Expressway service area vehicle-mounted lighting dynamic adjustment light energy-saving method and device

Abstract

This application provides a method and device for dynamic adjustment of vehicle lighting in highway service areas to save energy, belonging to the field of automatic control technology. The method includes: acquiring radar point cloud data of the highway service area; identifying multiple target vehicles based on the radar point cloud data; determining the spatial coordinates of each target vehicle based on the radar point cloud data corresponding to each target vehicle; matching multiple target vehicles with multiple sub-regions based on the spatial coordinates of each target vehicle to determine the target vehicles in each sub-region; counting the number of vehicles and average vehicle speed in each sub-region, and determining the lighting mode for each sub-region based on the number of vehicles and average vehicle speed; and dynamically adjusting the lighting brightness of each sub-region based on the lighting mode. The method and device for dynamic adjustment of vehicle lighting in highway service areas provided by this application can reduce the lighting energy consumption of highway service areas.

Description

Technical Field

[0001] This application belongs to the field of automatic control technology, and more specifically, relates to a method and device for dynamic adjustment of vehicle lighting in highway service areas to save energy. Background Technology

[0002] Highway service areas are dedicated areas set up along the main line of highways to provide temporary parking and comprehensive services for drivers and vehicles, and are an important supporting facility for highways. Highway service area lighting provides sufficient visibility for vehicles entering, exiting, parking, and turning, reducing the risk of traffic accidents. It is a key infrastructure for ensuring nighttime driving safety and improving the service experience.

[0003] Existing highway service area lighting facilities typically operate at full power 24 hours a day, resulting in high energy consumption and large electricity costs. Summary of the Invention

[0004] This application provides a method and device for dynamically adjusting vehicle lighting in highway service areas to save energy, thereby reducing lighting energy consumption in highway service areas.

[0005] According to one aspect of the embodiments of this application, a method for dynamically adjusting the energy-saving light of vehicle lighting in highway service areas is provided, comprising: Acquire radar point cloud data from highway service areas; Target identification is performed based on the radar point cloud data to obtain multiple target vehicles; The spatial coordinates of each target vehicle are determined based on the radar point cloud data corresponding to each target vehicle. Based on the spatial coordinates of each target vehicle, multiple target vehicles are matched with multiple sub-regions to determine the target vehicles in each sub-region. The number of vehicles and average vehicle speed in each sub-region are counted, and the lighting mode for each sub-region is determined based on the number of vehicles and average vehicle speed in each sub-region. The lighting mode includes an idle lighting mode, a passing lighting mode, and a parking lighting mode. The number of vehicles in each sub-region is the number of target vehicles in each sub-region, and the average vehicle speed in each sub-region is the average speed of the target vehicles in each sub-region. The lighting brightness of each sub-region is dynamically adjusted based on the lighting pattern of each sub-region.

[0006] According to one aspect of the embodiments of this application, a dynamic adjustment light-saving device for onboard lighting in highway service areas is provided, comprising: The data acquisition module is used to acquire radar point cloud data from highway service areas; The target recognition module is used to identify targets based on the radar point cloud data to obtain multiple target vehicles; The sub-region matching module is used to determine the spatial coordinates of each target vehicle based on the radar point cloud data corresponding to each target vehicle, and to match multiple target vehicles with multiple sub-regions based on the spatial coordinates of each target vehicle in order to determine the target vehicle in each sub-region. The lighting mode determination module is used to count the number of vehicles and the average speed of vehicles in each sub-region, and determine the lighting mode of each sub-region based on the number of vehicles and the average speed of vehicles in each sub-region; wherein, the lighting mode includes idle lighting mode, passing lighting mode and parking lighting mode, the number of vehicles in each sub-region is the number of target vehicles in each sub-region, and the average speed of vehicles in each sub-region is the average speed of target vehicles in each sub-region. The lighting adjustment module is used to dynamically adjust the lighting brightness of each sub-region based on the lighting mode of each sub-region.

[0007] According to one aspect of the embodiments of this application, an electronic device is provided, the electronic device including a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the above-described method for dynamic adjustment of vehicle lighting in highway service areas to save energy.

[0008] According to one aspect of the embodiments of this application, the computer program product includes a computer program stored in a computer-readable storage medium. A processor of an electronic device reads the computer program from the computer-readable storage medium and executes the computer program, causing the electronic device to perform the aforementioned method for dynamic adjustment of vehicle-mounted lighting in highway service areas to save energy.

[0009] The technical solutions provided in this application embodiment may have the following beneficial effects: This application embodiment divides highway service areas into multiple sub-regions, assigning multiple target vehicles within the service area to these sub-regions. Based on this, a lighting pattern for each sub-region is determined according to the number of vehicles and their average speed. This lighting pattern characterizes the lighting needs of each sub-region. Dynamically adjusting the brightness of each sub-region based on its lighting pattern enables intelligent vehicle-mounted lighting control—"bright when there are vehicles, dim when there are no vehicles, and dimming on demand"—thus reducing energy consumption in highway service areas while ensuring adequate lighting. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating the energy-saving method for dynamic adjustment of vehicle lighting in highway service areas provided in this application embodiment; Figure 2 This is a schematic diagram illustrating the principle of merging the minimum partitioned regions provided in an embodiment of this application. Figure 3 Structural block diagram of the dynamic adjustment light-saving device for onboard lighting in highway service areas provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0013] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0014] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] It should be understood that although the terms first, second, etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, a first parameter may also be referred to as a second parameter, and similarly, a second parameter may also be referred to as a first parameter. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0016] Figure 1 This is a flowchart of a method for dynamically adjusting vehicle lighting in highway service areas to save energy, as provided in an embodiment of this application. The method can be executed by an electronic device and includes: S101: Acquire radar point cloud data of highway service areas.

