Automatic test system and method for dynamic scene projection quality of intelligent vehicle lamp

Abstract

The invention provides an automatic test system and method for the dynamic scene projection quality of an intelligent vehicle lamp, and relates to the technical field of vehicle lamp projection detection. Comprising a test bench and a control device, and the test bench is provided with an intelligent vehicle lamp module and an image acquisition module and is arranged in a darkroom environment; control software and scene recharging software are integrated in the control device; through scene selection, camera position self-adaption, automatic focusing, projection triggering, sampling, data processing and quality verification, the problems of low precision, poor efficiency and narrow fixed camera application range of a traditional human eye test are solved, the automatic and precise test of the multi-dynamic scene projection quality of the intelligent vehicle lamp is realized, the method can be used for long-term stability test, and the blank in the field is filled.

Description

Technical Field

[0001] This invention relates to the field of vehicle headlight projection testing technology, specifically to an automated testing system and method for the dynamic scene projection quality of intelligent vehicle headlights. Background Technology

[0002] The projection function of the intelligent pixel headlights is a key highlight, providing drivers and other road users with safe supplemental lighting, interactive light signals, and visual effects, thus enhancing safety and interactivity. Testing of dynamic projection scenarios for the intelligent headlights requires triggering dynamic scenes on an indoor test bench to achieve high-precision projection interaction.

[0003] Traditional optical testing can only detect the light distribution performance of luminaires (such as luminous intensity, luminous flux, and spectral characteristics), and cannot detect the image quality of dynamic scene projections from intelligent vehicle lights. Existing testing methods suffer from two main problems: First, relying on human eyes to view the projected image, while highly flexible, is subjective, inefficient, and cannot track for extended periods. Furthermore, because dynamic scenes are video streams, the human eye cannot accurately capture each frame, making it difficult to identify issues such as missing frames and noise interference. It is also limited by visual persistence and motion blur, resulting in inaccurate and unscientific testing. Second, using a fixed camera to capture the display pattern of the matrix LED module requires the tested vehicle light module to be in a fixed position and shape, and manual focusing is necessary. This cannot meet the testing requirements of intelligent vehicle lights projecting different distances and patterns as the perceived scene changes. In conclusion, existing testing technologies for the dynamic scene projection quality of intelligent vehicle lights need further development. Summary of the Invention

[0004] The purpose of this invention is to address the problems of existing technologies that rely solely on human visual recognition, leading to issues such as missed detections due to visual fatigue, low efficiency, and inability to verify real-time performance; and the limitations of technologies that rely on fixed camera monitoring and analysis, which are only applicable to monitoring dynamic light strips or patterns (such as flowing light effects) displayed by matrix LED lighting modules (fixed lighting types). This invention provides an automated testing system and method for the projection quality of dynamic scenes in intelligent vehicle lights.

[0005] On one hand, this invention provides an automated testing system for the dynamic scene projection quality of intelligent vehicle lights. The system includes a test bench and a control device. The test bench is arranged in a darkroom environment, and an intelligent vehicle light module and an image acquisition module are installed on the test bench. The intelligent vehicle light module is used to project a corresponding projection image based on control commands issued by the control device. The image acquisition module is used to move to a corresponding position point according to the control commands issued by the control device and acquire the projection image. The control device integrates control software and scene re-feedback software. The control software is configured to perform the following steps: issuing a corresponding control command to the image acquisition module based on the scene mode selected by the user, causing the image acquisition module to move to the corresponding position point; receiving the image acquisition module... The returned arrival signal, based on the user-selected scene mode, controls the intelligent headlight module to project a standard image to the corresponding location point, and simultaneously drives the image acquisition module to acquire the standard image; receiving the standard image acquired by the image acquisition module, performing multi-region MTF calculation, and automatically adjusting the focus until the MTF value meets the preset MTF threshold to complete automatic focusing; after focusing is completed, controlling the scene backfeed software to backfeed scene data to the intelligent headlight module to trigger the intelligent headlight module to perform dynamic projection; simultaneously driving the image acquisition module to record video of the dynamic projection; processing the video data transmitted by the image acquisition module, extracting frames based on the projection instruction timestamp obtained from the intelligent headlight module, and automatically verifying the real-time performance, integrity, and correctness of the projection, generating a test report.

[0006] Furthermore, the intelligent vehicle lighting module includes a dual-lamp module and a light domain control board. The light domain control board includes a SOC and an MCU. After receiving control commands from the control device, the SOC communicates with the MCU via SPI, and the MCU controls the dual-lamp module to project.

