Head-up display control method, device and equipment, vehicle and storage medium

Abstract

The invention provides a head-up display control method, device and equipment, a vehicle and a storage medium. The method comprises the following steps: acquiring a front image of the vehicle; according to the front image, whether a light and shade boundary exists is determined, the light and shade boundary divides the front image into a light image and a dark image, and the difference value between the average brightness of the light image and the average brightness of the dark image is larger than a preset brightness difference value; and according to the position of the light and shade boundary line in the front image, determining a brightness boundary line of the imaging area of the HUD, and dividing the imaging area of the HUD into an upper imaging area and a lower imaging area by the brightness boundary line. And adjusting the brightness of the imaging area above according to the target image above in the position relationship between the bright image and the dark image. According to the method, when the vehicle goes out of the tunnel, the display brightness of the HUD in the hard light interference area can be dynamically adjusted along with the change of the light and shade boundary in the user visual angle, and the situation that a user cannot clearly see the display content due to external hard light is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle displays, and particularly relates to a control method, device, equipment, vehicle and storage medium for a head-up display. Background Art

[0002] With the continuous progress of automotive technology, the head-up display technology (Head-Up Display, abbreviated as HUD), as an important innovative technology, has gradually been widely applied to various vehicle models. The HUD technology projects driving information onto the windshield or transparent screen in front of the driver's line of sight, enabling the driver to view the instrument panel without lowering their head, thereby improving driving safety and convenience.

[0003] However, the existing HUD technology still has some limitations in practical applications, especially in environments with complex light conditions. Specifically, within a short period after the vehicle exits the tunnel, due to the significant brightness difference between the strong light outside the tunnel and the dim environment inside the tunnel, the display content of the HUD will also become unclear, affecting the driver's timely acquisition of information.

[0004] Therefore, how to ensure that the HUD image is clearly visible when the vehicle exits the tunnel has become an urgent technical problem to be solved. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a control method for a head-up display to solve the problem that the image displayed by the HUD is unclear due to the influence of strong light when the vehicle exits the tunnel in the prior art; the second purpose is to provide a control device for a head-up display; the third purpose is to provide an electronic device; the fourth purpose is to provide a vehicle; the fifth purpose is to provide a storage medium.

[0006] To achieve the above purposes, the technical solutions adopted by the present invention are as follows:

[0007] In a first aspect, the present application provides a control method for a head-up display, and the method includes:

[0008] Obtain the front image of the vehicle;

[0009] According to the front image, determine whether there is a light-dark boundary line that divides the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference;

[0010] If there is the light-dark boundary line, then according to the position of the light-dark boundary line in the front image, determine the brightness boundary line of the imaging area of the head-up display (HUD), and the brightness boundary line divides the imaging area of the HUD into an upper imaging area and a lower imaging area;

[0011] Adjust the brightness of the upper imaging area according to the target image that is above in the positional relationship between the bright image and the dark image.

[0012] According to the above technical means, by the above method, the front image of the vehicle is analyzed in real time to identify the light-dark boundary line. When the vehicle exits the tunnel, the display brightness of the area with strong light interference in the HUD can be dynamically adjusted as the light-dark boundary line changes in the user's perspective, avoiding visual interference caused by external strong light or light changes. In addition, according to the above method, when entering the tunnel, the display brightness of the HUD is dynamically reduced as the light-dark boundary line changes in the user's perspective, avoiding over-bright display, thereby improving the visual comfort and safety of the driver.

[0013] Further, the adjusting the brightness of the upper imaging area according to the target image that is above in the positional relationship between the bright image and the dark image includes:

[0014] Perform gray-scale processing on the target image to obtain gray-scale data;

[0015] Determine the average gray-scale value according to the gray-scale data;

[0016] Determine the target brightness of the upper imaging area according to the average gray-scale value and the preset mapping relationship between gray-scale and brightness, and the gray-scale and brightness are negatively correlated in the mapping relationship;

[0017] Adjust the brightness of the upper imaging area from the current brightness to the target brightness.

[0018] According to the above technical means, converting the image into gray-scale data for processing, representing the front brightness by gray-scale, has a lower delay in detecting light intensity changes compared with a light sensor.

[0019] Further, the obtaining the front image of the vehicle includes:

[0020] Obtain the image data of the front of the vehicle captured by the camera;

[0021] Crop out the front image that overlaps with the imaging area of the HUD from the image data in the user's perspective.

[0022] Further, the determining whether there is a light-dark boundary line according to the front image includes:

[0023] Convert the front image into a gray-scale image;

[0024] Determine the gradient magnitude of each pixel point according to the gray-scale image;

[0025] Among the gradient magnitudes of all pixel points, the pixel points with a gradient magnitude greater than a preset magnitude threshold are used as edge points to obtain an edge point set;

[0026] Based on the edge point set, determine whether there is such a light-dark boundary line.

[0027] According to the above technical means, the light-dark boundary line can be accurately identified by calculating the gradient magnitude of the brightness.

[0028] Furthermore, the determining whether there is such a light-dark boundary line based on the edge point set includes:

[0029] According to the vertical coordinates of each edge point in the edge point set, determine the average vertical coordinate, where the vertical coordinate is the coordinate in the pixel coordinate system of the grayscale image;

[0030] In the edge point set, delete the edge points that exceed the preset error range of the average vertical coordinate to obtain an optimized edge point set;

[0031] Based on the optimized edge point set, determine whether there is such a light-dark boundary line.

[0032] By the above method, the interference points are removed, which can avoid the interference of reflective objects.

[0033] Furthermore, the determining whether there is such a light-dark boundary line based on the optimized edge point set includes:

[0034] Connect the adjacent edge points within a preset distance in the optimized edge point set to obtain a candidate light-dark boundary line;

[0035] Determine the number of the candidate light-dark boundary lines.

