A DLP projection-based intelligent evaluation system for the comprehensive performance of automotive headlights
The intelligent evaluation system based on DLP projection enables comprehensive and automated inspection of automotive headlights, solving the problem of insufficient light field distribution information in traditional inspection methods and improving inspection accuracy and adaptability to complex environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUXI SHENGWEISI AUTOMATION EQUIP CO LTD
- Filing Date
- 2026-04-15
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional automotive headlight testing methods cannot obtain complete and continuous light field distribution information, lack objective and unified quantitative standards, and cannot simulate complex optical interaction environments, affecting testing accuracy and driving safety.
An intelligent evaluation system based on DLP projection is adopted, including a projection test driving module, an image acquisition and processing module, a performance evaluation module, and an evaluation report generation module. Through static and dynamic testing items, it automatically evaluates the optical performance of automotive headlights and simulates complex optical interaction environments.
It enables comprehensive, blind-spot-free evaluation of automotive headlights, improving testing efficiency, reducing costs, adapting to different types of headlights and evaluation standards, and providing objective and unified quantitative evaluation results.
Smart Images
Figure CN122409154A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of automotive headlight monitoring, and in particular to an intelligent evaluation system for the comprehensive performance of automotive headlights based on DLP projection. Background Technology
[0002] As the automotive industry rapidly evolves towards intelligence and high-end features, automotive headlights have evolved from traditional lighting tools into core safety components integrating adaptive adjustment and multi-scenario adaptation. Their optical performance, dynamic response capabilities, and adaptability to complex environments directly impact driving safety. Therefore, the market demands higher precision, comprehensiveness, and intelligence levels in the testing of automotive headlight performance.
[0003] Traditional automotive headlight inspection methods rely on static inspection systems with fixed screens and dot matrix lux meters. These systems can only acquire data from discrete points and cannot obtain complete and continuous information about the light field distribution. They lack the ability to evaluate indicators requiring global analysis, such as light pattern gradient and regional uniformity, and heavily depend on the experience of inspectors for visual judgment, lacking objective and unified quantitative standards. Traditional automotive headlight inspection methods also rely on vision inspection systems using industrial cameras. These systems acquire a two-dimensional brightness distribution image of the entire light field in a single shot and then calculate various parameters through image analysis. However, existing systems typically test against a uniform dark background, while real-world road environments contain various complex backgrounds that interact with the headlight beam, affecting the actual visual effect.
[0004] Therefore, there is an urgent need for an intelligent evaluation system for the comprehensive performance of automotive headlights that can resist interference and simulate complex optical interaction environments. Summary of the Invention
[0005] To address at least one of the above problems, this application provides an intelligent evaluation system for the comprehensive performance of automotive headlights based on DLP projection.
[0006] This application provides a comprehensive intelligent evaluation system for automotive headlight performance based on DLP projection, including: The projection test driver module drives relevant devices to display test images on a test screen based on a sequence of test requirements; the test requirements include static detection and dynamic detection. An image acquisition and processing module is positioned in front of the test screen and is used to synchronously acquire a sequence of light field distribution images related to the test image. The performance evaluation module is used to calculate a performance evaluation score based on the sequence of the test images and the corresponding sequence of the light field distribution images; The evaluation report generation module generates a comprehensive evaluation report containing improvement suggestions based on the performance evaluation score output; Wherein, when the test requirement is static detection, the test image is generated using a DLP projection lamp; when the test requirement is dynamic detection, the test image is generated using a non-DLP projection lamp.
[0007] As a further technical solution, the static detection includes a static base pattern adapted to different static optical performance testing items; The static optical performance testing items include distortion detection, MTF detection, uniformity detection, contrast detection, chromaticity and color difference detection, FOV detection, dead pixel detection, and stray light detection. The projection test driving module matches a static base pattern according to the detection requirements of the static optical performance test item, and projects the static base pattern onto the test screen to obtain the test parameters of the static optical performance test item, and further evaluates the static test parameters.
[0008] As a further technical solution, the method for static parameter detection and evaluation includes: The output test parameters are processed and converted into standard scores under a unified dimension. According to the preset weight allocation table, the test parameters obtained from the static optical performance testing items are assigned corresponding weight coefficients to obtain the sub-scores of each item of the static optical performance testing items. The weight coefficients are dynamically adjustable according to the importance and preset standards. The sub-scores are weighted and summed to obtain the total static detection score.
