DE102018101686B3 - Method for detecting pixel element states of a matrix headlight of a matrix headlight system - Google Patents

Abstract

The invention relates to a method for detecting pixel element states of a matrix headlight of a matrix headlight system, comprising the steps of: (a) detecting a wall onto which a light distribution can be projected by producing a low beam distribution by means of the matrix headlight system and capturing at least one reference point of the low beam distribution by means of a camera; b) generating a pattern projection (M) on the wall and embedding structures in the light distribution of the pattern projection (M), which differ from adjacent structures, c) extraction of all vertices (E1, E2, E3, E4) of the pixel elements from the pattern projection, d) Determination of actual pixel neighborhood relations for each of the corner points (E1, E2, E3, E4) determined in step c), e) assignment of the corner points (E1, E2, E3, E4) to pixel elements of individual headlight segments of the matrix headlights, f) examination whether in step e) at least one vertex (E1, E2, E3, E4) belong to one could not be assigned to the pixel element by comparing the actual pixel neighborhood relations with target pixel neighborhood relations.

Description

The present invention relates to a method of detecting pixel element states of a matrix headlamp of a matrix headlamp system.

In the development of motor vehicles, so-called matrix headlight systems, which typically have two matrix headlights, play an increasingly important role. These matrix headlights comprise a pixel matrix with selectively activatable or deactivatable, preferably also dimmable, pixel elements. It can be expected that in the future, and depending on the technology used, pixel resolutions of tens of thousands or hundreds of thousands of pixel elements can be achieved. With the pixel matrix quite different lighting functions can be implemented. One possible lighting function is, for example, a dazzle-free high-beam function, which does not dazzle oncoming road users when the high beam is activated. In this case, a vehicle-side camera (driver assistance camera) is used, which detects continuously oncoming as well as driving ahead of road users. The camera data is processed accordingly using image processing software. By means of a corresponding electronic control device, the individual pixel elements of the matrix headlamps of the matrix headlamp system are selectively controlled in order to achieve glare reduction.

Through active triangulation, the camera and the matrix headlights of the matrix headlight system can also be used for distance measurement in order to determine the distances between the vehicle equipped with the matrix headlight system and objects in the vehicle front-end scene.

According to the current state of the art, only actively-energized light sources, such as light-emitting diodes or laser light sources, have feedback between the electronic control device and the pixel matrix in order to obtain information about the pixel states. In contrast, this possibility of feedback is not given with switchable active display matrices, such as, for example, LCD matrices or DMD matrices. Consequently, in the case of the last-mentioned pixel matrices, no statement can be made (or at best using costly electronics) whether the corresponding pixel elements have changed state and thus can contribute, for example, to active glare reduction. Corresponding incorrect states of the pixel elements would, however, have far-reaching effects with regard to driver safety and the functional quality. In the case of a dazzle-free main beam, under certain circumstances this would result in dazzling oncoming traffic. With an active triangulation, an error-prone 3D reconstruction could possibly result from these false states.

From the DE 10 2016 109 030 A1 a method for evaluating detected features of a headlight of a motor vehicle is known, wherein during a drive of the motor vehicle with the headlight, a scene in a traffic space of the motor vehicle is irradiated with at least one specific light distribution, the illuminated scene from a camera of the motor vehicle in at least one Image is captured, in which at least one initial optical feature is dynamically searched and identified, this at least one initial optical feature is generated and analyzed, wherein at least one further optical feature is dynamically extracted in an environment of the at least one initial optical feature, the evaluated optical features are evaluated with a self-learning algorithm.

The DE 10 2014 015 796 A1 describes a method for testing the operability of a motor vehicle with a headlamp, which comprises a laser for generating light, by means of which a converter for emitting converted light is excited, that of the headlamp for generating a predefinable light distribution on a surface in a surrounding area of the motor vehicle is emitted, wherein the predetermined light distribution is adjusted by a control device of the headlamp. In order to check the state of the converter, a test pattern is set by the control device as light distribution. By means of an optical detection device of the motor vehicle, the test pattern is detected on the surface. By an evaluation device of the motor vehicle, the detected test pattern is compared with a predetermined reference pattern.

