A control method for self-adaptive brightness compensation of road flatness

Abstract

The application discloses a control method for road flatness self-adaptive brightness compensation, comprising the following steps: carrying out partition calibration to establish one-to-one correspondence between LED pixels and road surface areas; collecting three-dimensional flatness data of the current driving road surface; carrying out feature extraction to identify the position, area and depth parameters of the concave area; matching the target LED pixel group corresponding to the concave area; calculating the brightness compensation value of the target LED pixel group; and driving the target LED pixel group to execute brightness compensation. The application combines the vehicle-mounted laser radar and the visual sensor to collect the road flatness data, can accurately identify the position, depth and area of the concave area, solves the problem that the traditional technology cannot perceive the microscopic unevenness of the road surface, and provides reliable data support for brightness compensation.

Description

Technical Field

[0001] This invention belongs to the field of automotive lighting control technology, specifically relating to a control method for adaptive brightness compensation based on road surface smoothness. Background Technology

[0002] In the field of automotive lighting, the uniformity and accuracy of road lighting directly affect driving safety. Traditional MicroLED headlights often use fixed brightness or control methods that adjust solely based on ambient light, failing to consider the blind spots caused by differences in road surface smoothness.

[0003] In actual driving, road surfaces often have uneven surfaces such as depressions, potholes, and bumps. Depressions can easily create shadows, making it difficult for drivers to clearly identify the depth of the depression and key information such as obstacles, increasing the risk of hitting the chassis or losing control; while bumps may cause glare due to excessive light reflection, interfering with the vision of oncoming vehicles or pedestrians.

[0004] Existing brightness control technologies only focus on a single dimension such as ambient light intensity and user gear selection, without establishing a correlation mechanism between the headlight illumination area and road surface smoothness. Even some high-end models with adaptive lighting functions mostly adjust parameters such as steering angle and vehicle speed, failing to achieve precise brightness compensation for microscopic road surface unevenness.

[0005] In addition, the traditional overall brightness adjustment method of car lights cannot meet the pixel-level lighting needs, making it difficult to provide targeted supplementary lighting for individual recessed areas. This results in low supplementary lighting efficiency and energy waste, and may also cause new visual interference due to excessive brightness enhancement in non-target areas.

[0006] In summary, existing technologies have significant shortcomings in terms of adaptability to road surface smoothness and pixel-level precise illumination. There is an urgent need for a control algorithm that can sense road surface smoothness in real time and directionally compensate for lighting brightness in order to eliminate blind spots caused by uneven road surfaces and improve driving safety and comfort. Summary of the Invention

[0007] The purpose of this invention is to provide a control method for adaptive brightness compensation based on road surface smoothness, thereby solving the problems mentioned in the background art. The control method for adaptive brightness compensation based on road surface smoothness provided by this invention features real-time sensing of road surface smoothness, establishment of pixel-level LED-illuminated area correlation, and directional enhancement of light intensity in concave areas, achieving precise compensation of illumination brightness.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a control method for adaptive brightness compensation of road surface smoothness, comprising the following steps:

[0009] S1. Perform partition calibration on the pixel-level array of Micro LED vehicle lights, clarify the coordinates of the illumination area of ​​each LED and the road projection range, and establish a one-to-one correspondence between LED pixels and road areas.

[0010] S2. By combining vehicle-mounted lidar with vision sensors, three-dimensional smoothness data of the current road surface is collected and transmitted to the vehicle lighting controller.

[0011] S3, the vehicle lighting controller filters, reduces noise, and extracts features from road surface smoothness data to identify the location, area, and depth parameters of the depression area;

[0012] S4. Match the target LED pixel group corresponding to the concave area according to the correspondence between LED pixels and road surface area;

[0013] S5. Calculate the brightness compensation value of the target LED pixel group based on the depth and area of ​​the recessed region;

[0014] S6. Combining the base brightness value corresponding to the ambient light brightness with the brightness value set by the user, the final output brightness is calculated through a weighted fusion algorithm, and the target LED pixel group is driven to perform brightness compensation.

[0015] In this invention, further, in S1, the method for establishing a one-to-one correspondence between LED pixels and road surface areas includes the following steps:

[0016] S11. Divide the Micro LED vehicle headlight pixel array into several independent control units and record the LED pixel number and physical position coordinates;

[0017] S12. Collect the boundary coordinates of the road surface projection area of ​​each LED pixel at the standard test site;

[0018] S13. Establish a mapping database based on projection data;

[0019] S14. Verify and correct database deviations through actual testing to ensure accurate correspondence.

