A vehicle lamp illumination control method, a vehicle lamp illumination control system, a device, and a medium

Abstract

The application provides a vehicle lamp illumination control method, a vehicle lamp illumination control system, equipment and a medium. The method comprises the following steps: an image acquisition module acquires an initial environment image of a current driving environment of a vehicle, and sends the initial environment image to a central processing module; the central processing module generates a vehicle lamp control instruction according to the position of an actual reflection area in the current driving environment corresponding to a reflection area in a target environment image, and sends the vehicle lamp control instruction to a vehicle lamp control module; and the vehicle lamp control module controls a plurality of LED lamps to be turned off based on the vehicle lamp control instruction, so that the irradiation light of the matrix LED vehicle lamp avoids the actual reflection area in the current driving environment. Through the method and the system, the interference of reflected light and refracted light in the environment is effectively reduced, the active safety factor of the vehicle headlamp is improved, and the driving safety of the whole vehicle is improved, thereby providing the best road lighting for the driver.

Description

Technical Field

[0001] This application relates to the field of automotive lighting technology, and more specifically, to a vehicle lighting control method, a vehicle lighting control system, equipment, and medium. Background Technology

[0002] With the increasing prevalence of automobiles and the rapid rise in their numbers, road safety has become paramount. Traditional automotive headlights, with their simple distribution and mostly fixed light sources, present numerous problems and often perform poorly in certain driving environments. For instance, in rainy weather, the reflection of water on the road surface can impair the driver's vision, posing a significant safety hazard. Therefore, adaptively controlling the headlight beam to avoid reflective areas and provide optimal road illumination for drivers has become a crucial technical challenge. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a vehicle headlight lighting control method, vehicle headlight lighting control system, device and medium, which controls the vehicle headlight lighting route to avoid reflective areas on the road, effectively reduces the interference of reflected and refracted light in the environment, improves the active safety factor of vehicle headlights, and thus improves the overall driving safety of the vehicle and provides the best road lighting for the driver.

[0004] In a first aspect, embodiments of this application provide a vehicle lighting control method, which is applied to a vehicle lighting control system. The vehicle lighting control system includes an image acquisition module, a central processing module, and a vehicle lighting control module. The vehicle lighting control method includes:

[0005] The image acquisition module acquires an initial environmental image of the vehicle's current driving environment and sends the initial environmental image to the central processing module;

[0006] The central processing module performs image preprocessing on the received initial environmental image to obtain a target environmental image, and generates a headlight control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environmental image, and sends the headlight control command to the headlight control module; wherein, the headlight control command includes the identification information of multiple LEDs to be turned off in the matrix LED headlights of the vehicle, and the illumination light of the LEDs to be turned off passes through the actual reflective area;

[0007] The vehicle lighting control module controls multiple LEDs to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment.

[0008] Furthermore, the central processing module determines the reflective area from the target environment image through the following steps:

[0009] An edge detection algorithm is used to detect reflective areas in the target environment image to obtain reflective area edge images, and the reflective area edge images are then segmented into multiple region sub-images.

[0010] Multiple sub-images of the region are input into a pre-trained reflective region detection model to determine at least one reflective sub-image containing the reflective region from the multiple sub-images of the region;

[0011] For each reflective sub-image, the reflective area corresponding to the reflective sub-image is determined from the target environment image based on the position of the reflective sub-image in the target environment image.

[0012] Furthermore, the central processing module trains the reflective area detection model through the following steps:

[0013] Acquire multiple sample images;

[0014] For each of the multiple sample images containing a reflective sample area, determine the first location region of the reflective sample area in that sample image;

[0015] For each of the sample images, the sample image is input into the original detection model of the reflective area to obtain the second location region of the reflective prediction region;

[0016] Based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region, the original detection model of the reflective region is trained to obtain the reflective region detection model.

[0017] Furthermore, the process of training the original detection model of the reflective region based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region to obtain the reflective region detection model includes:

[0018] If the sample image corresponding to the reflection prediction region is an image without a reflection sample region, then adjust the training parameters of the original reflection region detection model until the reflection prediction region output by the original reflection region detection model is empty;

[0019] If the sample image corresponding to the reflective prediction region is an image with a reflective sample region, then the first position region of the reflective sample region in the sample image is compared with the second position region of the reflective prediction region, and the loss function of the original detection model of the reflective region in the current state is calculated.