Furthermore, from the DE 10 2013 211 876 A1 a method for checking the adjustment of a headlamp in a motor vehicle is known.

The present invention has for its object to provide a method for detecting pixel element states of a matrix headlight of a matrix headlight system, by means of which possible malfunction of individual pixel elements can be reliably detected in a simple manner.

The solution to this problem provides a method with the features of claim 1. The subclaims relate to advantageous developments of the invention.

1. a) Detektion einer Wand, auf die eine Lichtverteilung projizierbar ist, durch Erzeugen einer Abblendlichtverteilung mittels des Matrixscheinwerfersystems und Erfassen zumindest eines Referenzpunktes der Abblendlichtverteilung mittels einer Kamera,
2. b) Erzeugen einer Musterprojektion auf der Wand und Einbettung von Strukturen in die Lichtverteilung der Musterprojektion, die sich von benachbarten Strukturen unterscheiden,
3. c) Extraktion aller Eckpunkte der Pixelelemente aus der Musterprojektion,
4. d) Bestimmung von Ist-Pixel-Nachbarschaftsrelationen für jeden der in Schritt c) ermittelten Eckpunkte,
5. e) Zuordnung der Eckpunkte zu Pixelelementen einzelner Scheinwerfersegmente der Matrixscheinwerfer,
6. f) Prüfung, ob in Schritt e) zumindest ein Eckpunkt einem zugehörigen Pixelelement nicht zugeordnet werden konnte, durch einen Vergleich der Ist-Pixel-Nachbarschaftsrelationen mit Soll-Pixel-Nachbarschaftsrelationen.

An inventive method for detecting pixel element states of a matrix headlight of a matrix headlight system comprises the steps

1. a) detection of a wall, on which a light distribution can be projected, by generating a low-beam light distribution by means of the matrix headlight system and detecting at least one reference point of the low-beam light distribution by means of a camera,
2. b) generating a pattern projection on the wall and embedding structures in the light distribution of the pattern projection that differ from adjacent structures,
3. c) extraction of all vertices of the pixel elements from the pattern projection,
4. d) determination of actual pixel neighborhood relations for each of the vertices determined in step c),
5. e) assignment of the corner points to pixel elements of individual headlight segments of the matrix headlights,
6. f) checking whether in step e) at least one vertex could not be assigned to an associated pixel element by comparing the actual pixel neighborhood relations with nominal pixel neighborhood relations.

The method according to the invention makes it possible to determine the activity state of the individual pixel elements of the matrix headlights on the basis of pattern projections and segment assignments by comparing actual pixel neighborhood relations with nominal pixel neighborhood relations, so that defective pixel elements can be identified as such. This detection of the pixel states is performed in a pre-defined, controllable state (during vehicle standstill) to avoid any misdetection. For this reason, in a first step, wall detection is performed to ensure that the vehicle is in front of a wall. The projection of the pattern is done to provide characteristic properties for particular pixel elements. In the process, specific structures are embedded in the light distribution that differ in their neighborhood. Subsequently, for each of the pixel elements, its vertices are extracted from this pattern projection to determine the states of the pixel elements. In a next step, the actual pixel neighborhood relations for each of the determined vertices are determined and there is an assignment of the vertices to pixel elements in individual headlight segments of the matrix headlights. Subsequently, by comparing the actual pixel neighborhood relations with nominal pixel neighborhood relations, it is checked whether at least one vertex could not be assigned to a pertinent pixel element. This check can be used to determine whether individual pixel elements have a defect or not. If the detected corner points (i.e., the actual pixel neighborhood relations) agree with the projected information (i.e., the target pixel neighborhood relations), it can be concluded that the pixel element in question has no defect. If there is no match, this indicates a defect of the relevant pixel element.