[0020] In this invention, in step S12, the sampling distance is less than 10m.

[0021] In this invention, step S2, the method for collecting three-dimensional road surface smoothness data, includes the following steps:

[0022] S21. The vehicle-mounted LiDAR is used as the main sensor to scan the road ahead and obtain three-dimensional point cloud data. The forward-looking camera is used as an auxiliary visual sensor to simultaneously collect road images.

[0023] S22. Data is transmitted to the vehicle lighting controller in real time via the vehicle's CAN bus.

[0024] In a further step of this invention, in S21, the vehicle-mounted lidar scans the road surface 3-50 meters ahead at a sampling frequency of 100Hz.

[0025] In this invention, in step S22, the transmission delay of the vehicle CAN bus transmitting data to the headlight controller is within 50ms.

[0026] In a further step of this invention, in S3, the method for processing road surface smoothness data and identifying depression areas includes the following steps:

[0027] S31. Gaussian filtering is used to remove measurement noise from the vehicle-mounted LiDAR, and median filtering is used to remove salt-and-pepper noise from the visual image.

[0028] S32. The average height of the road surface within a continuous 10-meter range shall be used as the reference height.

[0029] S33. Set a depression judgment threshold. Areas whose height is 3cm or more below the reference height and are verified as real by visual images are judged as depression areas.

[0030] S34. Calculate and store the center coordinates, boundary range, maximum depth, and actual area parameters of the depression region.

[0031] In this invention, further, in step S4, the method for matching the target LED pixel group includes the following steps:

[0032] S41. Call the LED pixel-road area mapping database, and select the corresponding LED pixels to form the target supplementary light pixel group according to the boundary coordinates of the depression area and the illumination distance;

[0033] S42. Prioritize pixels according to the degree of overlap between the radiation range and the concave region;

[0034] S43. Perform validity checks on pixel groups to eliminate faulty and obstructed LED pixels.

[0035] In this invention, step S5, the method for calculating the brightness compensation value, includes the following steps:

[0036] S51. Establish the correspondence between the indentation depth and the brightness compensation coefficient. When the indentation depth is 3-5cm, set the compensation coefficient to 1.2-1.5. When the indentation depth is 5-10cm, set the compensation coefficient to 1.5-2.0. When the indentation depth exceeds 10cm, set the compensation coefficient to 2.0-2.5.

[0037] S52. Adjust the compensation coefficient based on the depression area. When the depression area is greater than 0.1㎡, add an additional compensation coefficient of 0.2 on the basis of the depth-corresponding compensation coefficient; when the depression area is less than 0.01㎡, reduce the compensation coefficient by 0.1.

[0038] S53. Assign compensation weights according to the priority of the target pixel group. First-priority LED pixels use 100% compensation value, and second-priority LED pixels use 80% compensation value. Calculate the final brightness compensation increment for each LED pixel.

[0039] In this invention, further, in step S6, the method for calculating the final output brightness and performing compensation using a weighted fusion algorithm includes the following steps:

[0040] S61. Calculate the base brightness value B based on the ambient light intensity and obtain the user-set brightness value F.

[0041] S62. Set the weight of the base brightness value to 0.4, the weight of the user's brightness level to 0.3, and the weight of the flatness compensation value to 0.3. Calculate the final brightness value using the formula L = 0.4 × B + 0.3 × F + 0.3 × compensation increment.

[0042] S63. Convert the final brightness value into a PWM drive signal to control the target LED pixel group to emit light, and update the compensation parameters every 10ms.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. This invention combines vehicle-mounted LiDAR with a vision sensor to collect road surface smoothness data, which can accurately identify the location, depth and area of ​​depressions, solving the problem that traditional technologies cannot detect microscopic unevenness of the road surface, and providing reliable data support for brightness compensation.

[0045] 2. This invention establishes a one-to-one correspondence between Micro LED pixels and road surface areas, achieving pixel-level directional supplementary lighting. It only increases the light intensity of the corresponding LEDs in the recessed areas, avoiding brightness redundancy in non-target areas, thus eliminating lighting blind spots and reducing energy waste.