[0009] As a further technical solution, the dynamic detection includes a curve illumination test; The projection test drive module loads a preset curve lighting test scheme that is adapted to different degrees of curvature and turning angles. Based on the aforementioned curve lighting test scheme, generate light pattern signals for the curve scene; The projection test drive module outputs corresponding control command lights based on the generated curve scene light pattern signal and projects them onto the test screen.
[0010] As a further technical solution, the dynamic detection also includes simulation of light pattern switching when vehicles meet; Based on the test standard for simulating oncoming vehicle light pattern switching, a virtual light spot is dynamically generated to simulate oncoming vehicle scenarios at different distances and positions. The performance evaluation module analyzes the light response time from the appearance of the virtual light spot to the start of light pattern adjustment, calculates the overlap between the shading area of the headlight of the vehicle under test and the oncoming driver's seat, and generates a simulated score for the oncoming light pattern switching.
[0011] As a further technical solution, the dynamic detection also includes simulation of lighting mode switching in rainy and foggy weather; According to the test requirements of the simulated switching of lighting modes in rainy and foggy weather, the dynamic information of the optical layer in rainy and foggy weather is adjusted to generate scene image signals corresponding to different weather conditions; The system detects whether the simulated weather is accurately identified and the correct lighting mode is switched, as well as the lighting mode switching time under the corresponding scene image signal, and generates a simulation score for the lighting mode switching in the rainy / foggy weather.
[0012] As a further technical solution, the performance evaluation module includes a multi-dimensional evaluation unit and a comprehensive performance evaluation unit; The multi-dimensional evaluation unit synchronously calculates and outputs test results based on the static optical performance test items and the results of the dynamic test. The comprehensive performance evaluation unit judges the test results and generates a performance evaluation score; The process of generating performance evaluation scores includes: A dynamic detection total score is generated based on the simulated score of the light pattern switching during vehicle encounters and the simulated score of the lighting mode switching during rain and fog. The performance evaluation score is output by combining the total static detection score and the total dynamic detection score.
[0013] As a further technical solution, the system also includes a power consumption monitoring sensor and a heat dissipation performance monitoring sensor; The power consumption monitoring sensor is used to collect power consumption data in real time under different working modes; The heat dissipation performance monitoring sensor is used to collect real-time temperature data and temperature change rate data to obtain heat dissipation performance data. The performance evaluation module calculates the energy conversion rate based on the operating power consumption data and the heat dissipation performance data, and judges the thermal stability based on the real-time temperature data and the temperature change rate.
[0014] As a further technical solution, the evaluation report generation module also includes a light pattern comparison module; The light pattern comparison module is used to generate a visual light pattern comparison chart, which includes the light field distribution image sequence obtained by the static optical performance test items and the dynamic test, and the standard light pattern image that should exist during the test. The light pattern comparison module compares the light field distribution image sequence with the standard light pattern image, marks the differences between the light field distribution image sequence and the standard light pattern image, and identifies the headlights of the vehicle under test that have more than a specified threshold of differences as abnormal headlights.
[0015] As a further technical solution, the system includes the following steps: Step S1: The system receives test requirements for static or dynamic testing, matches the corresponding test plan, and projects the test image onto the test screen to obtain the test image and light field distribution image sequence. Step S2: Simultaneously acquire the light field distribution image sequence to form a multi-source detection data set; Step S3: Output the static detection total score and the dynamic detection total score based on the sequence of the test images and the corresponding light field distribution image sequence; Step S4: Obtain the performance evaluation score based on the total static detection score and the total dynamic detection score; Step S5: Generate a comprehensive evaluation report containing improvement suggestions based on the performance evaluation output.
[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. This application covers eight key indicators for automotive headlight testing: distortion, MTF, uniformity, contrast, chromatic aberration, FOV, dead pixels, and stray light. This enables a comprehensive and thorough evaluation of the overall performance of automotive headlights, avoiding performance misjudgments caused by single-dimensional testing.
[0017] 2. The modules in this application work automatically and collaboratively without any manual intervention, which greatly improves the testing efficiency; the weight coefficients are dynamically adjustable and the test parameters are flexibly configurable, which can be adapted to different types of headlights and diverse evaluation standards, and is compatible with industry standards and enterprise-defined requirements, thereby reducing the investment cost of testing equipment. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of an intelligent evaluation system for the comprehensive performance of automotive headlights based on DLP projection.