[0017] In this embodiment, a lidar or millimeter-wave radar deployed in the highway service area can be used to scan the entire highway service area in real time to obtain radar point cloud data. The radar point cloud data can characterize the vehicle location, spatial distribution, and movement status of the highway service area. S102: Target identification is performed based on radar point cloud data to obtain multiple target vehicles.

[0018] In this embodiment, the original radar point cloud coordinates can be unified into a Cartesian coordinate system. Noise points are removed using statistical filtering and radius filtering methods. Ground points are removed using the Random Sample Consensus (RANSAC) algorithm. Static background points such as guardrails and streetlights are filtered using the inter-frame difference method, retaining effective dynamic point clouds. Then, a clustering algorithm is used to cluster the point clouds into clusters according to a preset distance threshold (0.3~0.5m), and candidate point cloud clusters that match the vehicle size are selected. Geometric features such as length, width, height, and aspect ratio of the candidate point cloud clusters are extracted and matched with a preset vehicle size template. Pedestrians, debris, and other non-vehicle targets are removed to obtain multiple target vehicles.

[0019] S103: Determine the spatial coordinates of each target vehicle based on the radar point cloud data corresponding to each target vehicle. Based on the spatial coordinates of each target vehicle, match multiple target vehicles with multiple sub-regions to determine the target vehicle in each sub-region.

[0020] In this embodiment, the radar's installation coordinates (X0, Y0, Z0) and attitude angles (yaw, pitch, roll) in the highway service area can be determined through radar extrinsic parameter calibration, establishing a radar local coordinate system. Simultaneously, an electronic map of the service area is constructed, defining a global geographic coordinate system with a fixed point in the service area (such as the center point of the entrance) as the origin. Then, feature points (such as the four corner points of the vehicle) are extracted from the radar point cloud cluster of the target vehicle to obtain the three-dimensional coordinates of the feature points in the radar local coordinate system. Next, the radar local coordinates of the feature points are converted into global geographic coordinates through a coordinate transformation matrix. Specifically, a rotation matrix can be used first to eliminate radar attitude angle deviations, and then the radar installation coordinates can be superimposed to complete the translation.

[0021] Taking any feature point (x', y', z') as an example, the global geographic coordinates (x, y, z) corresponding to that feature point can be obtained using the following formula: x = x'cosθ - y'sinθ + X0, y = x'sinθ + y'cosθ + Y0, z = z' + Z0, Where θ is the radar yaw angle.

[0022] Simultaneously, based on the actual layout of highway service areas and using a global geographic coordinate system as a benchmark, the highway service areas can be divided into multiple sub-regions. Each sub-region has a corresponding coordinate range and area type identifier (such as driving lanes, parking spaces, entrances / exits, passageways, etc.). Based on this, multiple target vehicles are matched with the coordinates of these sub-regions to determine the target vehicles in each sub-region.

[0023] S104: Count the number of vehicles and the average speed of vehicles in each sub-region, and determine the lighting mode of each sub-region based on the number of vehicles and the average speed of vehicles in each sub-region; wherein, the lighting mode includes idle lighting mode, passing lighting mode and parking lighting mode, the number of vehicles in each sub-region is the number of target vehicles in each sub-region, and the average speed of vehicles in each sub-region is the average speed of target vehicles in each sub-region.

[0024] In this embodiment, the number of target vehicles in each sub-region can be counted to obtain the total number of vehicles in each sub-region. Simultaneously, for each target vehicle, its position in two consecutive frames of radar point cloud data is recorded. Based on the time difference Δt and position difference Δs between the two frames of radar point cloud data, the speed v of the target vehicle is calculated. i =Δs / Δt, calculate the average speed of multiple target vehicles in each sub-region, and obtain these two average speeds for each sub-region.

[0025] Furthermore, considering that the number of vehicles and average vehicle speeds differ across multiple sub-regions, resulting in different lighting requirements, the lighting pattern for each sub-region can be determined based on the number of vehicles and average vehicle speed in each sub-region. For example, determining the lighting pattern for each sub-region based on its number of vehicles and average vehicle speed includes: If the number of vehicles in a sub-area is zero, the lighting mode for that sub-area is determined to be the idle lighting mode. If the number of vehicles in the sub-area is greater than zero and the average speed of vehicles in the sub-area is greater than or equal to the first speed threshold, the lighting mode of the sub-area is determined to be the traffic lighting mode. If the sub-area is a parking sub-area, and the number of vehicles in the sub-area is greater than zero and the average speed of the vehicles in the sub-area is less than the first speed threshold, then the lighting mode of the sub-area is determined to be the parking lighting mode.

[0026] In this embodiment, for each sub-region, if the number of vehicles in the sub-region is zero, it indicates that there is no vehicle activity in the sub-region, and only the minimum brightness needs to be maintained to meet basic lighting requirements; the corresponding mode is the idle lighting mode. If the number of vehicles in the sub-region is greater than zero, and the average speed of vehicles in the sub-region is greater than or equal to a first speed threshold, it indicates that vehicles are in motion, and sufficient brightness is needed to ensure driving visibility and traffic safety; the corresponding mode is the traffic lighting mode. If the sub-region is a parking sub-region (parking space area), and the number of vehicles in the sub-region is greater than zero, and the average speed of vehicles in the sub-region is less than a first speed threshold, it indicates that vehicles are in a low-speed stopped or parked state. The corresponding mode is the parking lighting mode. In this mode, there are more people getting in and out of vehicles and walking around, so the requirements for lighting brightness and uniformity are higher than in the traffic mode. If the sub-area is a parking sub-area (parking space area), and the number of vehicles in the sub-area is greater than zero and the average speed of vehicles in the sub-area is greater than or equal to the first speed threshold, it indicates that vehicles only pass through this area, and the corresponding mode is still the traffic lighting mode. If the sub-area is not a parking sub-area, and the number of vehicles in the sub-area is greater than zero and the average speed of vehicles in the sub-area is less than the first speed threshold, it indicates that there is vehicle congestion or illegal parking in the non-parking sub-area, and a corresponding prompt can be triggered for timely manual intervention.