[0036] If the number is 1, it is determined that there is such a light-dark boundary line, and the candidate light-dark boundary line is used as the light-dark boundary line.

[0037] If the number is greater than 1, it is determined that there is no such light-dark boundary line.

[0038] According to the above technical means, if multiple candidate light-dark boundary lines are recognized, it indicates that there may be problems or interference in the recognition of the light-dark boundary line. Only when there is one candidate light-dark boundary line can the light-dark boundary line be determined. By the above method, the recognition accuracy can be improved.

[0039] Furthermore, the determining the brightness dividing line of the imaging area of the head-up display (HUD) based on the position of the light-dark boundary line in the front image includes:

[0040] Divide the distance between the light-dark boundary line and the top line of the front image by the distance between the top line and the bottom line of the front image to obtain a position ratio.

[0041] Within the imaging area of the HUD, determine the brightness boundary line according to the position ratio.

[0042] In a second aspect, the present application provides a control device for a head-up display, the device comprising:

[0043] An acquisition module for acquiring a front image of a vehicle;

[0044] A first determination module for determining whether there is a light-dark boundary line according to the front image, the light-dark boundary line dividing the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image being greater than a preset brightness difference;

[0045] A second determination module for, if there is the light-dark boundary line, determining a brightness boundary line of an imaging area of a head-up display (HUD) according to the position of the light-dark boundary line in the front image, the brightness boundary line dividing the imaging area of the HUD into an upper imaging area and a lower imaging area;

[0046] A brightness adjustment module for adjusting the brightness of the imaging area above the brightness boundary line according to a target image located above in the positional relationship between the bright image and the dark image.

[0047] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor;

[0048] The memory stores computer-executable instructions;

[0049] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the method according to any one of the first aspect.

[0050] In a fourth aspect, the present application provides a vehicle, the vehicle comprising a controller, and the controller is used to implement the method according to any one of the first aspect.

[0051] In a fifth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspect.

[0052] The present application provides a control method, device, equipment, vehicle and storage medium for a head-up display. The method includes: obtaining a front image of the vehicle; determining whether there is a light-dark boundary line according to the front image, the light-dark boundary line divides the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference; determining a brightness boundary line of the imaging area of the HUD according to the position of the light-dark boundary line in the front image, and the brightness boundary line divides the imaging area of the HUD into an upper imaging area and a lower imaging area. Adjust the brightness of the upper imaging area according to the target image located above in the positional relationship between the bright image and the dark image. Through the above method, by analyzing the front image of the vehicle in real time and identifying the light-dark boundary line, when the vehicle exits the tunnel, the display brightness of the area with strong light interference of the HUD can be dynamically adjusted as the light-dark boundary line changes in the user's perspective, avoiding visual interference caused by external strong light or light changes. In addition, according to the above method, when entering the tunnel, the display brightness of the HUD is dynamically reduced as the light-dark boundary line changes in the user's perspective, avoiding over-bright display, thereby improving the visual comfort and safety of the driver. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are incorporated herein and form a part of this specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0054] Figure 1 It is a schematic top view of the imaging range of the HUD provided by the present application;

[0055] Figure 2 It is a schematic front view of the imaging range of the HUD provided by the present application;

[0056] Figure 3 It is a schematic flowchart of an embodiment of the control method for the head-up display provided by the present application Figure 1 ;

[0057] Figure 4 It is a schematic diagram of the field of view range of a camera provided by the present application;

[0058] Figure 5 It is a schematic diagram of the content of the imaging area of a HUD provided by the present application;

[0059] Figure 6 It is a schematic diagram of the dynamic change of the brightness of the imaging area of a HUD provided by the present application;

[0060] Figure 7 It is a schematic flowchart of an embodiment of the control method for the head-up display provided by the present application Figure 2 ;

[0061] Figure 8Schematic flow of the embodiment of the control method for the head-up display provided in this application Figure 3 ;

[0062] Figure 9 Schematic architecture of the control system for the head-up display provided in this application;

[0063] Figure 10 Schematic diagram of a brightness histogram provided in this application;

[0064] Figure 11 Schematic structural diagram of a control device for the head-up display provided in this application;

[0065] Figure 12 Schematic structural diagram of the electronic device provided in this application.

[0066] Through the above-mentioned drawings, the clear embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0067] The following will illustrate the implementation manners of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0068] Head-up display technology (abbreviated as HUD) refers to the display technology that projects key information into the user's field of view, enabling the user to directly view important information without having to lower their head or shift their line of sight. HUD has been widely applied in multiple fields such as vehicles, aviation, navigation, and augmented reality. The solution of the control method for the head-up display provided in this application can be applied to any of the above fields.

[0069] In the field of vehicles, HUD usually projects navigation information, vehicle speed, remaining fuel amount, incoming call information, etc. onto the front windshield, allowing the driver to obtain key information without having to lower their head or turn their line of sight, thereby improving safety and driving experience.

[0070] Figure 1 Schematic top view of the imaging range of the HUD provided in this application, Figure 2 Schematic front view of the imaging range of the HUD provided in this application. Refer to Figure 1 andFigure 2 In the vehicle application scenario, the HUD imaging area occupies a part of the user's forward view.

[0071] In actual applications, when the vehicle is driving out of the tunnel in a short period of time, the light outside the tunnel is relatively strong. As a result, there are obvious brightness differences in the user's view, which may cause the display content of the HUD to be unclear and potentially pose a safety risk.

