Method and system for offline detection and calibration of head-up display system

Abstract

The invention discloses a head-up display system offline detection calibration method and system, and relates to the technical field of automotive electronics and optical detection.The method comprises the steps that a detection camera is controlled to enter a vehicle, and the view center of the detection camera is accurately positioned to a theoretical center eye box position; the vehicle-mounted head-up display system is controlled to project a positioning cursor, a bar array diagram, a lattice diagram and other test images on the windshield glass in sequence, and the detection camera is controlled to shoot; lower visual angle deviation data, ghosting distance data of a virtual image and a reflection image, distortion rate data of an actual imaging point and inclination data of a feature point arrangement straight line are calculated through comparative analysis by an image recognition algorithm; finally, adjusting parameters including geometric correction and rotation compensation are generated according to the data and written into the vehicle-mounted head-up display system, closed-loop detection and automatic correction of the optical performance of the vehicle-mounted head-up display system in a mass production environment can be achieved, and the detection efficiency and the imaging quality are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics and optical testing technology, and in particular to a method and system for the offline testing and calibration of a head-up display system. Background Technology

[0002] Currently, head-up display (HUD) systems have evolved from an emerging "attractive" feature to a key configuration in smart cockpits. Driven by the deep integration of ADAS (Advanced Driver Assistance Systems) and AR (Augmented Reality) technologies, HUD technology continues to innovate. With its practicality, technological sophistication, and unique immersive experience, HUD systems have become a core focus for major automakers in developing intelligent driving experiences. However, in the pursuit of higher product appeal, a series of technical challenges remain. The ability to systematically overcome these challenges directly determines whether product refinement and user experience can achieve a qualitative leap. For example, when the HUD image is projected onto the windshield, due to the large off-axis magnification system used in the HUD's optical system and the limited aberration correction capability of the optical system itself, the image itself will exhibit some distortion. The windshield glass is part of the imaging optical path; the control and inspection of glass precision cannot be perfectly matched using optically required methods. Therefore, there is a certain error between the actual glass and the glass used in the design, which will affect the final image. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for offline testing and calibration of a head-up display system, which at least solves a technical problem in the prior art where the optical system itself has limited aberration correction capabilities, and the windshield glass, as part of the optical path, cannot be manufactured with precision that fully matches the optical requirements, resulting in unavoidable distortion and errors in the final image.

[0004] This invention provides the following solution:

[0005] According to one aspect of the present invention, a method for offline detection and calibration of a head-up display system is provided, comprising the following steps:

[0006] S1, Equipment positioning, control the detection camera to enter the vehicle and position its field of view center to the theoretical center eye box position of the vehicle head-up display system;

[0007] S2, Image Acquisition: Control the vehicle head-up display system to project preset test images sequentially onto the windshield, and control the detection camera to capture the projected images;

[0008] S3, Parameter Calculation: Based on the comparison between the captured image and the standard image, the following calculation steps are performed: Based on the test image with the positioning cursor, calculate the angular deviation between the image center point and the theoretical center point to obtain the downward viewing angle deviation data; Based on the strip array test image, analyze the distance between the virtual image and the reflected image to obtain the ghosting distance data; Based on the dot array test image, calculate the offset of the actual imaging point relative to the theoretical point to obtain the distortion rate data; Based on the dot array test image, fit the angle between the straight line of the feature points and the coordinate axis to obtain the tilt data.

[0009] S4, Calibration Writing: Generate corresponding calibration parameters based on the lower viewing angle deviation data, ghost distance data, distortion rate data, and tilt data, and write the calibration parameters into the vehicle head-up display system.

[0010] Furthermore, the device positioning specifically includes the following steps:

[0011] Perform position calibration on the vehicle to be inspected;

[0012] Obtain the vehicle's body height reference data;

[0013] Based on the position calibration and vehicle height reference data, the detection camera is moved into the vehicle so that the center of the detection camera's field of view is aligned with the center eye box of the vehicle.

[0014] Furthermore, the image acquisition specifically includes the following steps:

[0015] Send commands to control the vehicle head-up display system to display an image with a positioning cursor, and control the detection camera to capture the image;

[0016] Send commands to control the vehicle-mounted head-up display system to display a horizontal and vertical bar array, and control the detection camera to capture the image;

[0017] Send commands to control the vehicle-mounted head-up display system to display a dot matrix image and control the detection camera to capture the image.

[0018] Furthermore, the step of calculating the angular deviation between the image center point and the theoretical center point based on the test image with the positioning cursor, and obtaining the lower viewpoint deviation data, specifically includes the following steps:

[0019] Identify the captured test image with the positioning cursor and read the vertical angle value of the positioning cursor;

[0020] The difference between the vertical angle value and the preset reference angle value is calculated and used as the viewing angle data under the center eyebox;

[0021] Calculate the angular deviation between the center point of the positioning cursor displayed in the test image and the preset theoretical center point.

[0022] Furthermore, the step of calculating the offset of the actual imaging point relative to the theoretical point based on the dot matrix test image to obtain the distortion rate data specifically includes the following steps:

[0023] Identify the center point in the captured dot matrix image as the theoretical center point, and calculate the average row spacing and average column spacing within a preset area based on the theoretical center point.