[0046] 3. This invention combines the depth and area of ​​the depression with a dynamically adjusted compensation coefficient, while also integrating ambient light and user gear parameters. It balances the accuracy of supplementary lighting with the personalized needs of users, effectively improving the uniformity of lighting on uneven road sections and reducing driving risks. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the process of the present invention.

[0048] Figure 2 This is a schematic diagram illustrating the process of calibrating the correspondence between LED pixels and road surface areas according to the present invention.

[0049] Figure 3 This is a schematic diagram of the process for road surface smoothness data processing and depression identification in this invention.

[0050] Figure 4 This is a schematic diagram of the process for matching the target LED pixel group in this invention.

[0051] Figure 5 This is a schematic diagram of the process for calculating the brightness compensation value according to the present invention.

[0052] Figure 6 This is a schematic diagram of the weight fusion and brightness output process of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0055] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0056] In the description of this invention, the terms "upper," "lower," "right," and "left," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used only for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. Furthermore, the terms "first" and "second" are used merely for distinction in description and have no special meaning.

[0057] Please see Figures 1-6 This embodiment provides the following technical solution: a control method for adaptive brightness compensation of road surface smoothness, comprising the following steps.

[0058] S1. Calibration of the correspondence between LED pixels and road surface areas;

[0059] S11. Divide the pixel array of the Micro LED vehicle light into several independent control units in the horizontal and vertical directions. Each control unit corresponds to a single LED pixel or a group of LED pixels. Record the number and physical position coordinates of all LED pixels.

[0060] S12. Set up a road surface simulation platform with a distance of 10m in a standard test site, and use high-precision optical measurement equipment to collect the left and right boundary coordinates of the projection area formed on the road surface after each LED pixel is lit.

[0061] S13. Establish an LED pixel-road area mapping database based on projection data to clarify the illumination range of each LED pixel at a distance of 10m. The database contains key information such as LED pixel number and left and right coordinates of road projection.

[0062] S14. Verify the mapping database by actually lighting up a single LED pixel and taking a picture of the road surface projection. Compare the measured projection range with the database record value, correct the deviation, and ensure the accuracy of the correspondence.

[0063] S2, Road surface smoothness data collection;

[0064] S21. Select an onboard LiDAR as the main sensor and install it in the optimal installation position on the vehicle to ensure that the detection range covers the entire area illuminated by the headlights. At the same time, use a forward-looking camera as an auxiliary visual sensor.

[0065] S22. The lidar emits a laser beam at a sampling frequency of 100Hz to scan the road surface within a range of 3-50 meters ahead, and acquires three-dimensional point cloud data of the road surface, including the height coordinate information of each point; the visual sensor simultaneously acquires road surface images to help identify minute depressions that the lidar does not detect.

[0066] S23. The 3D point cloud data of the LiDAR and the image data of the vision sensor are transmitted to the headlight controller in real time via the vehicle CAN bus, with the transmission delay controlled within 50ms to ensure data timeliness.

[0067] S3. Road surface smoothness data processing and depression identification;

[0068] S31. The vehicle lighting controller preprocesses the received 3D point cloud data, uses a Gaussian filtering algorithm to remove the measurement noise of the lidar, and uses median filtering to remove salt-and-pepper noise from the visual image.

[0069] S32. Extract the height reference value of the three-dimensional point cloud data of the road surface, and use the average height of the road surface within a continuous 10-meter range as the reference height of the current road section.

[0070] S33. Set a depression judgment threshold. When the height of a road surface area is 3cm or more lower than the reference height, it is judged as a depression area that needs to be supplemented with light. At the same time, combine the grayscale characteristics of the visual image to verify the authenticity of the depression area and eliminate false depression signals such as shadows and stains.

[0071] S34. Calculate the key parameters of the depression region, including the center coordinates, boundary range, maximum depth and actual area of ​​the depression, and store them in the controller cache.

[0072] S4, Target LED pixel group matching;

[0073] S41. The vehicle headlight controller calls the LED pixel-road area mapping database established in S1, and filters out LED pixels whose illumination range completely or partially covers the recessed area based on the boundary coordinates of the recessed area and the illumination distance.

[0074] S42. The selected LED pixels are grouped into target supplementary light pixel groups, and the priority of each LED pixel in the group is marked. The LED pixel with the highest overlap between the illumination range and the concave area is set as the first priority, and the one with the second highest overlap is set as the second priority.