[0072] In view of the above problems, the present application obtains an external image in front of the vehicle through a camera and can adjust the brightness of the display content according to the image brightness. When it is recognized that there are bright and dark parts in the image, it means that the vehicle is in the scenario of entering or exiting the tunnel. According to the bright and dark parts in the image, the brightness corresponding to the bright part in the display screen is adjusted. In this way, when the vehicle exits the tunnel, the brightness of the display content corresponding to the area between the user's view and the external strong light object is increased, enabling the user to clearly observe the display content. In addition, the brightness of the display content corresponding to the non-strong light part in the user's view remains unchanged. As the vehicle moves forward, the strong light area in the display area of the user's view changes, and the corresponding highlighted display content area also changes, improving the observation ability and efficiency of the HUD from the driver's perspective and reducing accidents or other adverse consequences caused by unclear observation.

[0073] The following takes the vehicle controller as the execution subject and uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. These specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0074] Figure 3 Schematic flow of the control method embodiment for the head-up display provided by the present application Figure 1 As Figure 3 shown, the method includes:

[0075] S101. Obtain the forward image of the vehicle.

[0076] The forward image of the vehicle can be obtained by single-shot shooting with a camera or intercepted from a video stream. The camera captures a real-time video stream and provides image information of the road conditions ahead. Therefore, the image data of the vehicle ahead can be obtained in real time from the video stream.

[0077] In some embodiments, the camera can be a front-view camera installed at the front of the vehicle, as Figure 4 shown, Figure 4 which is a schematic diagram of the field of view of a camera provided by the present application.

[0078] In some embodiments, the camera can be the camera on a driving recorder that captures the forward image of the vehicle.

[0079] As Figure 4 shown, the field of view of the camera is much larger than the imaging range of the HUD. Therefore, it is necessary to preprocess the external image obtained by the camera and crop the front image within the imaging range of the HDU (i.e., the external image corresponding to the HUD imaging area from the user's perspective). So, it is necessary to crop the external image based on the size of the HUD eye box, and the overlapping area between the front image obtained after cropping and the HUD imaging is obtained.

[0080] In a specific implementation, due to the differences in height, seat position, and viewing angle of different drivers, the areas that need to be cropped corresponding to what different users see from the HUD imaging area are also different. Therefore, the cropping area needs to be determined according to the actual situation of each user. To ensure the accuracy of the cropped image, the user can calibrate according to their own height and seat position. The user can set the HUD imaging area corresponding to their viewing angle through a specific interface or system calibration. After this step is completed, the system can automatically crop the image according to the calibrated conversion information.

[0081] Optionally, when the camera uses an ultra-wide-angle camera, distortion reduction is also required before or after cropping to ensure that the cropped front image can reflect the real situation observed by the user.

[0082] S102. Determine whether there is a light-dark boundary line based on the front image. The light-dark boundary line divides the front image into a bright image and a dark image. The difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference.

[0083] Among them, the light-dark boundary line usually refers to the area where the light changes significantly, such as the boundary line between the area outside the tunnel exit irradiated by strong light and the area inside the tunnel. This boundary line divides the front image into a bright part and a dark part. To determine the existence of this boundary line, it is necessary to calculate the brightness difference between the two parts. If a light-dark boundary line is recognized in the front image, subsequent operations to adjust the brightness of the HUD display area by region are required. If no light-dark boundary line is recognized, only the brightness of the entire HUD display area needs to be adjusted according to the external brightness, or no adjustment is made.

[0084] There are several ways to determine whether there is a light-dark boundary line in the front image:

[0085] In one implementation, the front image is converted into a grayscale image. After conversion to grayscale, the calculation process can be simplified, focusing only on the brightness change to improve the calculation accuracy. Calculate the gradient magnitude of the brightness change around each pixel point in the grayscale image (i.e., the rate of change of the surrounding brightness). The area with a larger gradient magnitude is usually the position of the light-dark boundary line. In the vehicle driving scenario, the light-dark boundary line seen from the user's perspective is generally in the due front of the vehicle and extends left and right in the user's perspective, which means that the light-dark boundary line extends left and right in the front image (i.e., horizontally). Therefore, when calculating the gradient magnitude for the grayscale image, mainly focus on the brightness change in the vertical direction.

[0086] In one implementation, the front image is input into a pre-trained deep learning model, and the result of whether there is a light-dark boundary line is output. The deep learning model can be a convolutional neural network or a variant of the convolutional neural network, such as ResNet, VGG, U-Net, etc. To train the deep learning model, a large amount of front image data needs to be collected. Each image needs to be labeled whether it contains a light-dark boundary line. To enhance the robustness of the model, data augmentation processing can be performed on the images, such as rotation, scaling, flipping, etc. During the training process, the gradient descent method and the backpropagation algorithm are used to optimize the parameters of the model to obtain the trained deep learning model. The deep learning model is built into the vehicle. In addition, the output of the model can also be a probability value. For example, an output of 0.85 means that the probability that the image contains a light-dark boundary line is 85%.

[0087] To further determine whether the identified light-dark boundary line is correct, calculate the average brightness of the bright area (bright image) and the dark area (dark image) in the two calculated images obtained by dividing the front image by the light-dark boundary line, and then determine whether the difference between the two is greater than a preset brightness difference threshold. If it is greater than the brightness difference threshold, it indicates that there is an obvious light-dark boundary line in the image. If it is less than the brightness difference threshold, it means that the brightness change on both sides of the identified light-dark boundary line is not obvious, and the identification of the light-dark boundary line fails. The process of further determining whether the requirements are met based on the average brightness of the bright image and the dark image can be integrated into the step of determining the light-dark boundary line.

[0088] S103. If there is a light-dark boundary line, determine the brightness dividing line of the imaging area of the head-up display (HUD) according to the position of the light-dark boundary line in the front image. The brightness dividing line divides the imaging area of the HUD into an upper imaging area and a lower imaging area.

[0089] The front image is the image corresponding to the imaging area of the HUD. Therefore, there is a mapping relationship between the front image and the imaging area of the HUD. According to this mapping relationship and the position of the light-dark boundary line in the front image, the brightness dividing line corresponding to the imaging area of the HUD can be determined.