In an advantageous embodiment, it is proposed that in step a) H0V0 points of the low-beam light distribution be determined as reference points. A characteristic feature of a low beam distribution is the so-called HOVO point of the cut-off line. This HOVO point is defined as the intersection between a horizontal section and a subsequent increase in the cut-off line. These HOVO points of the matrix headlights are therefore particularly suitable as reference points, since their assignment to individual pixel elements of the matrix headlights of the matrix headlight system is clearly known. In wall detection, an attempt is made to capture the HOVO point of each of the two matrix headlights of the matrix headlight system by means of the camera. If these H0V0 points detected by the camera, it can be assumed that the motor vehicle is in front of a wall, which is mandatory for a downstream pattern projection.

By wall detection, however, can not be guaranteed that there are no inhomogeneities that can be caused for example by edge jumps or similar disturbances of the wall. In a preferred embodiment it can therefore be provided that between the steps a) and b) carried out a check whether at least one homogeneity criterion is met. Preferably, the examination of the fulfillment of the homogeneity criterion can be carried out by comparing the H0V0 points on the basis of their respective position in the camera image of the camera and by detecting a gray value variation in a defined environment of the H0V0 points.

In a preferred embodiment, it is proposed that the corner point extraction in step c) takes place on the basis of an image processing cascade.

In an advantageous embodiment, it is proposed that the pixel elements in the generation of the pattern projection in step b) are controlled such that each headlight segment produces the same light distribution in the defect-free state of all pixel elements.

In a particularly advantageous embodiment it can be provided that in step e) a global vertex assignment to the headlight segments is effected by the use of pixel trajectories. These pixel trajectories can be obtained in particular from corresponding calibration data and describe the pixel paths in the camera image. For example, if a motor vehicle equipped with the matrix headlight system drives toward an object remotely, the light distribution shifts on a defined pixel trajectory. Thus, all detected corner points can be assigned to the pixel elements in the individual headlight segments of the matrix headlights of the matrix headlight system on the basis of the pixel trajectories. Adjacent pixel elements can be correspondingly referenced to these pixel elements, so that a repetition of the light distributions (of the pattern) in the segments is unproblematic.

In order, for example, to avoid possible assignment errors etc. in the evaluation of potentially defective pixel elements, in a preferred embodiment it may be provided that a check is made in step f) whether in step e) a plurality of corner points, preferably all corner points, are not assigned to an associated pixel element could become. This advantageously results in the possibility of an additional plausibility check.

Preferably, step f) can be followed by at least one further step g) in which the positions of the detected defective pixel elements are checked. In this case, it can be checked, for example, whether the respective defective pixel element lies above or below the cut-off line and whether it lies in an outer or inner region of the light distribution, so that an evaluation of the relevance of the individual defective pixel elements for the generation of light distributions can be done by means of the matrix headlight system. Furthermore, for example, an entry into an error memory assigned to the matrix headlight system can take place and / or an error message can be generated for the vehicle driver. Depending on the relevant standards and on the position of the defective pixel element, it may also lead to a shutdown of the relevant matrix headlamp.

An advantageous embodiment, which can contribute to the reduction of misdetections, provides that after the detection of a (possibly) defective pixel element, a control check of the pixel element state of the relevant pixel element is carried out by further measurements (preferably without the pixel element state of the relevant pixel element being used for other measurement elements). or imaging techniques). Furthermore, if a malfunction of the pixel element is detected, the aforementioned measures (i.e., entry into a fault memory, generation of a warning message, and possibly shutdown of the respective matrix headlamp) may be initiated. Further, the pixel information of the pixel element concerned is ignored in the following wall position determinations. If the pixel element proves to be not defective in these control checks, it can continue to be used.

