Ar-HUD parameter configuration method and system, and vehicle terminal

Abstract

An AR-HUD parameter configuration method and system, and a vehicle-mounted terminal. The AR-HUD parameter configuration method comprises: acquiring lane line equations and image data within a preset range ahead of a vehicle in a current environment (S100); acquiring rendering coordinates of lane lines on the basis of the image data (S200); acquiring a target rendering parameter on the basis of the lane line equations and the rendering coordinates (S300); and writing the target rendering parameter into a software configuration so as to display a target picture on the basis of the target rendering parameter (S400). By means of parameter configuration of a display picture, the HUD display picture is more closely aligned with a real picture, thereby improving HUD display performance.

Description

An AR-HUD parameter configuration method, system and vehicle terminal Technical Field

[0001] This application belongs to the field of intelligent assisted driving technology, specifically relating to an AR-HUD parameter configuration method, system, and vehicle terminal. Background Technology

[0002] AR-HUD is an innovative solution that combines augmented reality technology with a vehicle's head-up display. By projecting information onto the windshield in the driver's field of vision, AR-HUD provides real-time information and enhances the driving experience. It can display AR content on common targets such as lane lines, road signs, and vehicles ahead, helping drivers maintain focus and reducing distractions when operating the dashboard or checking navigation. AR-HUD can also enhance the driver's perception of road conditions and the surrounding environment through real-time AR visuals, providing early warnings of potential hazards.

[0003] Since AR-HUD itself does not have a camera and cannot directly capture information in front of the vehicle, it can only share data from the ADAS (Advanced Driver Assistance System) forward-facing camera. This means it is impossible to calculate the correspondence between two-dimensional projection space coordinates and three-dimensional space pixel coordinates in the world coordinate system using pixel relationships in different image spaces. Due to differences in vehicle size and the installation location of the ADAS forward-facing camera, there is currently no mature and effective algorithm to calculate the ADAS information and the calibration parameters of the rendering engine (OpenGL) in the AR-HUD system. Summary of the Invention

[0004] To address the aforementioned issues, this technology innovatively proposes an AR-HUD parameter configuration method, system, and vehicle terminal, aiming to provide accurate target rendering parameter configuration for the HUD so that the HUD display can be aligned with the actual display on different rendering platforms.

[0005] Specifically, this application proposes an AR-HUD parameter configuration method, including: acquiring lane line equations and image data within a preset range in front of the vehicle under the current environment; acquiring rendering coordinates of the lane lines based on the image data; acquiring target rendering parameters based on the lane line equations and the rendering coordinates; and writing the target rendering parameters into software configuration to display a target image based on the target rendering parameters.

[0006] In the above technical solution, the calculation of target rendering parameters ensures that the HUD display screen accurately matches the real screen, improving the accuracy of the displayed information. By writing the target rendering parameters into the software configuration, the system can flexibly adjust the display effect, improving the system's adaptability and flexibility. Through precise lane line recognition, real-time rendering coordinate calculation, and flexible software configuration, not only is the display effect and user experience of AR-HUD improved, but driving safety and convenience are also enhanced.

[0007] In one implementation, the image data includes at least first image data and second image data; the acquisition of image data includes: obtaining the positioning endpoints of the lane lines according to the lane line equation, and obtaining the first image data based on the positioning endpoints.

[0008] By acquiring the positioning endpoints of the lane lines, the lane line positions can be accurately identified. The first image data acquired based on the positioning endpoints makes the lane lines displayed on the HUD more accurate, reduces errors, and improves the accuracy of the HUD display effect.

[0009] Furthermore, the acquisition of image data also includes: acquiring second image data based on a preset range of the HUD projection map. Acquiring the second image data within the range of the HUD projection map ensures that the displayed information is within the driver's line of sight, thereby improving the usability and practicality of the HUD's displayed information.

