Characterising of at least one object in the surroundings of a surroundings sensor system

Abstract

In a computer-implemented method for characterising at least one object (5, 14, 20, 22, 24) in the surroundings of a surroundings sensor system (3), sensor data are obtained, said sensor data being generated by means of the surroundings sensor system (3) and representing the surroundings in a three-dimensional manner. On the basis of the sensor data, a symbolic representation of the surroundings is generated, which contains a geometric figure (16a, 16b, 16c, 16d, 25) which approaches a region of the at least one object (5, 14, 20, 22, 24) in the surroundings, and at least one further geometric figure, which in each case approaches a corresponding further region of the at least one object (5, 14, 20, 22, 24). The region of the at least one object (5, 14, 20, 22, 24) is characterised by comparing the symbolic representation of the surroundings with a plurality of predefined reference representations.

Description

[0001] 2023PF02454

[0002] 1

[0003] Characterization of at least one object in the environment of an environmental sensor system

[0004] The present invention relates to a computer-implemented method for characterizing at least one object in the environment of an environmental sensor system, in particular an environmental sensor system of a vehicle, and to a method for at least partially automatic vehicle guidance, wherein such a computer-implemented method is carried out. The invention further relates to a data processing system for carrying out the aforementioned computer-implemented method, to an electronic vehicle guidance system with such a data processing system, and to a corresponding computer program product.

[0005] Environmental sensor systems, for example active optical sensor systems, especially lidar systems such as laser scanners or flash lidar systems, can be mounted on vehicles and used to implement a wide variety of driver assistance functions and / or other functions for partially automated vehicle control. In particular, distances to objects in the vehicle's vicinity can be determined, for example, by measuring the time of flight of light using active optical sensor systems of this type, and these distances can be used for driver assistance or other functions for at least partially automated vehicle control.

[0006] Known object classification algorithms that work based on data from lidar systems sometimes find it difficult to identify or separate overlapping or partially obscuring objects.

[0007] It is an object of the present invention to provide a way to characterize at least one object in the environment of an environmental sensor system that is more robust against partial occlusion.

[0008] This problem is solved by the subject matter of the independent claim. Advantageous further developments and preferred embodiments are the subject matter of the dependent claims. 2023PF02454

[0009] 2

[0010] The invention is based on the idea of ​​approximating several areas of at least one object by means of respective geometric figures and of characterizing one of these areas by comparing the symbolic representation thus obtained with given reference representations.

[0011] According to one aspect of the invention, a computer-implemented method for characterizing at least one object in the environment of an environmental sensor system is provided. Sensor data generated by the environmental sensor system, which depict the environment three-dimensionally, are obtained. Based on the sensor data, a symbolic representation of the environment is generated, containing a geometric figure, particularly in three-dimensional space, that approximates a region of the at least one object in the environment. Furthermore, the symbolic representation includes at least one other geometric figure, particularly in three-dimensional space, each of which approximates a corresponding further region of the at least one object. The region of the at least one object is characterized by comparing the symbolic representation of the environment with a plurality of predefined reference representations.

[0012] Unless otherwise specified, all steps of the computer-implemented method can be performed by a data processing system comprising at least one data processing device, in particular by a data processing system of the vehicle. Specifically, the at least one data processing device is configured or adapted to perform the steps of the computer-implemented method. For this purpose, the at least one data processing device may, for example, store a computer program containing instructions which, when executed by the at least one data processing device, cause it to execute the computer-implemented method. The terms "data processing system" and "at least one data processing device" may be used interchangeably.

[0013] All data processing devices of the at least one data processing device can be part of the vehicle. However, it is also possible that all data processing devices of the at least one data processing device are part of an external computing system outside the vehicle, for example, a mobile electronic device, a backend server, or a cloud computing system. It is also possible that the at least one data processing device is both at least one vehicle data processing device and at least one external 2023PF02454

[0014] 3

[0015] The data processing device of the external computing system is included. The at least one vehicle data processing device may, for example, include one or more electronic control units (ECUs), and / or one or more zone control units (ZCLIs), and / or one or more domain control units (DCLIs) of the vehicle and / or the environmental sensor system.

[0016] In the event that the at least one data processing device comprises two or more data processing devices, certain steps performed by the at least one data processing device can be understood, for example, as different data processing devices performing different steps or different parts of a step. In particular, it is not necessary for each data processing device to perform the steps completely. In other words, the execution of the steps can be distributed among the two or more data processing devices.