[0090] In one implementation, the distance between the light-dark boundary line and the top line of the front image is divided by the distance between the top line and the bottom line of the front image to obtain a position ratio. In the imaging area of the HUD, the brightness boundary line can be determined according to the position ratio. Exemplarily, if the light-dark boundary line is at the mid-position in the front image, it is determined that the brightness boundary line is at the mid-position in the imaging area of the HUD. Another example, if the light-dark boundary line is at the 1 / 4 position from the top of the front image, it is determined that the brightness boundary line is at the 1 / 4 position from the top of the imaging area of the HUD.

[0091] S104. Adjust the brightness of the upper imaging area according to the target image that is above in the positional relationship between the bright image and the dark image.

[0092] During the driving process of the vehicle moving forward, a light-dark boundary line appears. The brightness of the lower imaging area is the brightness that matches the current environment. The area where the brightness changes is the upper imaging area. Therefore, it is necessary to adjust the brightness of the upper imaging area. For example, in the daytime and non-cloudy scenarios, when exiting the tunnel, the image above the light-dark boundary line in the front image is bright, while the image below is relatively dim; when entering the tunnel, the image above the light-dark boundary line in the front image is dim, while the image below is bright. The first area to change is always the upper image. Therefore, it is necessary to adjust the brightness of the upper imaging area according to the target image above the light-dark boundary line (i.e., the target image that is above in the positional relationship between the bright image and the dark image).

[0093] For adjusting the brightness of the imaging area, it is necessary to adjust according to the brightness of the target image, that is, the brighter the target image, the higher the brightness of the upper area. If an ambient light sensor is used to obtain the external brightness, when the vehicle has not exited or entered the tunnel, the brightness change delay of the ambient light sensor is very high, which will introduce additional errors. However, the target image reflects the real situation of the road conditions ahead. Therefore, the brightness ahead can be determined through the target image. The target image is grayscale processed to obtain single-channel grayscale data. The grayscale data can represent the brightness ahead and is used to adjust the brightness of the upper imaging area.

[0094] In one implementation, according to the grayscale data, the average grayscale value in the target image is determined; according to the preset mapping relationship between grayscale and brightness, the target brightness of the upper imaging area is determined. In the mapping relationship, the grayscale and brightness are negatively correlated, that is, the larger the grayscale (closer to 255), the lower the corresponding brightness.

[0095] This embodiment provides a control method for a head-up display. The method includes: obtaining a front image of the vehicle; determining whether there is a light-dark boundary line according to the front image, which divides the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference; determining a brightness boundary line of the imaging area of the head-up display (HUD) according to the position of the light-dark boundary line in the front image, and the brightness boundary line divides the imaging area of the HUD into an upper imaging area and a lower imaging area. Adjust the brightness of the upper imaging area according to the target image that is above in the position relationship between the bright image and the dark image. Through the above method, by analyzing the front image of the vehicle in real time and identifying the light-dark boundary line, when the vehicle exits the tunnel, the display brightness of the area with strong light interference in the HUD can be dynamically adjusted as the light-dark boundary line changes in the user's perspective, avoiding visual interference caused by external strong light or light changes.

[0096] In addition, according to the above method, when entering the tunnel, as the light-dark boundary line changes in the user's perspective, the display brightness of the HUD is dynamically reduced to avoid over-bright display, thereby improving the visual comfort and safety of the driver.

[0097] In some embodiments, according to the vehicle navigation information, the above control method for the head-up display can be triggered only when the vehicle is within a preset distance from the tunnel or in the vehicle tunnel.

[0098] In some reverse scenarios, the first image to change is the image that is below in the position relationship between the bright image and the dark image, and then the brightness of the lower imaging area is adjusted according to the changed lower image.

[0099] Figure 5 It is a schematic diagram of the content in the imaging area of an HUD provided by this application. Combining Figure 1 and Figure 2 , the user can view information such as vehicle speed, navigation instructions, gear position, fuel information, music, phone, time, etc. in the HUD imaging area. The above-displayed information can be selected and set by the user, and there is no specific limitation.

[0100] Figure 6A schematic diagram of the dynamic change of the brightness of the imaging area of an HUD provided by this application. In the figure, the shadow represents the strong light seen from the user's perspective, the bold indicates an increase in the brightness of the displayed content, and the letters in the figure are only for the illustration of the displayed content. Figure a shows that the vehicle is in a tunnel, and the brightness of all the displayed content in the imaging area of the HUD is 1. In Figure b, the area of the strong light outside the tunnel seen through the imaging area of the HUD from the user's perspective accounts for 1 / 4 of the imaging area of the HUD. Then, the brightness level of the corresponding upper 1 / 4 area is adjusted to 4. In Figure c, as the vehicle moves forward, the area of the strong light outside the tunnel seen through the imaging area of the HUD from the user's perspective accounts for 1 / 2 of the imaging area of the HUD. Then, the brightness level of the corresponding upper 1 / 2 area is adjusted to 4. In Figure d, as the vehicle moves forward, the area of the strong light outside the tunnel seen through the imaging area of the HUD from the user's perspective accounts for the entire area of the HUD. Then, the brightness level of the entire area is adjusted to 4. The above brightness level is related to the external brightness, and here it is only for illustration of increasing the brightness. In the above process of dynamic change, the brightness of the display area corresponding to the strong light in the perspective can be adjusted according to the change of the strong light, and the area not involving the strong light in the perspective is not adjusted, which can avoid increasing the brightness of all areas and causing the entire HUD display area to be too dazzling. If the brightness of the entire display area is increased, it may cause the driver to feel visual fatigue during long-term driving. By only adjusting the brightness of the strong light area, the system can reduce the extra burden on the user's eyes and optimize the visual comfort. In addition, by only adjusting the brightness of the strong light area instead of the full-screen brightness adjustment, the energy consumption can be reduced.