[0024] Using the theoretical center point as a reference, the positions of other theoretical points in the dot matrix are expanded and located using the average row spacing and average column spacing to determine the coordinates of all theoretical points.

[0025] Calculate the distance from each actual imaging point in the captured image to the theoretical center point, and the distance from the corresponding theoretical point to the theoretical center point. Determine the distortion rate of the corresponding position based on the ratio of the two, and obtain the coordinate offset of the actual imaging point relative to the theoretical point.

[0026] Specifically, for feature points located within a preset range at the center of the image, their distortion rate values ​​are attenuated.

[0027] Furthermore, the step of analyzing the distance between the virtual image and the reflected image based on the strip array test image to obtain the ghost distance data specifically includes the following steps:

[0028] Multiple measurement locations were selected in the horizontal and vertical directions of the test image;

[0029] For each measurement location, identify the main lines of the virtual image and the ghost lines in the image;

[0030] If the main lines of the virtual image do not overlap with the doubled lines, then the distance between the center of the main lines of the virtual image and the center of the doubled lines is taken as the doubled distance data at that position.

[0031] If the main line of the virtual image overlaps with the ghost line, measure the total width of the overlapping line at that position, select the point where the gray value of the overlapping line is located as the center point of the main line of the virtual image, determine the width of the main line of the virtual image based on the gray distribution characteristics, calculate the center position of the ghost line, and obtain the ghost distance data at that position.

[0032] The maximum distance value calculated from all measurement positions in the horizontal and vertical directions is selected as the final ghost distance data.

[0033] Furthermore, the step of fitting the angle between the straight line arranging the feature points and the coordinate axis based on the dot matrix test image to obtain the tilt data specifically includes the following steps:

[0034] Extract feature points from multiple rows in the captured dot matrix test image;

[0035] For each row of feature points, perform line fitting and calculate the angle between each fitted line and the horizontal coordinate axis.

[0036] Calculate the average of the multiple included angles corresponding to the feature points in multiple rows, and use this average as the tilt data of the image.

[0037] Furthermore, the step of generating corresponding calibration parameters based on the lower viewing angle deviation data, ghost distance data, distortion rate data, and tilt data specifically includes the following steps:

[0038] The coordinate offset of each feature point in the distortion rate data is multiplied by a preset multiplier to obtain the value to be written.

[0039] Determine whether the value to be written is within a preset value range;

[0040] If the value to be written is within the range, it is used as the distortion adjustment parameter; if the value to be written exceeds the range, it is truncated to the boundary value of the range and used as the distortion adjustment parameter.

[0041] The distortion calibration parameters are jointly calculated using the tilt data to generate the final calibration parameters.

[0042] Furthermore, writing the calibration parameters into the vehicle head-up display system specifically includes the following steps:

[0043] Establish a communication connection with the vehicle head-up display system and send the calibration parameters to the internal storage unit of the vehicle head-up display system;

[0044] The projected image after the parameters are written is re-inspected. If the re-inspection result exceeds the preset tolerance range, the calibration parameters are recalculated and the image is rewritten.

[0045] According to two aspects of the present invention, a head-up display system offline testing and calibration system is provided, the system comprising an industrial control computer, a robotic arm, a testing camera, a vehicle centering platform, a vehicle height measuring device, and a gantry frame equipped with a screen;

[0046] The industrial control computer is connected to the robotic arm, the detection camera, and the vehicle height measurement equipment, and is configured to connect to the vehicle head-up display system via the vehicle OBD interface.

[0047] The industrial control computer is equipped with program modules that perform the following functions:

[0048] The equipment positioning module is used to combine the data obtained by the vehicle height measuring equipment to control the robotic arm to carry the detection camera into the vehicle and position the center of the detection camera's field of view to the theoretical center eye box position of the vehicle head-up display system.

[0049] The image acquisition module is used to control the vehicle head-up display system to project preset test images onto the windshield sequentially via the vehicle's OBD interface, and to control the detection camera to capture the projected images.

[0050] The parameter calculation module performs the following calculations based on the comparison between the captured image and the standard image: Based on the test image with a positioning cursor, it calculates the angular deviation between the image center point and the theoretical center point to obtain the downward viewing angle deviation data; based on the strip array test image, it analyzes the distance between the virtual image and the reflected image to obtain the ghosting distance data; based on the dot array test image, it calculates the offset of the actual imaging point relative to the theoretical point to obtain the distortion rate data; based on the dot array test image, it fits the angle between the straight line arranging the feature points and the coordinate axis to obtain the tilt data.

[0051] The calibration writing module is used to generate corresponding calibration parameters based on the lower viewing angle deviation data, ghost distance data, distortion rate data and tilt data, and write the calibration parameters into the vehicle head-up display system through the OBD interface.

[0052] The above solution achieves the following beneficial technical effects:

[0053] This application achieves fully automated optical inspection with high consistency in mass production by using vehicle centering, laser height measurement, and robotic arm collaborative positioning to automatically compensate for vehicle position and attitude deviations, ensuring that the inspection camera is accurately aligned with the theoretical eye box.

[0054] This application effectively overcomes the difficulty of separating and calculating virtual images and ghost images when they overlap by adopting a grayscale feature analysis algorithm, and realizes high-precision quantitative analysis of the small ghost images and nonlinear distortions introduced by the windshield.