[0007] Furthermore, the image acquisition module includes a high-speed camera, a horizontal slide for driving the high-speed camera to move horizontally, and a servo motor; the servo motor is used to drive the high-speed camera to move on the horizontal slide to a corresponding position point based on the control commands issued by the control software, including: when the user selects the scene mode as a far scene, driving the high-speed camera to move on the horizontal slide to a first position point, and when the user selects the scene mode as a near scene, driving the high-speed camera to move on the horizontal slide to a second position point.

[0008] Furthermore, the control software integrates a PLC controller, which is electrically connected to the servo motor and is used to control the servo motor to drive the high-speed camera to move to a preset first position point or a second position point according to the scene mode.

[0009] Furthermore, the testing system also includes a synchronization trigger module, which is a hardware trigger line connecting the output port of the intelligent vehicle headlight module and the GPIO port of the high-speed camera, used to achieve microsecond-level synchronization between projection commands and image acquisition.

[0010] Furthermore, the testing system also includes a communication module, which includes an Ethernet for connecting the control software and the intelligent vehicle lighting module, and a PCIe data bus for connecting the image acquisition module and the control software.

[0011] Furthermore, the control software is also used to obtain MTF data of N projection blocks from the standard image, and to calculate the comprehensive evaluation MTF value by averaging the MTF data of the N projection blocks, including: determining the edge positions of the N projection blocks respectively, measuring the edge spread function (ESF) at the edges; calculating the line spread function (LSF) by differentiating or fitting the derivative of the edge spread function (ESF); performing a Fourier transform on the line spread function (LSF) to obtain the MTF value of each projection block; and using the average value obtained by averaging the MTF values ​​of each projection block as the evaluation basis for focus sharpness.

[0012] Furthermore, the processing of the video data transmitted by the image acquisition module, and the frame extraction based on the projection command timestamp obtained from the intelligent vehicle headlight module, includes: obtaining the precise timestamp of the projection command issued from the domain control board log of the intelligent vehicle headlight module; decoding the video data to convert the RAW data from the high-speed camera into an RGB image, and associating the frame sequence number with the timestamp; setting a time window centered on the timestamp, and extracting video frames within the time window from the video data acquired by the high-speed camera as valid samples; automatically verifying the valid samples, and selecting video data frames whose timestamp and the error value of the projection command time T recorded by the MCU are less than or equal to the allowable range as key frames for the projection action.

[0013] Furthermore, the automated verification of the real-time performance, integrity, and correctness of the projection includes: determining whether there are projection action key frames within the time window; if there are projection action key frames, the real-time performance verification is satisfied; calculating the time difference between adjacent projection commands based on the log information output by the MCU to determine the theoretical or actual total number of projection frames; if the error between the total number of projection action key frames and the calculated theoretical or actual total number of projection frames is less than or equal to a preset frame number threshold, the integrity verification is satisfied; performing image recognition on the extracted projection action key frames, parsing their projection content, and comparing it with the command content in the log to determine whether the projection content is consistent with the command content; if they are consistent, the correctness verification is satisfied.

[0014] Secondly, embodiments of the present invention provide an automated testing method for the dynamic scene projection quality of intelligent vehicle lights. The method is applied to the aforementioned automated testing system for the dynamic scene projection quality of intelligent vehicle lights. The method includes: Step S1, issuing a control command corresponding to the scene mode selected by the user to the image acquisition module, causing the image acquisition module to move to the corresponding position point; Step S2, receiving the arrival signal returned by the image acquisition module, controlling the intelligent vehicle light module to project a standard image to the corresponding position point based on the scene mode selected by the user, and synchronously driving the image acquisition module to acquire the standard image; Step S3, receiving… Step S4: After focusing, control the scene backfeed software to backfeed scene data to the intelligent headlight module to trigger the intelligent headlight module to perform dynamic projection; Step S5: Synchronously drive the image acquisition module to record video of the dynamic projection; Step S6: Process the video data transmitted by the image acquisition module, extract frames based on the projection instruction timestamp obtained from the intelligent headlight module, and automatically verify the real-time performance, integrity and correctness of the projection to generate a test report.

[0015] In another aspect, the present invention also provides a computer-readable storage medium storing one or more instructions, the computer instructions being used to cause the computer to execute the above-described automated testing method for the dynamic scene projection quality of intelligent vehicle lights.

[0016] In another aspect, the present invention provides an electronic device, comprising: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the above-mentioned automated testing method for the dynamic scene projection quality of intelligent vehicle lights.

[0017] The beneficial effects of this invention are: (1) The automated testing system and method for intelligent vehicle headlight projection quality proposed in this invention can automatically verify the real-time performance, integrity and correctness of interactive dynamic projection scenes, filling the gap in verification methods in this field.

[0018] (2) The automated testing system and method for intelligent vehicle headlight projection quality proposed in this invention can dynamically adjust the position and focal length of the high-speed camera according to the scene mode, and capture clear projection patterns according to different projection modes and projection distances. This solves the limitation of existing testing schemes that require manual intervention.

