5. Normalize the energy consumption and light pollution objective function F1 and the driver's psychological load objective function F2; ； ； In the formula, , are the minimum values ​​of objective functions F1 and F2 respectively, , are the maximum values ​​of objective functions F1 and F2 respectively, , are the standardized values ​​of objective functions F1 and F2 respectively; Step 6. Establish a comprehensive objective function F, and determine the lighting solution by solving the Pareto frontier optimal solution of the comprehensive objective function F; ； The constraints are: ; In the formula, θ is the weight coefficient, Represents the optimal brightness of the cross section of the ith lamp near the entrance or exit, in cd·m −2 ; and Represents the maximum and minimum brightness values ​​that meet the driver's visual comfort requirements; is the standard brightness value specified in the specification; Representative The maximum value in P i refers to the power of the ith lamp near the inlet or outlet, P max Refers to the maximum power of a single lamp, S i is the distance between the ith lamp and the i+1th lamp near the entrance or exit, in meters. f min and f max is the minimum and maximum value of the flicker frequency, v t represents the tunnel design speed, in km / h; represents the rate of change of the driver's pupil area at the ith lamp near the entrance or exit; d total Represents the actual total length of the tunnel in meters.

2. The tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics according to claim 1 is characterized in that: The relationship model between the driver's pupil area and the ambient light intensity in the entrance, transition and exit sections of the tunnel is: Q=e 8.509 ×E -0.101； Where: Q is the driver's pupil area, in px; E is the ambient light intensity in the tunnel, in lx.

3. The tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics according to claim 1 is characterized in that: The pupil area change rate is calculated as: ； Where: Represents the rate of change of the driver's pupil area at the i+1th lamp near the tunnel entrance or exit; represents the pupil area of ​​the driver at the ith lamp near the tunnel entrance or exit; Represents the pupil area of ​​the driver at the i+1th lamp near the tunnel entrance or exit.

4. The tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics according to claim 1 is characterized in that: Average scanning speed The expression is: ； Where: represents the average saccadic velocity in ° / s; Representative The amplitude of the saccade, in degrees; Representative The time of a scan, in seconds; U j Represents the total number of glances from the jth luminaire to the j+1th luminaire.

5. The tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics according to claim 1 is characterized in that: The calculation formula for the total average illumination of the road surface in the tunnel for the ith lamp near the entrance or exit is: ； Where: E 外i It represents the actual illumination of the natural light from the outside at the i-th lamp near the entrance or exit of the tunnel when there is no lamp lighting in the tunnel; ; where d i Represents the length of the section where the ith lamp is located near the entrance or exit of the tunnel from the entrance or exit; L 外 represents the natural light outside the tunnel; k is a coefficient with a value range of 0.05 m -1 0.5 m -1 ; The average illumination level of the lamp on the road surface is E av The expression is: ； Where: is the lighting arrangement coefficient, is the utilization coefficient, W is the tunnel road width, in meters; S is the distance between two adjacent lamps, in meters; It is the rated luminous flux of the lamp, in lm.

6. The tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics according to claim 1 is characterized in that: When i At the entrance section TH1, =L th1 When d i At the entrance section TH2, =L th2 When d i In the transition section TR1, =L tr1 When d i In the transition section TR2, =L tr2 When d i In the transition section TR3, =L tr3 When d i At the exit section EX1, =L ex1 When d i At the exit section EX2, =L ex2 ; , Represents the brightness of the entrance sections TH1 and TH2, , , Represents the brightness of transition segments TR1, TR2, and TR3, , Represents the brightness of the exit sections EX1 and EX2, calculated according to China's "Highway Tunnel Lighting Design Regulations"; the brightness of the entrance section , ; Transition , ; Export section , .

7. The tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics according to claim 1 is characterized in that: Genetic algorithm is used to solve the comprehensive objective function F and generate the optimal solution of Pareto frontier.

Images