[0075] S43. Verify the effectiveness of the target supplementary lighting pixel group, eliminate LED pixels that cannot emit light normally due to malfunction or obstruction, and ensure the reliability of supplementary lighting execution.

[0076] S5. Calculation of brightness compensation value;

[0077] S51. Establish the correspondence between recess depth and brightness compensation coefficient: when the recess depth is 3-5cm, the compensation coefficient is set to 1.2-1.5; when the recess depth is 5-10cm, the compensation coefficient is set to 1.5-2.0; when the recess depth exceeds 10cm, the compensation coefficient is set to 2.0-2.5 to ensure that the compensated brightness is adapted to the recess depth.

[0078] S52. Adjust the compensation value based on the depression area: When the depression area is greater than 0.1㎡, add an additional compensation coefficient of 0.2 on the basis of the depth-corresponding compensation coefficient; when the depression area is less than 0.01㎡, reduce the compensation coefficient by 0.1 to avoid excessive or insufficient supplementary lighting.

[0079] S53. Assign compensation weights according to the priority of the target LED pixel group: use 100% compensation value for first-priority LED pixels and 80% compensation value for second-priority LED pixels, and calculate the final brightness compensation increment for each LED pixel.

[0080] S6, Weighted fusion and brightness output;

[0081] S61. The vehicle light controller obtains the current ambient light brightness through the vehicle brightness sensor and calculates the basic brightness value B under the current environment by combining the preset ambient light-basic brightness correspondence curve.

[0082] S62. Obtain the brightness value F set by the user. The brightness level is set from 1 to N (N≥4). Different brightness levels correspond to different basic brightness gain coefficients, and the gain coefficient range is 0.8-1.3.

[0083] S63. The final output brightness is calculated using a weighted fusion algorithm: the weight of the base brightness value is set to 0.4, the weight of the user's brightness level value is set to 0.3, the weight of the flatness compensation value is set to 0.3, and the final brightness value L = 0.4 × B + 0.3 × F + 0.3 × compensation increment.

[0084] S64, the vehicle headlight controller converts the final brightness value into a PWM drive signal to precisely control the luminous intensity of the target LED pixel group, thereby achieving directional brightness compensation for the concave area; at the same time, it monitors the changes in road surface smoothness data in real time and updates the compensation parameters every 10ms to ensure that the supplementary lighting effect dynamically adapts to the road surface conditions.

[0085] In summary, this invention combines vehicle-mounted LiDAR with visual sensors to collect road surface smoothness data, accurately identifying the location, depth, and area of ​​depressions. This solves the problem of traditional technologies being unable to detect microscopic road surface unevenness, providing reliable data support for brightness compensation. This invention establishes a one-to-one correspondence between Micro LED pixels and road surface areas, achieving pixel-level directional lighting. It only increases the light intensity of corresponding LEDs in depression areas, avoiding brightness redundancy in non-target areas, thus eliminating blind spots and reducing energy waste. This invention dynamically adjusts the compensation coefficient based on depression depth and area, while also incorporating ambient light and user-defined settings, balancing lighting accuracy with personalized user needs. This effectively improves the uniformity of lighting on uneven road sections and reduces driving risks.

[0086] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control method for adaptive brightness compensation of road surface smoothness, characterized in that, Includes the following steps: S1. Perform partition calibration on the pixel-level array of Micro LED vehicle lights, clarify the coordinates of the illumination area of ​​each LED and the road projection range, and establish a one-to-one correspondence between LED pixels and road areas. S2. By combining vehicle-mounted lidar with vision sensors, three-dimensional smoothness data of the current road surface is collected and transmitted to the vehicle lighting controller. S3, the vehicle lighting controller filters, reduces noise, and extracts features from road surface smoothness data to identify the location, area, and depth parameters of the depression area; S4. Match the target LED pixel group corresponding to the concave area according to the correspondence between LED pixels and road surface area; S5. Calculate the brightness compensation value of the target LED pixel group based on the depth and area of ​​the recessed region; S6. Combining the base brightness value corresponding to the ambient light brightness with the brightness value set by the user, the final output brightness is calculated through a weighted fusion algorithm, and the target LED pixel group is driven to perform brightness compensation.

