Tunnel illumination dynamic optimization method based on driver visual adaptation characteristics

Abstract

The invention belongs to the field of illumination light control, and relates to a dynamic tunnel illumination optimization method based on driver visual adaptation characteristics, which comprises the following steps of: establishing a relation model between a driver pupil area and ambient illuminance in a tunnel, and calculating a pupil area change rate according to the driver pupil area; establishing a corresponding relationship among the average glancing speed, the pupil area change rate and the ambient light in the tunnel according to the historical data; constructing a calculation model of the total average illumination of the lamps on the road surface in the tunnel based on the natural illumination outside the tunnel and the average horizontal illumination of the lamps on the road surface; an energy consumption and light pollution objective function and a driver psychological load objective function are established based on parameters such as total average illumination of lamps on a road surface in a tunnel and the length from an entrance or an exit, and multi-source data such as illumination, brightness, lamp power, pupil change rate and glancing speed are deeply fused, so that human-vehicle-environment multi-dimensional collaborative optimization is realized. The driving safety and the visual comfort of a driver are improved, and meanwhile, the energy consumption is considered.

Description

Technical Field

[0001] The invention belongs to the field of lighting control, relates to the control of lighting in a tunnel, and in particular to a tunnel lighting dynamic optimization method based on the visual adaptation characteristics of a driver. Background Art

[0002] With the continuous expansion of my country's expressway network, highway tunnel construction has also seen significant growth. In complex mountainous terrain, although tunnels effectively shorten driving distances, their operational safety and energy consumption issues have gradually become the focus of attention. On the one hand, due to the visual effects caused by sudden changes in brightness at the entrances and exits of tunnels, drivers need a long time to adapt, which makes the accident rate in this area significantly higher than that of ordinary roads, and the accident handling time is prolonged, increasing the risk of secondary accidents; on the other hand, the existing lighting system has high energy consumption, and some operators adopt intermittent lighting to save costs, resulting in large fluctuations in illumination in the tunnel, which not only aggravates the driver's visual fatigue, but also increases safety hazards.

[0003] Chinese patent (application number 202011494429.2) discloses a tunnel lighting adjustment system and method based on the driver's pupil change characteristics. The power supply voltage of the tunnel lamp is adjusted according to the obtained pupil area data, thereby adjusting the lighting brightness of the light at the tunnel entrance, solving the "black hole" effect on the driver when entering the tunnel, and reducing traffic accidents. Chinese patent (application number 202310209767.4) discloses a driving comfort improvement device and method based on illumination and pupil monitoring; Chinese patent (application number 201910557709.4) discloses a method for selecting a tunnel entrance lighting source based on the driver's visual adaptation. According to the light color data of the sun in different periods outside the tunnel, the LED light source color suitable for the tunnel entrance lighting is selected to reduce the driver's dark adaptation time. However, the existing methods focus on optimizing the lighting of the entrance and exit sections, and less research is done on the lighting of the transition section in the tunnel, and energy consumption is not considered. Therefore, it is urgent to develop a tunnel lighting optimization method that can simultaneously optimize visual adaptation and adjust 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 method for dynamic optimization of tunnel lighting based on the driver's visual adaptation characteristics. The method establishes energy consumption and light pollution objective functions and driver psychological load objective functions, and deeply integrates multi-source data of engineering parameters (illuminance, brightness, lamp power) and psychological parameters (pupil change rate, scanning speed) to achieve multi-dimensional collaborative optimization of "people-vehicle-environment", thereby improving driving safety and driver's visual comfort while taking energy consumption into consideration.

[0005] To achieve the above object, the present invention adopts the following technical solution: A tunnel lighting dynamic optimization method based on driver's visual adaptation characteristics 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 F 1 and the driver's mental load objective function F 2 ; ; ; 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. Energy consumption and light pollution objective function F 1 and the driver's mental load objective function F 2 Perform normalization; ; ; In the formula, , The objective function F 1 、F 2 The minimum value of , The objective function F 1 、F 2 The maximum value of , The objective function F 1 、F 2Normalized value; 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.