[0010] Furthermore, before obtaining the rendering coordinates of the lane line based on the image data, the method further includes: determining whether there are currently confirmed rendering parameters; if so, writing the confirmed rendering parameters as target rendering parameters into the software configuration; otherwise, obtaining the rendering coordinates of the current lane line based on the image data.

[0011] If confirmed rendering parameters exist, the target rendering parameters can be quickly applied to the software configuration, thereby enabling rapid adjustment and optimization of the rendering effect. This avoids the computational burden of recalculating the target rendering parameters and improves rendering efficiency. In the absence of confirmed rendering parameters, the target rendering parameters are calculated by obtaining the rendering coordinates of the current lane line based on image data. This ensures the practicality and effectiveness of the target rendering parameters, allowing for flexible adjustment under different circumstances and ensuring the accuracy and consistency of the rendering effect.

[0012] Furthermore, obtaining the rendering coordinates of the lane line based on the image data includes: detecting all pixel endpoints of the lane line according to the first image data, and obtaining the pixel coordinates of the lane line according to all the pixel endpoints and the lane line equation.

[0013] Detecting the pixel endpoints of lane lines using the first image data ensures accurate lane line identification, effectively identifying lane lines even in complex situations, and improving the robustness of obtaining the pixel coordinates of lane lines.

[0014] Furthermore, obtaining the rendering coordinates of the lane lines based on the image data further includes: converting the pixel coordinates into the rendering coordinates based on the second image data using a coordinate transformation algorithm.

[0015] The coordinate transformation algorithm can be the TPS algorithm. By converting the pixel coordinates into rendering coordinates through the coordinate transformation algorithm, the data can adapt to different rendering environments, thereby improving the versatility and adaptability of the data.

[0016] Furthermore, before writing the target rendering parameters into the software configuration, the method further includes: detecting 3D virtual objects based on the image data and converting the 3D virtual objects into 2D images.

[0017] By detecting 3D virtual objects and converting them into 2D images, complex scenes are simplified and processed, making rendering more efficient. This ensures that virtual objects are displayed correctly on the screen, avoiding visual distortion.

[0018] Furthermore, before writing the target rendering parameters into the software configuration, the method further includes: verifying whether the target rendering parameters can make the lane lines and the 2D image in the HUD display screen match the real image; if so, the target rendering parameters are written into the software configuration; otherwise, the target rendering parameters are recalculated by re-collecting the lane line equations and image data until the target rendering parameters pass the verification.

[0019] By verifying the target rendering parameters, it is ensured that these parameters can be applied to the HUD, thereby optimizing the rendering process and avoiding display errors or poor effects caused by improper parameter settings. Based on the verified target rendering parameters, seamless integration of lane lines and the 2D image in the HUD with the real-world image is achieved. This enhances the user's perception of the surrounding environment while driving and ensures consistency between the displayed information and the real-world scene. The verified target rendering parameters can be adjusted in real time to adapt to display needs under different lighting and weather conditions, thereby improving the applicability and reliability of the HUD. By verifying the parameters before writing them into the software configuration, the risk of system errors or crashes caused by improper parameters is reduced, enhancing system stability.

[0020] Based on the same inventive concept, this application also proposes an AR-HUD parameter configuration system, which includes at least a domain controller, a sensor device, and a HUD display device; the sensor device is used to acquire lane line equations and image data within a preset range in front of the vehicle; the domain controller is used to acquire target rendering parameters based on the image data and lane line equations and write them into the software configuration; the HUD display device is used to display the target image based on the target rendering parameters.

[0021] Furthermore, the sensor device includes at least a lane line sensing device and an image sensing device; the lane line sensing device is used to acquire the lane line equation within a preset range in front of the vehicle under the current environment; the image sensing device is used to acquire image data in front of the vehicle under the current environment.

[0022] Furthermore, the domain controller includes at least a memory and a processor; the memory is used to store computer instructions for obtaining target rendering parameters based on image data and lane line equations acquired by sensor devices and writing them into multiple functional modules configured in software; the processor communicates with the memory via a bus and is used to execute each computer instruction of the functional modules stored in the memory.