[0020] Based on the loss function of the original reflective area detection model, the training parameters of the original reflective area detection model are continuously adjusted until the original reflective area detection model reaches a convergence state, thus obtaining the reflective area detection model.

[0021] Furthermore, the central processing module generates headlight control commands based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, including:

[0022] For each reflective area in the target environment image, determine the position coordinates of the reflective area in the target environment image;

[0023] Based on the calibration parameters of the image acquisition module and the position coordinates of the reflective area in the target environment image, the three-dimensional spatial coordinates of the actual reflective area corresponding to the reflective area in the current driving environment are determined.

[0024] Based on the optical path information of each LED in the matrix LED vehicle headlight, the LED to be turned off is determined from multiple LEDs whose light propagation path passes through the spatial position corresponding to the three-dimensional spatial coordinates.

[0025] The vehicle light control command is generated based on the identification information of the LED light to be turned off.

[0026] Furthermore, the vehicle lighting control system also includes a light detection module, a rain detection module, and a central control module, and the vehicle lighting control method further includes:

[0027] The illumination detection module detects the illumination intensity of the current driving environment in real time and sends the illumination intensity to the central control module;

[0028] The rainfall detection module detects the rainfall value of the current driving environment in real time and sends the rainfall value to the central control module;

[0029] The central control module displays the light intensity and the rainfall value.

[0030] Furthermore, the vehicle lighting control method also includes:

[0031] The central control module responds to the user's operation on the lighting control function, generates a lighting control command, and sends the lighting control command to the image acquisition module, the central processing module, and the headlight control module, so that the image acquisition module, the central processing module, and the headlight control module start working.

[0032] Secondly, embodiments of this application also provide a vehicle lighting control system, the vehicle lighting control system comprising:

[0033] The image acquisition module is used to acquire an initial environmental image of the vehicle's current driving environment and send the initial environmental image to the central processing module;

[0034] The central processing module is used to perform image preprocessing on the received initial environmental image to obtain a target environmental image, and generate a headlight control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environmental image, and send the headlight control command to the headlight control module; wherein, the headlight control command includes the identification information of multiple LEDs to be turned off in the matrix LED headlights of the vehicle, and the illumination light of the LEDs to be turned off passes through the actual reflective area;

[0035] The vehicle lighting control module is used to control multiple LED lights to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment.

[0036] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the vehicle lighting control method described above are performed.

[0037] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the vehicle lighting control method described above.

[0038] This application provides a vehicle lighting control method, vehicle lighting control system, device, and medium. First, an image acquisition module acquires an initial environmental image of the vehicle's current driving environment and sends the initial environmental image to a central processing module. Then, the central processing module performs image preprocessing on the received initial environmental image to obtain a target environmental image, and generates a vehicle lighting control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environmental image, and sends the vehicle lighting control command to the vehicle lighting control module. Finally, the vehicle lighting control module controls multiple LED lights to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment.

[0039] Compared to existing lighting control methods, this application turns off multiple LEDs in the matrix LED headlights that would otherwise illuminate reflective areas, thereby controlling the headlight illumination area and guiding the headlight path to avoid reflective areas on the road. This effectively reduces interference from reflected and refracted light in the environment, improves the active safety factor of the vehicle's headlights, and ultimately enhances overall vehicle driving safety, providing drivers with optimal road lighting.

[0040] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart of a vehicle lighting control method provided in an embodiment of this application;

[0043] Figure 2 This is one of the structural schematic diagrams of a vehicle lighting control system provided in the embodiments of this application;

[0044] Figure 3 This is a second schematic diagram of a vehicle lighting control system provided in an embodiment of this application;

[0045] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0047] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of automotive lighting technology.

[0048] With the increasing prevalence of automobiles and the rapid rise in their numbers, road safety has become paramount. Research has revealed that traditional automotive headlights, with their simple, fixed light source distribution, present numerous problems and often perform poorly in certain driving environments. For instance, in rainy weather, the reflection of water on the road surface can impair the driver's vision, posing a significant safety hazard. Therefore, adaptively controlling the headlight beam to avoid reflective areas and provide optimal road illumination for drivers has become a crucial technical challenge.

[0049] Based on this, this application provides a vehicle headlight lighting control method to reduce interference from reflected and refracted light in the environment, improve the active safety factor of vehicle headlights, thereby improving overall vehicle driving safety and providing the driver with optimal road lighting.