[0017] Each embodiment of the computer-implemented method results in a corresponding embodiment of a method for characterizing the at least one object that is not purely computer-implemented, by including corresponding steps for generating the sensor data using the active optical sensor system.

[0018] An environmental sensor system is, for example, a sensor system for the surroundings of a vehicle, particularly a motor vehicle such as a car, van, truck, motorcycle, and so on. The environmental sensor system is specifically mounted on the vehicle, so the sensor data, which represents the environment of the environmental sensor system, is the environment of the vehicle, specifically its external environment. In other words, the environment of the environmental sensor system is the environment of the vehicle.

[0019] The environmental sensor system can, in particular, be an active optical sensor system. By definition, an active optical sensor system has a light source for emitting light or light pulses. The light source can, in particular, be a laser, for example, an infrared laser. Furthermore, by definition, an active optical sensor system, in this case the detector array, has at least one optical detector to detect reflected components of the emitted light. The active optical sensor system is, in particular, 2023PF02454

[0020] 4 is set up to generate and process or output one or more sensor signals based on the detected components of the light.

[0021] Here and in the following, the term "light" can be understood to encompass electromagnetic waves in the visible, infrared, and / or ultraviolet ranges. Accordingly, the term "optical" can also be understood to refer to light as defined in this way.

[0022] It should be noted that a single detector pixel of the detector array does not necessarily consist of a single optical detector. Rather, it is also possible for a group of several adjacent optical detectors to form a detector pixel. The latter is particularly possible when using single photon avalanche diodes (SPADs). In other embodiments, however, it is also possible for a pixel to consist of exactly one optical detector, for example, a single photodiode or avalanche photodiode (APD).

[0023] The active optical sensor system is specifically designed as a lidar sensor system, for example as a laser scanner.

[0024] Laser scanners are a well-known type of lidar sensor system in which a laser beam is deflected by a light deflection device, allowing for various deflection angles. The light deflection device can, for example, contain a rotatably mounted mirror. Alternatively, the light deflection device can have a mirror element with a tiltable and / or swiveling surface. The mirror element can be designed, for example, as a microelectromechanical system (MEMS). In the environment, the emitted laser beams can be partially reflected, and the reflected portions can then strike the laser scanner, specifically the light deflection device, which can direct them onto the detector array of the laser scanner. Each detector pixel of the detector array, or each optical detector of the detector array, generates a corresponding detector signal based on the respective detected portions.Based on the spatial arrangement of each detector pixel, together with the current position of the light deflection device, in particular its rotational position or its tilting and / or swiveling position, the direction of incidence of the detected reflected components can be determined. An evaluation unit can also, for example, perform a time-of-flight measurement to determine the direction of incidence of the detected reflected components.

[0025] 5. To determine the radial distance of the reflecting object. Alternatively or additionally, a method can be used to determine the distance, according to which a phase difference between emitted and detected light is evaluated.

[0026] Other types of lidar sensor systems are flash lidar sensor systems. These are non-scanning systems that do not require such a light deflection arrangement. Instead, the laser light generated by the light source is scattered by an optical element, so that it is emitted in a single flash over a wide angle.

[0027] Typically, data generated by an active optical sensor system can be presented as a point cloud, which is understood as a set of points, each identified by corresponding coordinates in a two- or three-dimensional coordinate system. In the case of a three-dimensional point cloud, the three-dimensional coordinates can be determined, for example, by the direction of incidence of the reflected light and the corresponding light travel time or the radial distance measured for that particular point. However, the information can also be preprocessed to obtain three-dimensional Cartesian coordinates for each point. Generally, the points in a point cloud can be presented in an unordered or unsorted manner, unlike, for example, a camera image.In addition to spatial information, namely the two- or three-dimensional coordinates, the point cloud can also contain additional information or measured values ​​for the individual points, such as an echo pulse width (EPW) of the respective sensor signal.