[0101] The following will introduce in detail how to determine the light-dark boundary line in the front image.

[0102] Figure 7 Flow schematic of the embodiment of the control method of the head-up display provided by this application Figure 2 in the above flow schematic Figure 1 Based on the corresponding embodiment, step S102 may include the following steps:

[0103] S1021. Convert the front image into a grayscale image.

[0104] In this step, the obtained front image usually contains three channels of RGB (red, green, blue), and each pixel point has the values of these three colors. The following conversion formula can be used to convert the three-channel image into a grayscale image:

[0105] Grayscale value = 0.2989×R + 0.5870×G + 0.1140×B

[0106] After being converted into a grayscale image, the details of the image will become simplified, only retaining the change of brightness, which is suitable for further edge detection.

[0107] S1022. Determine the gradient magnitude of each pixel point based on the grayscale image.

[0108] By calculating the gradient magnitude of each pixel point, regions with large brightness changes in the image, i.e., possible edge regions, are identified. The gradient is the rate of change of pixel brightness in the image and is calculated using the first derivative of the change in grayscale values. There are multiple gradient directions, such as the horizontal and vertical directions. The gradient magnitude represents the magnitude of the brightness change considering all gradient directions.

[0109] The Sobel operator or Prewitt operator can be used to calculate the gradient of the grayscale image. Specifically, the Sobel operator is usually divided into two convolution kernels in the horizontal and vertical directions to calculate the horizontal and vertical gradients respectively. The convolution kernel for the horizontal gradient is: [-1, 0, 1], and the convolution kernel for the vertical gradient is [-1, 0, 1] T , by applying the above convolution kernels to each pixel point, the gradients (rates of change) in the horizontal and vertical directions of the grayscale image can be obtained. The gradient magnitude of any target pixel can be calculated by the following formula:

[0110]

[0111] where G X represents the gradient of the target pixel in the horizontal direction, and G Y represents the gradient of the target pixel in the vertical direction.

[0112] By the above method, the gradient magnitudes of all pixel points can be calculated.

[0113] S1023. Among the gradient magnitudes of all pixel points, the pixel points with a gradient magnitude greater than the preset magnitude threshold are regarded as edge points to obtain an edge point set.

[0114] At the light-dark boundary line, the gradient change of brightness should be drastic. Therefore, find the points with prominent brightness changes in the grayscale image, and these points may form the determined light-dark boundary line. A gradient magnitude threshold is preset, and only the pixel points with a gradient magnitude greater than the gradient magnitude threshold will be marked as edge points. Through threshold screening, the edge information of brightness can be effectively extracted from the image, removing unimportant noise and small brightness changes to obtain a clear edge point set.

[0115] S1024. Determine whether there is a light-dark boundary line based on the edge point set.

[0116] The set of edge points recognized here may be the edge points of the light-dark boundary line, or the edges of some strongly reflective objects, or there may be both the edge points of the light-dark boundary line and the edges of some reflective objects. Therefore, it is necessary to determine whether there is a light-dark boundary line according to the set of edge points. Since the light-dark boundary line is a horizontal line running through the grayscale image (the thickness of the line may be several pixel sizes), and the edge points on the light-dark boundary line are continuously and densely arranged, the light-dark boundary line can be determined by the following method.

[0117] In one implementation, the light-dark boundary line usually presents a linear or relatively smooth structure. Therefore, connect the points in the set of edge points (the distance between the two connected points needs to be restricted within a preset distance) to form a connection line, and check whether the connection line is a continuous line that runs through the left and right sides of the grayscale image. If so, it is determined that there is a light-dark boundary line; if not, it is determined that there is no light-dark boundary line.

[0118] In one implementation, use morphological operations (such as dilation and erosion) to detect whether the edge points can be merged into a continuous line and run through the left and right sides of the grayscale image. If so, it is determined that there is a light-dark boundary line, and the formed line belongs to the light-dark boundary line.

[0119] In one implementation, use a clustering algorithm to group the edge points and check whether the set of edge points gathers together to form an obvious area. If the clustering result shows that the edge points are distributed in a continuous specific area that runs through the left and right sides of the grayscale image, it is determined that there is a light-dark boundary line, and the symmetry line of the continuous specific area is the light-dark boundary line. If the clustering result shows that the edge points are distributed in multiple discontinuous areas, it is determined that there is no light-dark boundary line.

[0120] After converting the front image into a grayscale image in this embodiment, the details of the image will be simplified, only the brightness change will be retained, which is suitable for further edge detection and avoids the interference of multiple colors. By determining the edge points through the gradient magnitude of the pixel points and then determining whether there is a light-dark boundary line, the situation of misjudgment can be avoided and the recognition ability of the light-dark boundary line can be improved.

[0121] If both the light-dark boundary line and some small reflective objects appear in the front image, and the brightness of the reflective objects is also relatively high, it may affect the determination of the light-dark boundary line and cause misjudgment. The following introduces how to determine whether there is a light-dark boundary line in this case with an embodiment.

[0122] Figure 8 Schematic flow of the embodiment of the control method for the head-up display provided by this application Figure 3 in the above schematic flow Figure 2Based on the corresponding embodiments, step S1024 may include the following steps:

[0123] S10241. Determine the average ordinate according to the ordinates of each edge point in the edge point set. The ordinate is the coordinate in the pixel coordinate system of the grayscale image.

[0124] In this step, the pixel coordinate system may be a coordinate system formed with any point in the grayscale image as the origin, such as the lower left pixel corner point, or the central pixel point, etc. Traverse all edge points, obtain the ordinates of each edge point, and calculate the average value.

[0125] In one implementation, directly solve the average value of the ordinates of all edge points.