[0027] The first speed threshold is a preset constant. In actual operation, when a vehicle is driving, turning, or passing through a service area, its speed is usually significantly higher than 5 km / h. However, when preparing to park, enter a parking space, or wait, the vehicle speed will drop below 5 km / h. Therefore, the first speed threshold can be set to 5 km / h to distinguish between passing and parking states.

[0028] S105: Dynamically adjust the lighting brightness of each sub-region based on the lighting pattern of each sub-region.

[0029] In this embodiment, the controller can send dimming commands to the lighting fixtures in the corresponding sub-areas according to the lighting mode of each sub-area, and adjust the brightness and power in real time and smoothly to achieve intelligent vehicle lighting control that is "bright when there is a vehicle, dim when there is no vehicle, and dimmed on demand".

[0030] For example, for each sub-region, the illumination brightness of the sub-region is dynamically adjusted based on the illumination pattern of that sub-region, including: If the lighting mode of the sub-area is idle lighting mode, the preset first brightness will be used as the target brightness; If the lighting mode of the sub-area is the traffic lighting mode, the target brightness is determined based on the number of vehicles and the average speed of vehicles in the sub-area; where the number of vehicles and the average speed of vehicles are both positively correlated with the target brightness; If the lighting mode of the sub-area is parking lighting mode, the preset second brightness is used as the target brightness; wherein, the first brightness is less than the second brightness.

[0031] The lighting brightness of the sub-region is dynamically adjusted based on the target brightness.

[0032] In this embodiment, for each sub-area, if the sub-area is in idle lighting mode, the target brightness of the sub-area can be set to a relatively small first brightness (e.g., 10 lux) to meet only basic lighting needs, thereby reducing ineffective energy consumption; if the lighting mode of the sub-area is traffic lighting mode, the target brightness can be determined based on the number of vehicles and the average speed of vehicles in the area. The larger the number of vehicles and the higher the average speed of vehicles, the higher the visibility distance and safety requirements for passage, and the higher the target brightness; if the lighting mode of the sub-area is parking lighting mode, the target brightness of the sub-area can be set to a relatively large second brightness (e.g., 50 lux) to meet the needs of parking, getting in and out of vehicles, and personnel activities.

[0033] As can be seen from the above, this embodiment divides the highway service area into multiple sub-areas, assigning multiple target vehicles within the service area to these sub-areas. Based on this, the lighting pattern for each sub-area is determined according to the number of vehicles and their average speed. Each sub-area's lighting pattern characterizes its lighting needs. Dynamically adjusting the brightness of each sub-area based on its lighting pattern enables intelligent vehicle-mounted lighting control—"bright when there are vehicles, dark when there are no vehicles, and dimming on demand"—thus reducing energy consumption in highway service areas while ensuring adequate lighting.

[0034] In one embodiment of this application, for each sub-region, the target brightness is determined based on the number of vehicles and the average speed of vehicles in that sub-region, including: Obtain the preset third brightness, and the first quantity threshold and the second speed threshold corresponding to the third brightness; The first adjustment coefficient is determined based on the relative magnitude of the number of vehicles in the sub-region and the first quantity threshold. The second adjustment coefficient is determined based on the relative magnitude of the average vehicle speed and the second speed threshold in the sub-region; The third adjustment factor is obtained by weighted summing of the first and second adjustment factors. The instantaneous brightness is obtained by adjusting the third brightness based on the third adjustment coefficient; If the instantaneous brightness is less than or equal to the third brightness, the third brightness shall be used as the target brightness; If the instantaneous brightness is greater than the third brightness, the second brightness is taken as the target brightness; where the second brightness is greater than the third brightness.

[0035] In this embodiment, a common combination of third brightness, number of vehicles (first quantity threshold), and average vehicle speed (second speed threshold) can be determined based on historical data, and this combination data can be used as a reference value for setting the target brightness.

[0036] Specifically, a first adjustment coefficient can be determined first based on the relative magnitude of the number of vehicles in the sub-region and a first quantity threshold. For example, the first adjustment coefficient can be calculated using the following formula: ; in, This represents the first adjustment factor. Indicates the number of vehicles. This represents the first quantity threshold.

[0037] Meanwhile, a second adjustment coefficient can be determined based on the relative magnitude of the average vehicle speed and the second speed threshold in the sub-region. For example, the first adjustment coefficient can be calculated using the following formula: ; in, This represents the second adjustment factor. Indicates the average speed of the vehicle. This indicates the second speed threshold.

[0038] Furthermore, the first and second adjustment coefficients are weighted and summed to obtain the third adjustment coefficient: ; in, As the first weighting coefficient, It is the second weighting coefficient, and .

[0039] The methods for determining the first and second weighting coefficients for each sub-region include: If the sub-region is a long straight lane region, set the first weight coefficient to the first value and the second weight coefficient to the first value; wherein, the first value is less than the second value, for example, the first value is 0.4 and the second value is 0.6; If the sub-region is a region other than the long straight lane region, set the first weight coefficient to the second value and the second weight coefficient to the first value. In this embodiment, each sub-region has a corresponding region type identifier. The region type identifier can be used to determine whether a sub-region belongs to a long straight-ahead lane area. If the sub-region is a long straight-ahead lane area, vehicles travel at high speeds, requiring high visibility distances. The average vehicle speed has a greater impact on target brightness. Therefore, the second weight corresponding to the second adjustment coefficient can be set to a larger second value to prioritize the target brightness of the long straight-ahead lane in response to changes in average vehicle speed, meeting high visibility safety requirements. If the sub-region is an area other than a long straight-ahead lane area, vehicles travel at low speeds, and there are many lane changes / stops / starts. The more vehicles there are, the higher the risk of mixed pedestrian and vehicle traffic, and the higher the requirement for target brightness. Therefore, the first weight corresponding to the first adjustment coefficient can be set to a larger second value to prioritize the target brightness in response to changes in the number of vehicles, adapting to low-speed, frequent stop / start, and mixed pedestrian / vehicle traffic scenarios, improving lighting safety.