• 1 ein Blockdiagramm, welches die Realisierung einer blendfreien Fernlichtverteilung mittels eines Matrixscheinwerfersystems veranschaulicht,
• 2 ein Blockdiagramm mit den Verfahrensschritten eines Verfahrens zur Erkennung von Pixelelementzuständen eines Matrixscheinwerfers eines Matrixscheinwerfersystems,
• 3 eine Musterprojektion, anhand derer eine Zustandserkennung der Pixelelemente des Matrixscheinwerfersystems erfolgen kann,
• 4 eine schematische Darstellung, die das Prinzip der Bildung von Ist-Pixel-Nachbarschaftsrelationen auf Basis detektierter Eckpunkte eines Pixelelements veranschaulicht, wobei das betrachtete Pixelelement keinen Defekt aufweist,
• 5 eine schematische Darstellung, die das Prinzip der Bildung von Ist-Pixel-Nachbarschaftsrelationen auf Basis detektierter Eckpunkte eines Pixelelements veranschaulicht, wobei das betrachtete Pixelelement einen Defekt aufweist.

Further features and advantages of the present invention will become apparent from the following description of a preferred embodiment with reference to the accompanying drawings. Show

• 1 a block diagram illustrating the realization of a dazzle-free high beam distribution by means of a matrix headlight system,
• 2 a block diagram with the method steps of a method for detecting pixel element states of a matrix headlight of a matrix headlight system,
• 3 a pattern projection, by means of which a state recognition of the pixel elements of the matrix headlight system can take place,
• 4 a schematic representation illustrating the principle of the formation of actual pixel neighborhood relations based on detected vertices of a pixel element, wherein the pixel element under consideration has no defect,
• 5 a schematic diagram illustrating the principle of the formation of actual pixel neighborhood relations based on detected vertices of a pixel element, wherein the considered pixel element has a defect.

A matrix headlight system, by means of which different illumination functions, such as a dazzle-free high-beam function can be realized, usually comprises two matrix headlights (ie, a left matrix headlight and a right-hand matrix headlamp) having a pixel matrix with a plurality of selectively activatable or deactivatable, preferably also dimmable, pixel elements , In order to be able to realize the illumination function "glare-free high beam" with such a matrix headlight system, according to 1 in a first step 10 by means of a vehicle-side camera (driver assistance camera) and an image processing system, which can evaluate the camera images, detects objects that are in a Vehicle front scene are located. These objects may, in particular, be preceding or oncoming road users who are not to be blinded by the matrix headlight system.

In a second step 20 On the basis of the object positions, the (target) pixel states for the matrix headlights of the matrix headlight system are calculated, which prevent glare of the detected objects.

In a third step 30 Then, the calculated (target) pixel states are transmitted to an electronic control device of the relevant matrix headlight. By means of the electronic control device can then in a fourth step 40 the states of the pixel elements corresponding to those in step 30 adjusted calculations. As a result, so in step 50 achieved glare the detected objects in the vehicle front scene, so that a glare-free high beam can be realized.

In these matrix headlamp systems, where the pixel matrices of the matrix headlamps have a very high resolution (ie, a very high number of pixel elements), there may be a problem that individual pixel elements may malfunction and therefore not the desired, in step 30 take calculated (target) pixel states. This may in particular lead to an undesired glare of objects in the vehicle front scene or to an insufficient illumination of certain areas in the vehicle front scene.

Therefore, a method is presented below, by means of which the states of the individual pixel elements of a matrix headlight can be reliably determined so that possible malfunctions of individual pixel elements can be detected. A block diagram with the individual process steps is in 2 shown.

This method must be carried out in a previously defined, controllable state, so that any misrecognition, in particular due to inhomogeneities, etc., can be excluded. The method is performed when the motor vehicle equipped with the matrix headlight system is at a standstill in front of a wall.