[0028] The data generated by the active optical sensor system during a frame can be interpreted as a 2.5-dimensional point cloud, provided the radial spacing is appropriately determined. This means that while a three-dimensional position is stored for each point in the point cloud, the entire three-dimensional space is not represented within the active optical sensor system's field of view. This is because the emitted light pulses only reach the side of objects in the environment facing the active optical sensor system. Therefore, parts of the objects behind this side, or other objects or parts thereof obscured by the objects, are not represented by the point cloud, similar to a camera image. Thus, the sensor data can potentially represent the environment three-dimensionally even with a 2.5-dimensional point cloud. 2023PF02454

[0029] 6

[0030] The view of data generated by an active optical sensor system as a point cloud stems, among other things, from the fact that the detector arrays of earlier active optical sensor systems had only a very small number of detector pixels, for example, two to four detector pixels arranged in a row. In modern active optical sensor systems, however, this is sometimes different, as the detector pixels are arranged in a large number of rows and columns, for example, in 100 to 1,000 rows and / or 100 to 1,000 columns. As a result, a point cloud of a frame can also be viewed or displayed as an image, where the number of image pixels corresponds to the number of detector pixels, and the spatial two-dimensional arrangement of the detector pixels defines the corresponding image plane and directly corresponds to the arrangement of the image pixels.The individual pixel values ​​of the image can be given by the radial distance of the reflecting point, measured by the corresponding detector pixel, or by the corresponding depth, which is the perpendicular distance of the reflecting point from the detector pixel and can be calculated from the radial distance. The image can then be referred to as a depth image, in particular a monochromatic depth image.

[0031] However, it is also possible, for example, to generate corresponding images, especially monochromatic images, based on other parameters instead of depth, such as the EPW or the light intensity of the corresponding reflected components as detected by the detector pixel. It is also possible to use the detector array to capture ambient light without emitting light pulses and detecting reflected components, essentially like a 2D camera, particularly a 2D infrared camera.

[0032] The symbolic representation of the environment is not necessarily a representation of the entire environment, especially of all objects within it. In particular, the symbolic representation of the environment can also be understood or described as a symbolic representation of at least one object. It is a symbolic representation in the sense that the symbolic representation does not necessarily contain a realistic image of the at least one object, but rather the area and the further areas of the at least one object are symbolically represented by the geometric figure and the further geometric figure or figures. 2023PF02454

[0033] 7

[0034] A geometric figure can, in this and subsequent discussion, be a curve or a surface in three-dimensional space. The curve or surface is fundamentally defined in three-dimensional space. However, this does not preclude the possibility that, in some cases, the curve or surface lies in a two-dimensional plane. This two-dimensional plane is not necessarily parallel to the image plane but can, for example, extend wholly or partially in the depth direction.

[0035] For example, the type of geometric figure and / or at least one other geometric figure can be specified. It can be specified, for instance, that the geometric figure and / or at least one other geometric figure are straight lines, segments of circular arcs, semicircles, segments of elliptical arcs, arcs of ellipses, parts of a sphere's surface or an ellipsoid's surface, and so on.

[0036] A region of the at least one object can, for example, be an edge of the at least one object. An edge can be approximated, in particular, by a curve as a geometric figure. A region of the at least one object can also be a surface region, in particular a surface region of the at least one object. A surface region can be approximated, in particular, by a surface as a geometric figure.

[0037] The at least one object can be exactly one object. In that case, the area and the further areas of the at least one object are different areas of the same object. The at least one object can also contain two or more objects. In that case, the area and the further areas of the at least one object are different areas of the same object or of different objects. Each further geometric figure of the at least one further geometric figure, in particular, approximates another further area of ​​the at least one object.

[0038] The geometric figure and at least one other geometric figure are, in particular, all given in a common coordinate system. In other words, the symbolic representation includes not only the shapes of the geometric figure and the at least one other geometric figure, but also, explicitly or implicitly, the relative position of the geometric figure and the at least one other geometric figure to each other, in particular the position of the at least one other geometric figure with respect to the geometric figure. 2023PF02454

[0039] 8

[0040] The characterization of the at least one object can consist of the characterization of the domain. The geometric figure can also be referred to as the anchor figure or anchor feature. The at least one further geometric figure, on the other hand, can be referred to as at least one auxiliary figure or at least one auxiliary feature.

[0041] The characterization of at least one object can also include the characterization of at least one further domain or a subset thereof. In particular, the steps of the computer-implemented procedure can be repeated accordingly, with, for example, one of the further geometric figures taking the place of the geometric figure as the anchor figure, and so on.

[0042] The comparison can be performed, for example, by direct individual comparison (brute force). Less computationally intensive methods for comparison can be used, for example, if the symbolic representation is stored as a binary tree or hash map.