[0006] As a preferred embodiment of the present invention, the relationship model between the driver's pupil area and the ambient light illumination of the entrance section, transition section, and exit section 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.

[0007] As a preferred embodiment of the present invention, the pupil area change rate calculation formula is: ; 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.

[0008] As the preferred embodiment of the present invention, the 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.

[0009] As a preferred embodiment of the present invention, the calculation formula for the total average illumination of the road surface in the tunnel for the i-th 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 the coefficient, and its value range is 0.05m -1 0.5m -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.

[0010] As the preferred embodiment of the present invention, when d i At the entrance TH 1 When the road section =L th1 ; When d i At the entrance TH 2 hour, =L th2 ; When d i In transition stage TR 1 hour, =L tr1 When d i In transition section TR 2 hour, =L tr2 When d i In transition section TR 3 hour, =L tr3 When d i In the exit section EX 1 hour, =L ex1 When d i In the exit section EX 2 hour, =L ex2 ; , Represents the entry section TH 1 , TH 2 Brightness, , , Represents the transition segment TR 1 ,TR 2 ,TR 3 Brightness, , Exit section EX 1 、EX 2 The brightness is calculated according to China's "Highway Tunnel Lighting Design Specifications"; Entrance section , ; Transition , ; Export section , .

[0011] As a preferred embodiment of the present invention, a genetic algorithm is used to solve the comprehensive objective function F and generate a Pareto frontier optimal solution.

[0012] Advantages and beneficial effects of the present invention: 1. The present invention uses an illuminance sensor to monitor the natural light intensity outside the tunnel in real time, and combines the light attenuation function related to vehicle displacement and the lamp utilization coefficient curve to accurately calculate and dynamically adjust the average illuminance in the tunnel, and realizes real-time dynamic dimming to achieve real-time response of the lighting system. Compared with traditional fixed strategies, it is more flexible and has a stronger ability to respond to emergencies, and is superior to traditional static or segmented dimming solutions.

[0013] 2. The present invention innovatively combines driver physiological indicators such as pupil area change rate and average scanning speed to quantitatively evaluate visual comfort and psychological load. It is more scientific and comprehensive than the traditional method that only relies on illumination or pupil area. By deeply integrating multi-source data of engineering parameters (illuminance, brightness, lamp power) and psychological parameters (pupil change rate, scanning speed), it realizes multi-dimensional collaborative optimization of "people-vehicle-environment", improving driving safety and driver visual comfort while taking energy consumption into consideration.

[0014] 3. The present invention establishes a natural light attenuation model in the tunnel (E 外 =L 外 ×e -kd ), quantify the impact of external light on the interior of the tunnel, and improve the prediction ability of dynamic dimming; control the flicker frequency (2.5-15Hz) through lamp spacing constraints, combined with the optimization of lamp layout, reduce the driver's frequent eye scans, and significantly reduce visual fatigue and safety hazards.

[0015] 4. The present invention establishes a comprehensive objective function (F 1 is the energy consumption and light pollution target, F 2 The weight coefficient method is used to dynamically balance energy saving and safety, and synergistically optimize energy consumption and safety. Moreover, the dynamic weight can be adapted to different scenarios. For example, the weight coefficient θ can be dynamically adjusted according to the difference between day and night scenarios. Energy saving can be emphasized during the day, and safety can be prioritized at night.

[0016] 5. The present invention dynamically adjusts the lighting brightness of the tunnel entrance and exit sections and the transition section, effectively alleviating the "black hole effect" and "white hole effect" caused by the sudden change of natural light from the outside and the light inside the tunnel, significantly shortening the visual adaptation time and reducing the risk of accidents caused by visual discomfort; based on the relationship model between the driver's pupil area and the ambient light intensity (Q = e 8.509 ×E -0.101 ), the system accurately adapts to visual needs, and uses the pupil area change rate (less than 20% is a comfortable state) as an evaluation indicator. Through a smooth illumination gradient design (such as a gradual change from high illumination at the entrance to basic illumination in the middle section), it reduces visual fatigue and psychological tension, thereby improving the safety and comfort of drivers in tunnel driving.