[0023] Furthermore, the functional modules include at least: a data acquisition module, a coordinate detection module, a parameter calculation module, and a parameter configuration module; the data acquisition module includes computer instructions for acquiring lane line equations and image data within a preset range in front of the vehicle under the current environment; the coordinate detection module includes computer instructions for acquiring the rendering coordinates of the lane lines based on the image data; the parameter calculation module includes computer instructions for acquiring target rendering parameters based on the lane line equations and the rendering coordinates; and the parameter configuration module includes computer instructions for writing the target rendering parameters into the software configuration to display the target image based on the target rendering parameters.

[0024] Furthermore, the coordinate detection module is preceded by a parameter judgment module; the parameter judgment module includes a computer instruction for determining whether there are currently confirmed rendering parameters. If so, the confirmed rendering parameters are written into the software configuration as target rendering parameters; otherwise, the computer instruction is used to obtain the rendering coordinates of the current lane line based on the image data.

[0025] Furthermore, before the parameter configuration module, a parameter verification module is also included; the parameter verification module includes a function to verify whether the target rendering parameters can make the lane lines and the 2D image in the HUD display screen match the real image. If so, a verification pass signal is sent to the parameter configuration module; otherwise, the target rendering parameters are recalculated by re-acquiring the lane line equations and image data until the target rendering parameters pass the verification.

[0026] Based on the same inventive concept, this application also proposes an in-vehicle terminal, which is equipped with at least an AR-HUD parameter configuration system. The AR-HUD parameter configuration system uses the AR-HUD parameter configuration method to realize the parameter configuration of the HUD.

[0027] Compared with the prior art, this application has at least the following beneficial effects:

[0028] The AR-HUD parameter configuration method described in this application provides an effective parameter configuration scheme for AR-HUD in practical applications. This application obtains the rendering coordinates of lane lines through real-time acquired image data, ensuring that the rendered lane lines match the lane lines on the actual road, thus improving the accuracy of the rendered lane lines. By writing the target rendering parameters into the software configuration and displaying the target image through these parameters, the flexibility and accuracy of the HUD display are improved. Based on the displayed target image, the driver's understanding and reaction to the environment are enhanced, thereby improving driving safety. Attached Figure Description

[0029] Figure 1 is a flowchart illustrating the AR-HUD parameter configuration method according to an embodiment of this application.

[0030] Figure 2 is a schematic diagram of an AR-HUD parameter configuration system shown in an embodiment of this application.

[0031] Figure 3 is a schematic diagram of the structure of the sensor device shown in an embodiment of this application.

[0032] Figure 4 is a schematic diagram of the structure of a domain controller shown in an embodiment of this application.

[0033] Figure 5 is a schematic diagram of the memory structure shown in an embodiment of this application.

[0034] Figure 6 is a schematic diagram of the structure of the vehicle terminal shown in an embodiment of this application. Detailed Implementation

[0035] 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. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0037] Example 1:

[0038] Please refer to Figure 1. The AR-HUD parameter configuration method can mainly include steps S100 to S400.

[0039] Step S100 includes: acquiring lane line equations and image data within a preset range in front of the vehicle under the current environment. The lane line equations can be acquired via an ADAS forward-facing camera, and these equations can primarily be:

[0040] ,

[0041] in, Let C be the coordinate position of the lane line in the ADAS coordinate system, and C be the lane line equation function. The preset range in front of the vehicle can be 10 meters or 20 meters, but those skilled in the art can adjust the preset range according to actual conditions, and are not limited to this. The image data can be captured by an eye-position camera to simulate human eye image data in front of the vehicle.