[0126] In one implementation, to increase the redundancy of the solution, in the sorting of the ordinates of the edge point set, a certain number of maximum values and minimum values are removed. This can avoid the interference of outliers (such as the influence of noise or reflective objects) on the average value, and calculate the average value according to the ordinates of the remaining edge points. Exemplarily, the first 5% of the maximum values and the last 10% of the minimum values can be removed.

[0127] S10242. In the edge point set, delete the edge points that exceed the preset error range of the average ordinate to obtain an optimized edge point set.

[0128] According to the set error range, for each edge point, check whether its ordinate is within the error range of the average value. If the ordinate of a certain edge point exceeds this range, it is deleted. After deleting the edge points that exceed the error range, the remaining edge points form an optimized edge point set.

[0129] By deleting the edge points that exceed the preset error range of the average ordinate, the interference of some reflective objects can be eliminated.

[0130] S10243. Determine whether there is a light-dark boundary line according to the optimized edge point set.

[0131] Based on the optimized edge point set, the next step is to determine whether there is a light-dark boundary line. For the optimized edge point set, the method for determining whether there is a light-dark boundary line in the edge point set in the above embodiments can be adopted. The following details one way of connecting the points in the edge point set, which specifically includes the following steps:

[0132] S102431. Connect the adjacent edge points within a preset distance in the optimized edge point set to obtain a candidate light-dark boundary line.

[0133] First, adjacent edge points need to be connected to form potential light-dark boundaries. A preset distance (e.g., a preset number of pixel distances) is predefined. If the difference between two edge points is less than the preset distance, the two edge points are considered adjacent and are connected. Traverse the optimized set of edge points and connect adjacent edge points in pairs in the order from left to right or from right to left according to the abscissa.

[0134] S102432. Determine whether the number of candidate light-dark boundaries is 1.

[0135] Traverse all the connected sets of edge points and count the number of formed boundary segments. If there is only one connected boundary segment, it indicates that there may be one light-dark boundary; if there are multiple independent connected segments, it indicates that there is no clear light-dark boundary.

[0136] S102433. If the number is 1, determine that there is a light-dark boundary and use the candidate light-dark boundary as the light-dark boundary.

[0137] S102434. If the number is greater than 1, determine that there is no light-dark boundary.

[0138] If the number of candidate light-dark boundaries is greater than 1, it means that the set of edge points cannot form a clear light-dark boundary. At this time, it is determined that there is no light-dark boundary.

[0139] The method of this embodiment includes calculating the average value of the ordinates from the set of edge points and processing outliers, optimizing the set of edge points, and determining whether there is a light-dark boundary by connecting adjacent edge points. By the above method, the influence of reflective objects can be avoided. Through the number of candidate light-dark boundaries, only when a clear candidate light-dark boundary is formed can it be determined that a light-dark boundary is formed.

[0140] In the above way, the light-dark boundary can be accurately identified in the following scenarios. For example, when following a vehicle in a tunnel, the taillights or reflective strips may be misidentified as the light-dark boundary through grayscale processing. By optimizing the set of edge points as described above and then determining the number of light-dark boundaries, the above misjudgment can be avoided.

[0141] Figure 9 It is a schematic diagram of the architecture of the head-up display control system provided by this application, as Figure 9 shown, the architecture of the control system includes a front-view camera, a transmission module, an in-vehicle infotainment system, and a HUD display unit.

[0142] The front camera outside the vehicle, the in-vehicle infotainment system, the HUD display module, and the in-vehicle infotainment system are connected by a Low-Voltage Differential Signaling (LVDS) harness. The front camera outside the vehicle takes pictures or records videos in a loop at a certain time interval, and transmits them to the in-vehicle infotainment system through LVDS. The HUD software package performs image statistical analysis and processing. Within the HUD eyebox range, area division is carried out, such as Figure 5 . Combining the image information of the front camera outside the vehicle, optical parameter modulation is performed on the main content display area, and modulation processing is carried out in two dimensions of color and brightness according to the calibrated target parameter range.

[0143] Taking the front camera in the surround view camera system (AVM) commonly installed in vehicles as the image capture device, which is usually arranged in the front of the vehicle body. In a dynamic scenario, the camera needs to take images at a cycle of 2 to 5 seconds, and the image data is transmitted to the in-vehicle infotainment system through LVDS signals.

[0144] After receiving the graphic data, the in-vehicle infotainment system distributes it to HUD_Service through an internal interface, which performs noise removal, distortion reduction, image cropping, and grayscale processing. Specifically:

[0145] 1. Noise removal, using Gaussian filtering to remove the noise in the image to ensure that the image parameters referred to by the subsequent histogram are more accurate and real.

[0146] 2. Distortion reduction, the surround view camera generally uses an ultra-wide-angle fish-eye camera, and distortion will inevitably occur. This method is based on a preset standard image to calculate radial and tangential distortions, and uses inverse mapping or other transformation algorithms to correct image distortion.

[0147] Regularly calibrate the internal parameters of the camera, take multiple images using a standard pattern (such as a checkerboard), and obtain data from different angles and positions; use image processing algorithms (such as Harris corner detection or Shi-Tomasi corner detection) to identify the corner positions in the pattern. Through the spatial coordinates and image coordinates of these corners, use the Zhang calibration method or other models to calculate the internal parameter matrix of the camera, including focal length, principal point position, and distortion coefficient.

[0148] Establish a model including radial and tangential distortion parameters, and possibly higher-order coefficients are needed to more accurately describe complex distortion situations.

[0149] After establishing the model of distortion parameters, the coordinates of each pixel in the original image are mapped through the inverse mapping algorithm to calculate its position in the normal coordinate system. The interpolation algorithm (such as bilinear interpolation or bicubic interpolation) is used to obtain the pixel value at this position, and an undistorted image is generated.