[0050] Please see Figure 1 , Figure 1 This is a flowchart illustrating a vehicle lighting control method provided in an embodiment of this application. The vehicle lighting control method is applied to a vehicle lighting control system, which includes an image acquisition module, a central processing module, and a vehicle lighting control module. Figure 1 As shown in the figure, the vehicle lighting control method provided in this application embodiment includes:

[0051] S101, the image acquisition module acquires an initial environmental image of the vehicle's current driving environment and sends the initial environmental image to the central processing module.

[0052] It should be noted that the initial environment image refers to the image of the current driving environment captured by the image acquisition module when the vehicle is in motion.

[0053] Specifically, the image acquisition module is mainly used to acquire images of the current driving environment of the vehicle. Regarding step S101 above, in specific implementation, the image acquisition module acquires an initial environmental image of the vehicle's current driving environment and sends the acquired initial environmental image to the central processing module. Here, according to the embodiments provided in this application, the image acquisition module can be one or more low-light, high-dynamic, high-frame-rate, and high-resolution vehicle-mounted cameras, installed in front of the vehicle to effectively sense and detect the location of the headlight illumination range in the vehicle's direction of travel, such as the location of the rearview mirror inside the windshield, but not limited to this location.

[0054] S102, the central processing module performs image preprocessing on the received initial environment image to obtain a target environment image, and generates a headlight control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, and sends the headlight control command to the headlight control module.

[0055] It should be noted that image preprocessing includes operations such as noise reduction and grayscale conversion. The target environment image is the image obtained after preprocessing the initial environment image. The reflective area is the location of the actual reflective area in the current driving environment captured by the image acquisition module. Here, the actual reflective area in the current driving environment can be a waterlogged area on the road, or a location with reflective or refractive properties such as raindrops, snowflakes, or glass windows of buildings. The headlight control command refers to the command used to control multiple LEDs in the vehicle's matrix LED headlights to turn off. The headlight control command includes the identification information of multiple LEDs to be turned off in the vehicle's matrix LED headlights. The identification information can be the serial number or row / column number of the LEDs to be turned off, etc., which is not specifically limited in this application. The illumination light from the LEDs to be turned off passes through the actual reflective area in the current driving environment.

[0056] Specifically, the central processing module is mainly used for image processing, identification of reflective areas in the image, and generation of headlight control commands. Regarding step S102 above, in practice, after receiving the initial environmental image acquired by the image acquisition module, the central processing module first performs image preprocessing on the initial environmental image. Here, denoising and grayscale operations are performed on the initial environmental image to obtain the target environmental image, thereby improving the recognition effect of reflective areas. Then, reflective areas in the target environmental image are identified, and headlight control commands are generated based on the positions of the reflective areas in the target environmental image, and the headlight control commands are sent to the headlight control module.

[0057] Specifically, regarding step S102 above, the central processing module determines the reflective area from the target environment image through the following steps:

[0058] A: An edge detection algorithm is used to detect reflective areas in the target environment image to obtain reflective area edge images, and the reflective area edge images are then segmented into multiple region sub-images.

[0059] Edge detection is a fundamental problem in image processing and computer vision. Its purpose is to identify points in a digital image where brightness changes significantly. Regarding step A above, in specific implementation, an edge detection algorithm is used to detect reflective regions in the target environment image, identifying areas with significant brightness changes and obtaining reflective region edge images. Here, the edge detection algorithm can be the Canny algorithm, Sobel algorithm, etc., and this application does not specifically limit its application. Then, the reflective region edge images are segmented to obtain multiple region sub-images. Image segmentation algorithms, binary image segmentation, and horizontal segmentation can be used for segmenting the reflective region edge images, and this application does not specifically limit its application.

[0060] B: Input multiple sub-images of the region into a pre-trained reflective region detection model, and determine at least one reflective sub-image containing the reflective region from the multiple sub-images of the region.

[0061] It should be noted that the reflective area detection model is a pre-trained convolutional neural network model used to detect reflective areas in images.

[0062] Regarding step B above, in specific implementation, multiple region sub-images are input into a pre-trained reflective region detection model, and reflective regions are detected in each region sub-image. At least one reflective sub-image containing reflective regions is determined from the multiple region sub-images.

[0063] Specifically, regarding step B above, the central processing module trains the reflective area detection model through the following steps:

[0064] I: Acquire multiple sample images.