In a first step 100 First, a wall detection is performed, in which the matrix headlights of the matrix headlight system project a conventional (and ECE-compliant) low-beam distribution, so that the determination of the pixel elements by the camera can be ensured. A characteristic feature of a low beam distribution is the so-called HOVO point of the cut-off line. This H0V0 point is defined as the intersection between a horizontal section and a subsequent increase in the cut-off line. These HOVO points of the matrix headlights are considered as reference points, since their assignment to individual pixel elements of the matrix headlights of the matrix headlight system is clearly known. Because the observed H0V0 "kink" of the cut-off line is characterized by switching off or switching on defined pixel elements. Accordingly, the detected vertices of the pixel elements are always assigned at this point and can be considered as reference points.

In wall detection, an attempt is made to detect the H0V0 point of each of the two matrix headlights of the matrix headlight system by means of the camera. If these HOVO points are detected by the camera, it can be assumed that the motor vehicle is in front of a wall, which is absolutely necessary for a downstream pattern projection. However, this wall detection can not guarantee that no inhomogeneities exist between the characteristic H0V0 points of the matrix headlights.

Accordingly, therefore, in a next step 200 a test is carried out to determine whether at least one homogeneity criterion has been fulfilled. This is done for example by comparing the detected characteristic HOVO points of the two matrix headlights (left and right) of the matrix headlight system based on their respective position in the camera image of the camera and by detecting a gray scale variation in a defined environment of the HOVO points.

The aim is to detect as homogeneous as possible projection walls that have no edge jumps or similar disturbances that lead to inhomogeneities. If the wall has been detected by the above-described detection of the HOVO points of the two matrix headlights of the matrix headlight system, now in this second step 200 the environment of the HOVO points checked for their homogeneity. For this purpose, numerous homogeneity criteria known from the prior art can be used. For example, for this purpose, the so-called Blur effect can be used, which considers edge elements in an image window and classifies them. Highly defined edges, when folded with a Gaussian kernel, are subject to severe edge degradation, whereas very blurred edges tend to have lower degradation. If that of the Camera detected edge elements were caused exclusively by the projected by the matrix headlights on the wall light distribution, the homogeneity criterion is set to the status "satisfied". If, on the other hand, strong edges are present in the image, such as edge jumps in the middle, etc., the homogeneity criterion is set to "not satisfied". If no edge elements are detected, the homogeneity criterion is also set to "satisfied". Thus, inhomogeneities or edge jumps in the vehicle front scene can be excluded. In this way it can be ensured that all projected pixel elements can be resolved by the camera and can be detected.

1. a) das Pixelelement ist aktiviert und weist seine maximale Helligkeit auf (nachfolgend: Zustand „1“),
2. b) das Pixelelement ist deaktiviert (nachfolgend: Zustand „0“) und leistet somit keinen aktiven Beitrag zur Beleuchtung der Fahrzeugfrontszene oder
3. c) das Pixelelement ist gedimmt (nachfolgend: Zustand „2“) und nimmt somit einen Zustand zwischen minimaler und maximaler Helligkeit ein.

If a wall has been detected in the manner described above and, in addition, the homogeneity criterion has also been met, in a next step 300 projected by the matrix headlight system a defined pattern projection M on the wall. The projection of the pattern is done to thereby provide characteristic properties for certain pixel elements. In the process, structures are specifically embedded in the light distribution that differ in their neighborhood and are defined by target pixel-neighborhood relations. In the further course, for each of the pixel elements, its vertices are extracted from this pattern projection M in order to determine the states of the pixel elements. To simplify the later considerations, it should be assumed in a simplified manner that each individual pixel element can assume one of three different states that can be captured by the camera:

1. a) the pixel element is activated and has its maximum brightness (hereinafter: state "1"),
2. b) the pixel element is deactivated (hereinafter: state "0") and thus makes no active contribution to the lighting of the vehicle front scene or
3. c) the pixel element is dimmed (hereafter: state "2") and thus assumes a state between minimum and maximum brightness.