[0042] Step S200 includes: obtaining the rendering coordinates of the lane lines based on the image data. This is primarily achieved using the TPS (Thin Plate Spline) algorithm to convert the pixel coordinates of the lane lines in the image data into rendering coordinates. The TPS algorithm is a commonly used interpolation and deformation method in computer graphics and image processing. The TPS algorithm constructs a set of control points and uses these points to perform smooth interpolation in two-dimensional or three-dimensional space. This algorithm can perform non-linear deformation of shapes, ensuring that the deformed shape remains smooth and continuous. The core of the TPS algorithm lies in its energy minimization principle; by solving for the shape transformation with minimum energy, smooth deformation of the original shape is achieved.

[0043] Step S300 includes: obtaining target rendering parameters based on the lane line equation and the rendering coordinates. The target rendering parameters can be calculated primarily using the PNL (Piecewise Nonlinear) algorithm. The PNL algorithm is mainly a piecewise nonlinear modeling method, primarily used to handle nonlinear relationships in complex datasets. The PNL algorithm can be used to describe and predict lane line changes for better vehicle control and navigation. By analyzing the image data acquired by the camera, lane line features are extracted, and the obtained target rendering parameters can be adapted to different lane line shapes, such as straight lines and curves, to model complex road conditions.

[0044] Furthermore, step S400 includes: writing the target rendering parameters into the software configuration to display the target image based on the target rendering parameters. The target rendering parameters can primarily be the MVP (Model-View-Projection) relationship between the lane equation and the rendering coordinates. The MVP relationship is an important concept in computer graphics used to convert 3D coordinates into 2D screen coordinates. The MVP relationship involves three matrices: the Model Matrix, the View Matrix, and the Projection Matrix.

[0045] The model matrix is ​​primarily used to convert the local coordinates of an object into world coordinates to describe the object's position, rotation, and scaling in 3D space. Through translation, rotation, and scaling operations, it defines the transformation of the object relative to the world coordinate system. The view matrix is ​​primarily used to convert the scene's world coordinates into view coordinates to describe the camera's position and orientation, defining how the scene is viewed from the camera's perspective. The projection matrix is ​​primarily used to convert view coordinates into clipping coordinates to define how the 3D scene is projected onto a 2D plane.

[0046] In some embodiments, the image data includes at least first image data and second image data; acquiring the image data includes: obtaining the positioning endpoints of the lane lines according to the lane line equation, and obtaining the first image data based on the positioning endpoints. The second image data is then acquired based on a preset range of the HUD projection chart.

[0047] The location endpoints are primarily obtained from real-time image data captured by the vehicle's forward-facing camera. Image processing techniques, such as grayscale conversion, edge detection, and morphological transformation, are used to extract lane line features. Based on the processed image data, the lane line equation is applied to the image coordinate system. By considering the camera's viewpoint and camera parameters, the corresponding points of the lane lines in the image are determined. These camera parameters mainly include focal length and distortion parameters. The endpoints of the lane lines within a specific coordinate range are calculated based on the lane line equation. For example, if the lane line is a linear equation, the left and right boundaries of the image can be selected, i.e., x = 0 or x = the image width, to calculate the corresponding y values ​​and obtain the two endpoints of the lane lines.

[0048] Optionally, before obtaining the rendering coordinates of the lane line based on the image data, the method further includes: determining whether there are currently confirmed rendering parameters; if so, writing the confirmed rendering parameters as target rendering parameters into the software configuration; otherwise, obtaining the rendering coordinates of the current lane line based on the image data.

[0049] By determining whether there are already confirmed rendering parameters, the system can ensure a fast response and reduce unnecessary calculations. If confirmed rendering parameters are detected, they are used as the target rendering parameters. These parameters are directly written into the software configuration for use in subsequent rendering processes. This approach not only improves efficiency but also maintains rendering consistency and stability, especially in multi-driving scenarios.

[0050] Optionally, obtaining the rendering coordinates of the lane line based on the image data includes: detecting all pixel endpoints of the lane line according to the first image data, and obtaining the pixel coordinates of the lane line according to all the pixel endpoints and the lane line equation.

[0051] The formula for calculating pixel coordinates can be:

[0052]

[0053] Where G is the pixel coordinate of the lane line, L is the pixel endpoint of the lane line, and C is the lane line equation function.