[0040] Furthermore, the third adjustment coefficient is multiplied by the third brightness to adjust the third brightness, thus obtaining the instantaneous brightness that changes in real time with the overall traffic flow.

[0041] Furthermore, to avoid frequent adjustments to the target brightness, this embodiment compares the instantaneous brightness with the third brightness. When the instantaneous brightness is lower than the third brightness, the target brightness is set to the third brightness to ensure a minimum level of traffic lighting. When the instantaneous brightness is higher than the third brightness, the target brightness is set to the second brightness to meet the lighting needs of high-density, high-speed traffic.

[0042] As can be seen from the above, for each sub-region, this embodiment first determines the instantaneous brightness based on the number of vehicles and the average speed of vehicles in that sub-region, so that the instantaneous brightness is dynamically adjusted as the number of vehicles and the average speed of vehicles change; furthermore, the instantaneous brightness is compared with the third brightness, and the target brightness is locked at the third brightness or the second brightness according to the comparison result, retaining only two fixed target brightness levels, which can eliminate the frequent brightness jumps caused by the instantaneous small fluctuations in the number of vehicles or the average speed of vehicles, thereby improving the stability of the lighting system operation.

[0043] In one embodiment of this application, for each sub-region, the illumination brightness of the sub-region is dynamically adjusted based on the target brightness, including: Obtain the ambient brightness of this sub-region; The compensation brightness of this sub-region is determined based on the ambient brightness and the target brightness. The lighting equipment in the sub-area is adjusted based on the compensation brightness to dynamically adjust the lighting brightness of the sub-area.

[0044] In this embodiment, after determining the target brightness of a sub-region, the ambient brightness of that region can be collected in real time using a light sensor. This accurately captures changes in ambient light, such as natural light and external stray light, and calculates the difference between the ambient brightness and the target brightness to determine the compensation brightness. That is, when the ambient brightness is too high, the compensation brightness is reduced accordingly, and when the ambient brightness is too low, the compensation brightness is increased accordingly. Based on this, the compensation brightness is used as the basis for actual output control of the lighting equipment in that sub-region. Dynamically adjusting the power and illuminance output of the lighting equipment can offset the illuminance deviation caused by fluctuations in ambient light, ensuring that the actual lighting brightness of the sub-region always stably matches the actual needs of that sub-region.

[0045] It should be noted that when installing light sensors in each sub-area, the light sensors can be installed at the edge of the sub-area, in locations where there is no direct light from lamps and no obstruction from vehicles / obstacles, such as guardrail brackets, the top of signs, etc., to avoid distortion of the collected ambient brightness data.

[0046] In one embodiment of this application, the method for determining multiple sub-regions includes: Obtain the total number of all target vehicles in the highway service area; If the total number of all target vehicles is less than a preset second quantity threshold, multiple sub-regions are determined based on a preset first coordinate matrix; wherein, each row of the first coordinate matrix corresponds to the coordinate range and region type identifier of a sub-region; If the total number of all target vehicles is greater than or equal to the preset second quantity threshold, the value of N is determined based on the relative size of the total number of all target vehicles and the second quantity threshold; rows with the same region type identifier in the first coordinate matrix are taken as target rows, and multiple target rows are grouped according to the arrangement order of the target rows. Each group includes N adjacent target rows. The coordinate ranges corresponding to the N target rows in each group are merged to obtain the second coordinate matrix, and multiple sub-regions are determined based on the second coordinate matrix.

[0047] In this embodiment, the highway service area can be divided into regions based on the actual layout of the highway service area and the global geographic coordinate system. Multiple minimum division regions are obtained. A first coordinate matrix is ​​constructed based on the coordinate range and region type identifier of each minimum division region. The coordinate range and region type identifier of each minimum division region are used as a row in the preset first coordinate matrix.

[0048] The area type identifier can be in the form of a number to distinguish different area types. For example, when the area type identifier is 1, the corresponding area type is a long straight lane area; when the area type identifier is 2, the corresponding area type is an entrance / exit ramp area; when the area type identifier is 3, the corresponding area type is an intersection / roundabout area; when the area type identifier is 4, the corresponding area type is a parking perimeter passage area; and when the area type identifier is 5, the corresponding area type is a parking space area.

[0049] Based on this, the total number of all target vehicles in the highway service area is obtained using radar point cloud data. If the total number of all target vehicles is less than a preset second threshold, it indicates that the traffic density in the highway service area is low. In this case, fine-grained partitioning can be performed based on the first coordinate matrix to ensure the accuracy of subsequent lighting control and traffic monitoring, avoiding resource waste and accuracy loss caused by unified management of large areas. The second threshold is a preset constant that can be obtained by statistically analyzing historical data of the highway service area; for example, the second threshold could be 30 vehicles.

[0050] Specifically, a preset first coordinate matrix can be directly called to divide the area into sub-regions, with each row of the first coordinate matrix serving as the coordinate range and area type identifier for a sub-region. For example, the data in the first row of the first coordinate matrix can be [1, 1, (X11, Y11), (X12, Y12), (X13, Y13), (X14, Y14), 2]. Here, the first element "1" indicates that the minimum division area corresponding to this row is numbered 1, the second element "1" indicates that the sub-region corresponding to this row contains a minimum division area, (X11, Y11), (X12, Y12), (X13, Y13), and (X14, Y14) represent the coordinate values ​​of the upper left, upper right, lower right, and lower left of the sub-region corresponding to this row, respectively, and the last element "2" indicates that the area identifier of the sub-region corresponding to this row is 2 (entrance / exit ramp area). Based on the above information, a sub-region containing only one minimum division area (the minimum division area is numbered 1) can be identified, and the coordinate range of this sub-region is (X11, Y11), (X12, Y12), (X13, Y13) and (X14, Y14). The region type of this sub-region is an entrance / exit ramp area.