[0065] II: For each of the multiple sample images containing a reflective sample area, determine the first location region of the reflective sample area in that sample image.

[0066] It should be noted that the sample image refers to each training sample used in the model training set to train the prediction model. The sample image can be an image with or without reflective areas. As an optional implementation, the sample image can be an image captured by a camera or an image uploaded by a user; this application does not impose specific limitations on this. The reflective sample area refers to a real reflective area existing in the sample image. The first position region is used to characterize the position of the reflective sample area in the sample image. For example, when the sample image has a reflective area, the reflective sample area here is the reflective area in the sample image, and the first position region here is the position of that reflective area in the sample image.

[0067] Regarding steps I-II above, in specific implementation, multiple sample images are acquired for training the prediction model. Then, for each sample image containing a reflective sample region, the first location region of the reflective sample region within that sample image is determined.

[0068] III: For each of the sample images, input the sample image into the original detection model of the reflective area to obtain the second location region of the reflective prediction region.

[0069] It should be noted that the original reflective region detection model refers to the initial model used to detect reflective regions in the sample image. The reflective prediction region refers to the reflective region detected by the original reflective region detection model for the sample image. Since the sample image may contain reflective regions or not, the second location region of the reflective prediction region identified by the original reflective region detection model may not exist.

[0070] Regarding step III above, in specific implementation, for each sample image, the sample image is input into the original detection model of the reflective area, and the convolutional neural network in the original detection model of the reflective area is used to determine the second location region of the reflective prediction region in the sample image.

[0071] IV: Based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region, the original detection model of the reflective region is trained to obtain the reflective region detection model.

[0072] For step IV above, after determining the first location region of the reflective sample area and the second location region of the reflective prediction area, the original detection model of the reflective area is trained using the above two parameters to obtain the reflective area detection model.

[0073] Specifically, regarding step IV above, the step of training the original detection model of the reflective region based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region to obtain the reflective region detection model includes:

[0074] i: If the sample image corresponding to the reflective prediction region is an image without a reflective sample region, then adjust the training parameters of the original reflective region detection model until the reflective prediction region output by the original reflective region detection model is empty.

[0075] Regarding step i above, the sample images include images with reflective areas and images without reflective areas. When the original reflective area detection model identifies an image without reflective areas, it obtains a predicted reflective area. At this point, the model's identification is considered incorrect, and its training parameters need adjustment. The original reflective area detection model iteratively adjusts its training parameters. In each iteration, it outputs a new predicted reflective area. As long as the predicted reflective area is not empty, the training parameters are continuously adjusted, and new parameters result in new predicted reflective areas. This process continues until the trained model outputs an empty predicted reflective area, at which point the model's identification is considered accurate.

[0076] ii: If the sample image corresponding to the reflective prediction region is an image with a reflective sample region, then the first position region of the reflective sample region in the sample image is compared with the second position region of the reflective prediction region, and the loss function of the original detection model of the reflective region in the current state is calculated.

[0077] iii: Based on the loss function of the original detection model of the reflective area, continuously adjust the training parameters of the original detection model of the reflective area until the original detection model of the reflective area reaches a convergent state, and obtain the reflective area detection model.

[0078] It should be noted that a loss function is a function that maps the values ​​of a random event or its related random variables to non-negative real numbers to represent the "risk" or "loss" of that random event. In applications, the loss value is often used as a learning criterion in relation to the optimization problem; that is, the model is solved and evaluated by minimizing the loss function.

[0079] Regarding steps ii-iii above, in specific implementation, when the original reflective region detection model identifies sample images containing reflective regions, for each sample image with a reflective region, the first position region of the reflective sample region in the sample image is compared with the second position region of the reflected prediction region. By comparing the reflective sample region and the reflected prediction region in the sample image, the accuracy of the original reflective region detection model is determined. If the first and second position regions of the sample image are not the same, the original reflective region detection model is considered inaccurate. At this point, it is necessary to calculate the loss function of the original reflective region detection model under the current state. The method for calculating the loss function is explained in detail in existing technologies and will not be elaborated further here. Then, the training parameters of the original reflective area detection model are continuously adjusted. The original reflective area detection model continuously minimizes the loss through iteration. At each iteration, the loss value of the original reflective area detection model is calculated. When the loss value of the original reflective area detection model fails to reach the loss threshold, the model parameter weight coefficients of the original reflective area detection model are continuously updated. The new parameters will calculate a new loss value, thus making the loss value fluctuate and decrease during the iteration process. Finally, when the loss value reaches a smooth state, that is, when the loss value of the trained original reflective area detection model is no greater than the loss threshold, that is, when the loss value does not decrease significantly compared with the previously calculated loss value, the original reflective area detection model is considered to have reached a convergence state. At this point, the detection accuracy of the original reflective area detection model is relatively high, and the training ends, resulting in the reflective area detection model.