[0054] Optionally, obtaining the rendering coordinates of the lane lines based on the image data further includes: converting the pixel coordinates into the rendering coordinates based on the second image data using a coordinate transformation algorithm.

[0055] The coordinate transformation algorithm can be primarily the TPS algorithm, and the main calculation formula is as follows:

[0056]

[0057] Where etc represents the extended vector of TPS, Q represents the rendering coordinates, and G represents the pixel coordinates of the lane lines.

[0058] After obtaining the rendering coordinates of the lane lines, the target rendering parameters can be calculated using the PNL algorithm based on the lane line equation and the rendering coordinates. The main calculation formula is as follows: Calculate the MVP relationship between the lane line equation and the rendering coordinates using the PNL algorithm:

[0059]

[0060] Where P is the lane line coordinate in the ADAS coordinate system, and Q is the rendered coordinate.

[0061] Optionally, before writing the target rendering parameters into the software configuration, the method further includes: detecting 3D virtual objects based on the image data and converting the 3D virtual objects into 2D images.

[0062] The 3D virtual object can be detected from the image data using computer vision algorithms. For example, a deep learning model or a convolutional neural network can be used to detect the 3D virtual object in the image data. The 3D virtual object can be mapped to a 2D plane through projection transformation.

[0063] Optionally, before writing the target rendering parameters into the software configuration, the method further includes: verifying whether the target rendering parameters can make the lane lines and the 2D image in the HUD display screen match the real image; if so, writing the target rendering parameters into the software configuration; otherwise, re-collecting the lane line equations and image data to calculate the target rendering parameters until the target rendering parameters pass the verification.

[0064] The process involves verifying the target rendering parameters to evaluate the matching degree between the lane lines displayed on the HUD and the actual lane lines. A high-precision positioning system can be used to record the position information of the actual lanes and compare it with the lane lines displayed on the HUD. If the target rendering parameter verification fails, the target rendering parameters are recalculated by re-acquiring lane line equations and image data until the target rendering parameters pass verification before being written into the software configuration.

[0065] The target rendering parameters primarily affect the rendering engine (OpenGL, an open graphics library). Based on these parameters, the rendering engine directly draws the target image on the HUD display, ensuring that the target image closely matches the real-world image. The OpenGL rendering engine is a cross-platform graphics API used for rendering 2D and 3D graphics. It provides a series of functions that allow developers to interact directly with graphics hardware to create high-performance graphics applications such as games, simulations, and scientific visualizations, supporting the rendering of various graphics effects, such as lighting, texture mapping, and shadows.

[0066] Example 2:

[0067] Referring to Figure 2, this application also proposes an AR-HUD parameter configuration system, which includes at least a domain controller 100, a sensor device 200, and a HUD display device 300; the sensor device 200 is used to acquire lane line equations and image data within a preset range in front of the vehicle; the domain controller 100 is used to acquire target rendering parameters based on the image data and lane line equations and write them into the software configuration; the HUD display device 300 is used to display the target image based on the target rendering parameters.

[0068] Furthermore, referring to Figure 3, the sensor device 200 includes at least a lane line sensing device 210 and an image sensing device 220; the lane line sensing device 210 is used to acquire the lane line equation within a preset range in front of the vehicle under the current environment; the image sensing device 220 is used to acquire image data in front of the vehicle under the current environment.

[0069] Further, referring to Figure 4, the domain controller 100 includes at least a memory 110 and a processor 120; the memory 110 is used to store computer instructions for obtaining target rendering parameters based on image data and lane line equations collected by sensor device 200 and writing them into software configuration multiple functional modules; the processor 120 communicates with the memory 110 through bus 130 and is used to execute each computer instruction of the functional modules stored in the memory 110.

[0070] The memory 110 includes, but is not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), and CD-ROM (Compact Disc Read-Only Memory); the processor 120 includes a central processing unit or a device or module with processing capabilities.