[0051] Correspondingly, if the total number of all target vehicles is greater than or equal to the preset second quantity threshold, it indicates that the traffic density of the highway service area is relatively high. At this time, the traffic characteristics of the smallest division areas with dense vehicles and the same area type in the highway service area tend to be similar. Therefore, multiple smallest division areas can be merged to reduce the number of sub-areas and simplify the control logic.

[0052] Specifically, the ratio between the total number of all target vehicles and a second quantity threshold can be calculated first. Based on this ratio, a preset mapping relationship is used to obtain the N value (the number of target rows to be merged). The larger the ratio, the larger the corresponding N value, and the more target rows need to be merged. For example, the preset mapping relationship can be shown in Table 1 below: Table 1 - Mapping Relationship Between Ratio and N Value

[0053] Based on the determined value of N, rows with the same region type identifier in the first coordinate matrix can be used as target rows. According to the order of the target rows, multiple target rows are grouped, with each group including N adjacent target rows. The coordinate ranges corresponding to the N target rows in each group are merged to obtain the second coordinate matrix, and multiple sub-regions are determined based on the second coordinate matrix.

[0054] For example, N=2, and the first, second, and third rows of the first coordinate matrix have the same region type identifier, such as... Figure 2 As shown, the smallest subdivision region corresponding to the first row is numbered 1 (hereinafter referred to as smallest subdivision region 1), the smallest subdivision region corresponding to the second row is numbered 2 (hereinafter referred to as smallest subdivision region 2), and the smallest subdivision region corresponding to the third row is numbered 3 (hereinafter referred to as smallest subdivision region 3). Smallest subdivision region 1 and smallest subdivision region 2 are adjacent, and the lower right coordinate point (X13, Y13) of smallest subdivision region 1 coincides with the upper right coordinate point (X22, Y22) of smallest subdivision region 2, and the lower left coordinate point (X14, Y14) of smallest subdivision region 1 coincides with the upper left coordinate point (X21, Y21) of smallest subdivision region 2. Therefore, the first, second, and third rows in the first coordinate matrix are taken as target rows. According to the order of the target rows, the first and second rows are divided into the first group, and the remaining third row is divided into the second group. When merging the coordinate ranges corresponding to the two target rows in the first group, all coordinate points corresponding to the first and second rows can be extracted. The minimum x-coordinate, minimum y-coordinate, maximum x-coordinate, and maximum y-coordinate of all coordinate points are then combined to form the new coordinate range after the first group is merged. The second group contains only the single target row, the third row, and the coordinate range remains unchanged. The coordinate ranges of the merged first group and the second group are used as the corresponding rows of the second coordinate matrix, and each row retains its original region type identifier, thus obtaining the second coordinate matrix.

[0055] Through the above process, the data in the first row of the second coordinate matrix is ​​[1, 2, (X11, Y11), (X12, Y12), (X23, Y23), (X24, Y24), 2]. The first element "1" indicates that the number of the smallest division area corresponding to this row is 1. The second element "2" indicates that the sub-region corresponding to this row (denoted as sub-region 1) contains 2 smallest division areas. (X11, Y11), (X12, Y12), (X23, Y23), and (X24, Y24) represent the coordinate values ​​of the upper left, upper right, lower right, and lower left of the sub-region 1 corresponding to this row, respectively. The last element "2" indicates that the area identifier of the sub-region 1 corresponding to this row is 2 (entrance / exit ramp area). The data in the second row of the second coordinate matrix is ​​[3, 1, (X31, Y31), (X32, Y32), (X33, Y33), (X34, Y34), 2]. The first element "3" indicates that the minimum division area corresponding to this row is numbered 3. The second element "1" indicates that the sub-region corresponding to this row (denoted as sub-region 2) contains 1 minimum division area. (X31, Y31), (X32, Y32), (X33, Y33), and (X34, Y34) represent the coordinates of the upper left, upper right, lower right, and lower left of the sub-region 2 corresponding to this row, respectively. The last element "2" indicates that the area identifier of the sub-region 2 corresponding to this row is 2 (entrance / exit ramp area).

[0056] As can be seen from the above, this embodiment first constructs a first coordinate matrix based on the coordinate range and area type identifier of multiple minimum division areas. When the total number of all target vehicles in the highway service area is less than a preset second quantity threshold, multiple sub-areas of the highway service area are determined based on the preset first coordinate matrix, which can realize fine-grained zoning lighting, making brightness adjustment accurately match the local traffic flow status, and avoiding the waste of resources caused by unified management of large areas. When the total number of all target vehicles in the highway service area is greater than or equal to the preset second quantity threshold, the rows with the same function type identifier in the first coordinate matrix are merged to obtain a second coordinate matrix. Multiple sub-areas of the highway service area are determined based on the second coordinate matrix, which can realize the automatic merging of multiple minimum division areas, thereby simplifying the management logic and reducing the computational burden.

[0057] In one embodiment of this application, if the total number of all target vehicles is greater than or equal to a preset second quantity threshold, multiple target vehicles are matched with multiple sub-regions based on the spatial coordinates of each target vehicle to determine the target vehicles in each sub-region, including: Cluster analysis was performed on multiple target vehicles to obtain multiple cluster regions; For each cluster region, calculate the intersection between the coordinate range of the cluster region and each sub-region, and take the sub-region corresponding to each intersection as the sub-region to which the intersection belongs; If there are isolated target vehicles in the cluster analysis results that do not belong to any cluster region, the coordinate range of each isolated target vehicle is compared with the coordinate range of each sub-region, and the sub-region to which each isolated target vehicle belongs is determined based on the comparison results. Merge subsets belonging to the same sub-region with isolated target vehicles to obtain the target vehicles for each sub-region.