[0150] During the inverse mapping process, some holes or incomplete regions may occur because some pixels may not be able to be mapped to the new coordinates. To ensure the integrity and visual effect of the image, methods such as filling or mirror extension are usually used to handle these holes.

[0151] 3. Image cropping. The field of view of the surround camera is much larger than the HUD imaging range, so it is necessary to crop based on the size of the HUD eye box. After cropping, the overlapping area between the image captured by the camera and the HUD imaging is obtained.

[0152] 4. Grayscale processing. The cropped color image is grayscale processed to obtain a single-channel grayscale image. The brightness value range of each pixel point in the grayscale image is generally from 0 to 255.

[0153] 5. Identification of the light-dark boundary line and the brightness boundary line. Specific details will not be introduced.

[0154] For the imaging area above the light-dark boundary line, analysis is carried out from two dimensions of color and brightness, and two histogram calculations are required. In the brightness dimension, the grayscale image obtained by preprocessing is analyzed, and in the color dimension, the color image without grayscale processing is analyzed.

[0155] Drawing and analysis of the brightness histogram: Each pixel point of the grayscale image is analyzed to count its grayscale value and generate a brightness histogram. An example is shown in Figure 10 , Figure 10 which is a schematic diagram of a brightness histogram provided by this application. Among them, the horizontal axis represents the grayscale value, that is, the brightness value, and the vertical axis is the frequency of the appearance of this brightness value.

[0156] Analyze the distribution of the brightness histogram. If it is concentrated on the left, it means that there is insufficient exposure and the brightness level of the display module needs to be increased. If it is concentrated on the right, it means that there is overexposure and the brightness level of the display module needs to be reduced. Calculate the average brightness of the grayscale image through the parameters of the brightness histogram. Here, the average brightness is represented by the grayscale value. Quantify the overall brightness level of the upper imaging area with the average brightness, and specifically refer to the mapping relationship between grayscale and brightness in the foregoing embodiments. The calculation formula for the average brightness is:

[0157]

[0158] Calculate the average contrast of the grayscale image through the parameters of the brightness histogram to quantify the obvious degree of brightness change in the image. If the contrast is too low, the image will be dull or blurred. If it is too high, there will be excessive bright-dark contrast, resulting in serious loss of picture details. The calculation formula is as follows:

[0159]

[0160] The HUD_Service continuously calculates based on the image data sent in a cycle of every 2 to 5 seconds. When the average brightness or contrast of the partition exceeds the threshold, perform enhanced processing on the picture display effect.

[0161] According to the analysis result of the brightness histogram, adjust the brightness of the image in the upper imaging area so that it can maintain a good display effect under different lighting conditions.

[0162] In addition to analyzing the brightness dimension, analyze the color dimension of the color image, count its color values, and generate a color histogram. By adjusting the gamut range of the image, improve the saturation and contrast of the image, making the image more vivid and clear.

[0163] After image modulation processing, output the image to the HUD display module through the audio-video transmission interface to achieve enhanced HUD display effects.

[0164] Figure 11 It is a schematic structural diagram of a control device for a head-up display provided by this application. As Figure 11 shown, the control device 40 for the head-up display provided in this embodiment includes:

[0165] An acquisition module 401, configured to acquire the front image of the vehicle;

[0166] A first determination module 402, configured to determine whether there is a light-dark boundary line according to the front image. The light-dark boundary line divides the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference;

[0167] A second determination module 403, configured to, if there is the light-dark boundary line, determine the brightness boundary line of the imaging area of the head-up display (HUD) according to the position of the light-dark boundary line in the front image. The brightness boundary line divides the imaging area of the HUD into an upper imaging area and a lower imaging area;

[0168] A brightness adjustment module 404, configured to adjust the brightness of the imaging area above the brightness boundary line according to the target image in the upper position of the positional relationship between the bright image and the dark image.

[0169] Optionally, the brightness adjustment module 404 is specifically configured to:

[0170] Perform grayscale processing on the target image to obtain grayscale data;

[0171] Determine the average grayscale value according to the grayscale data;

[0172] Determine the target brightness of the upper imaging area according to the average grayscale value and a preset mapping relationship between grayscale and brightness, where the grayscale and brightness are negatively correlated in the mapping relationship;

[0173] Adjust the brightness of the upper imaging area from the current brightness to the target brightness.

[0174] Optionally, the acquisition module 401 is specifically configured to:

[0175] Acquire image data of the front of the vehicle captured by a camera;

[0176] Crop out the front image that overlaps with the imaging area of the HUD from the image data from the perspective of the user.

[0177] Optionally, the first determination module 402 is specifically configured to:

[0178] Convert the front image into a grayscale image;

[0179] Determine the gradient magnitude of each pixel point according to the grayscale image;

[0180] Among the gradient magnitudes of all pixel points, use the pixel points with a gradient magnitude greater than a preset magnitude threshold as edge points to obtain an edge point set;

[0181] Determine whether there is the light-dark boundary line according to the edge point set.

[0182] Optionally, the first determination module 402 is specifically configured to:

[0183] Determine the average ordinate according to the ordinates of each edge point in the edge point set, where the ordinate is the coordinate in the pixel coordinate system of the grayscale image;

[0184] Delete the edge points in the edge point set that exceed the preset error range of the average ordinate to obtain an optimized edge point set;

[0185] Determine whether there is the light-dark boundary line according to the optimized edge point set.

[0186] Optionally, the first determination module 402 is specifically configured to:

[0187] Connect adjacent edge points within a preset distance in the optimized edge point set to obtain a candidate light-dark boundary line;

[0188] Determine the number of candidate light-dark boundaries to be selected.

[0189] If the number is 1, it is determined that the light-dark boundary exists, and the candidate light-dark boundary is used as the light-dark boundary.

[0190] If the number is greater than 1, it is determined that the light-dark boundary does not exist.