[0071] Further, referring to Figure 5, the functional module includes at least: a data acquisition module 111, a coordinate detection module 112, a parameter calculation module 113, and a parameter configuration module 114; the data acquisition module 111 includes computer instructions for acquiring lane line equations and image data within a preset range in front of the vehicle under the current environment; the coordinate detection module 112 includes computer instructions for acquiring rendering coordinates of the lane lines based on the image data; the parameter calculation module 113 includes computer instructions for acquiring target rendering parameters based on the lane line equations and the rendering coordinates; and the parameter configuration module 114 includes computer instructions for writing the target rendering parameters into the software configuration to display the target image based on the target rendering parameters.

[0072] Furthermore, the coordinate detection module 112 is preceded by a parameter judgment module 115; the parameter judgment module 115 includes a computer instruction for determining whether there are currently confirmed rendering parameters. If so, the confirmed rendering parameters are written into the software configuration as target rendering parameters; otherwise, the computer instruction is used to obtain the rendering coordinates of the current lane line based on the image data.

[0073] Furthermore, before the parameter configuration module 114, a parameter verification module 116 is also included; the parameter verification module 116 includes a function to verify whether the target rendering parameters can make the lane lines and the 2D image in the HUD display screen match the real image. If so, a verification pass signal is sent to the parameter configuration module 114; otherwise, the target rendering parameters are recalculated by re-acquiring the lane line equations and image data until the target rendering parameters pass the verification.

[0074] Example 3:

[0075] Referring to Figure 6, this application also proposes an in-vehicle terminal, which is equipped with at least an AR-HUD parameter configuration system. The AR-HUD parameter configuration system adopts the AR-HUD parameter configuration method as described in Embodiment 1 to realize the parameter configuration of the HUD.

[0076] In the vehicle terminal, image data and lane line equations are acquired by sensor devices in the AR-HUD parameter configuration system. Based on the lane line equations and image data, target rendering parameters are acquired by the domain controller and written into the software configuration. The target image is then displayed by the HUD display device after parameter configuration based on the target rendering parameters.

[0077] In summary, this application obtains the rendering coordinates of lane lines through real-time image data acquisition, ensuring that the lane lines displayed on the HUD match the lane lines on the actual road. Calculation of target rendering parameters ensures accurate alignment between the HUD display and the real-world image, improving the accuracy of the displayed information. By incorporating the target rendering parameters into the software configuration, the system can flexibly adjust the display effect, enhancing its adaptability and flexibility. Through precise lane line recognition, real-time rendering coordinate calculation, and flexible software configuration, the display effect of the AR-HUD is improved, enhancing the driver's understanding and reaction to the environment, thus strengthening driving safety and user experience.

[0078] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0079] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a 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 an electronic device 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.

[0080] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for configuring AR-HUD parameters, comprising: Obtain lane line equations and image data within a preset range in front of the vehicle under the current environment (S100). The rendering coordinates of the lane lines are obtained based on the image data (S200). Target rendering parameters are obtained based on the lane line equation and the rendering coordinates (S300). And, the target rendering parameters are written into the software configuration to display the target image based on the target rendering parameters (S400).

2. The AR-HUD parameter configuration method according to claim 1, wherein the image data includes at least first image data and second image data; the acquisition of image data includes: The lane line positioning endpoints are obtained according to the lane line equation, and the first image data is obtained based on the positioning endpoints.

3. The AR-HUD parameter configuration method according to claim 2, wherein acquiring image data further includes: The second image data is acquired based on the preset range of the HUD projection card.

4. The AR-HUD parameter configuration method according to claim 3, before obtaining the rendering coordinates of the lane lines based on the image data, further includes: Determine if there are any confirmed rendering parameters. If so, write the confirmed rendering parameters as target rendering parameters into the software configuration. Otherwise, the rendering coordinates of the current lane line are obtained based on the image data.