As an alternative, these three states ("0", "1" and "2") can also be formed, for example, by three activated states of the pixel elements having different brightness values, which can be detected in particular as gray values by the camera. It is also understood that more than these three states can be assigned to each pixel element, for example if the dimmed state is subdivided into further states.

In step 400 For each of the pixel elements, the vertices are extracted from the pattern projection detected by the camera, so that the states of the pixel elements can be determined. This Eckpunktxtraktion can be done in particular on the basis of an image processing cascade.

An example of a pattern projection M is in 3 shown. This pattern projection M , which in this case forms a checkerboard pattern, this was simplified into only three segments S1 . S2 . S3 , each corresponding to individual headlight segments, divided to simplify the following explanations of the basic principle of the state recognition of the pixel elements. It should be noted at this point that the pixel matrices of the matrix headlights of the matrix headlight system can generate a large number of further segments. The states of some selected pixel elements that produce the pattern projection were in 1 denoted by "0", "1" and "2". The other pixel elements have deliberately not been labeled with their states so as not to complicate the drawing. The pattern projection M is thereby generated in such a way that the light distribution in each of the three segments S1 . S2 . S3 repeated, provided that the corresponding pixel elements work without errors.

On the basis of in 3 shown pattern projection M It can be seen that each pixel element has a rectangular, in particular a square outline. As explained below with reference to 4 will be explained, each of these pixel elements by four vertices E1 . E2 . E3 . E4 characterize. In one on the detection of the vertices (step 400 ) subsequent step 500 For all detected vertices, actual pixel neighborhood relations to adjacent pixel elements are determined. This will be described below by way of example with reference to the pixel element P1 within the first segment S1 be explained.

Each of these vertices E1 . E2 . E3 . E4 of the pixel element P1 may be determined by the pixel states ("0", "1" or "2") of the pixel element in question P1 and defined by the pixel states of three adjacent pixel elements. The corner point E1 (top left) of the pixel element P1 is defined (read in the arrow direction) by the pixel states "1", "2", "1" (state of the pixel element P1 yourself) and "2", leaving that vertex E1 an ID "1212" can be assigned. Analog can be the corner E2 (top right) are assigned the ID "2221". The corner E3 (bottom right) the ID "1212" can be assigned. Finally, the corner point E4 (bottom left) are assigned the ID "2122". All detected vertices of all pixel elements can be assigned corresponding IDs in the manner explained above.

As already briefly mentioned above, the pattern projection M in the way that generates the IDs of vertices E1 . E2 . E3 . E4 are different for adjacent pixel elements, whereby the pixel elements can be clearly distinguished from each other. As for the characterization of a vertex E1 . E2 . E3 . E4 In each case the states ("0", "1" or "2") of four neighboring pixel elements are considered, resulting in three possible different states a total of 81 possible IDs, through which the vertices E1 . E2 . E3 . E4 the pixel elements can be characterized. With only two different states ("0" and "1"), only 16 different IDs would result. Since, therefore, depending on the number of possible (also detectable by the image processing system) states of the pixel elements only a limited number of IDs available, the IDs must be repeated if the matrix headlight system has a correspondingly large number of pixel elements. For this reason, the light distribution of the pattern projection M in each of the (here three) segments S1 . S2 and S3 repeated. For each pixel element, for each of its four vertices E1 . E2 . E3 . E4 the corresponding actual pixel neighborhood relations are determined in the manner described above and assigned to corresponding IDs.