[0019] (3) The MTF multi-area evaluation method proposed in this invention is more suitable for the multi-element combination of intelligent vehicle headlight projection animation patterns, and can more comprehensively reflect the clarity of the entire projection surface, greatly avoiding the problem of focusing difficulties caused by misjudgment and single evaluation deviation.

[0020] (4) The automated testing system and method for intelligent vehicle headlight projection quality proposed in this invention solves the drawbacks of human eye testing of dynamic projection quality, improves detection accuracy and efficiency, and can be used for long-term high-load testing of long-stability testing, and the reliability of projection quality is verified. Attached Figure Description

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] Figure 1 This is a structural diagram of an automated testing system for the dynamic scene projection quality of intelligent vehicle lights, provided in Embodiment 1 of the present invention.

[0023] Figure 2 This is a structural diagram of another automated testing system for the dynamic scene projection quality of intelligent vehicle lights provided in Embodiment 1 of the present invention.

[0024] Figure 3 This is a flowchart of an automated testing method for the dynamic scene projection quality of intelligent vehicle lights, provided in Embodiment 2 of the present invention.

[0025] Figure 4 This is a partial block diagram of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0027] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] The present invention will now be described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0029] Example 1 The specific implementation method is as follows: like Figure 1 The diagram shown is a structural diagram of an automated testing system for the dynamic scene projection quality of intelligent vehicle lights provided by the present invention.

[0030] As an example, the system includes a test bench 1 and a control device 2; the test bench 1 is arranged in a darkroom environment, and an intelligent vehicle light module 10 and an image acquisition module 11 are installed on the test bench 1; the intelligent vehicle light module 10 is used to project a corresponding projection image based on the control commands issued by the control device 2; the image acquisition module 11 is used to move to a corresponding position point according to the control commands issued by the control device 2 and acquire the projection image; the control device 2 integrates control software 20 and scene feedback software 21; the control software 20 is configured to perform the following steps: issuing a corresponding control command to the image acquisition module 11 based on the scene mode selected by the user, causing the image acquisition module 11 to move to the corresponding position point; receiving the position signal returned by the image acquisition module 11, based on the user's... The selected scene mode controls the intelligent headlight module 10 to project a standard image to the corresponding location point, and simultaneously drives the image acquisition module 11 to acquire the standard image; the standard image acquired by the image acquisition module 11 is received, and multi-region MTF calculation is performed on it, automatically adjusting the focal length until the MTF value meets the preset MTF threshold to complete automatic focusing; after focusing is completed, the scene backfeed software 21 is controlled to backfeed scene data to the intelligent headlight module 10 to trigger the intelligent headlight module 10 to perform dynamic projection; the image acquisition module 11 is simultaneously driven to record video of the dynamic projection; the video data transmitted by the image acquisition module 11 is processed, and frames are extracted based on the projection instruction timestamp obtained from the intelligent headlight module 10, and the real-time performance, integrity and correctness of the projection are automatically verified to generate a test report.

[0031] In some feasible implementations, the testing process begins with the user selecting a scenario, including near-field and far-field scenarios. In intelligent vehicle headlight projection, the distinction between near-field and far-field scenarios is primarily based on functional requirements and optical characteristics. Scenario modes are differentiated according to different projection distances; for example, welcoming and farewell projections belong to near-field scenarios, while pedestrian yielding and following distance warnings belong to far-field scenarios. Near-field projection distances are typically 1-3 meters, and far-field projection distances are typically 5-15 meters. Users select the near-field / far-field scenario on the scenario control app and send a scenario flag.

[0032] In some feasible implementations, the intelligent vehicle lighting module 10 includes a dual-lamp module 101 and a light domain control board 102. The light domain control board 102 includes a SOC and an MCU. After receiving control commands from the control device, the SOC communicates with the MCU via SPI, and the MCU controls the dual-lamp module 10 to project.

[0033] In some feasible implementations, combined with Figure 2 As shown, the image acquisition module 11 includes a high-speed camera 110, a horizontal slide 111 for driving the high-speed camera to move horizontally, and a servo motor. The servo motor is used to drive the high-speed camera 110 to move on the horizontal slide 111 to a corresponding position point based on the control commands issued by the control software 20. This includes: when the user selects the scene mode as a far scene, driving the high-speed camera 110 to move on the horizontal slide 111 to a first position point; and when the user selects the scene mode as a near scene, driving the high-speed camera 110 to move on the horizontal slide 111 to a second position point.

[0034] Preferably, the control software 20 integrates a PLC controller 201, which is electrically connected to the servo motor and is used to control the servo motor to drive the high-speed camera 110 to a preset first position point or a second position point according to the scene mode.