[0055] This application establishes a closed-loop calibration process from acquisition and calculation to automatic OBD writing, and avoids image overcorrection through a central region distortion attenuation strategy, significantly improving the refinement of the final image and the user's viewing comfort. Attached Figure Description

[0056] Figure 1 This is a flowchart of a method for offline detection and calibration of a head-up display system provided by one or more embodiments of the present invention.

[0057] Figure 2 This is a schematic diagram of a standard crosshair cursor with a border provided in a specific embodiment of the present invention.

[0058] Figure 3 This is a schematic diagram of a ghosting detection image provided in a specific embodiment of the present invention.

[0059] Figure 4 This is a bordered black and white horizontal and vertical bar array provided in a specific embodiment of the present invention.

[0060] Figure 5 This is a frameless distortion measurement dot matrix provided in a specific embodiment of the present invention.

[0061] Figure 6 This is a schematic diagram of distortion measurement provided in a specific embodiment of the present invention.

[0062] Figure 7 This is a schematic diagram of tilt measurement provided in a specific embodiment of the present invention. Detailed Implementation

[0063] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Figure 1 This is a flowchart of a method for offline detection and calibration of a head-up display system provided by one or more embodiments of the present invention.

[0065] like Figure 1 The method for offline detection and calibration of a head-up display system, as shown, includes:

[0066] S1, Equipment positioning, control the detection camera to enter the vehicle and position its field of view center to the theoretical center eye box position of the vehicle head-up display system;

[0067] Furthermore, the equipment positioning specifically includes the following steps:

[0068] Perform position calibration on the vehicle to be inspected;

[0069] Obtain the vehicle's body height reference data;

[0070] By combining position calibration and vehicle height reference data, the detection camera is moved into the vehicle so that the center of the detection camera's field of view is aligned with the center eye box of the vehicle.

[0071] Specifically, in the initial stage of the calibration process, the primary task is to perform the equipment positioning step, which involves using an industrial control computer to control a robotic arm to carry a detection camera into the vehicle and precisely position the center of the detection camera's field of view to the theoretical center eye box position of the vehicle's head-up display system, in order to simulate the driver's actual observation perspective.

[0072] Specifically, the vehicle to be tested is first driven into and parked on the centering platform. The centering platform uses mechanical limiters or push rods to physically calibrate the vehicle's tires, thereby constraining the vehicle in the preset standard test position in the X and Y axes on the horizontal plane. This eliminates the left and right deviations and angle skews when the vehicle is parked, ensuring that the longitudinal center plane of the vehicle is parallel or coincident with the reference plane of the robotic arm coordinate system.

[0073] After completing the horizontal position calibration, considering that the suspension status, tire pressure, and load conditions of different batches of vehicles may cause slight fluctuations in vehicle height, these fluctuations will directly cause vertical displacement of the head-up display eye box area located inside the vehicle relative to the external robotic arm reference coordinate system. Therefore, the system activates the laser wheel arch height measuring device installed on the gantry or a specific bracket. This device emits a laser beam towards the wheel arch position of the vehicle and uses the laser triangulation or time-of-flight principle to obtain the reference data of the current vehicle height in real time. After receiving the height data, the industrial control computer compares and calculates it with the preset standard vehicle height data.

[0074] The industrial computer runs a coordinate transformation algorithm to calculate the final target coordinates of the robotic arm's end effector. Let the theoretical center eyebox coordinates in the robotic arm coordinate system be... Its expression is ,in , , These represent the x, y, and vertices of the center of the eye box in a Cartesian coordinate system under standard conditions. Let the preset standard wheel arch height be... The laser measurement equipment collects the current wheel arch height of the vehicle in real time. At this point, the industrial control computer calculates the vertical compensation amount caused by the change in vehicle height. The calculation formula is: Based on this, the industrial control computer generates the actual target coordinates that the robotic arm carrying the detection camera needs to reach. The calculation formula is as follows:

[0075] ;

[0076] In this formula, and Maintaining the position is based on the premise that the middle platform has completed horizontal calibration. If offset feedback data is available for the middle platform, then... and A corresponding level compensation amount also needs to be added.

[0077] The industrial control computer calculates the actual target coordinates. Motion control commands are generated and sent to the robotic arm controller. The robotic arm responds to the commands and drives the movement of each joint to smoothly deliver the detection camera installed at its end into the vehicle through the side window. During the movement, the robotic arm adjusts its posture through inverse kinematics calculations to ensure that the optical axis center of the detection camera is precisely aligned with the theoretical center eye box position of the vehicle's head-up display system in space, and that the camera's attitude angle is consistent with the driver's line of sight.

[0078] By combining physical alignment with laser height compensation, the problem of inaccurate eyepoint positioning caused by vehicle parking errors and suspension height tolerances in mass production environments is effectively solved. This ensures that the inspection camera can completely simulate the driver's perspective under design conditions, providing a highly consistent optical benchmark for subsequent detection of downward angle, ghosting, distortion, and tilt, and improving the accuracy and calibration quality of head-up display system off-line inspection.