[0019] Figure 2 It is a distortion detection image.
[0020] Figure 3 It is an MTF detection image.
[0021] Figure 4 It is a uniformity detection image.
[0022] Figure 5 It is a contrast detection image.
[0023] Figure 6 It is a color difference detection image.
[0024] Figure 7 This is an image detected by FOV.
[0025] Figure 8 This is a defective pixel detection image.
[0026] Figure 9 This is a stray light detection image. Detailed Implementation
[0027] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.
[0028] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0029] This application discloses an intelligent evaluation system for the comprehensive performance of automotive headlights based on DLP projection, which performs performance testing on a novel DLP automotive headlight. Figure 1 As shown, it includes: The projection test driver module drives relevant devices to display test images on the test screen based on the test requirement sequence. The test requirements include static detection and dynamic detection; wherein, when the test requirement is static detection, the test image is generated using a DLP projection lamp; when the test requirement is dynamic detection, the test image is generated using a non-DLP projection lamp. The static inspection includes static base patterns adapted to different static optical performance testing items. The static optical performance testing items include distortion detection, MTF detection, uniformity detection, contrast detection, chromaticity and color difference detection, FOV detection, dead pixel detection, and stray light detection. The projection test driving module loads the static basic pattern that matches the detection requirements based on the information of the headlight under test, and projects the static basic pattern onto the test screen to obtain the static test parameters of the headlight under test, and further performs static parameter detection and evaluation. The headlight under test executes corresponding operations based on the received control signals, enabling it to operate stably in a preset working state. This working state is maintained continuously until a new control signal is received, ensuring that the headlight's illumination direction remains aligned with the test screen throughout the entire test process, thus meeting the image acquisition and processing module's acquisition requirements.
[0030] The image acquisition and processing module is located in front of the test screen and uses an SA2000 imaging colorimeter camera. Based on preset detection area parameters, such as the effective range of static detection and the core area of dynamic detection, it locates the specified detection area on the test screen and synchronously acquires a sequence of light field distribution images of the test image at a preset acquisition frame rate and image resolution. The performance evaluation module is communicatively connected to the image acquisition and processing module and is used to calculate the performance evaluation score based on the sequence of the test images and the corresponding sequence of the light field distribution images. The performance evaluation module includes a multi-dimensional evaluation unit and a comprehensive performance evaluation unit; The multi-dimensional evaluation unit synchronously calculates and outputs test results based on the static optical performance test items and the results of the dynamic test. The comprehensive performance evaluation unit judges the test results and generates a performance evaluation score; In this embodiment, the method for obtaining the static test parameters of the headlight to be tested includes: like Figure 2 As shown, the methods for obtaining distortion detection results include:
[0031] in, The distortion variable is the result of the projection point map of the headlight of the car under test. The height of the center point of the four sides and the height difference between the four vertices and the center point of the four sides when the projection point diagram of the headlight of the car under test is shown. The height of the center of the four sides of the projection point diagram of the headlight of the vehicle under test is given; the distortion rate of the headlight under test is calculated to be 0.8%. like Figure 3 As shown, the methods for obtaining MTF detection results include:
[0032] in, The maximum brightness obtained from the line-pairing diagram of the car headlight under test is given. The minimum luminance obtained from the line-pair plot of the headlight under test is given; the MTF value of the headlight under test is calculated to be 0.58. like Figure 4 As shown, the methods for obtaining uniformity detection results include:
[0033]
[0034] in, The color deviation value of the headlight under test. The actual chromaticity value of the car headlight under test at a specific test point. The value is the average of the actual chromaticity values at all test points of the headlight under test; the calculated illumination uniformity of the headlight under test is 88%. like Figure 5 As shown, the methods for obtaining contrast detection results include:
[0035] in, The contrast ratio of the headlight under test is [value]. The value represents the average brightness of the black area under the illumination of the car's headlights. The average brightness of the white area under the illumination of the headlight of the car under test is given; the contrast ratio of the headlight of the car under test is calculated to be 120:1. like Figure 6 As shown, the methods for obtaining colorimetric and color difference detection results include:
[0036] in, The color difference value of the car headlight being tested. , This represents the actual color coordinate value of a specific test point on the headlight of the vehicle under test. , The standard color coordinates of a certain test point of the headlight under test are given; the color difference of the headlight under test is calculated to be 1.5. like Figure 7 As shown, the method for obtaining FOV detection results includes projecting a white image, capturing the length and width of the projection area, calculating the horizontal and vertical projection angles, and determining whether the field of view is within the design standard.