[0058] In this embodiment, when matching multiple target vehicles with multiple sub-regions to determine the target vehicles in each sub-region, different matching methods can be determined based on the total number of all target vehicles in the highway service area.

[0059] Specifically, if the total number of all target vehicles is greater than or equal to a preset second threshold, it indicates that the traffic density is high. In this case, using the spatial coordinates of each target vehicle as the data basis, a clustering algorithm is used to perform cluster analysis on all target vehicles, resulting in multiple cluster regions. Each cluster region includes multiple spatially adjacent and concentrated target vehicles. For each cluster region, the intersection between the coordinate range of the cluster region and each sub-region can be calculated. For example, if the cluster region has an intersection 1 with sub-region 1 and an intersection 2 with sub-region 2, then intersection 1 belongs to sub-region 1, and intersection 2 belongs to sub-region 2.

[0060] If the cluster analysis results show isolated target vehicles that do not belong to any cluster region, the coordinate range of each isolated target vehicle is compared with the coordinate range of each sub-region, and the sub-region to which each isolated target vehicle belongs is determined based on the comparison results.

[0061] Finally, subsets belonging to the same sub-region and isolated target vehicles are merged to obtain the target vehicles for each sub-region.

[0062] In the above process, cluster analysis can be implemented using the existing density-based spatial clustering algorithm (DBSCAN). The DBSCAN clustering algorithm can automatically identify cluster regions and outliers without pre-setting the number of clusters. The specific steps are as follows: (1) Extract the spatial coordinates of all target vehicles to form a coordinate dataset; (2) Clustering parameter settings: Set the neighborhood radius ε = 3~5 meters, and the minimum number of vehicles in the cluster MinPts = 3~5 vehicles; (3) For each target vehicle, find the number of vehicles in its ε neighborhood. If the number of vehicles in its ε neighborhood is greater than or equal to MinPts, then mark the target vehicle as the core point and create a clustering region. (4) For each cluster region, include all target vehicles in the neighborhood of the core point ε into that cluster region; (5) For a target vehicle whose number of vehicles in the ε neighborhood is less than MinPts, it is determined to be an isolated target vehicle.

[0063] Correspondingly, if the total number of all target vehicles is less than the preset second quantity threshold, it indicates that the traffic density is low. In this case, cluster analysis is no longer necessary. Instead, the coordinate range of each target vehicle is directly compared with the coordinate range of each sub-region, and the sub-region to which each target vehicle belongs is determined based on the comparison results.

[0064] As can be seen from the above, when the traffic density is high, this embodiment first uses cluster analysis to aggregate a large number of discrete vehicles into a small number of cluster regions, and then determines the target vehicles in each sub-region by calculating the intersection of each cluster region and each sub-region. Compared with the method of determining the sub-region to which each target vehicle belongs one by one, this method can reduce the number of spatial coordinate comparisons, thereby reducing the amount of computation.

[0065] Corresponding to the above embodiment of the energy-saving method for dynamic adjustment of vehicle lighting in highway service areas, Figure 3 This is a structural block diagram of a dynamic adjustment and energy-saving device for onboard lighting in highway service areas, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 3 The highway service area vehicle lighting dynamic adjustment energy-saving device 20 includes: a data acquisition module 21, a target recognition module 22, a sub-area matching module 23, a lighting mode determination module 24, and a lighting adjustment module 25.

[0066] Among them, the data acquisition module 21 is used to acquire radar point cloud data of highway service areas; Target recognition module 22 is used to identify targets based on radar point cloud data to obtain multiple target vehicles; The sub-region matching module 23 is used to determine the spatial coordinates of each target vehicle based on the radar point cloud data corresponding to each target vehicle, and to match multiple target vehicles with multiple sub-regions based on the spatial coordinates of each target vehicle in order to determine the target vehicle in each sub-region. The lighting mode determination module 24 is used to count the number of vehicles and the average speed of vehicles in each sub-region, and determine the lighting mode of each sub-region based on the number of vehicles and the average speed of vehicles in each sub-region; wherein, the lighting mode includes idle lighting mode, passing lighting mode and parking lighting mode, the number of vehicles in each sub-region is the number of target vehicles in each sub-region, and the average speed of vehicles in each sub-region is the average speed of target vehicles in each sub-region. The lighting adjustment module 25 is used to dynamically adjust the lighting brightness of each sub-region based on the lighting mode of each sub-region.

[0067] In one embodiment of this application, for each sub-region, the lighting pattern determination module 24 is specifically used for: If the number of vehicles in a sub-area is zero, the lighting mode for that sub-area is determined to be the idle lighting mode. If the number of vehicles in the sub-area is greater than zero and the average speed of vehicles in the sub-area is greater than or equal to the first speed threshold, the lighting mode of the sub-area is determined to be the traffic lighting mode. If the sub-area is a parking sub-area, and the number of vehicles in the sub-area is greater than zero and the average speed of the vehicles in the sub-area is less than the first speed threshold, then the lighting mode of the sub-area is determined to be the parking lighting mode.

[0068] In one embodiment of this application, for each sub-region, the lighting adjustment module 25 is specifically used for: If the lighting mode of the sub-area is idle lighting mode, the preset first brightness will be used as the target brightness; If the lighting mode of the sub-area is the traffic lighting mode, the target brightness is determined based on the number of vehicles and the average speed of vehicles in the sub-area; where the number of vehicles and the average speed of vehicles are both positively correlated with the target brightness; If the lighting mode of the sub-area is parking lighting mode, the preset second brightness is used as the target brightness; wherein the first brightness is less than the second brightness. The lighting brightness of the sub-region is dynamically adjusted based on the target brightness.