[0079] S2, Image Acquisition: Control the vehicle head-up display system to project preset test images sequentially onto the windshield, and control the detection camera to capture the projected images;

[0080] Furthermore, image acquisition specifically includes the following steps:

[0081] Send commands to control the vehicle head-up display system to display an image with a positioning cursor, and control the detection camera to capture the image;

[0082] Send commands to control the vehicle-mounted head-up display system to display a horizontal and vertical bar array, and control the detection camera to capture the image;

[0083] Send commands to control the vehicle-mounted head-up display system to display a dot matrix image and control the detection camera to capture the image.

[0084] Specifically, after the equipment positioning is completed so that the center of the test camera's field of view is precisely aligned with the theoretical center of the vehicle's eye box, the system enters the image acquisition stage. The industrial control computer, as the core control unit of the entire calibration system, establishes a connection with the vehicle's OBD diagnostic interface through a communication cable. It uses the vehicle's Ethernet or CAN bus protocol to send UDS diagnostic commands to the head-up display controller inside the vehicle to ensure the synchronization of the generated projected image and the image acquisition. The industrial control computer strictly controls the head-up display to project different types of test images in sequence according to the preset timing logic, and triggers the test camera to take high-resolution pictures after each frame of image is stably displayed.

[0085] First, the downward viewing angle and basic optical parameters are acquired. The industrial control computer sends the first set of display commands through the OBD interface, controlling the head-up display system to project a test image with a positioning cursor onto the windshield, such as... Figure 4 As shown, the test image consists of a centered crosshair cursor and a white border, with the crosshair cursor's line width designed to be... The line length is The white border line width is also [missing information]. The detection camera adjusts its exposure parameters under the trigger of the industrial control computer, captures a projected image containing the crosshair cursor, and transmits it back to the industrial control computer's memory. The image acquired in this step contains key information such as the field of view, imaging distance, and image center position, which is crucial for subsequent calculations of the viewing angle deviation. And the basic data source for determining whether the center points of the coordinate system coincide.

[0086] Subsequently, image acquisition for ghost detection is performed. The industrial control computer sends a second set of display commands to switch the projection content of the head-up display to a black and white horizontal and vertical bar array for ghost analysis, such as... Figure 3 and Figure 4 As shown, the image consists of a series of horizontal and vertical lines, arranged in a tic-tac-toe or grid pattern to evenly divide the display area. The line width of all lines is set to [value missing]. The image is surrounded by lines with a width of [missing information]. The white border indicates that the camera is re-capturing the image, acquiring a high-contrast striped image. This image, utilizing high-frequency line features, can significantly expose the optical ghosting caused by the curved reflection of the windshield, providing a basis for subsequent algorithms in the horizontal direction. Position and vertical direction The distance between the virtual image main line and the ghost line is calculated at each position to provide a high signal-to-noise ratio input image.

[0087] Finally, distortion and tilt detection images are acquired. The industrial control computer sends the third set of display commands to control the head-up display system to display a high-density dot matrix image, such as... Figure 5 As shown, the dot matrix consists of circles arranged in a rectangular array, with an array size of [missing information]. Multiply Columns, total With a set of feature points, and the image designed without borders, after the detection camera captures the dot matrix image, the industrial control computer can obtain geometric feature information covering the entire field of view. These discrete dot matrix data are the core of the subsequent construction of coordinate mapping relationship. By calculating the offset of the actual imaging point relative to the theoretical design point, the system can accurately quantify the distortion rate distribution of the entire field of view and the degree of rotation and tilt of the image relative to the horizontal axis.

[0088] The OBD interface enables fully automated image switching and synchronous acquisition, avoiding fluctuations in the testing process introduced by manual operation. This ensures that the digital source of the test images is completely consistent with the design standards. At the same time, special test patterns are designed for different optical defect characteristics, such as using crosshairs to determine the center, using fine lines to measure ghosting, and using high-density dot matrix to measure geometric distortion. This decouples the optical inspection tasks. The step-by-step acquisition strategy allows the calculation of each type of optical parameter to be based on the image most suitable for its characteristics. As a result, the robustness of the head-up display system's offline inspection and the accuracy of calibration parameter calculation are significantly improved in the complex tolerance environment of mass-produced windshields.

[0089] S3, Parameter Calculation: Based on the comparison between the captured image and the standard image, the following calculation steps are performed: Based on the test image with the positioning cursor, calculate the angular deviation between the image center point and the theoretical center point to obtain the downward viewing angle deviation data; Based on the strip array test image, analyze the distance between the virtual image and the reflected image to obtain the ghosting distance data; Based on the dot array test image, calculate the offset of the actual imaging point relative to the theoretical point to obtain the distortion rate data; Based on the dot array test image, fit the angle between the straight line of the feature points and the coordinate axis to obtain the tilt data.

[0090] Furthermore, based on the test image with the positioning cursor, the angular deviation between the image center point and the theoretical center point is calculated to obtain the lower viewpoint deviation data. This specifically includes the following steps:

[0091] Identify the captured test image with the positioning cursor and read the vertical angle value of the positioning cursor;

[0092] Calculate the difference between the vertical angle value and the preset reference angle value, and use it as the viewing angle data under the center eyebox;

[0093] Calculate the angular deviation between the center point of the positioning cursor displayed in the test image and the preset theoretical center point.