[0017] 6. The present invention significantly reduces the flickering effect caused by the periodic change of light and dark bands on the tunnel pavement and sidewalls by optimizing the lamp spacing design (the flickering frequency is controlled to be lower than 2.5 Hz or higher than 15 Hz) and power constraints, thereby reducing the interference with the driver's attention. At the same time, by strictly controlling the light intensity of the lamps within the specified range, the glare is effectively suppressed and the negative impact of light pollution on the driver's vision is reduced; this design improves driving comfort while enhancing the safety and stability of tunnel lighting.

[0018] 7. The present invention can adaptively adjust the illumination of the entrance section to 10%-20% of the external natural light according to external variables such as weather conditions (such as 5000-10000 lx on a sunny day and 100-1000 lx on a cloudy day), 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, can effectively cope with complex environmental changes, and improve the applicability and accuracy of lighting solutions.

[0019] 8. The present invention supports parameter adjustment for different design speeds (20-120km / h), tunnel types (one-way / two-way), design traffic volumes, and weather conditions (sunny / cloudy). It is suitable for multiple types of tunnels in mountainous areas, cities, etc., and can adapt to a wide range of scenarios.

[0020] 9. The present invention adopts genetic algorithm to solve the multi-objective optimization model, generates the Pareto frontier optimal solution, supports flexible selection of multiple lighting schemes, realizes automatic generation and dynamic adjustment of lighting schemes, and takes into account both engineering feasibility and intelligent requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention is elaborated and illustrated in detail through the following detailed description of the embodiments in conjunction with the accompanying drawings.

[0022] Figure 1 A flow chart of a tunnel lighting dynamic optimization method based on driver vision adaptation characteristics provided by the present invention; Figure 2 The following are schematic diagrams of common lighting fixture layouts; a) is a midline layout; b) is a staggered or symmetrical layout on both sides; c) is a midline side-off layout; Figure 3 Examples of lamp spacing under different lighting fixture arrangements are shown below; Figure 4 It is a section diagram of the lighting system of a one-way traffic highway tunnel; Figure 5 This is a section diagram of the lighting system for a two-way traffic highway tunnel. DETAILED DESCRIPTION

[0023] The present invention is further described in detail below with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.

[0024] like Figure 1 As shown, the present invention provides a tunnel lighting dynamic optimization method based on the driver's visual adaptation characteristics, the method comprising 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 F 1 and the driver's mental load objective function F 2 ; Step 5. Energy consumption and light pollution objective function F 1 and the driver's mental load objective function F 2 Normalize: Step 6. Establish a comprehensive objective function F, and determine the lighting plan by solving the Pareto frontier optimal solution of the comprehensive objective function F.

[0025] Specifically, the present invention uses an eye tracker to collect the driver's pupil area, and records the natural light illumination outside the tunnel and the actual road illumination inside the tunnel at the corresponding time and place.

[0026] In order to reduce the impact of demographic characteristics such as gender and driving experience on the physiological characteristics of drivers, the present invention selects pupil area data samples of multiple drivers when driving in tunnels, and performs mean analysis based on these data. Since the overall data distribution is unclear, non-parametric test methods (non-parametric tests are flexible and widely applicable, especially for situations where the overall distribution is unclear or skewed) are used for inference and comparison.

[0027] First, the single-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 the Kolmogorov-Smirnov test statistic D and the bilateral asymptotic significance value P of the mean pupil area of ​​drivers are higher than the average significance level, and the null hypothesis cannot be rejected. Therefore, the mean size of the driver's pupil area obeys a normal distribution, and this conclusion provides an important reference for subsequent research.

[0028] Since the Kolmogorov-Smirnov test did not find significant differences, the multi-paired sample non-parametric test method was further used for verification. The multi-paired sample non-parametric test method is mainly used to compare the distribution patterns of two or more related samples. Commonly used methods include the signed rank test, Friedman test, and Page trend test. 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 each group of samples has a significant difference in rank. Through the Friedman test, it was found that there were significant differences in the mean distribution of pupil area of ​​drivers on different road sections.