[0080] C: For each reflective sub-image, the reflective area corresponding to the reflective sub-image is determined from the target environment image based on the position of the reflective sub-image in the target environment image.

[0081] Regarding step C above, in specific implementation, after the reflective area in each reflective sub-image is determined, for each reflective sub-image, the corresponding reflective area in the target environment image is determined based on the position of the reflective sub-image in the target environment image. Here, since each reflective sub-image has a corresponding area in the target environment image, for each reflective sub-image, the pixel area where the reflective sub-image is located is first determined in the target environment image, and then the corresponding reflective area in the target environment image can be determined based on the pixel range of the reflective area in the reflective sub-image in the target environment image.

[0082] Specifically, regarding step S102 above, the central processing module generates headlight control commands based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, including:

[0083] (1) For each reflective area in the target environment image, determine the position coordinates of the reflective area in the target environment image.

[0084] (2) Based on the calibration parameters of the image acquisition module and the position coordinates of the reflective area in the target environment image, determine the three-dimensional spatial coordinates of the actual reflective area corresponding to the reflective area in the current driving environment.

[0085] It should be noted that when the image acquisition module is an in-vehicle camera, the calibration parameters can be the in-vehicle camera's intrinsic parameters, translation matrix, and rotation matrix, etc. Three-dimensional spatial coordinates refer to the three-dimensional coordinates of the actual reflective area's relative position to the vehicle in actual space.

[0086] In the specific implementation of steps (1)-(2) above, for each reflective area in the target environment image, the position coordinates of the reflective area in the target environment image are first determined. Then, using techniques such as geometric transformation and affine transformation, the relationship information between the actual three-dimensional space and the two-dimensional space captured by the image acquisition module is calculated. Based on the calibration parameters of the image acquisition module and the position coordinates of the reflective area in the target environment image, the three-dimensional spatial coordinates of the actual reflective area corresponding to the reflective area in the current driving environment are determined.

[0087] (3) Based on the optical path information of each LED in the matrix LED vehicle light, determine the LED to be turned off from multiple LEDs whose light propagation path passes through the spatial position corresponding to the three-dimensional spatial coordinate.

[0088] (4) Generate the vehicle light control command based on the identification information of the LED light to be turned off.

[0089] It's important to note that matrix LED headlights are not single light sources. Their key feature is modular design; they typically contain dozens or even hundreds of light-emitting units, known as LEDs. Each LED in a matrix LED headlight can be independently adjusted for brightness and controlled via on / off functions. Optical path information refers to the path of light emitted by an LED in actual space.

[0090] Regarding steps (3)-(4) above, in specific implementation, firstly, the optical path information of each LED in the matrix LED headlight is determined, that is, the propagation path of the light emitted by each LED in the actual space is determined. Since the three-dimensional spatial coordinates of each actual reflective area in the actual space have been determined in step (2) above, for each LED, it is necessary to determine whether the light propagation path of the LED will pass through the spatial position corresponding to the three-dimensional spatial coordinates of the actual reflective area. If so, it is considered that the light of the LED will be reflected or refracted by the actual reflective area, and the LED needs to be turned off to reduce the impact on the driver's vision. At this time, the LED is designated as the LED to be turned off. Then, headlight control commands are generated based on the identification information of the LED to be turned off.

[0091] S103, the vehicle light control module controls multiple LED lights to be turned off based on the vehicle light control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment.

[0092] Specifically, the headlight control module is mainly used to execute headlight control commands sent by the central processing module to effectively control the illumination area of ​​the matrix LED headlights. Regarding step S103 above, in practice, the headlight control module controls multiple LEDs to be turned off based on the received headlight control commands, so that the illumination light from the matrix LED headlights avoids the actual reflective areas in the current driving environment. Here, since the headlight control commands carry identification information of the LEDs to be turned off, the headlight control module can determine the LEDs to be turned off from the multiple LEDs of the matrix LED headlights based on the identification information, and then control the LEDs to be turned off. In this way, the LEDs whose light would illuminate the actual reflective areas are turned off, controlling the headlight illumination path to avoid reflective areas on the driving road, effectively reducing the interference of reflected and refracted light in the environment, improving the active safety factor of the vehicle's headlights, and thus improving overall vehicle driving safety, providing the driver with optimal road lighting.