[0094] Furthermore, based on the dot matrix test image, the offset of the actual imaging point relative to the theoretical point is calculated to obtain the distortion rate data. This specifically includes the following steps:

[0095] Identify the center point in the captured dot matrix image as the theoretical center point, and calculate the average row spacing and average column spacing within a preset area based on the theoretical center point.

[0096] Using the theoretical center point as a reference, the positions of other theoretical points in the dot matrix are expanded and located using the average row spacing and average column spacing to determine the coordinates of all theoretical points.

[0097] Calculate the distance from each actual imaging point to the theoretical center point in the captured image, and the distance from the corresponding theoretical point to the theoretical center point. Determine the distortion rate at the corresponding position based on the ratio of the two, and obtain the coordinate offset of the actual imaging point relative to the theoretical point.

[0098] Specifically, for feature points located within a preset range at the center of the image, their distortion rate values ​​are attenuated.

[0099] Furthermore, based on the strip array test image, the distance between the virtual image and the reflected image is analyzed to obtain the ghosting distance data, specifically including the following steps:

[0100] Multiple measurement locations were selected in the horizontal and vertical directions of the test image;

[0101] For each measurement location, identify the main lines of the virtual image and the ghost lines in the image;

[0102] If the main lines of the virtual image do not overlap with the doubled lines, then the distance between the center of the main lines of the virtual image and the center of the doubled lines is taken as the doubled distance data at that position.

[0103] If the main line of the virtual image overlaps with the ghost line, measure the total width of the overlapping line at that position, select the point where the gray value of the overlapping line is located as the center point of the main line of the virtual image, determine the width of the main line of the virtual image based on the gray distribution characteristics, calculate the center position of the ghost line, and obtain the ghost distance data at that position.

[0104] The maximum distance value calculated from all measurement positions in the horizontal and vertical directions is selected as the final ghost distance data.

[0105] Furthermore, based on the dot matrix test image, the angle between the straight line arranging the feature points and the coordinate axis is fitted to obtain the tilt data, specifically including the following steps:

[0106] Extract feature points from multiple rows in the captured dot matrix test image;

[0107] For each row of feature points, perform line fitting and calculate the angle between each fitted line and the horizontal coordinate axis.

[0108] Calculate the average of the multiple included angles corresponding to the feature points in multiple rows, and use this average as the tilt data of the image.

[0109] Specifically, after the industrial control computer completes image acquisition and obtains multiple sets of standard test image data, including crosshair patterns, horizontal and vertical bar patterns, and dot patterns, the system enters the parameter calculation stage. The industrial control computer retrieves the pre-stored standard image data and compares and analyzes it with the actual image data acquired by the camera.

[0110] To calculate the downward viewing angle deviation data, the system first performs image recognition processing on the captured test image with the positioning cursor, such as... Figure 2 As shown, the system locks the geometric center of the crosshair cursor in the image and reads the vertical angle value of the cursor in the projected coordinate system. The industrial computer stores preset reference angle values, which are usually set to... The system calculates the difference between the vertical angle value and the reference angle value, that is:

[0111] ;

[0112] This difference directly represents the downward viewing angle data of the central eyepiece. Simultaneously, the system calculates the angular deviation between the actual coordinates of the positioning cursor's center point displayed in the test image and the preset theoretical center point coordinates. If this deviation exceeds the preset tolerance range, such as... If the value is 0, it is recorded as the downward angle deviation that needs to be corrected.

[0113] The ghosting distance data is then calculated, a step primarily based on the horizontal and vertical stripe test images, such as... Figure 3 , Figure 4 As shown, the industrial control computer divides the horizontal direction into 42 line segments, and selects 3 positions at equal intervals for each line segment, for a total of 126 horizontal measurement points. It also divides the vertical direction into 42 line segments, and selects 1 position for each line segment, for a total of 42 vertical measurement points. For each measurement position, the system first uses a grayscale threshold, such as 40, to segment the image to identify the main lines of the virtual image and the ghost lines produced by the reflection of the windshield glass.

[0114] Non-overlapping case: Directly calculate the Euclidean distance between the center of the main line and the center of the ghost line, and convert the pixel distance to angular minutes, denoted as . .

[0115] Overlap condition: The system measures the total width of the overlapping lines. Search for the maximum gray value in the overlapping region. Its coordinates are the center of the main lines. Take grayscale values ​​in The effective width is calculated from the pixels in the interval. And calculate the center of the ghosting:

[0116] ;

[0117] Ghost value The maximum horizontal ghosting value is obtained by traversing all horizontal measurement points:

[0118] ;

[0119] The maximum vertical ghost value is obtained by traversing all vertical measurement points.

[0120] ;

[0121] Take the larger of the two values ​​or use them separately as the final ghost distance data.

[0122] Next, the distortion rate data is calculated based on... Figure 5 and Figure 6 The industrial control computer identifies the point in the 7th row and 20th column as the actual center point, which is tentatively designated as the theoretical center reference. The center is then determined by this point. Calculate the average row and column spacing within the dot matrix area, and then apply it to the entire image. Generate ideal, distortion-free theoretical coordinates at the point Calculate the actual distance from each actual point to the center. Distance from theory to center The distortion rate is:

[0123] ;

[0124] Coordinate offset:

[0125] ;

[0126] For those located in the center The distortion rate or offset of the feature points in the region is then divided by 2 for attenuation, in order to reduce the correction sensitivity of the central region.