[0035] Specifically, the PLC controller 201 receives scene flag signals from the control software 20 in real time, performs judgments and calculations according to the set logic (far and near field positions, stepping), and outputs control commands to the servo motor to drive the high-speed camera 110 on the horizontal slide 111 to move to the corresponding position. After the camera is in position, it returns the in-position status to the control software 20.

[0036] In some feasible implementations, the test system further includes a synchronization trigger module, which is a hardware trigger line connecting the output port of the intelligent vehicle headlight module 10 and the GPIO port of the high-speed camera 110, used to achieve microsecond-level synchronization between projection commands and image acquisition.

[0037] Preferably, in the ready state, the intelligent vehicle headlight module 10 chip output port is directly connected to the high-speed camera 110 GPIO via a hardware-triggered synchronization mechanism, achieving a latency of less than 10µs and almost synchronous video recording. Based on the refresh rate of the vehicle headlight projection, this embodiment selects a camera frame rate at least twice the projection refresh rate. For example, if the vehicle headlight projection refresh rate is 30FPS, this embodiment sets the camera's recording frame rate to 60FPS. This allows the high-speed camera's oversampling capability to avoid motion blur and aliasing effects, and also avoids the massive amount of redundant data generated by setting the sampling frame rate too high.

[0038] In some feasible implementations, the test system further includes a communication module, which includes an Ethernet for connecting the control software 20 and the intelligent vehicle lighting module 10, and a PCIe data bus for connecting the image acquisition module 11 and the control software 20.

[0039] In some feasible implementations, the control software 20 is further configured to obtain MTF data of N projection blocks from the standard image, and calculate the average of the MTF data of the N projection blocks to obtain a comprehensive evaluated MTF value, including: determining the edge positions of the N projection blocks respectively, measuring the edge spread function (ESF) at the edges; calculating the line spread function (LSF) by differentiating or fitting the derivative of the edge spread function (ESF); performing a Fourier transform on the line spread function (LSF) to obtain the MTF value of each projection block; and using the average value obtained by averaging the MTF values ​​of each projection block as the evaluation basis for focus sharpness.

[0040] Preferably, after receiving the positioning status signal returned by the high-speed camera 110, the control software 20 controls the intelligent headlight module 10 to project a standard pattern to the corresponding distance position according to the selection of near and far fields. This standard pattern is preferably a checkerboard pattern. Simultaneously, the high-speed camera 110 captures the checkerboard image. By analyzing the brightness distribution of the image, edges are detected and MTF is calculated, allowing for rapid MTF estimation and significantly improving computational efficiency. The main steps are as follows: First, determine the edge position, measure the pixel intensity distribution ESF at the edge, then differentiate the ESF or calculate the LSF using a fitted function derivative, then calculate the PSF (Point Spread Function), and finally perform a Fourier transform on the PSF to obtain the MTF. It should be noted that, alternatively, the method of determining the edge position, measuring the pixel intensity distribution ESF (Edge Spread Function) at the edge, then differentiating the ESF or calculating the LSF (Line Spread Function) using a fitted function derivative, and finally performing a Fast Fourier Transform on the LSF can also be used to obtain the MTF. Since the calculation method of MTF is already very mature in the existing technology, no restrictions are placed on the calculation method of MTF here.

[0041] Since the projected image or video stream of the vehicle headlights is a combination of multiple elements, including multiple squares, lines, and text, and to reduce the impact of local projection deviations, this embodiment uses MTF data of 17 projected squares obtained from a standard projection image for multi-region comprehensive evaluation. The image positions are: center, top left, bottom left, top right, bottom right, top left center, bottom left center, top right center, and bottom right center. The algorithm results of the current projection image are then automatically compared. If the MTF value reaches 0.2~0.15 (this threshold has been verified to ensure that the edges of the projected image of the vehicle headlights are not significantly blurred and details are not lost, meeting the user's visual recognition requirements), it indicates that the camera is properly focused and the current image is clear; otherwise, the system automatically adjusts the focus step according to the MTF change trend (e.g., if the MTF increases with the focus step, it adjusts in the forward direction; otherwise, it adjusts in the reverse direction), and re-captures the image for MTF calculation until the MTF meets the set threshold, then returns the camera focus OK status to the control software.

[0042] In some feasible implementations, the processing of the video data transmitted by the image acquisition module, and the frame extraction based on the projection command timestamp obtained from the intelligent vehicle headlight module, includes: obtaining the precise timestamp of the projection command issued from the domain control board log of the intelligent vehicle headlight module; decoding the video data to convert the RAW data from the high-speed camera into an RGB image, and associating the frame sequence number with the timestamp; setting a time window centered on the timestamp, and extracting video frames within the time window from the video data acquired by the high-speed camera as valid samples; automatically verifying the valid samples, and selecting video data frames whose timestamp and the error value of the projection command time T recorded by the MCU are less than or equal to the allowable range as key frames for the projection action.

