Patent Application
20230382293
This invention is related to the field of automotive lighting devices, and more particularly, to the management of the electronic data derived from the control of the lighting sources.
Current lighting devices comprises an increasing number of light sources which has to be controlled, to provide adaptive lighting functionalities.
This number of light sources involves a big amount of data, which has to be managed by the control unit. The CAN protocol is often used, in some of their variants (CAN-FD is one of the most used ones) to transfer data between the PCM and the light module. However, some car manufacturers decide to limit the bandwidth of the CAN protocol, and this affects the management operations, which usually requires about 5 Mbps.
Current compression methods are not very efficient for light patterns, and this compromises the bandwidth reduction which is requested by car manufacturers. Higher compression rates always involve a loss of data which may not be acceptable by automotive regulations.
A solution for this problem is sought.
The invention provides a solution for these problems by means of a method for manufacturing an automotive lighting arrangement, an automotive lighting arrangement and a method for operating said automotive lighting arrangement.
Unless otherwise defined, all terms (including technical and scientific terms) used herein are to be interpreted as is customary in the art. It will be further understood that terms in common usage should also be interpreted as is customary in the relevant art and not in an idealised or overly formal sense unless expressly so defined herein.
In this text, the term “comprises” and its derivations (such as “comprising”, etc.) should not be understood in an excluding sense, that is, these terms should not be interpreted as excluding the possibility that what is described and defined may include further elements, steps, etc.
In a first inventive aspect, the invention provides a method for manufacturing an automotive lighting arrangement, comprising the steps of
This method is aimed to manage the image data which is exchanged between a control unit and a light module. Instead of using methods of linearizing the values, a convolutional neural network, such as a deep autoencoder, is used to produce compressed data in a encoder block based on image data. The same autoencoder is able to restore the original version of the image data in the decoder block.
The main advantage of this method is the ability to define a flexible data loss, improving the given compression rate. The training may reduce the data loss and the compression rate may be defined for the neural network.
In some particular embodiments, the encoder block comprises a convolution layer, a rectified linear unit layer and a normalization layer.
The presence of several layers contribute to decrease the image size of the training data used in the learning functionality and increase the compression rate.
In some particular embodiments, the method further comprises the step of choosing the ratio between the size of convolution layer of the encoder block and the normalization layer of the encoder block.
This choice is directly related to the compression rate, and will also have influence on the processing speed.
In some particular embodiments, the decoder block comprises an unsampling convolution layer, a rectified linear unit layer and a normalization layer.
The presence of several layers of decoder block contribute to increase the amount of data compressed by the encoder, to find the decompressed image needed to learn the functionality and increase the compression rate.
In a second inventive aspect, the invention provides an automotive lighting arrangement manufactured by a method according to the first inventive aspect, the automotive lighting arrangement comprising
This automotive lighting arrangement may be installed in an automotive vehicle for a better operation of the lighting process. Since the encoder block and the decoder block have been trained as parts of the same deep autoencoder, they are perfectly coordinated to obtain an accurate copy of the original image data.
In some particular embodiments, the lighting module comprises solid-state light sources, such as LEDs.
The term “solid state” refers to light emitted by solid-state electroluminescence, which uses semiconductors to convert electricity into light. Compared to incandescent lighting, solid state lighting creates visible light with reduced heat generation and less energy dissipation. The typically small mass of a solid-state electronic lighting device provides for greater resistance to shock and vibration compared to brittle glass tubes/bulbs and long, thin filament wires. They also eliminate filament evaporation, potentially increasing the life span of the illumination device. Some examples of these types of lighting comprise semiconductor light-emitting diodes (LEDs), organic light-emitting diodes (OLED), or polymer light-emitting diodes (PLED) as sources of illumination rather than electrical filaments, plasma or gas.
In a third inventive aspect, the invention provides a method for operating an automotive lighting arrangement according to the previous inventive aspect, comprising the steps of
This method allows the automotive arrangement to operate with a lower communication bandwidth than the traditional ones. The processed images data produced as the output of the encoder represents the compressed image. Then, the decoder receives the compressed data as input to restore an image as closer as possible to the original image data.
In some particular embodiments, the method comprises a step of normalizing the image data before operating the encoder block to reduce its data size. Particularly, the step of normalizing the image data comprises converting each value of the image data in a converted value comprised between 0 and 1.
This normalization is used to improve the autoencoder operation, since these normalized values are optimal for its operation.
In some particular embodiments, the method comprising a step of dividing the image data in data subarrays of the same format, before operating the encoder block to reduce its data size.
Each type of lighting pattern (low beam, high beam . . . ) may operate with particular features. If these features are isolated in different subarrays, and treated independently, the compression and operation of the arrangement will be improved.
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:
In these figures, the following reference numbers have been used:
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.
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.
In this first step, a deep autoencoder 1 is trained to process image data. This autoencoder 1 comprises one encoder block 2, one decoder block 3 and a loss function unit 7 to minimize the error produced by the autoencoder 1.
This training may be performed with real light patterns that will be provided by the automotive manufacturer, so that the compression may be optimized and the autoencoder may provide the minimum data loss possible for a given compression rate.
The encoder block 2 comprises a convolution layer 21, a rectified linear unit layer 22 and a normalization layer 23, while the decoder block 3 comprises an unsampling convolution layer 31, a rectified linear unit layer 32 and a normalization layer 33. The presence of several layers contribute to decrease the amount of training data needed to learn the functionality and increase the compression rate.
The compression rate is given by the ratio between the size of the convolution layer 21 and the size of the normalization layer 23 of the encoder block 2, which will provide the size of the data which is transmitted to the decoder block 3. This is relevant, since, once this autoencoder 1 has been trained, the decoder block 3 will be separated from the encoder block 2, and each element will be installed in different parts of an automotive vehicle.
This lighting device 5 therefore achieves a good quality projection with an improved transmission bandwidth.
Firstly, an image pattern 6 is produced by the control unit of the vehicle. This image pattern is sent to the encoder block, which divides the image in different portions, according to the nature of the same.
In this case, since the image is a low beam pattern, a first portion 61 comprises the flat and a second portion 62 comprises the kink. These two images are normalized so that the luminous intensity of each pixel is scaled to the range between 0 and 1. Then, the normalized data undergo the encoding processing, where two vectors 71, 72 are produced.
These two vectors 71, 72, which have a size which is substantially lower than the original pattern 6, are transmitted to the lighting device.
There, the decoder receives the vectors 71, 72 and processes them, producing two restored images 81, 82, one for the flat and another one for the kink. They are joint and sent to the lighting module so that they are finally projected.
| Number | Date | Country | Kind |
|---|---|---|---|
| FR2011162 | Oct 2020 | FR | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/079941 | 10/28/2021 | WO |