5. The AR-HUD parameter configuration method according to claim 4, wherein obtaining the rendering coordinates of the lane lines based on the image data includes: Detect all pixel endpoints of the lane line based on the first image data, and obtain the pixel coordinates of the lane line based on all the pixel endpoints and the lane line equation.

6. The AR-HUD parameter configuration method according to claim 5, wherein obtaining the rendering coordinates of the lane lines based on the image data further includes: Based on the second image data, the pixel coordinates are converted into the rendering coordinates using a coordinate transformation algorithm.

7. The AR-HUD parameter configuration method according to claim 6, further comprising, before writing the target rendering parameters into the software configuration: 3D virtual objects are detected based on the image data, and the 3D virtual objects are converted into 2D images.

8. The AR-HUD parameter configuration method according to claim 7, further comprising, before writing the target rendering parameters into the software configuration: Verify whether the target rendering parameters can make the lane lines and the 2D image in the HUD display match the real image. If so, write the target rendering parameters into the software configuration; otherwise, re-collect the lane line equations and image data to calculate the target rendering parameters until the target rendering parameters pass the verification.

9. An AR-HUD parameter configuration system, comprising at least a domain controller (100), a sensor device (200), and a HUD display device (300); wherein the sensor device (200) is used to acquire lane line equations and image data within a preset range in front of the vehicle; the domain controller (100) is used to acquire target rendering parameters based on the image data and lane line equations and write them into software configuration; and the HUD display device (300) is used to display a target image based on the target rendering parameters.

10. The AR-HUD parameter configuration system according to claim 9, wherein the sensor device (200) includes at least a lane line sensing device (210) and an image sensing device (220). The lane line sensing device (210) is used to obtain the lane line equation within a preset range in front of the vehicle under the current environment; The image sensing device (220) is used to acquire image data of the front of the vehicle in the current environment.

11. According to the AR-HUD parameter configuration system of claim 10, the domain controller (100) includes at least a memory (110) and a processor (120). The memory (110) is used to store computer instructions for obtaining target rendering parameters based on image data and lane line equations collected by sensor devices (200) and writing them into multiple functional modules configured in the software. The processor (120) communicates with the memory (110) via a bus (130) to execute each computer instruction of the functional module stored in the memory (110).

12. The AR-HUD parameter configuration system according to claim 11, wherein the functional module includes at least: The data acquisition module (111), coordinate detection module (112), parameter calculation module (113), and parameter configuration module (114) are included. The data acquisition module (111) includes computer instructions for acquiring lane line equations and image data within a preset range in front of the vehicle under the current environment; The coordinate detection module (112) includes computer instructions for obtaining the rendering coordinates of the lane lines based on the image data; The parameter calculation module (113) includes computer instructions for obtaining target rendering parameters based on the lane line equation and the rendering coordinates; In addition, the parameter configuration module (114) includes computer instructions for writing the target rendering parameters into software configuration to display the target image based on the target rendering parameters.

13. According to the AR-HUD parameter configuration system according to claim 12, a parameter judgment module (115) is further included before the coordinate detection module (112). The parameter judgment module (115) includes a computer instruction for judging whether there are currently confirmed rendering parameters. If so, the confirmed rendering parameters are written into the software configuration as target rendering parameters; otherwise, the computer instruction is used to obtain the rendering coordinates of the current lane line based on the image data.

14. According to the AR-HUD parameter configuration system according to claim 13, a parameter verification module (116) is further included before the parameter configuration module (114). The parameter verification module (116) includes a function to verify whether the target rendering parameters can make the lane lines and the 2D image in the HUD display screen match the real image. If so, a verification pass signal is sent to the parameter configuration module (114); otherwise, the target rendering parameters are recalculated by re-acquiring the lane line equation and image data until the target rendering parameters pass the verification.

15. A vehicle-mounted terminal, the vehicle-mounted terminal being equipped with at least an AR-HUD parameter configuration system, the AR-HUD parameter configuration system employing the AR-HUD parameter configuration method as described in claim 1 to realize HUD parameter configuration.

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