[0029] The Wilcoxon signed-rank test method (assuming α=0.05) in the two-paired sample nonparametric test was continued to test the pairwise differences between the four road sections. The Wilcoxon signed-rank test is a hypothesis test method based on the rank sum test criterion. By analyzing two paired samples, it is inferred whether the distribution of the samples from the two wholes is different. The test results show that the size of the driver's pupil changes with the change of the road section during driving. In the non-tunnel section, the average pupil area of ​​the driver is smaller, probably because the light is strong and the pupil size needs to be adjusted to adapt to the light. In the tunnel, due to the weak light, the driver's pupil will naturally expand to improve visual sensitivity.

[0030] In addition, there are significant differences in the average pupil area between the entrance section, exit section and transition section, which is related to the different light intensity and visual range of these sections. The entrance section is the first lighting section entering the tunnel, which requires drivers to quickly adapt from the high-brightness environment outside the tunnel to the low-brightness environment inside the tunnel to eliminate the "black hole" phenomenon; the transition section is between the entrance section and the middle section. Due to the large difference in brightness between the two, the transition section allows drivers to fully adapt their vision; the exit section helps drivers transition from the low brightness level in the tunnel to the high brightness level to avoid the glare effect caused by natural light.

[0031] The driver's pupil area varies greatly in these sections, reflecting that the driver's visual comfort is poor and he may be in a tense psychological state. These conditions pose great safety hazards. Therefore, the present invention optimizes the lighting of the entrance section, exit section and transition section in the tunnel.

[0032] The collected driver data were taken to construct scatter plots of ln(Q / E) and ln(E) for each driver. The scatter plots suggested that there was a linear relationship between the two. Linear regression was performed, and the obtained model was tested for significance and homogeneity of variance. It was found that the model had a good fitting effect on the sample data points, and the correlation between the two variables ln(Q / E) and ln(E) was very significant. Overall, the model had a good fitting effect and was statistically significant.

[0033] The t-test analysis results of the model coefficients show that the Sig. values ​​of the coefficients are all less than 0.05, indicating that the coefficients in the model are significant and can be used in the model. This result further proves the reliability and validity of the model.

[0034] The present invention comprehensively analyzes the relationship model between ln(Q / E) and ln(E) of each driver, and obtains the relationship model suitable for the pupil area of ​​most drivers and the ambient light illumination of the entrance section, transition section and exit section of the tunnel through calculation: Q=e 8.509 ×E -0.101 ; Where: Q is the driver's pupil area (px); E is the ambient light intensity in the tunnel (lx).

[0035] The pupil area change rate can reflect the driver's nervousness. The pupil area change rate is selected as an evaluation index to analyze the psychological state of the subject when the driver is affected by the side wall effect under different illumination and different linear induction schemes. When the pupil area change rate is less than 20%, the driver is in a comfortable state; when the pupil area is greater than 20%, the driver is in a nervous state. Due to the high frequency of eye tracker acquisition and the large number of data points, the average pupil area of ​​the subject (driver) within 0.5s is selected as the pupil area at that moment.

[0036] The pupil area change rate is calculated as: ; Where: Represents the i+1th lamp near the tunnel entrance or exit (d i+1 ) The driver’s pupil area change rate; Represents the i-th lamp near the tunnel entrance or exit (d i ) The driver’s pupil area; Represents the i+1th lamp near the tunnel entrance or exit (d i+1 ) The driver’s pupil area.

[0037] In tunnels, if the brightness or spacing of lamps is not designed properly, periodic light and dark strips will appear on the road surface and side walls. This "flickering effect" will make the driver feel uncomfortable. In order to adapt to this light change, the driver's eyes will move frequently, trying to capture a clear view. This rapid eye movement is called "saccade". The more severe the flickering effect, the faster the driver's eyes move, and the stronger the visual discomfort. Therefore, it is possible to determine whether the driver feels comfortable by measuring the scanning speed of the driver's eye movement.