[0043] Preferably, taking the far-field vehicle distance reminder scenario as an example, during the driving scenario feedback, the SOC at the headlight domain control board receives the control software enable signal (scenario opening) and perception data (targets such as vehicles and CAN signals). After calculation and processing, it obtains the algorithm post-processing result message and sends it to the MCU. The MCU outputs projection commands, sending a projection pattern at a distance of 15 meters at time T1, a projection pattern at a distance of 10 meters at time T2, and a projection pattern at a distance of 5 meters at time T3. If no update projection command is received, the current projection pattern is maintained for 5 seconds and then turned off. The MCU records the command log in real time, using it as the truth value.

[0044] Scene backfeedback is complete, and high-speed camera sampling is finished. The data generated by the high-speed camera has been synchronously transmitted to the control software via the PCIe bus. The control software processes the sampled data. The steps are as follows: First, format decoding is performed to convert the RAW data of the high-speed camera into RGB images, and the frame sequence number and timestamp are associated; then, frame extraction is triggered based on time from the massive data frames. In the above example, frames are extracted based on time T recorded by the MCU, and frames within T1±0.5s, T2±0.5s, and T3±0.5s are extracted to directly generate valid samples. This embodiment uses time-triggered frame extraction, which can greatly improve the frame extraction efficiency, reduce invalid frames, and save computing power.

[0045] Preferably, the valid samples are automatically categorized based on the principles of feature extraction and similarity clustering, and these are used as the core data to be verified. The core data to be verified is automatically validated against the ground truth data, and frames with a timestamp error of ≤1 / T (e.g., ±0.05s, with the threshold determined according to the accuracy requirements of the intelligent vehicle lighting scenario) are selected and recorded as key frames for the projection action.

[0046] In some feasible implementations, the automated verification of the real-time performance, integrity, and correctness of the projection includes: determining whether there are projection action key frames within the time window; if there are projection action key frames, the real-time performance verification is satisfied; calculating the time difference between adjacent projection commands based on the log information output by the MCU to determine the theoretical or actual total number of projection frames; if the error between the total number of projection action key frames and the calculated theoretical or actual total number of projection frames is less than or equal to a preset frame count threshold, the integrity verification is satisfied; performing image recognition on the extracted projection action key frames, parsing their projection content, and comparing it with the command content in the log to determine whether the projection content is consistent with the command content; if they are consistent, the correctness verification is satisfied.

[0047] Preferably, taking the above-mentioned far-field vehicle distance reminder scenario as an example, the real-time verification of projection quality is as follows: if the number of projection action keyframes is greater than or equal to 1, it means that the headlight projection is successful and there is no obvious lag, which meets the real-time requirements of projection, and the real-time judgment is OK; if the number of projection action keyframes is 0, it means that the headlight projection fails or does not meet the real-time requirements, and the real-time judgment is NG.

[0048] Preferably, taking the above-mentioned far-field vehicle distance reminder scenario as an example, the verification of projection quality integrity is as follows: Based on the log output by the MCU, first determine the difference between the timestamp of the next instruction and the timestamp of the previous instruction. If T2-T1, T3-T2, and end timestamp-T3 are all greater than 5 seconds, it means that the projection display of T1, T2, and T3 has not been interrupted. Theoretically, this projection process requires at least 5 seconds. 30 frames / second equals 150 frames; if T2-T1, T3-T2, and end timestamp-T3 are less than 5 seconds, then calculate the actual projection time X seconds for T1, T2, and T3 respectively. Similarly, calculate the total number of projection frames as X. 30 frames / second. Finally, count the total number of frames from the projection action keyframe to the next projection action keyframe / 2. If the total number of projection action keyframes is equal to or the error is ≤ 3 frames, it means that the car headlight projection process is complete and there are no missing frames, and the projection integrity is judged as OK; otherwise, the projection integrity is judged as NG.

[0049] Preferably, taking the far-field vehicle distance reminder scenario mentioned above as an example, the verification of whether the actual projection matches the triggering scenario is as follows: Extract the actual distance data (e.g., 15 meters at T1, 10 meters at T2, and 5 meters at T3) from the algorithm post-processing result message output by the SOC, and perform semantic segmentation and understanding on the keyframe images of the projection action to determine whether the projection pattern matches the current scene. If the match is consistent, the projection correctness is judged as OK; otherwise, the projection correctness is judged as NG.

[0050] Finally, the system outputs the automated judgment results of all test items (automatically generated based on MCU logs) and test metrics for this scenario.