[0093] As an optional implementation, the vehicle lighting control system provided in this application embodiment further includes a light detection module, a rain detection module, and a central control module, and the vehicle lighting control method further includes:

[0094] The light detection module detects the light intensity of the current driving environment in real time and sends the light intensity to the central control module.

[0095] The rainfall detection module detects the rainfall value of the current driving environment in real time and sends the rainfall value to the central control module.

[0096] The central control module displays the light intensity and the rainfall value.

[0097] Here, the light detection module is mainly used to detect the light intensity of the current driving environment. This module can be a high-precision photosensitive detection device, and this application does not specifically limit its application. The rainfall detection module is mainly used to detect the rainfall in the current driving environment. This module can be a rain sensor, and this application does not specifically limit its application. The central control module is mainly used to display the light intensity and rainfall values ​​and respond to user operations.

[0098] In implementation, the light detection module continuously monitors the ambient light intensity and sends it to the central control module. Similarly, the rainfall detection module continuously monitors the rainfall and sends it to the central control module. The central control module then displays the light intensity and rainfall data for the user to view.

[0099] As an optional implementation, the vehicle lighting control method provided in this application embodiment further includes:

[0100] The central control module responds to the user's operation on the lighting control function, generates a lighting control command, and sends the lighting control command to the image acquisition module, the central processing module, and the headlight control module, so that the image acquisition module, the central processing module, and the headlight control module start working.

[0101] Here, the driver can actively choose whether to activate the lighting control function based on the light intensity and rainfall level. When the driver selects to activate the lighting control function, they can perform related operations on the lighting control function in the central control module, such as clicking the corresponding button. At this time, the central control module responds to the user's operation on the lighting control function, generates a lighting control command, and sends the lighting control command to the image acquisition module, central processing module, and headlight control module to start working.

[0102] This application provides a vehicle lighting control method applied to a vehicle lighting control system. First, an image acquisition module acquires an initial environmental image of the vehicle's current driving environment and sends the initial environmental image to a central processing module. Then, the central processing module performs image preprocessing on the received initial environmental image to obtain a target environmental image, and generates a vehicle lighting control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environmental image, and sends the vehicle lighting control command to the vehicle lighting control module. Finally, the vehicle lighting control module controls multiple LED lights to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment.

[0103] Compared to existing lighting control methods, this application turns off multiple LEDs in the matrix LED headlights that would otherwise illuminate reflective areas, thereby controlling the headlight illumination area and guiding the headlight path to avoid reflective areas on the road. This effectively reduces interference from reflected and refracted light in the environment, improves the active safety factor of the vehicle's headlights, and ultimately enhances overall vehicle driving safety, providing drivers with optimal road lighting.

[0104] Please see Figure 2 , Figure 3 , Figure 2 This is one of the structural schematic diagrams of a vehicle lighting control system provided in the embodiments of this application. Figure 3 This is a second schematic diagram of a vehicle lighting control system provided in an embodiment of this application. Figure 2 As shown, the vehicle lighting control system 200 includes:

[0105] The image acquisition module 201 is used to acquire an initial environmental image of the vehicle's current driving environment and send the initial environmental image to the central processing module 202.

[0106] The central processing module 202 is used to perform image preprocessing on the received initial environment image to obtain a target environment image, and generate a headlight control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, and send the headlight control command to the headlight control module 203; wherein, the headlight control command includes the identification information of multiple LED lights to be turned off in the matrix LED headlights of the vehicle, and the illumination light of the LED lights to be turned off passes through the actual reflective area;

[0107] The vehicle lighting control module 203 is used to control multiple LED lights to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment.

[0108] Furthermore, the central processing module 202 is also configured to determine the reflective area from the target environment image through the following steps:

[0109] An edge detection algorithm is used to detect reflective areas in the target environment image to obtain reflective area edge images, and the reflective area edge images are then segmented into multiple region sub-images.

[0110] Multiple sub-images of the region are input into a pre-trained reflective region detection model to determine at least one reflective sub-image containing the reflective region from the multiple sub-images of the region;

[0111] For each reflective sub-image, the reflective area corresponding to the reflective sub-image is determined from the target environment image based on the position of the reflective sub-image in the target environment image.