[0038] Specifically, the scanning speed is obtained by recording the driver's eye movements over a period of time using an eye tracker. The speed of each scan can be measured by the angle of eye rotation (called the scanning amplitude). ) divided by the time it takes to rotate ( ) to calculate and get a speed value However, due to the complex light conditions in the tunnel, the driver's scanning speed at different positions may vary greatly. If we only look at the data of a certain scan, it may be interfered by accidental factors and cannot reflect the overall situation. Therefore, the present invention adds up the speed of all scans in a period of time and takes the average value, which is defined as the average scanning speed , the formula is as follows: ; 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.

[0039] In this embodiment, the relationship between the average scanning speed and the scanning time is established. The scanning time is related to the light. By fitting the historical data using the existing method, the relationship between the scanning time and the ambient light in the tunnel can be determined, thereby determining the corresponding relationship between the average scanning speed and the ambient light in the tunnel.

[0040] Average scanning speed It can be directly measured by an eye tracker. A more accurate understanding of the driver’s visual experience can be obtained. For example, if If the light intensity is low (e.g. 5-10° / s), it means the driver's eye movement is stable, the lighting environment is comfortable, and the driver is focused. If it is higher (for example, more than 15° / s), it may indicate that the flickering effect is obvious, and the driver needs to frequently adjust his sight, feeling nervous or tired. This average value method can reduce the fluctuation of data, help better evaluate the effect of tunnel lighting, and provide a basis for optimizing design.

[0041] The present invention establishes average scanning speed based on historical research data The corresponding relationship between the pupil area change rate and the ambient lighting in the tunnel, and the corresponding relationship between the pupil area change rate and the ambient lighting in the tunnel; in actual application, the corresponding average scanning speed and pupil area change rate are selected according to the ambient lighting in the tunnel.

[0042] The intensity of natural light outside the tunnel is affected by factors such as weather and time, and is usually between 5000-10000 lx (sunny day) and 100-1000 lx (cloudy day). When there is no lighting inside the tunnel, it mainly relies on natural light at the entrance. Therefore, the intensity of external natural light directly affects the illumination design inside the tunnel.

[0043] The optimization scheme provided by the present invention mainly optimizes the lighting of the entrance section, transition section and exit section of the tunnel. The lamps are arranged in sequence from the outside to the inside according to 1, 2, 3...i near the entrance, and are arranged in sequence from the outside to the inside according to 1, 2, 3...i near the exit.

[0044] The present invention uses an illumination sensor to measure the natural illumination outside the tunnel and the natural illumination of the road surface when there is no lighting in the tunnel, and establishes the actual illumination function E of the external natural illumination in the tunnel related to the vehicle displacement. 外 (L 外 , d).

[0045] ; Where: E 外i 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, 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 represents a coefficient related to the material and shape of the tunnel wall, the area of ​​the tunnel opening, and the angle between the natural light and the tunnel axis. The value range of k is about 0.05 m -1 0.5 m -1 .

[0046] The present invention calculates the average horizontal illumination E of the lamp on the road surface according to the lamp utilization coefficient curve. av , the expression is: ; Where: is the lamp arrangement coefficient, which is 2 for symmetrical arrangement and 1 for staggered, midline and central side single light band arrangement; is the utilization coefficient, which can be obtained from the lamp utilization coefficient curve diagram. For the lamp utilization coefficient curve diagram, please refer to the "Highway Tunnel Lighting Design Regulations"; W is the tunnel road width (m); S is the distance between two adjacent lamps (m). The distance between lamps under different lighting fixture arrangements is as follows: Figure 3 As shown; M is the maintenance coefficient of the lamp. Under normal circumstances, the maintenance coefficient M is 0.7. The maintenance coefficient M of special lamps with a longitudinal slope greater than 2% and a large vehicle ratio greater than 50% is 0.6; is the rated luminous flux of the lamp (lm).