[0191] Optionally, the second determination module 403 is specifically configured to:

[0192] Divide the distance between the light-dark boundary and the top line of the front image by the distance between the top line and the bottom line of the front image to obtain a position ratio;

[0193] Within the imaging area of the HUD, determine the brightness dividing line according to the position ratio.

[0194] The head-up display control device provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effects are similar, which will not be elaborated here in this embodiment.

[0195] Figure 12 It is a schematic structural diagram of the electronic device provided in this application. As Figure 12 shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. Among them, the processor 501, the memory 502, and the communication component 503 are connected through a bus 504.

[0196] In a specific implementation process, at least one processor 501 executes the computer execution instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0197] The specific implementation process of the processor 501 can refer to the above method embodiment, and its implementation principle and technical effects are similar, which will not be elaborated here in this embodiment.

[0198] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU for short), or other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0199] The memory may include a random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk memory.

[0200] The bus may be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the buses in the drawings of this application are not limited to only one bus or one type of bus.

[0201] This application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0202] This application also provides a vehicle, including a controller, which is used to execute the method of the above embodiments.

[0203] This application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the above method is implemented.

[0204] The above-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. The readable storage medium may be any available medium accessible by a general-purpose or special-purpose computer.

[0205] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium may also be a component of the processor. The processor and the readable storage medium may be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium may also exist as discrete components in a device.

[0206] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0207] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0208] In addition, the functional units in various embodiments of the present invention may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0209] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, and other various media that can store program codes.

[0210] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, and other various media that can store program codes.

[0211] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will easily think of other implementation manners of the present invention. The present invention is intended to cover any variations, uses, or adaptive changes of the present invention. These variations, uses, or adaptive changes follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A control method for a head-up display, characterized in that, The method includes: Obtaining a front image of the vehicle; According to the front image, determining whether there is a light-dark boundary line that divides the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference; If there is the light-dark boundary line, then according to the position of the light-dark boundary line in the front image, determining a brightness boundary line of the imaging area of the head-up display (HUD), and the brightness boundary line divides the imaging area of the HUD into an upper imaging area and a lower imaging area; Adjusting the brightness of the upper imaging area according to the target image that is above in the positional relationship between the bright image and the dark image.

2. The method according to claim 1, characterized in that, The adjusting the brightness of the upper imaging area according to the target image that is above in the positional relationship between the bright image and the dark image includes: Performing grayscale processing on the target image to obtain grayscale data; Determining an average grayscale value according to the grayscale data; Determining the target brightness of the upper imaging area according to the average grayscale value and a preset mapping relationship between grayscale and brightness, and in the mapping relationship, the grayscale and the brightness are negatively correlated; Adjusting the brightness of the upper imaging area from the current brightness to the target brightness.

3. The method according to claim 1, characterized in that, The obtaining a front image of the vehicle includes: Obtaining image data of the front of the vehicle captured by a camera; Cropping out the front image that overlaps with the imaging area of the HUD from the image data from the perspective of the user.

4. The method according to any one of claims 1-3, characterized in that, The determining whether there is a light-dark boundary line according to the front image includes: Converting the front image into a grayscale image; Determining the gradient magnitude of each pixel point according to the grayscale image; Among the gradient magnitudes of all pixel points, taking the pixel points greater than a preset magnitude threshold as edge points to obtain an edge point set; Determining whether there is the light-dark boundary line according to the edge point set.

5. The method according to claim 4, wherein The determining whether there is the light-dark boundary line according to the edge point set includes: Determining an average ordinate according to the ordinates of each edge point in the edge point set, and the ordinate is the coordinate in the pixel coordinate system of the grayscale image; In the edge point set, deleting the edge points that exceed the preset error range of the average ordinate to obtain an optimized edge point set; Determining whether there is the light-dark boundary line according to the optimized edge point set.

6. The method according to claim 5, wherein The determining whether there is the light-dark boundary line according to the optimized edge point set includes: Connecting adjacent edge points within a preset distance in the optimized edge point set to obtain a candidate light-dark boundary line; Determining the number of the candidate light-dark boundary lines; If the number is 1, determining that there is the light-dark boundary line and taking the candidate light-dark boundary line as the light-dark boundary line; If the number is greater than 1, determining that there is no light-dark boundary line.

7. The method according to any one of claims 1 to 3, characterized in that, The determining a brightness boundary line of the imaging area of the head-up display (HUD) according to the position of the light-dark boundary line in the front image includes: Divide the distance between the light-dark boundary line and the top line of the front image by the distance between the top line and the bottom line of the front image to obtain a position ratio; Within the imaging area of the HUD, determine the brightness boundary line according to the position ratio.

8. A control device for a head-up display, characterized in that The device includes: An acquisition module for acquiring a front image of the vehicle; A first determination module for determining, according to the front image, whether there is a light-dark boundary line that divides the front image into a bright image and a dark image, and the difference between the average brightness of the bright image and the average brightness of the dark image is greater than a preset brightness difference; A second determination module for, if there is the light-dark boundary line, determining a brightness boundary line of the imaging area of the head-up display (HUD) according to the position of the light-dark boundary line in the front image, and the brightness boundary line divides the imaging area of the HUD into an upper imaging area and a lower imaging area; A brightness adjustment module for adjusting the brightness of the imaging area above the brightness boundary line according to the target image that is above in the positional relationship between the bright image and the dark image.

9. An electronic device, characterized in that, Comprising: A memory and a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1-7.

10. A vehicle, characterized in that, The vehicle includes a vehicle body, a display, and a controller, and the controller is used to implement the method according to any one of claims 1-7.

11. A computer-readable storage medium, characterized in that, Computer execution instructions are stored in the computer-readable storage medium, and when the computer execution instructions are executed by the processor, they are used to implement the method according to any one of claims 1-7.

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