[0051] In some feasible implementations, specific examples are given here to facilitate understanding of the above embodiments: Example scenario: Testing the "far-field distance prompt" function; Test function: While the vehicle is in motion, a "keep distance" prompt icon or number (e.g., "10m") is projected onto the ground in front; Projection distance: 10 meters (far field); Headlight projection refresh rate: 30Hz; Test device: A DLP intelligent pixel headlight.

[0052] The testing process is as follows: Step 1: Scene Selection and Coarse Camera Positioning: The engineer selects "Test Scene Library" → "Far Field" → "Distance Prompt_10m" on the control software UI; the control software sends the "Far Field Mode" flag to the PLC controller; the PLC controller, according to the preset program, knows that the camera calibration position corresponding to "Far Field" is the coordinate value X=1500mm of the horizontal slide; the PLC sends a command to the servo motor, which drives the motor to move the slide carrying the camera precisely to the position X=1500mm; the servo motor sends a "in position" signal back to the PLC, which then forwards it to the control software.

[0053] Step 2: Autofocus Process (MTF Multi-Area Evaluation Method): Now, the camera is roughly in place, but the focus is inaccurate. The system begins to perform fine autofocus. The control software sends a command to the headlight domain controller via Ethernet: "Project standard pattern: checkerboard"; the intelligent headlight projects a high-contrast black and white checkerboard pattern onto the ground 10 meters away; Initial Shooting and MTF Calculation: The control software commands the camera to capture a projected checkerboard image; the algorithm automatically selects 17 evaluation points on the image; the algorithm calculates the MTF value for each point. Assuming the camera is currently out of focus, the calculated average MTF value of the 17 points is 0.08 (far below the target threshold of 0.15); Initial MTF Measurement Result: 0.08 (Failure). Based on this, the distance is adjusted for the first time. Since it's the first adjustment, the system performs a trial step: the camera motor moves a small step (e.g., step value = 20) in the "far" direction; the headlights are commanded to project the checkerboard pattern again and a picture is taken; a new average MTF value is calculated = 0.12; trend analysis: the MTF has increased from 0.08 to 0.12, indicating the focus direction is correct. Therefore, focus adjustment continues (second adjustment): the system continues to move the motor in the same direction ("far") (step value = 20); a picture is taken, and a new average MTF value = 0.18 is calculated; the system determines: 0.18 > 0.15, and is within the ideal range of 0.15~0.2; focus is successful, the loop ends; the control software records the number of motor steps for the current focal length and displays the "focus complete" status. The autofocus log at this point is as follows: Position: Far field | Target MTF: 0.15; Iteration 1: MTF=0.08 → Direction probe: Step 20 further away; Iteration 2: MTF=0.12 (Trend: Upward) → Continue stepping 20 further away; Iteration 3: MTF=0.18 → Success, exit loop.

[0054] Step 3: Triggering Projection and Synchronous Sampling: The control software sends instructions to the "Scene Re-feedback Software"; the re-feedback software simulates a scenario of "a vehicle suddenly decelerating ahead," sending data packets (including: target vehicle ID, distance 10m, relative speed, etc.) to the headlight domain controller via Ethernet; the SOC on the headlight domain controller board processes this data and determines that a "10m" distance indicator icon needs to be projected; the SOC sends the instruction to "project the '10m' icon" to the MCU; the MCU performs the following two operations almost simultaneously: Operation A: Through the headlight driver chip, control the DMD micromirror array to form a "10m" pattern and project it; Operation B: Simultaneously, through the hardware GPIO trigger line, send a 5V pulse signal to the high-speed camera; as soon as the high-speed camera receives the pulse, its electronic shutter immediately begins exposure; the camera begins recording video at a frame rate of 60 fps (twice the headlight's 30Hz refresh rate) to ensure perfect capture of every frame of projection change.

[0055] Step 4: Data Processing and Automated Verification: Assume the MCU log records the following "truth value": [LOG] T1=12:00:05.000-CMD: Project "10m". The automated script reads the log and obtains the key timestamp T1=12:00:05.000; then, in the large-scale video file recorded by the camera, it locates the time window from 12:00:04.500 to 12:00:05.500; the script extracts all video frames (approximately 60 frames) within this time period as "candidate frames" to be analyzed. Real-time performance verification: The script detected a clear "10m" icon in the frame at 12:00:05.020. This indicates that the projection appeared within 20 milliseconds after the command was issued, so the real-time performance is OK.

[0056] Integrity check: The MCU log shows that the icon should be projected continuously for 5 seconds (150 frames @ 30fps). The script analyzes the candidate frames and, through an image similarity algorithm, identifies 149 frames where the "10m" icon appears consecutively. The difference (150-149=1 frame) is within the allowable error range of 3 frames, so integrity is OK.