[0047] Therefore, the total average illumination of the road surface in the tunnel for the ith lamp near the entrance or exit is .

[0048] According to the above formula, the present invention combines it with multiple factors such as balancing light pollution and energy consumption (energy saving and environmental protection), driver psychological load (safety and comfort), etc., and adopts the weight coefficient method to construct a comprehensive objective function to achieve multi-objective optimization.

[0049] Define the sub-objective function: Energy consumption and light pollution objective function F 1 : ; Where: E i represents the total average illuminance (lx) of the road surface in the tunnel for the ith lamp near the entrance or exit; 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 (m); a and r represent adjustment coefficients, reflecting the sensitivity of illumination and vehicle displacement to energy consumption, a∈[0.5, 2], r∈[1, 3], and the specific values ​​can be adjusted according to the actual application; if the model focuses on energy saving, a can be appropriately increased; if it is necessary to optimize the uniformity of lighting on long sections, r can be increased; Driver psychological load objective function F 2 : ; Where: E i0 In the tunnel lighting system, it represents the ideal illumination threshold (lx) of the ith lamp near the entrance or exit in the tunnel, which is determined based on the driver's visual adaptation characteristics. It represents the illumination within a specific tunnel section that can keep the driver's pupil area change rate within the comfortable range, that is, when AQ=20%, while meeting the minimum illumination requirements of safe driving needs and national standards; E i -E i0 represents the deviation between the actual illumination of the i-th lamp in the tunnel and the ideal illumination threshold. The larger the deviation, the higher the psychological load; is the adjustment coefficient, which is used to balance the relative importance of illumination deviation and scanning speed deviation. Its value range is (0, 1). The specific value needs to be calibrated experimentally. Represents the deviation between the average scanning speed and the ideal scanning speed value, Usually 10° / s is taken.

[0050] To eliminate the influence of dimension, the sub-objective function is normalized: ; ; In the formula, , The objective function F 1 、F 2 The minimum value of , The objective function F 1 、F 2 The maximum value of , The objective function F 1 、F 2 The standardized value; after standardization, , ∈[0,1].

[0051] The comprehensive objective function F balances the two sub-objectives through the weight coefficients θ∈[0,1], (1-θ)∈[0,1]: ; When θ→1, priority is given to optimizing energy consumption and light pollution; when θ→0, priority is given to reducing the driver’s psychological burden.

[0052] In addition, since the optimization problem needs to meet actual engineering and safety specifications, the constraints are analyzed. The main constraints are as follows: The optimal brightness should be within the driver's visual comfort range, and the illumination of each section of the tunnel must meet national standards.

[0053] ; Where: represents the optimal brightness (cd·m −2 ); and Represents the maximum and minimum brightness values ​​that meet the driver's visual comfort requirements (cd·m −2 ); usually taken from the entrance section , ; Transition , ; Export section , ; is the standard brightness value specified in the specification, cd·m −2 ; Representative The maximum value in; when d i At the entrance TH 1 When the road section =L th1 When d i At the entrance TH 2 hour, =L th2 When di In transition section TR 1 hour, =L tr1 When d i In transition section TR 2 hour, =L tr2 When d i In transition section TR 3 hour, =L tr3 When d i In the exit section EX 1 hour, =L ex1 When d i In the exit section EX 2 hour, =L ex2 .

[0054] like Figure 4 , Figure 5 As shown in the figure, the lighting system of a one-way traffic highway tunnel is divided into an entrance section, a transition section, a middle section, and an exit section; the lighting system of a two-way traffic highway tunnel is divided into an entrance section, a transition section, and a middle section; according to China's "Highway Tunnel Lighting Design Rules" (JTG / T D70 / 2-01-2014), the brightness calculation formula for the entrance section (entrance area) and the transition section (transition area) is: ; ; ; ; ; Where: , Represents the entry section TH 1 , TH 2 Brightness (cd· ); , , Represents the transition segment TR 1 ,TR 2 ,TR 3 Brightness (cd· ); Represents the brightness of the section outside the cave close to the entrance (approaching section) (cd· ); is the brightness reduction coefficient of the entrance section, and the value can be obtained according to Table 1.