2. The control method for adaptive brightness compensation of road surface smoothness according to claim 1, characterized in that: The method for establishing a one-to-one correspondence between LED pixels and road surface areas in step S1 includes the following steps: S11. Divide the Micro LED vehicle headlight pixel array into several independent control units and record the LED pixel number and physical position coordinates; S12. Collect the boundary coordinates of the road surface projection area of ​​each LED pixel at the standard test site; S13. Establish a mapping database based on projection data; S14. Verify and correct database deviations through actual testing to ensure accurate correspondence.

3. The control method for adaptive brightness compensation of road surface smoothness according to claim 2, characterized in that: In S12, the sampling distance is less than 10m.

4. The control method for adaptive brightness compensation of road surface smoothness according to claim 1, characterized in that: The method for collecting three-dimensional road surface smoothness data in step S2 includes the following steps: S21. The vehicle-mounted LiDAR is used as the main sensor to scan the road ahead and obtain three-dimensional point cloud data. The forward-looking camera is used as an auxiliary visual sensor to simultaneously collect road images. S22. Data is transmitted to the vehicle lighting controller in real time via the vehicle's CAN bus.

5. The control method for adaptive brightness compensation of road surface smoothness according to claim 4, characterized in that: In step S21, the vehicle-mounted lidar scans the road surface 3-50 meters ahead at a sampling frequency of 100Hz.

6. The control method for adaptive brightness compensation of road surface smoothness according to claim 4, characterized in that: In S22, the transmission delay of the vehicle CAN bus to the headlight controller is within 50ms.

7. The control method for adaptive brightness compensation of road surface smoothness according to claim 1, characterized in that: The method for processing road surface smoothness data and identifying depression areas in step S3 includes the following steps: S31. Gaussian filtering is used to remove measurement noise from the vehicle-mounted LiDAR, and median filtering is used to remove salt-and-pepper noise from the visual image. S32. The average height of the road surface within a continuous 10-meter range shall be used as the reference height. S33. Set a depression judgment threshold. Areas whose height is 3cm or more below the reference height and are verified as real by visual images are judged as depression areas. S34. Calculate and store the center coordinates, boundary range, maximum depth, and actual area parameters of the depression region.

8. The control method for adaptive brightness compensation of road surface smoothness according to claim 1, characterized in that: In step S4, the method for matching the target LED pixel group includes the following steps: S41. Call the LED pixel-road area mapping database, and select the corresponding LED pixels to form the target supplementary light pixel group according to the boundary coordinates of the depression area and the illumination distance; S42. Prioritize pixels according to the degree of overlap between the radiation range and the concave region; S43. Perform validity checks on pixel groups to eliminate faulty and obstructed LED pixels.

9. The control method for adaptive brightness compensation of road surface smoothness according to claim 1, characterized in that: The method for calculating the brightness compensation value in step S5 includes the following steps: S51. Establish the correspondence between the indentation depth and the brightness compensation coefficient. When the indentation depth is 3-5cm, set the compensation coefficient to 1.2-1.

5. When the indentation depth is 5-10cm, set the compensation coefficient to 1.5-2.

0. When the indentation depth exceeds 10cm, set the compensation coefficient to 2.0-2.

5. S52. Adjust the compensation coefficient based on the depression area. When the depression area is greater than 0.1㎡, add an additional compensation coefficient of 0.2 on the basis of the depth-corresponding compensation coefficient; when the depression area is less than 0.01㎡, reduce the compensation coefficient by 0.

1. S53. Assign compensation weights according to the priority of the target pixel group. First-priority LED pixels use 100% compensation value, and second-priority LED pixels use 80% compensation value. Calculate the final brightness compensation increment for each LED pixel.

10. The control method for adaptive brightness compensation of road surface smoothness according to claim 1, characterized in that: In step S6, the method for calculating the final output brightness and performing compensation using a weighted fusion algorithm includes the following steps: S61. Calculate the base brightness value B based on the ambient light intensity and obtain the user-set brightness value F. S62. Set the weight of the base brightness value to 0.4, the weight of the user's brightness level to 0.3, and the weight of the flatness compensation value to 0.

3. Calculate the final brightness value using the formula L = 0.4 × B + 0.3 × F + 0.3 × compensation increment. S63. Convert the final brightness value into a PWM drive signal to control the target LED pixel group to emit light, and update the compensation parameters every 10ms.

Images