[0037] like Figure 8 As shown, the method for obtaining the defect detection results includes projecting a white image to detect the presence of black defective pixels, projecting a black image to detect the presence of white defective pixels, and deducting 20 points for each defective pixel detected; the headlight of the car under test was found to have no defective pixels. like Figure 9 As shown, the method for obtaining stray light detection results includes projecting a white image, capturing the maximum brightness value of a set area at the edge of the projection area, and determining that the stray light ratio of the headlight of the vehicle under test is 3%.
[0038] In this embodiment, the method for static parameter detection and evaluation includes: The output test parameters are processed and converted into standard scores under a unified dimension. According to the preset weight allocation table, the test parameters obtained from the static optical performance testing items are assigned corresponding weight coefficients to obtain the sub-scores of each item of the static optical performance testing items. The weight coefficients are dynamically adjustable according to the importance and preset standards. The sub-scores are weighted and summed to obtain the total static detection score;
[0039] in, The total static test score for the headlights of the vehicle under test. For the first Scoring of each static optical performance test item For the first Weighting coefficients for each static optical performance testing item Given the total number of test items, the total static test score for the headlights of the vehicle under test is calculated to be 95 points.
[0040] In this embodiment, dynamic detection can also be provided for the headlight testing of the vehicle under test. The dynamic detection simulation includes curve illumination test, oncoming traffic light pattern switching simulation and rain and fog weather lighting mode switching simulation. The dynamic detection includes a curve illumination test; The projection test drive module loads a preset curve lighting test scheme that is adapted to different degrees of curvature and turning angles. Based on the aforementioned curve lighting test scheme, generate light pattern signals for the curve scene; The projection test drive module outputs corresponding control command lights based on the generated curve scene light pattern signal and projects them onto the test screen.
[0041] In this embodiment, the method also includes simulating the switching of oncoming light patterns during nighttime driving. The method dynamically generates virtual light spots of oncoming vehicles based on the test standards of the oncoming light pattern switching simulation, and simulates oncoming scenarios at different distances and positions. In this embodiment, the headlights of the car under test encountered three groups of oncoming vehicles in succession, namely group A, group B, and group C. The performance evaluation module analyzes the light response time from the appearance of the virtual light spot to the start of light pattern adjustment, which is 0.24 seconds, 0.25 seconds, and 0.25 seconds, respectively. It calculates the overlap between the obscured area of the headlight of the vehicle under test and the oncoming driver's seat as 3%, 3%, and 3.5%, respectively. The method for generating the simulated light pattern switching score for oncoming traffic includes:
[0042]
[0043]
[0044] in, The overall score for the simulated light pattern switching during vehicle encounters. Score the time it takes for the light to move away. For the overlap sub-score, For the time it takes for the lights to be moved away, The standard light removal time, This is the preset maximum allowable time for the light to be removed. The degree of overlap between the area obscured by the headlights of the car under test and the oncoming driver's seat. The standard value for the overlap between the obscured area of the headlights of the vehicle under test and the oncoming driver's seat is given. This is the preset maximum allowable overlap value. The weighting coefficients for the delayed sub-ratings, The weighting coefficients for the overlap sub-scores; =0.2 seconds, =0.5 seconds, =3%, =4%, =0.4, =0.6; The overall score for the first group's simulated light pattern switching during vehicle encounters is: Calculated A=86.7, A=100, A=94.7; The overall score for the second group's simulated light pattern switching during vehicle encounters is: Calculated B=83.33, B=100, B=93.33; The overall score for the third group's simulated light pattern switching during vehicle encounters was: Calculated C=83.33; =95, C=90.33; The average of the three sets of simulated light pattern switching scores for passing vehicles is taken as the final output of the simulated light pattern switching score for passing vehicles. =92.79; In this embodiment, the dynamic detection also includes simulation of lighting mode switching in rainy and foggy weather; The simulation of switching lighting modes in rainy / foggy weather includes: According to the test requirements of the simulated switching of lighting modes in rainy and foggy weather, the dynamic information of the optical layer in rainy and foggy weather is adjusted to generate scene image signals corresponding to different weather conditions; The system detects whether the simulated weather is accurately identified and the correct lighting mode is switched to, as well as the lighting mode switching time under the corresponding scene image signal, and generates a simulation score for the rain and fog weather lighting mode switching. The acquired actual light pattern images are analyzed to detect whether the system accurately identifies the simulated weather conditions and automatically switches to the correct lighting mode, and the lighting mode switching time is recorded as 0.4 seconds; The method for generating the simulation score for switching lighting modes in rainy and foggy weather includes: The simulation score for switching lighting modes in rainy and foggy weather is divided into a sub-score for weather recognition and mode switching accuracy, a sub-score for rainy and foggy weather response delay, and a sub-score for lighting capability.