[0112] Furthermore, the central processing module 202 is also used to train the reflective area detection model through the following steps:

[0113] Acquire multiple sample images;

[0114] For each of the multiple sample images containing a reflective sample area, determine the first location region of the reflective sample area in that sample image;

[0115] For each of the sample images, the sample image is input into the original detection model of the reflective area to obtain the second location region of the reflective prediction region;

[0116] Based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region, the original detection model of the reflective region is trained to obtain the reflective region detection model.

[0117] Furthermore, when the central processing module 202 trains the original detection model of the reflective region based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region to obtain the reflective region detection model, the central processing module 202 is also used to:

[0118] If the sample image corresponding to the reflection prediction region is an image without a reflection sample region, then adjust the training parameters of the original reflection region detection model until the reflection prediction region output by the original reflection region detection model is empty;

[0119] If the sample image corresponding to the reflective prediction region is an image with a reflective sample region, then the first position region of the reflective sample region in the sample image is compared with the second position region of the reflective prediction region, and the loss function of the original detection model of the reflective region in the current state is calculated.

[0120] Based on the loss function of the original reflective area detection model, the training parameters of the original reflective area detection model are continuously adjusted until the original reflective area detection model reaches a convergence state, thus obtaining the reflective area detection model.

[0121] Furthermore, when the central processing module 202 generates headlight control commands based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, the central processing module 202 is also used to:

[0122] For each reflective area in the target environment image, determine the position coordinates of the reflective area in the target environment image;

[0123] Based on the calibration parameters of the image acquisition module and the position coordinates of the reflective area in the target environment image, the three-dimensional spatial coordinates of the actual reflective area corresponding to the reflective area in the current driving environment are determined.

[0124] Based on the optical path information of each LED in the matrix LED vehicle headlight, the LED to be turned off is determined from multiple LEDs whose light propagation path passes through the spatial position corresponding to the three-dimensional spatial coordinates.

[0125] The vehicle light control command is generated based on the identification information of the LED light to be turned off.

[0126] Furthermore, such as Figure 3 As shown, the vehicle lighting control system 200 further includes:

[0127] The illumination detection module 204 is used to detect the illumination intensity of the current driving environment in real time and send the illumination intensity to the central control module 206;

[0128] The rainfall detection module 205 is used to detect the rainfall value of the current driving environment in real time and send the rainfall value to the central control module 206;

[0129] The central control module 206 is used to display the light intensity and the rainfall value.

[0130] Furthermore, the central control module 206 is also used to generate lighting control commands in response to user operations related to lighting control functions, and send the lighting control commands to the image acquisition module, the central processing module, and the vehicle light control module, so that the image acquisition module, the central processing module, and the vehicle light control module start working.

[0131] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

[0132] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, they can perform the operations described above. Figure 1The steps of the vehicle lighting control method in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0133] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the vehicle lighting control method in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0134] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0140] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling vehicle lighting, characterized in that, The vehicle lighting control method is applied to a vehicle lighting control system, which includes an image acquisition module, a central processing module, and a vehicle lighting control module. The vehicle lighting control method includes: The image acquisition module acquires an initial environmental image of the vehicle's current driving environment and sends the initial environmental image to the central processing module; The central processing module performs image preprocessing on the received initial environmental image to obtain a target environmental image, and generates a headlight control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environmental image, and sends the headlight control command to the headlight control module; wherein, the headlight control command includes the identification information of multiple LEDs to be turned off in the matrix LED headlights of the vehicle, and the illumination light of the LEDs to be turned off passes through the actual reflective area; The vehicle lighting control module controls multiple LED lights to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment; The central processing module generates headlight control commands based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, including: For each reflective area in the target environment image, determine the position coordinates of the reflective area in the target environment image; Based on the calibration parameters of the image acquisition module and the position coordinates of the reflective area in the target environment image, the three-dimensional spatial coordinates of the actual reflective area corresponding to the reflective area in the current driving environment are determined. Based on the optical path information of each LED in the matrix LED vehicle headlight, the LED to be turned off is determined from among multiple LEDs whose light propagation path passes through the spatial position corresponding to the three-dimensional spatial coordinates; where optical path information refers to the propagation path of the light emitted by the LED in the actual space; The vehicle light control command is generated based on the identification information of the LED light to be turned off.

