METHOD FOR OPERATING AN AUTOMOTIVE LIGHTING DEVICE AND AUTOMOTIVE LIGHTING DEVICE
20230347812 ยท 2023-11-02
Assignee
Inventors
Cpc classification
B60Q1/0023
PERFORMING OPERATIONS; TRANSPORTING
G06V10/774
PHYSICS
B60Q1/085
PERFORMING OPERATIONS; TRANSPORTING
G06V10/60
PHYSICS
International classification
B60Q1/00
PERFORMING OPERATIONS; TRANSPORTING
G06V20/56
PHYSICS
G06V10/774
PHYSICS
Abstract
This invention provides a method for operating an automotive lighting device including a plurality of solid-state light sources. This method includes acquiring image data of a working zone in front of the automotive lighting device, transforming the image data into a luminance map, providing a desired light pattern, calculating an adapted light pattern which provides the desired light pattern when projected over the luminance map and projecting the adapted light pattern.
Claims
1. A method for operating an automotive lighting device with a matrix arrangement of light pixels, the method comprising: acquiring image data of a working zone in front of the automotive lighting device; transforming the image data into a luminance map; providing a desired light pattern; calculating an adapted light pattern which provides the desired light pattern when projected over the luminance map; and projecting the adapted light pattern.
2. The method according to claim 1, wherein the method is performed more than about twice per second.
3. The method according to claim 1, wherein the transforming the image data into a luminance map is carried out by a control unit which is configured to transform the image data into a luminance map by: training the control unit to provide a luminance map with a training dataset of image data; and testing the control unit comparing the luminance map provided by the control unit with measured luminance map.
4. The method according to claim 3, wherein the training the control unit includes the use of a machine learning algorithm.
5. The method according to claim 1, wherein the calculating the adapted light pattern is carried out by a control unit which is configured to calculate the adapted light pattern by: training the control unit to calculate an adapted light pattern in response to a training dataset of luminance map and desired light pattern; and testing the control unit comparing the calculated adapted light pattern with measured light pattern.
6. The method according to claim 5, wherein the training the control unit includes the use of a machine learning algorithm.
7. The method according to claim 1, wherein the luminance map isolates the position of the road from the position of other objects in the working zone.
8. The method according to claim 1, wherein the image data comprises RGB pixels.
9. The method according to claim 1, wherein the image data is acquired by an infrared camera.
10. (canceled)
11. A computer program including instructions which, when the program is executed by a control unit, cause the control unit to: acquire image data of a working zone from the camera; transform the image data into a luminance map; provide a desired light pattern; calculate an adapted light pattern which provides the desired light pattern when projected over the luminance map; and project the adapted light pattern with the matrix arrangement of solid-state light sources.
12. An automotive lighting system comprising: a matrix arrangement of solid-state light sources; a camera configured to acquire image data; and a control 9unit configured to: acquire image data of a working zone from the camera; transform the image data into a luminance map; provide a desired light pattern; calculate an adapted light pattern which provides the desired light pattern when projected over the luminance map; and project the adapted light pattern with the matrix arrangement of solid-state light sources.
13. The automotive lighting system according to claim 12, wherein the matrix arrangement comprises at least 2000 solid-state light sources.
Description
BRIEF DESCRIPTION OF DRAWINGS
[0042] To complete the description and in order to provide for a better understanding of the invention, a set of drawings is provided. Said drawings form an integral part of the description and illustrate an embodiment of the invention, which should not be interpreted as restricting the scope of the invention, but just as an example of how the invention can be carried out. The drawings comprise the following figures:
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DETAILED DESCRIPTION OF THE INVENTION
[0048] In In these figures, the following reference numbers have been used: [0049] 1 Headlamp [0050] 2 LED [0051] 3 Control unit [0052] 4 Camera [0053] 5 Adapted light pattern [0054] 100 Automotive vehicle
[0055] The example embodiments are described in sufficient detail to enable those of ordinary skill in the art to embody and implement the systems and processes herein described. It is important to understand that embodiments can be provided in many alternate forms and should not be construed as limited to the examples set forth herein.
[0056] Accordingly, while embodiment can be modified in various ways and take on various alternative forms, specific embodiments thereof are shown in the drawings and described in detail below as examples. There is no intent to limit to the particular forms disclosed. On the contrary, all modifications, equivalents, and alternatives falling within the scope of the appended claims should be included.
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[0058] This headlamp 1 is installed in an automotive vehicle 100 and comprises [0059] a matrix arrangement of LEDs 2, intended to provide a light pattern; [0060] a control unit 3 to perform a control of the operation of the LEDs 2; and [0061] a camera 4 intended to provide some external data.
[0062] This matrix configuration is a high-resolution module, having a resolution greater than 2000 pixels. However, no restriction is attached to the technology used for producing the projection modules.
[0063] The control unit, previously to its installation in the automotive headlamp, has undergone two training processes.
[0064] Both training processes comprise some machine learning steps, where the control unit is trained with training data provided by the plurality of sensors.
[0065] The first training process is concerning the transformation of the image data into a luminance map. This first training process comprises [0066] training the control unit to provide a luminance map with a training dataset of image data; and [0067] testing the control unit comparing the luminance map provided by the control unit with measured luminance map.
[0068] Different image data are provided, and the correspondent luminance map is created, due to the aforementioned algorithm.
[0069] The second training process is concerning the calculation of the adapted light pattern. This second training process comprises [0070] training the control unit to calculate an adapted light pattern in response to a training dataset of luminance map and desired light pattern; and [0071] testing the control unit comparing the calculated adapted light pattern with measured light pattern.
[0072] In this case, different luminance maps are provided, together with different desired light patterns. The control unit is trained to create the optimal light pattern which, in the particular light circumstances, achieves a final desired light pattern.
[0073] Once both training processes are finished, the control unit is installed in an automotive vehicle 100 of
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[0075] Each 0.2 seconds, the control unit of the lighting device receives an RGB image data which has been acquired by the camera 220. From this image, objects are classified, extracting the road feature 230 and the rest of the objects present in the image. Then, these data is converted into a luminance map 240 by the control unit. The control unit compares the luminance map with the desired light pattern 250 and then creates an adapted light pattern which 260, in combination with the luminance map estimated in the previous steps, provides the user with the desired light pattern.
[0076] The headlamp will project the adapted light pattern, which provides an adequate lighting at the minimum power consumption.
[0077] This process is repeated each 0.2 seconds.
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