[0057] Correctness check: The script performs semantic segmentation image recognition on the frame at 12:00:05.020, and the recognized content is also "10m", which is completely consistent with the instruction. Correctness: OK.

[0058] Step 5: Report Generation: The system automatically generates and pops up the test report: Test time: 2025-08-27 12:00:05; Real-time performance: OK (Latency: 20ms); Integrity: OK (Number of dropped frames: 1); Correctness: OK (content matches); MTF focus value: 0.18; Overall result: PASS.

[0059] The above implementation method solves the problems of low accuracy and poor efficiency of traditional human eye testing and narrow applicability of fixed cameras by scene selection, camera position adaptation, automatic focus, projection triggering, sampling, data processing and quality verification. It realizes automated and accurate testing of projection quality of intelligent vehicle lights in multiple dynamic scenes, which can be used for long-term stability testing and fills the gap in the field.

[0060] It is worth mentioning that all modules involved in this embodiment are logical units. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0061] Example 2 Please see Figure 3 The above is a flowchart of an automated testing method for the dynamic scene projection quality of intelligent vehicle lights, provided by an embodiment of the present invention.

[0062] As an example, the method is applied to the automated testing system for the dynamic scene projection quality of intelligent vehicle lights described in Embodiment 1, and the method includes: Step S1: Based on the scene mode selected by the user, send a corresponding control command to the image acquisition module to move the image acquisition module to the corresponding position point.

[0063] Step S2: Receive the arrival signal returned by the image acquisition module, control the intelligent vehicle headlight module to project a standard image to the corresponding position point based on the scene mode selected by the user, and synchronously drive the image acquisition module to acquire the standard image.

[0064] Step S3: Receive the standard image acquired by the image acquisition module, perform multi-region MTF calculation on it, and automatically adjust the focal length until the MTF value meets the preset MTF threshold to complete the autofocus.

[0065] Step S4: After focusing is completed, control the scene backflow software to backflow scene data to the intelligent vehicle headlight module to trigger the intelligent vehicle headlight module to perform dynamic projection.

[0066] Step S5: Synchronously drive the image acquisition module to record video of the dynamic projection.

[0067] Step S6: Process the video data transmitted by the image acquisition module, extract frames based on the projection instruction timestamp obtained from the intelligent vehicle headlight module, and automatically verify the real-time performance, integrity and correctness of the projection to generate a test report.

[0068] It is not difficult to see that this embodiment is a method embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0069] Example 3 This invention also proposes a storage medium storing an automated testing method for the dynamic scene projection quality of intelligent vehicle lights. When the automated testing program for the dynamic scene projection quality of intelligent vehicle lights is executed by a processor, it implements the steps of the automated testing method for the dynamic scene projection quality of intelligent vehicle lights as described above. Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0070] Example 4 Please see Figure 4 The present invention also provides an electronic device, including: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the automated testing method for the dynamic scene projection quality of intelligent vehicle lights provided in Embodiment 2.

[0071] The memory 702 and processor 701 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 701 and memory 702 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 701 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 701.

[0072] Processor 701 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 702 can be used to store data used by processor 701 during operation.

[0073] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, based on the guidance provided in this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An automated testing system for the dynamic scene projection quality of intelligent vehicle lights, characterized in that, The system includes a test bench and a control device; The test bench is set up in a darkroom environment, and the test bench is equipped with an intelligent vehicle light module and an image acquisition module. The intelligent vehicle lighting module is used to project a corresponding image based on the control command issued by the control device. The image acquisition module is used to move to the corresponding position point according to the control command issued by the control device and acquire the projected image; The control device integrates control software and scene re-feedback software. The control software is configured to perform the following steps: Based on the scene mode selected by the user, a corresponding control command is sent to the image acquisition module to move the image acquisition module to the corresponding position point. The system receives the arrival signal from the image acquisition module, controls the intelligent vehicle headlight module to project a standard image to the corresponding location point based on the scene mode selected by the user, and simultaneously drives the image acquisition module to acquire the standard image. The system receives a standard image acquired by the image acquisition module, performs multi-region MTF calculation on it, and automatically adjusts the focal length until the MTF value meets the preset MTF threshold to complete the autofocus. After focusing is completed, the scene backfeed software controls the scene data backfeed to the intelligent vehicle headlight module to trigger the intelligent vehicle headlight module to perform dynamic projection. The image acquisition module is synchronously driven to record video of the dynamic projection. The video data transmitted by the image acquisition module is processed, and frames are extracted based on the projection instruction timestamp obtained from the intelligent vehicle headlight module. The real-time performance, integrity and correctness of the projection are automatically verified, and a test report is generated.

2. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 1, characterized in that, The intelligent vehicle lighting module includes a dual-lamp module and a light domain control board. The light domain control board includes a SOC and an MCU. After receiving control commands from the control device, the SOC communicates with the MCU via SPI. The MCU controls the dual-lamp module to project.

3. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 1, characterized in that, The image acquisition module includes a high-speed camera, a horizontal slide for driving the high-speed camera to move horizontally, and a servo motor. The servo motor is used to drive the high-speed camera to move to a corresponding position on the horizontal slide based on control commands issued by the control software, including: When the user selects the scene mode as far scene, the high-speed camera is driven to move to the first position point on the horizontal slide. When the user selects the scene mode as near scene, the high-speed camera is driven to move to the second position point on the horizontal slide.

4. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 3, characterized in that, The control software integrates a PLC controller, which is electrically connected to the servo motor and is used to control the servo motor to drive the high-speed camera to move to a preset first position point or a second position point according to the scene mode.

5. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 1, characterized in that, The testing system also includes a synchronization trigger module, which is a hardware trigger line that connects the output port of the intelligent vehicle headlight module to the GPIO port of the high-speed camera, and is used to achieve microsecond-level synchronization between projection commands and image acquisition.

6. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 1, characterized in that, The testing system also includes a communication module, which includes an Ethernet for connecting the control software and the intelligent vehicle lighting module, and a PCIe data bus for connecting the image acquisition module and the control software.

7. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 1, characterized in that, The control software is also used to obtain MTF data of N projection blocks from the standard image, and to calculate the average of the MTF data of the N projection blocks to obtain a comprehensive evaluation MTF value, including: Determine the edge positions of the N projection blocks respectively, and measure the edge spread function (ESF) at the edges; The line spread function (LSF) is obtained by differentiating the edge spread function (ESF) or calculating the derivative of the fitted function. Perform a Fourier transform on the line spread function (LSF) to obtain the MTF value for each projection block; The average value obtained by averaging the MTF values ​​of each projection square is used as the basis for evaluating focus sharpness.

8. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 1, characterized in that, The process of processing the video data transmitted by the image acquisition module, including frame extraction based on the projection instruction timestamp obtained from the intelligent vehicle headlight module, includes: Obtain the precise timestamp of the projection command issued from the domain control board log of the intelligent vehicle lighting module; The video data is format-decoded to convert the RAW data from the high-speed camera into an RGB image, and the frame number is associated with the timestamp. Centered on the timestamp, a time window is set, and video frames within the time window are extracted from the video data collected by the high-speed camera as valid samples. The valid samples are automatically verified, and video data frames whose timestamps and the error values ​​of the projection command T recorded by the MCU are less than or equal to the allowable range are selected as key frames for the projection action.

9. The automated testing system for dynamic scene projection quality of intelligent vehicle lights according to claim 8, characterized in that, The automatic verification of the projection's real-time performance, completeness, and correctness includes: Determine whether there is a projection action keyframe within the time window. If there is a projection action keyframe, the real-time verification is satisfied. Based on the log information output by the MCU, calculate the time difference between adjacent projection commands to determine the theoretical or actual total number of projection frames. If the error between the total number of key frames of the projection action and the calculated theoretical or actual total number of projection frames is less than or equal to the preset frame number threshold, then the integrity check is satisfied. Image recognition is performed on the extracted keyframes of the projection action to parse their projection content, and then compared with the instruction content in the log to determine whether the projection content is consistent with the instruction content. If they are consistent, the correctness check is satisfied.

10. An automated testing method for the dynamic scene projection quality of intelligent vehicle lights, the method being applied to the automated testing system for the dynamic scene projection quality of intelligent vehicle lights as described in any one of claims 1-9, characterized in that, The method includes: Step S1: Based on the scene mode selected by the user, send a corresponding control command to the image acquisition module to move the image acquisition module to the corresponding position point; Step S2: Receive the arrival signal returned by the image acquisition module, control the intelligent vehicle headlight module to project a standard image to the corresponding position point based on the scene mode selected by the user, and synchronously drive the image acquisition module to acquire the standard image. Step S3: Receive the standard image acquired by the image acquisition module, perform multi-region MTF calculation on it, and automatically adjust the focal length until the MTF value meets the preset MTF threshold to complete the autofocus; Step S4: After focusing is completed, control the scene backflow software to backflow scene data to the intelligent vehicle headlight module to trigger the intelligent vehicle headlight module to perform dynamic projection. Step S5: Synchronously drive the image acquisition module to record video of the dynamic projection; Step S6: Process the video data transmitted by the image acquisition module, extract frames based on the projection instruction timestamp obtained from the intelligent vehicle headlight module, and automatically verify the real-time performance, integrity and correctness of the projection to generate a test report.

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