2. The vehicle lighting control method according to claim 1, characterized in that, The central processing module determines the reflective area from the target environment image through the following steps: An edge detection algorithm is used to detect reflective areas in the target environment image to obtain reflective area edge images, and the reflective area edge images are then segmented into multiple region sub-images. Multiple sub-images of the region are input into a pre-trained reflective region detection model to determine at least one reflective sub-image containing the reflective region from the multiple sub-images of the region; For each reflective sub-image, the reflective area corresponding to the reflective sub-image is determined from the target environment image based on the position of the reflective sub-image in the target environment image.

3. The vehicle lighting control method according to claim 2, characterized in that, The central processing module trains the reflective area detection model through the following steps: Acquire multiple sample images; For each of the multiple sample images containing a reflective sample area, determine the first location region of the reflective sample area in that sample image; For each of the sample images, the sample image is input into the original detection model of the reflective area to obtain the second location region of the reflective prediction region; Based on the first location region of the reflective sample region in the sample image and the second location region of the reflective prediction region, the original detection model of the reflective region is trained to obtain the reflective region detection model.

4. The vehicle lighting control method according to claim 3, characterized in that, The process of training the original reflection region detection model based on the first location region of the reflection sample region in the sample image and the second location region of the reflection prediction region to obtain the reflection region detection model includes: If the sample image corresponding to the reflection prediction region is an image without a reflection sample region, then adjust the training parameters of the original reflection region detection model until the reflection prediction region output by the original reflection region detection model is empty; If the sample image corresponding to the reflective prediction region is an image with a reflective sample region, then the first position region of the reflective sample region in the sample image is compared with the second position region of the reflective prediction region, and the loss function of the original detection model of the reflective region in the current state is calculated. Based on the loss function of the original reflective area detection model, the training parameters of the original reflective area detection model are continuously adjusted until the original reflective area detection model reaches a convergence state, thus obtaining the reflective area detection model.

5. The vehicle lighting control method according to claim 1, characterized in that, The vehicle lighting control system further includes a light detection module, a rain detection module, and a central control module; the vehicle lighting control method further includes: The illumination detection module detects the illumination intensity of the current driving environment in real time and sends the illumination intensity to the central control module; The rainfall detection module detects the rainfall value of the current driving environment in real time and sends the rainfall value to the central control module; The central control module displays the light intensity and the rainfall value.

6. The vehicle lighting control method according to claim 5, characterized in that, The vehicle lighting control method also includes: The central control module responds to the user's operation on the lighting control function, generates a lighting control command, and sends the lighting control command to the image acquisition module, the central processing module, and the headlight control module, so that the image acquisition module, the central processing module, and the headlight control module start working.

7. A vehicle lighting control system, characterized in that, The vehicle lighting control system includes: The image acquisition module is used to acquire an initial environmental image of the vehicle's current driving environment and send the initial environmental image to the central processing module; The central processing module is used to perform image preprocessing on the received initial environmental image to obtain a target environmental image, and generate a headlight control command based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environmental image, and send the headlight control command to the headlight control module; wherein, the headlight control command includes the identification information of multiple LEDs to be turned off in the matrix LED headlights of the vehicle, and the illumination light of the LEDs to be turned off passes through the actual reflective area; The vehicle lighting control module is used to control multiple LED lights to be turned off based on the vehicle lighting control command, so that the illumination light of the matrix LED vehicle lights avoids the actual reflective area in the current driving environment; When the central processing module generates headlight control commands based on the position of the actual reflective area in the current driving environment corresponding to the reflective area in the target environment image, the central processing module is further configured to: For each reflective area in the target environment image, determine the position coordinates of the reflective area in the target environment image; Based on the calibration parameters of the image acquisition module and the position coordinates of the reflective area in the target environment image, the three-dimensional spatial coordinates of the actual reflective area corresponding to the reflective area in the current driving environment are determined. Based on the optical path information of each LED in the matrix LED vehicle headlight, the LED to be turned off is determined from among multiple LEDs whose light propagation path passes through the spatial position corresponding to the three-dimensional spatial coordinates; where optical path information refers to the propagation path of the light emitted by the LED in the actual space; The vehicle light control command is generated based on the identification information of the LED light to be turned off.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the vehicle lighting control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the vehicle lighting control method as described in any one of claims 1 to 6.

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