[0055] Table 1 Value table of brightness reduction coefficient k at the entrance section

[0056] Note: When the traffic volume is at its intermediate value, the value is obtained by linear interpolation.

[0057] For example: when the design speed is 120km / h, one-way traffic ≥1200 or two-way traffic ≥650, k is taken as 0.070; when the design speed is 20-40km / h, one-way traffic ≥1200 or two-way traffic ≥650, k is taken as 0.012.

[0058] Exit section (exit area) brightness calculation formula: ; ; Where: Represents the brightness of the middle segment (cd· ), the brightness values ​​of the middle segment are shown in Table 2; , Exit section EX 1 、EX 2 Brightness (cd· ).

[0059] Table 2 Middle segment brightness table L in (cd / m 2 )

[0060] Note: When the design speed v t When the design speed is 120km / h, the brightness of the middle section can be taken as the brightness corresponding to 100km / h; when the design speed v t When the design speed is 100km / h, the brightness of the middle section can be taken as the brightness corresponding to 80km / h; when the design speed v t When the speed is ≤80km / h, the brightness can be set according to the table; For example: when the design speed is 100 km / h, one-way traffic ≥ 1200 or two-way traffic ≥ 650, L in Take 3.5, when the design speed is 120km / h, one-way traffic ≥1200 or two-way traffic ≥650, L in Take 6.5, when the design speed is 60km / h, one-way traffic ≥1200 or two-way traffic ≥650, L in Take 2.

[0061] The relationship between brightness and illumination and the calculation method: In tunnel lighting design, illuminance and brightness are two key parameters; illuminance reflects the light intensity, which can be directly measured by an illuminance sensor or calculated based on lamp parameters; brightness reflects the light intensity perceived by the human eye and is directly related to the driver's visual comfort; the relationship between the two is: ; Where: Represents brightness ( ); E represents illumination ( ); Represents the reflectivity of the wall or road surface, with a value range of 0 1. Rough concrete , smooth coating .

[0062] Lamp power and layout constraints: Single lamp power limit: ; Where 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. Represents that the single lamp power limit applies to each lamp numbered i; Luminaire spacing restrictions: When the driver travels at the design speed in the tunnel for more than 20 s, the spacing of lighting fixtures should have a flickering frequency lower than 2.5 Hz or higher than 15 Hz.

[0063] Flicker frequency formula: ; The available lamp spacing is: ; Where: v t represents the tunnel design speed (km / h); f min and f max are the minimum and maximum values ​​of the flashing frequency, which are set to 2.5 Hz and 15 Hz respectively; S i is the distance (m) between the i-th luminaire and the i+1-th luminaire near the entrance or exit.

[0064] Displacement dynamic constraint, the length of the section where the i-th lamp is located from the entrance or exit (travel displacement) d i Need to match the actual tunnel length: 0≤d i ≤d total Where: d total Represents the actual total length of the tunnel (m).

[0065] The optimization model (comprehensive objective function) of the present invention can be obtained as follows: ; Constraints: ; The model can be solved by using algorithms such as optimization algorithms based on genetic algorithms. The model effectively balances the energy-saving requirements and safety goals in tunnel lighting through the weight coefficient method and range standardization. The constraints 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.

[0066] The optimization method provided by the present invention can be used for dynamic weight adjustment in practical applications. In daytime scenes, when the light is sufficient, the driver has strong adaptability and can increase To save energy and reduce consumption, for example, 0.75; Night scene: high visual sensitivity, need to be reduced , for example, you can control The value is 0.25, giving priority to the psychological load index. And by solving the Pareto frontier optimal solution of the comprehensive objective function F, a variety of lighting solutions can be provided. The model also has certain engineering feasibility. The model supports real-time adjustment of lamp parameters (such as dimming system) to adapt to changes in traffic flow.

[0067] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art of the present invention, according to the idea of ​​the present invention, some simple deductions, deformations or substitutions can be made. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

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.

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