[0127] Finally, the tilt data is calculated, also based on... Figure 7 The system extracts feature points from the upper, middle, and lower rows, and fits straight lines using the least squares method to obtain the angles with the horizontal axis. The inclination is taken as the arithmetic mean of the three angles:

[0128] ;

[0129] This average value represents the tilt data of the projected image.

[0130] The technical advantage of the above-described implementation method lies in its ability to accurately quantify various optical indicators of the head-up display system through step-by-step and targeted algorithm design. Especially in the case of overlapping lines during ghost detection, the center estimation algorithm based on grayscale width overcomes the blind spots of traditional methods. The central region attenuation mechanism introduced in distortion calculation balances the contradiction between small central aberrations and the high sensitivity of the human eye's central fixation area, avoiding over-correction and making the final calibration parameters more in line with the actual needs of human visual comfort.

[0131] S4, Calibration Writing: Generate corresponding calibration parameters based on the downward viewing angle deviation data, ghost distance data, distortion rate data, and tilt data, and write the calibration parameters into the vehicle head-up display system.

[0132] Furthermore, generating corresponding calibration parameters based on downward angle deviation data, ghost distance data, distortion rate data, and tilt data specifically includes the following steps:

[0133] The coordinate offset of each feature point in the distortion rate data is multiplied by a preset multiplier to obtain the value to be written.

[0134] Determine whether the value to be written is within the preset value range;

[0135] If the value to be written is within the range, it is used as the distortion adjustment parameter; if the value to be written is outside the range, it is truncated to the boundary value of the range and used as the distortion adjustment parameter.

[0136] The distortion calibration parameters are jointly calculated using tilt data to generate the final calibration parameters.

[0137] Furthermore, writing the calibration parameters into the vehicle head-up display system specifically includes the following steps:

[0138] Establish a communication connection with the vehicle head-up display system and send the calibration parameters to the internal storage unit of the vehicle head-up display system;

[0139] The projected image after the parameters are written is re-inspected. If the re-inspection result exceeds the preset tolerance range, the calibration parameters are recalculated and the image is rewritten.

[0140] Specifically, after the industrial control computer completes the precise calculation of key optical data such as downward viewing angle deviation, ghost distance, distortion rate and tilt, the system enters the calibration writing stage. This stage aims to convert the calculated physical quantities into electronic calibration parameters that the head-up display controller can recognize and execute, and write them into the device through the vehicle network.

[0141] The industrial control computer first constructs a coordinate offset matrix for HUD correction based on the coordinate offsets of each feature point relative to its theoretical position, calculated from the distortion rate data. Let the test image have a total of... OK The industrial computer generates a horizontal offset matrix. offset matrix in the vertical direction ,in and Represented by pixel values, it represents the offset relative to the original image coordinates. The specific matrix expression is as follows:

[0142] ;

[0143] The system sets a preset multiplier coefficient, which is set to 4 in this embodiment. The industrial control computer multiplies each element in the matrix by this multiplier coefficient to obtain the actual numerical matrix to be written. Subsequently, the system performs a validity range check on the values ​​in the matrix, with the preset numerical range being [missing value]. If the calculated value is within the closed interval, it is written directly as the actual value; if the value exceeds the interval, for example, if an element value is greater than 127, it is forcibly truncated to the boundary value of 127 and written; if it is less than -127, it is truncated to -127 and written, in order to prevent logical errors in the display controller caused by parameter overflow.

[0144] After generating the basic distortion parameters, the industrial control computer (ICC) uses the previously calculated tilt data to perform joint calculations and optimizations on the parameters. The system uses an image rotation matrix algorithm to superimpose the rotation components required for tilt correction onto the aforementioned distortion calibration parameter matrix, thus forming a final calibration parameter set that includes both geometric distortion correction and overall rotation correction functions. Subsequently, the ICC establishes a communication link with the vehicle's OBD diagnostic interface via a physical connection cable and initializes a diagnostic session with the head-up display system control unit. The ICC packages the generated final calibration parameter set into a specific format of instructions and sends it to the head-up display's internal storage unit.

[0145] After the parameters are written, the system immediately starts the re-inspection process. The industrial control computer controls the head-up display system to project the distortion dot matrix image or comprehensive test image again, and controls the inspection camera to take pictures and analyze them. The system compares the various optical indicators of the re-inspection image with the preset tolerance range, such as whether the distortion rate is less than 5% and the tilt angle is less than 0.5°. If the re-inspection result meets all tolerance requirements, the calibration is considered successful, and the system prompts for release. If the re-inspection result still exceeds the tolerance range, the industrial control computer will recalculate a new compensation parameter matrix based on the residual error of this re-inspection using an iterative algorithm, and then rewrite it through the OBD interface.

[0146] By constructing a matrix-based parameter model, independent pixel-level control can be performed on each local area of ​​the head-up display, achieving high-degree-of-freedom nonlinear distortion correction. At the same time, the introduction of a gain multiplier and numerical truncation protection mechanism ensures both the accuracy and sensitivity of the correction, and effectively prevents abnormal data from impacting the hardware system, thus ensuring the calibration safety and product consistency of the vehicle off-line inspection.