[0069] In one embodiment of this application, for each sub-region, the lighting adjustment module 25 is further configured to: Obtain the preset third brightness, and the first quantity threshold and the second speed threshold corresponding to the third brightness; The first adjustment coefficient is determined based on the relative magnitude of the number of vehicles in the sub-region and the first quantity threshold. The second adjustment coefficient is determined based on the relative magnitude of the average vehicle speed and the second speed threshold in the sub-region; The third adjustment factor is obtained by weighted summing of the first and second adjustment factors. The instantaneous brightness is obtained by adjusting the third brightness based on the third adjustment coefficient; If the instantaneous brightness is less than or equal to the third brightness, the third brightness shall be used as the target brightness; If the instantaneous brightness is greater than the third brightness, the second brightness is taken as the target brightness; where the second brightness is greater than the third brightness.

[0070] In one embodiment of this application, for each sub-region, the lighting adjustment module 25 is further configured to: Obtain the ambient brightness of this sub-region; The compensation brightness of this sub-region is determined based on the ambient brightness and the target brightness. The lighting equipment in the sub-area is adjusted based on the compensation brightness to dynamically adjust the lighting brightness of the sub-area.

[0071] In one embodiment of this application, the sub-region matching module 23 is specifically used for: Obtain the total number of all target vehicles in the highway service area; If the total number of all target vehicles is less than a preset second quantity threshold, multiple sub-regions are determined based on a preset first coordinate matrix; wherein, each row of the first coordinate matrix corresponds to the coordinate range and region type identifier of a sub-region; If the total number of all target vehicles is greater than or equal to the preset second quantity threshold, the value of N is determined based on the relative size of the total number of all target vehicles and the second quantity threshold; rows with the same region type identifier in the first coordinate matrix are taken as target rows, and multiple target rows are grouped according to the arrangement order of the target rows. Each group includes N adjacent target rows. The coordinate ranges corresponding to the N target rows in each group are merged to obtain the second coordinate matrix, and multiple sub-regions are determined based on the second coordinate matrix.

[0072] In one embodiment of this application, if the total number of all target vehicles is greater than or equal to a preset second quantity threshold, the sub-region matching module 23 is further configured to: Cluster analysis was performed on multiple target vehicles to obtain multiple cluster regions; For each cluster region, calculate the intersection between the coordinate range of the cluster region and each sub-region, and take the sub-region corresponding to each intersection as the sub-region to which the intersection belongs; If there are isolated target vehicles in the cluster analysis results that do not belong to any cluster region, the coordinate range of each isolated target vehicle is compared with the coordinate range of each sub-region, and the sub-region to which each isolated target vehicle belongs is determined based on the comparison results. Merge subsets belonging to the same sub-region with isolated target vehicles to obtain the target vehicles for each sub-region.

[0073] It should be noted that the specific limitations of the above-described embodiment of the dynamic adjustment light-saving device 20 for vehicle lighting in highway service areas can be found in the limitations of the dynamic adjustment light-saving method for vehicle lighting in highway service areas described above, and will not be repeated here. Each module of the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the computer device in hardware form or independent of the processor, or it can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0074] This application also provides a computer device, which includes: a processor and a memory, wherein the memory stores a computer program; the processor is used to execute the computer program in the memory to implement the energy-saving method for dynamic adjustment of on-board lighting in highway service areas provided in the above-described method embodiments.

[0075] This application also provides a computer device, which includes a processor and a memory, wherein at least one computer program is stored in the memory. The at least one computer program is loaded and executed by one or more processors to enable the computer device to implement any of the aforementioned copy repair methods. The computer device can be a server or a terminal; the structures of servers and terminals will be described below.

[0076] In this application embodiment, the electronic device may be a server. Figure 4 This is a schematic diagram of a server structure provided in an embodiment of this application. The server can vary significantly due to differences in configuration or performance. It may include one or more Central Processing Units (CPUs) 31 and one or more memories 32. The one or more memories 32 store at least one computer program, which is loaded and executed by the one or more processors 31 to enable the server to implement the copy repair methods provided in the various method embodiments described above. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be elaborated upon here.

[0077] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one computer program, which is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-described methods for dynamic adjustment of vehicle lighting in highway service areas to save energy.

[0078] In one possible implementation, the aforementioned computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a solid-state drive (SSD), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc. The random access memory can include resistive random access memory (ReRAM) and dynamic random access memory (DRAM).

[0079] In an exemplary embodiment, a computer program or computer program product is also provided, which includes computer instructions loaded and executed by a processor to enable a computer to implement any of the above-described methods for dynamic adjustment of vehicle lighting in highway service areas to save energy.

[0080] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0081] In other words, the data collection and processing in this application should strictly comply with the requirements of relevant national laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0082] It should be further noted that the terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The implementation methods described in the above exemplary embodiments do not represent all implementation methods consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0083] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0084] Furthermore, the step numbers described herein are merely illustrative of one possible execution order between steps. In some other embodiments, the steps may not be executed in the order of their numbers, such as two steps with different numbers being executed simultaneously, or two steps with different numbers being executed in the reverse order of the illustration. This application does not limit this.

[0085] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. Optionally, the program is stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0086] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for dynamically adjusting vehicle lighting in highway service areas to save energy, characterized in that, include: Acquire radar point cloud data from highway service areas; Target identification is performed based on the radar point cloud data to obtain multiple target vehicles; The spatial coordinates of each target vehicle are determined based on the radar point cloud data corresponding to each target vehicle. Based on the spatial coordinates of each target vehicle, multiple target vehicles are matched with multiple sub-regions to determine the target vehicles in each sub-region. The number of vehicles and average vehicle speed in each sub-region are counted, and the lighting mode for each sub-region is determined based on the number of vehicles and average vehicle speed in each sub-region. The lighting mode includes an idle lighting mode, a passing lighting mode, and a parking lighting mode. The number of vehicles in each sub-region is the number of target vehicles in each sub-region, and the average vehicle speed in each sub-region is the average speed of the target vehicles in each sub-region. The lighting brightness of each sub-region is dynamically adjusted based on the lighting pattern of each sub-region.