As explained above, with three possible states of the pixel elements ("0", "1" or "2"), a total of 81 IDs are available. Because in all segments S1 . S2 and S3 the same light distributions are generated, the IDs are repeated in defined pixel intervals from segment to segment. In one on the step 500 following process step 600 The vertices become the pixel elements in the segments S1 . S2 . S3 associated with the matrix headlight of the matrix headlight system. To a online capable and robust assignment of the characteristic features (in this case the vertices E1 . E2 . E3 . E4 ) extracted from the camera image to allow the pixel matrix of the respective matrix headlamp can preferably have pixel trajectories T1 . T2 . T3 be used. These pixel trajectories T1 . T2 . T3 , which are obtained from corresponding calibration data, describe the pixel paths in the camera image. For example, if a motor vehicle equipped with the matrix headlight system drives toward an object remotely, the light distribution shifts on a defined pixel trajectory T1 . T2 . T3 , Thus, all captured vertices E1 . E2 . E3 . E4 using the pixel trajectories T1 . T2 . T3 the pixel elements in the individual segments S1 . S2 . S3 be assigned to the matrix headlights of the matrix headlight system. Adjacent pixel elements can be referenced accordingly to these pixel elements, allowing a repetition of the IDs in the segments S1 . S2 . S3 is not a problem. If it is known which of the segments S1 . S2 . S3 the detected vertices E1 . E2 . E3 . E4 These may be based on the unique neighborhood relations of a pixel element within that segment S1 . S2 . S3 be assigned. This assignment can be made for all detected vertices, so that all pixel elements are the respective segments S1 . S2 . S3 can be assigned, provided that they function properly.

When the global and local vertex mapping has finished, you can now go to a subsequent step 700 be checked whether individual pixel segments could be faulty under certain circumstances. This should be based on the pixel element considered above P1 , whose cornerstones E1 . E2 . E3 . E4 are determined in the manner described above and provided with a characteristic ID, will be explained in more detail. Due to the repetitive light distributions in the segments S2 and S3 is expected to be the cornerstones E1 . E2 . E3 . E4 also in these segments S2 . S3 can be assigned successfully, provided that with the pixel element P1 of the first segment S1 corresponding pixel elements P1 ' respectively P1 " have no defect.

First, the corresponding pixel element P1 ' within the second segment S2 to be viewed as. If the pixel element P1 ' no defect, the vertices would have to E1 . E2 . E3 . E4 of the pixel element P1 ' of the second segment S2 have the same IDs as the vertices E1 . E2 . E3 . E4 of the pixel element P1 within the first segment S1 , This is the case here because of the IDs: ID (E1) = "1212", ID (E2) = "2221", ID (E3) = "1212" and ID (E4) = "2122". The IDs of the vertices E1 . E2 . E3 and E4 of the pixel element P1 ' of the second segment S2 thus correspond to the IDs of the vertices E1 . E2 . E3 and E4 of the corresponding pixel element P1 of the first segment S1 , Thus, the actual pixel neighborhood relation of the pixel element corresponds P1 ' within the second segment S2 the desired pixel neighborhood relation. The pixel element P1 ' thus has no error.

Below is the corresponding pixel element P1 " within the third segment S3 be considered closer. In the segment S3 assigns the pixel element P1 " a defect / malfunction so that it assumes the state "0" (instead of "1"). This results in a vertex assignment, as in 5 is shown.

For the corner E1 (top left) results in the pixel states "1", "2", "0" (defect of the pixel element P1 " ), "2", leaving that vertex E1 the ID "1202" is assigned. Already this single, not correctly assigned vertex E1 gives an indication that the pixel element P1 " within the third segment S3 has an error, so that it can be marked as defective. However, there is a single faulty referencing one of the vertices E1 . E2 . E3 . E4 may be due to errors in the assignment, etc., it can not be concluded with certainty that the relevant pixel element P1 " is faulty. For this reason, it is advantageous to perform an additional plausibility check. This is achieved by the fact that the other corner points E2 . E3 . E4 the supposedly defective pixel element P1 " be referenced. Present may be the vertex E2 (top right) the ID "2220" will be assigned. The corner point E3 (bottom right) has the ID "0212". Finally, the corner point E4 the ID "2022" are assigned. Thus, the IDs of the three other key points E2 . E3 and E4 for the displayed pattern projection M not valid, so the pixel element P1 " can be set to the state "not functioning / defective".