[0045]
[0046]
[0047]
[0048] in, Simulated scoring for switching lighting modes in rainy and foggy weather. Sub-scores for weather recognition and mode switching accuracy. For rain and fog weather response delay sub-score, For the lighting capability sub-score, For lighting mode switching time, Standard time for switching lighting modes The preset lighting mode switching limit time, For the number of errors, To measure the lighting capability of the car headlight under test, For standard lighting capability, This represents the minimum lighting capability threshold. Detected =0.3 seconds, =0.4 seconds, =1.0 seconds, =25.7; =0, =40; =80%, =78%, =50%, =28.8; =94.5.
[0049] The average of the simulated light pattern switching score for oncoming traffic and the simulated lighting mode switching score for rain and fog weather is taken as the total dynamic detection score. ; The static detection score and the dynamic detection score are combined to output a performance evaluation score for the tested vehicle headlight. ; Calculated .
[0050] In this embodiment, to further determine the durability and thermal stability of the headlight under test, the system also includes a power consumption monitoring sensor and a heat dissipation performance monitoring sensor. The power consumption monitoring sensor is used to collect the operating power consumption data of the headlight under test in different working modes in real time; the power consumption monitoring sensor collects the operating power consumption data of the headlight under test in static mode, cornering mode and rain and fog mode in real time, which are 45W, 50W and 52W respectively, with an average power consumption of 49W. The heat dissipation performance monitoring sensor is used to collect real-time temperature data and temperature change rate data of the headlight under test. The sensor collects temperature data of the headlight under test over one hour of operation: initial temperature 25℃, stable temperature 105℃, and temperature change rate 3℃ / min. The projection test driving module calculates the energy conversion rate of the headlight under test based on the operating power consumption data and the heat dissipation performance data. At the same time, it judges the thermal stability of the headlight under test based on the real-time temperature data and the temperature change rate, combined with a preset safe temperature threshold.
[0051] The evaluation report generation module outputs the non-compliant test items of the headlight under test based on the performance evaluation score and generates a comprehensive evaluation report containing improvement suggestions; The evaluation report generation module also includes a light pattern comparison module; The light pattern comparison module is used to generate a visual light pattern comparison chart. The visual light pattern comparison chart includes the actual light pattern image of the headlight under test after the static optical performance test and the dynamic test, and also includes the standard light pattern image that the headlight under test should have during the test. The light pattern comparison module compares the actual light pattern image with the standard light pattern image, marks the differences between the actual light pattern image and the standard light pattern image, and marks 2 difference points. If the number of difference points does not exceed the specified threshold of 5, it is not marked as an abnormal headlight.
[0052] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A comprehensive intelligent evaluation system for automotive headlight performance based on DLP projection, characterized in that, include: The projection test driver module drives relevant devices to display test images on the test screen based on the test requirement sequence. The testing requirements include static testing and dynamic testing; An image acquisition and processing module is positioned in front of the test screen and is used to synchronously acquire a sequence of light field distribution images related to the test image; The performance evaluation module is used to calculate a performance evaluation score based on the sequence of the test images and the corresponding sequence of the light field distribution images. The evaluation report generation module generates a comprehensive evaluation report containing improvement suggestions based on the performance evaluation score output; Specifically, when the test requirement is static detection, the test image is generated using a DLP projection lamp; when the test requirement is dynamic detection, the test image is generated using a non-DLP projection lamp.
2. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 1, characterized in that, The static inspection includes static base patterns adapted to different static optical performance testing items. The static optical performance testing items include distortion detection, MTF detection, uniformity detection, contrast detection, chromaticity and color difference detection, FOV detection, dead pixel detection, and stray light detection. The projection test driving module matches a static base pattern according to the detection requirements of the static optical performance test item, and projects the static base pattern onto the test screen to obtain the test parameters of the static optical performance test item, and further evaluates the static test parameters.
3. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 2, characterized in that, The method for static parameter detection and evaluation includes: The output test parameters are processed and converted into standard scores under a unified dimension. According to the preset weight allocation table, the test parameters obtained from the static optical performance testing items are assigned corresponding weight coefficients to obtain the sub-scores of each item of the static optical performance testing items. The weight coefficients are dynamically adjustable according to the importance and preset standards. The sub-scores are weighted and summed to obtain the total static detection score.
4. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 2, characterized in that, The dynamic detection includes a curve illumination test; The projection test drive module loads a preset curve lighting test scheme that adapts to different degrees of curvature and turning angles. Based on the aforementioned curve lighting test scheme, generate light pattern signals for the curve scene; The projection test drive module outputs corresponding control command lights based on the generated curve scene light pattern signal and projects them onto the test screen.
5. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 4, characterized in that, The dynamic detection also includes simulation of light pattern switching when vehicles meet; Based on the test standard for simulating oncoming vehicle light pattern switching, a virtual light spot is dynamically generated to simulate oncoming vehicle scenarios at different distances and positions. The performance evaluation module analyzes the light response time from the appearance of the virtual light spot to the start of light pattern adjustment, calculates the overlap between the shading area of the headlight of the vehicle under test and the oncoming driver's seat, and generates a simulated score for the oncoming light pattern switching.
6. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 4, characterized in that, The dynamic detection also includes simulation of lighting mode switching in rainy and foggy weather; According to the test requirements of the simulated switching of lighting modes in rainy and foggy weather, the dynamic information of the optical layer in rainy and foggy weather is adjusted to generate scene image signals corresponding to different weather conditions; The system detects whether the simulated weather is accurately identified and the correct lighting mode is switched, as well as the lighting mode switching time under the corresponding scene image signal, and generates a simulation score for the lighting mode switching in the rainy / foggy weather.
7. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 1, characterized in that, The performance evaluation module includes a multi-dimensional evaluation unit and a comprehensive performance evaluation unit; The multi-dimensional evaluation unit synchronously calculates and outputs test results based on the static optical performance test items and the results of the dynamic test. The comprehensive performance evaluation unit judges the test results and generates a performance evaluation score; The process of generating performance evaluation scores includes: A dynamic detection total score is generated based on the simulated score of the light pattern switching during vehicle encounters and the simulated score of the lighting mode switching during rain and fog. The performance evaluation score is output by combining the total static detection score and the total dynamic detection score.
8. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 1, characterized in that, The system also includes a power consumption monitoring sensor and a heat dissipation performance monitoring sensor; The power consumption monitoring sensor is used to collect power consumption data in real time under different working modes; The heat dissipation performance monitoring sensor is used to collect real-time temperature data and temperature change rate data to obtain heat dissipation performance data. The performance evaluation module calculates the energy conversion rate based on the operating power consumption data and the heat dissipation performance data, and judges the thermal stability based on the real-time temperature data and the temperature change rate.
9. The intelligent evaluation system for comprehensive performance of automotive headlights based on DLP projection according to claim 1, characterized in that, The evaluation report generation module also includes a light pattern comparison module; The light pattern comparison module is used to generate a visual light pattern comparison chart, which includes the light field distribution image sequence obtained by the static optical performance test items and the dynamic test, and the standard light pattern image that should exist during the test. The light pattern comparison module compares the light field distribution image sequence with the standard light pattern image, marks the differences between the light field distribution image sequence and the standard light pattern image, and identifies the headlights of the vehicle under test that have more than a specified threshold of differences as abnormal headlights.
10. A method for intelligent evaluation of the comprehensive performance of automotive headlights based on DLP projection, characterized in that, The method includes the following steps: Step S1: The system receives test requirements for static or dynamic testing, matches the corresponding test plan, and projects the test image onto the test screen to obtain the test image and light field distribution image sequence. Step S2: Simultaneously acquire the light field distribution image sequence to form a multi-source detection data set; Step S3: Output the static detection total score and the dynamic detection total score based on the sequence of the test images and the corresponding light field distribution image sequence; Step S4: Obtain the performance evaluation score based on the total static detection score and the total dynamic detection score; Step S5: Generate a comprehensive evaluation report containing improvement suggestions based on the performance evaluation output.