2. The energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in claim 1, characterized in that, For each sub-region, the number of vehicles and their average speed are counted, and the lighting pattern for that sub-region is determined based on these statistics, including: If the number of vehicles in a sub-area is zero, the lighting mode for that sub-area is determined to be the idle lighting mode. If the number of vehicles in the sub-area is greater than zero and the average speed of vehicles in the sub-area is greater than or equal to the first speed threshold, the lighting mode of the sub-area is determined to be the traffic lighting mode. If the sub-area is a parking sub-area, and the number of vehicles in the sub-area is greater than zero and the average speed of the vehicles in the sub-area is less than the first speed threshold, then the lighting mode of the sub-area is determined to be the parking lighting mode.

3. The energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in claim 1, characterized in that, For each sub-region, the lighting brightness of that sub-region is dynamically adjusted based on its lighting pattern, including: If the lighting mode of the sub-area is idle lighting mode, the preset first brightness will be used as the target brightness; If the lighting mode of the sub-area is the traffic lighting mode, the target brightness is determined based on the number of vehicles and the average speed of the vehicles in the sub-area; wherein the number of vehicles and the average speed of the vehicles are both positively correlated with the target brightness; If the lighting mode of the sub-area is parking lighting mode, the preset second brightness is used as the target brightness; wherein the first brightness is less than the second brightness. The illumination brightness of the sub-region is dynamically adjusted based on the target brightness.

4. The energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in claim 3, characterized in that, For each sub-region, determining the target brightness based on the number of vehicles and the average vehicle speed in that sub-region includes: Obtain a preset third brightness, and a first quantity threshold and a second speed threshold corresponding to the third brightness; The first adjustment coefficient is determined based on the relative magnitude of the number of vehicles in the sub-region and the first quantity threshold. The second adjustment coefficient is determined based on the relative magnitude of the average vehicle speed in the sub-region and the second speed threshold. The first adjustment coefficient and the second adjustment coefficient are weighted and summed to obtain the third adjustment coefficient; The instantaneous brightness is obtained by adjusting the third brightness based on the third adjustment coefficient. If the instantaneous brightness is less than or equal to the third brightness, the third brightness shall be taken as the target brightness; If the instantaneous brightness is greater than the third brightness, the second brightness is taken as the target brightness; wherein the second brightness is greater than the third brightness.

5. The energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in claim 3, characterized in that, For each sub-region, the dynamic adjustment of the illumination brightness of that sub-region based on the target brightness includes: Obtain the ambient brightness of this sub-region; The compensation brightness of the sub-region is determined based on the ambient brightness and the target brightness. The lighting equipment in the sub-region is adjusted based on the compensated brightness to dynamically adjust the lighting brightness of the sub-region.

6. The energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in claim 1, characterized in that, The methods for determining the multiple sub-regions include: Obtain the total number of all target vehicles in the highway service area; If the total number of all target vehicles is less than a preset second quantity threshold, the multiple sub-regions are determined based on a preset first coordinate matrix; wherein, each row of the first coordinate matrix corresponds to the coordinate range and region type identifier of a sub-region; If the total number of all target vehicles is greater than or equal to a preset second quantity threshold, the value of N is determined based on the relative size of the total number of all target vehicles and the second quantity threshold; rows with the same region type identifier in the first coordinate matrix are taken as target rows, and multiple target rows are grouped according to the arrangement order of the target rows, with each group including N adjacent target rows, and the coordinate ranges corresponding to the N target rows in each group are merged to obtain a second coordinate matrix, and the multiple sub-regions are determined based on the second coordinate matrix.

7. The energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in claim 6, characterized in that, If the total number of all target vehicles is greater than or equal to a preset second quantity threshold, the process of matching multiple target vehicles with multiple sub-regions based on the spatial coordinates of each target vehicle to determine the target vehicles in each sub-region includes: Cluster analysis was performed on multiple target vehicles to obtain multiple cluster regions; For each clustered region, the intersection between the coordinate range of the clustered region and each sub-region is calculated, and the sub-region corresponding to each intersection is taken as the sub-region to which the intersection belongs; If there are isolated target vehicles in the cluster analysis results that do not belong to any of the cluster regions, the coordinate range of each isolated target vehicle is compared with the coordinate range of each sub-region, and the sub-region to which each isolated target vehicle belongs is determined based on the comparison results. Merge subsets belonging to the same sub-region with isolated target vehicles to obtain the target vehicles for each sub-region.

8. A dynamic adjustment and energy-saving device for vehicle lighting in highway service areas, characterized in that, include: The data acquisition module is used to acquire radar point cloud data from highway service areas; The target recognition module is used to identify targets based on the radar point cloud data to obtain multiple target vehicles; The sub-region matching module is used to determine the spatial coordinates of each target vehicle based on the radar point cloud data corresponding to each target vehicle, and to match multiple target vehicles with multiple sub-regions based on the spatial coordinates of each target vehicle in order to determine the target vehicle in each sub-region. The lighting mode determination module is used to count the number of vehicles and the average speed of vehicles in each sub-region, and determine the lighting mode of each sub-region based on the number of vehicles and the average speed of vehicles in each sub-region; wherein, the lighting mode includes idle lighting mode, passing lighting mode and parking lighting mode, the number of vehicles in each sub-region is the number of target vehicles in each sub-region, and the average speed of vehicles in each sub-region is the average speed of target vehicles in each sub-region. The lighting adjustment module is used to dynamically adjust the lighting brightness of each sub-region based on the lighting mode of each sub-region.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program, which is loaded and executed by the processor to implement the energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is loaded and executed by a processor to implement the energy-saving method for dynamic adjustment of vehicle lighting in highway service areas as described in any one of claims 1 to 7.

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