Thus, the actual pixel neighborhood relations of the detected vertices are correct E1 . E2 . E3 . E4 with the projected information (ie, the target pixel-neighborhood relations) becomes the pixel element in question P1 . P1 ' marked as "active / functional". On the other hand, if the actual pixel neighborhood relations do not match the projected information and thus do not match the expected target pixel neighborhood relations, the pixel element in question will become P1 " marked as "non-functional / defective" and preferably no longer considered for further generation of light distributions.

In response to a detected defective pixel element, the position of the defective pixel element can first be checked. In particular, it can be checked whether the defective pixel element lies above or below the cut-off line and whether it lies in an outer or inner region of the light distribution.

Furthermore, for example, an entry into an error memory assigned to the matrix headlight system can take place and / or an error message can be generated for the vehicle driver. Depending on the relevant standards and on the position of the defective pixel element, it may also lead to a shutdown of the relevant matrix headlamp.

An optional procedure for the reduction of misdetections provides that after the detection of a possibly defective pixel element (and preferably a corresponding marking of this pixel element as "defective"), a control check of the pixel state of the relevant pixel element is carried out by further measurements without the pixel state for other measurement - or imaging method to use. If, furthermore, a defect of the pixel element is detected, the above-mentioned measures (entry into an error memory, generation of a warning message and, if appropriate, switching off of the relevant matrix headlamp) can be initiated. Further, the pixel information of the pixel element concerned is ignored in the following wall position determinations. If the pixel element proves to be not defective in these control checks, it can continue to be used.

Claims (10)

A method of detecting pixel element states of a matrix headlamp of a matrix headlamp system, comprising the steps a) detection of a wall, on which a light distribution can be projected, by generating a low-beam light distribution by means of the matrix headlight system and detecting at least one reference point of the low-beam light distribution by means of a camera, b) generating a pattern projection (M) on the wall and embedding structures in the light distribution of the pattern projection (M) that differ from adjacent structures, c) extraction of all vertices (E1, E2, E3, E4) of the pixel elements from the pattern projection, d) determination of actual pixel neighborhood relations for each of the vertices (E1, E2, E3, E4) determined in step c), e) assignment of the corner points (E1, E2, E3, E4) to pixel elements of individual headlight segments of the matrix headlights, f) Checking whether in step e) at least one vertex (E1, E2, E3, E4) could not be assigned to an associated pixel element by comparing the actual pixel neighborhood relations with nominal pixel neighborhood relations. Method according to Claim 1 , characterized in that in step a) HOVO points of the low-beam light distribution are determined as reference points. Method according to one of Claims 1 or 2 , characterized in that between the steps a) and b) carried out a check whether at least one homogeneity criterion is met. Method according to Claim 3 , characterized in that the examination of the fulfillment of the homogeneity criterion by comparing the HOVO points based on their respective location in the camera image of the camera and by detecting a gray value variation in a defined environment of the HOVO points. Method according to one of Claims 1 to 4 , characterized in that the corner point extraction in step c) takes place on the basis of an image processing cascade. Method according to one of Claims 1 to 5 , characterized in that the pixel elements are driven in the generation of the pattern projection in step b) such that each headlight segment in the defect-free state of all pixel elements generates the same light distribution. Method according to one of Claims 1 to 6 , characterized in that in step e) a global vertex assignment to the headlight segments by the use of pixel trajectories. Method according to one of Claims 1 to 7 , characterized in that in step f) a check is made, whether in step e) several vertices, preferably all vertices, an associated pixel element could not be assigned. Method according to one of Claims 1 to 8th , characterized in that the step f) is followed by at least one further step g), in which a check of the positions of the detected defective pixel elements takes place. Method according to one of Claims 1 to 9 , characterized in that after the detection of a defective pixel element, a control check of the pixel state is carried out by further measurements.

Images