[0147] This application also provides a head-up display system offline testing and calibration system, the system including an industrial control computer, a robotic arm, a testing camera, a vehicle centering platform, a vehicle height measuring device, and a gantry equipped with a screen;

[0148] The industrial control computer communicates with the robotic arm, the detection camera, and the vehicle height measurement equipment, and is configured to connect to the vehicle head-up display system via the vehicle's OBD interface;

[0149] The industrial computer is equipped with program modules that perform the following functions:

[0150] The equipment positioning module is used to combine the data obtained by the vehicle height measuring equipment to control the robotic arm to carry the detection camera into the vehicle and position the center of the detection camera's field of view to the theoretical center eye box position of the vehicle head-up display system.

[0151] The image acquisition module is used to control the vehicle head-up display system to project preset test images onto the windshield sequentially via the vehicle's OBD interface, and to control the detection camera to capture the projected images.

[0152] The parameter calculation module performs the following calculations based on the comparison between the captured image and the standard image: Based on the test image with a positioning cursor, it calculates the angular deviation between the image center point and the theoretical center point to obtain the downward viewing angle deviation data; based on the strip array test image, it analyzes the distance between the virtual image and the reflected image to obtain the ghosting distance data; based on the dot array test image, it calculates the offset of the actual imaging point relative to the theoretical point to obtain the distortion rate data; based on the dot array test image, it fits the angle between the straight line arranging the feature points and the coordinate axis to obtain the tilt data.

[0153] The calibration writing module is used to generate corresponding calibration parameters based on the downward viewing angle deviation data, ghost distance data, distortion rate data, and tilt data, and write the calibration parameters into the vehicle head-up display system through the OBD interface.

[0154] Specifically, the system mainly consists of an industrial control computer, a robotic arm, a detection camera, a vehicle centering platform, a vehicle height measuring device, and a gantry frame equipped with a black screen. The vehicle centering platform is used to physically limit and calibrate the tires of parked vehicles to eliminate horizontal positioning errors. The vehicle height measuring device uses a laser rangefinder mounted on the gantry frame or a specific bracket to collect real-time data on the wheel arch height to obtain the vehicle's vertical attitude changes. The robotic arm is preferably a six-axis collaborative robot, whose end effector is rigidly connected to the detection camera via a fixing device. It can respond to motion commands from the industrial control computer and, combined with height compensation data, accurately place the detection camera into the vehicle and position it at the theoretical center eyepiece location. The gantry frame is positioned in front of the vehicle's windshield, and the black screen suspended on it serves as the background for projection imaging, absorbing ambient light. The system provides a high-contrast black background to highlight the features of the virtual image lines. The industrial control computer, as the core control unit, is connected to the robotic arm, the inspection camera, and the vehicle height measurement equipment via an industrial communication bus. It also establishes a data interaction channel with the head-up display controller through the vehicle's OBD diagnostic interface. The industrial control computer is equipped with image acquisition and analysis software, which can control the head-up display to project multiple sets of standard test images. Based on the feedback data captured by the inspection camera, it calculates the lower viewing angle deviation, ghost distance, distortion rate, and tilt data. Finally, it generates calibration parameters with multiplier gain and numerical truncation protection, which are written to the head-up display through the OBD interface to complete the closed-loop calibration.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for offline testing and calibration of a head-up display system, characterized in that, Includes the following steps: S1, Equipment positioning, control the detection camera to enter the vehicle and position its field of view center to the theoretical center eye box position of the vehicle head-up display system; S2, Image Acquisition: Control the vehicle head-up display system to project preset test images sequentially onto the windshield, and control the detection camera to capture the projected images; S3, Parameter Calculation: Based on the comparison between the captured image and the standard image, the following calculation steps are performed respectively: Based on the test image with the positioning cursor, calculate the angular deviation between the image center point and the theoretical center point to obtain the lower viewing angle deviation data; Based on the strip array test image, analyze the distance between the virtual image subject and the reflected image to obtain the ghost distance data; Based on the dot array test image, calculate the offset of the actual imaging point relative to the theoretical point to obtain the distortion rate data. Based on the dot matrix test image, the angle between the straight line arranging the feature points and the coordinate axis is fitted to obtain the tilt data; S4, Calibration Writing: Generate corresponding calibration parameters based on the lower viewing angle deviation data, ghost distance data, distortion rate data, and tilt data, and write the calibration parameters into the vehicle head-up display system.

2. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The device positioning specifically includes the following steps: Perform position calibration on the vehicle to be inspected; Obtain the vehicle's body height reference data; Based on the position calibration and vehicle height reference data, the detection camera is moved into the vehicle so that the center of the detection camera's field of view is aligned with the center eye box of the vehicle.

3. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The image acquisition specifically includes the following steps: Send commands to control the vehicle head-up display system to display an image with a positioning cursor, and control the detection camera to capture the image; Send commands to control the vehicle-mounted head-up display system to display a horizontal and vertical bar array, and control the detection camera to capture the image; Send commands to control the vehicle-mounted head-up display system to display a dot matrix image and control the detection camera to capture the image.

4. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The process of calculating the angular deviation between the image center point and the theoretical center point based on the test image with a positioning cursor, and obtaining the lower viewpoint deviation data, specifically includes the following steps: Identify the captured test image with the positioning cursor and read the vertical angle value of the positioning cursor; The difference between the vertical angle value and the preset reference angle value is calculated and used as the viewing angle data under the center eyebox; Calculate the angular deviation between the center point of the positioning cursor displayed in the test image and the preset theoretical center point.

5. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The process of calculating the offset of actual imaging points relative to theoretical points based on the dot matrix test image to obtain distortion rate data specifically includes the following steps: Identify the center point in the captured dot matrix image as the theoretical center point, and calculate the average row spacing and average column spacing within a preset area based on the theoretical center point. Using the theoretical center point as a reference, the positions of other theoretical points in the dot matrix are expanded and located using the average row spacing and average column spacing to determine the coordinates of all theoretical points. Calculate the distance from each actual imaging point in the captured image to the theoretical center point, and the distance from the corresponding theoretical point to the theoretical center point. Determine the distortion rate of the corresponding position based on the ratio of the two, and obtain the coordinate offset of the actual imaging point relative to the theoretical point. Specifically, for feature points located within a preset range at the center of the image, their distortion rate values ​​are attenuated.

6. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The process of analyzing the distance between the virtual image and the reflected image based on the strip array test image to obtain the ghost distance data specifically includes the following steps: Multiple measurement locations were selected in the horizontal and vertical directions of the test image; For each measurement location, identify the main lines of the virtual image and the ghost lines in the image; If the main lines of the virtual image do not overlap with the doubled lines, then the distance between the center of the main lines of the virtual image and the center of the doubled lines is taken as the doubled distance data at that position. If the main line of the virtual image overlaps with the ghost line, measure the total width of the overlapping line at that position, select the point where the gray value of the overlapping line is located as the center point of the main line of the virtual image, determine the width of the main line of the virtual image based on the gray distribution characteristics, calculate the center position of the ghost line, and obtain the ghost distance data at that position. The maximum distance value calculated from all measurement positions in the horizontal and vertical directions is selected as the final ghost distance data.

7. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The process of fitting the angle between the straight line arranging feature points and the coordinate axis based on the dot matrix test image to obtain the tilt data specifically includes the following steps: Extract feature points from multiple rows in the captured dot matrix test image; For each row of feature points, perform line fitting and calculate the angle between each fitted line and the horizontal coordinate axis. Calculate the average of the multiple included angles corresponding to the feature points in multiple rows, and use this average as the tilt data of the image.

8. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The process of generating corresponding calibration parameters based on the lower angle deviation data, ghost distance data, distortion rate data, and tilt data specifically includes the following steps: The coordinate offset of each feature point in the distortion rate data is multiplied by a preset multiplier to obtain the value to be written. Determine whether the value to be written is within a preset value range; If the value to be written is within the range, it is used as the distortion adjustment parameter; if the value to be written exceeds the range, it is truncated to the boundary value of the range and used as the distortion adjustment parameter. The distortion calibration parameters are jointly calculated using the tilt data to generate the final calibration parameters.

9. The method for offline detection and calibration of a head-up display system according to claim 1, characterized in that, The process of writing the calibration parameters into the vehicle head-up display system specifically includes the following steps: Establish a communication connection with the vehicle head-up display system and send the calibration parameters to the internal storage unit of the vehicle head-up display system; The projected image after the parameters are written is re-inspected. If the re-inspection result exceeds the preset tolerance range, the calibration parameters are recalculated and the image is rewritten.

10. A head-up display system offline detection and calibration system, characterized in that, A method for offline testing and calibration of a head-up display system according to any one of claims 1-9, the system comprising an industrial control computer, a robotic arm, a testing camera, a vehicle centering platform, a vehicle height measuring device, and a gantry frame equipped with a screen; The industrial control computer is connected to the robotic arm, the detection camera, and the vehicle height measurement equipment, and is configured to connect to the vehicle head-up display system via the vehicle OBD interface. The industrial control computer is equipped with program modules that perform the following functions: The equipment positioning module is used to combine the data obtained by the vehicle height measuring equipment to control the robotic arm to carry the detection camera into the vehicle and position the center of the detection camera's field of view to the theoretical center eye box position of the vehicle head-up display system. The image acquisition module is used to control the vehicle head-up display system to project preset test images onto the windshield sequentially via the vehicle's OBD interface, and to control the detection camera to capture the projected images. The parameter calculation module is used to perform the following calculations based on the comparison between the captured image and the standard image: based on the test image with the positioning cursor, calculate the angular deviation between the image center point and the theoretical center point to obtain the downward viewing angle deviation data; based on the strip array test image, analyze the distance between the virtual image subject and the reflected image to obtain the ghost distance data; based on the dot array test image, calculate the offset of the actual imaging point relative to the theoretical point to obtain the distortion rate data. Based on the dot matrix test image, the angle between the straight line arranging the feature points and the coordinate axis is fitted to obtain the tilt data; The calibration writing module is used to generate corresponding calibration parameters based on the lower viewing angle deviation data, ghost distance data, distortion rate data and tilt data, and write the calibration parameters into the vehicle head-up display system through the OBD interface.

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