[0043] The reference representations are structurally identical to the symbolic representation. That is, a reference representation includes a reference anchor figure and one or more reference auxiliary figures. The type of reference anchor figure and the reference auxiliary figures is, in particular, the same as the type of anchor figure and auxiliary figures in the symbolic representation of the environment. The number of reference auxiliary figures does not necessarily have to correspond to the number of auxiliary figures.

[0044] The characterization of the domain can therefore include, in particular, determining which reference representation or representations from the multitude of possibilities correspond sufficiently well with the symbolic representation, especially according to a predefined agreement criterion. A reference representation can, in particular, include or enable a semantic interpretation of the reference anchor figure or the domain of a reference object represented by it. By identifying the reference representations that correspond sufficiently well with the symbolic representation through comparison, a corresponding semantic interpretation can thus be assigned to the domain of at least one object. A semantic interpretation can, for example, be a class in the sense of an object classification, such as "part of a vehicle," "part of a pedestrian," "part of a building," "part 2023PF02454."

[0045] 9. of vegetation,” “part of a road marking,” “part of a structural road boundary,” and so on. More specific classes are also possible, such as “part of a vehicle body,” “part of a fender,” “side panel of a vehicle body,” “wheel of a vehicle,” “part of a guardrail,” and so on.

[0046] It should be noted that it is possible for several reference representations to correspond sufficiently well with the symbolic representation, and consequently, several corresponding semantic interpretations can be assigned to the domain of at least one object. The characterization therefore does not necessarily provide a unique semantic interpretation of the domain. However, this is not required for many applications in the field of at least partially automated vehicle control. For example, the information that the domain most likely belongs to another vehicle may be sufficient, without needing to know which component of the other vehicle the domain belongs to, or similar details.

[0047] The result of the characterization, in particular the information about which of the reference representations the symbolic representation corresponds sufficiently well to, and / or the associated semantic interpretations, can be used in a variety of ways for applications in the context of at least partially automated vehicle control. Examples of possible applications include, but are not limited to, the selection of at least one object for object tracking, obstacle detection, recognition of lane markings and road boundaries, route planning, and so on.

[0048] A key advantage of the computer-implemented method according to the invention is that it does not classify an entire object, but rather characterizes one or more areas of the at least one object individually. Accordingly, partial occlusion of the at least one object is less relevant for the computer-implemented method according to the invention than for conventional object recognition algorithms, semantic segmentation algorithms, or the like. In particular, when using the computer-implemented method according to the invention, a prior semantic segmentation can be dispensed with, which would be resource-intensive and, in some cases, for example, in the case of partial occlusion, would not be reliably possible. 2023PF02454

[0049] 10

[0050] Mathematically, the symbolic representation and the reference representations can each be represented as a vector, for example, where entries of the vector contain or are derived from corresponding properties or parameters of the geometric figures or the reference anchor figure and the reference auxiliary figures.

[0051] According to at least one embodiment, each reference representation of the plurality of predefined reference representations, in particular the reference anchor figure of the corresponding reference representation, is assigned an object class. Characterizing the first area involves determining a subset of the plurality of predefined reference representations such that the symbolic representation corresponds to each reference representation of the subset up to a predefined tolerance, and assigning the object classes of the reference representations of the subset to the first area.

[0052] Object classes can be understood as semantic interpretations in the sense explained above. In particular, object classes can also refer only to parts of an object.

[0053] The subset of the multitude of given reference representations can include one or more of the reference representations.

[0054] The definition of tolerance depends on the specific application and also on the specific mathematical formulation of the symbolic representation and the reference representations.

[0055] For example, when formulating with vectors, a similarity measure or distance measure for vectors between the symbolic representation and the respective reference representation can be calculated and compared with a given minimum similarity measure or a given maximum distance measure to determine whether the symbolic representation matches the corresponding reference representation within the given tolerance.

[0056] The reference representations can, for example, correspond to simulated data from an active optical sensor system, which is particularly advantageous for assigning object classes to the reference representations, since these are essentially automatically provided by the simulation. It is also possible that the 2023PF02454

[0057] 11

[0058] Reference representations are generated based on real measurement data. Object classes can be assigned manually and / or automatically.

[0059] According to at least one embodiment, the geometric figure includes a curve in three-dimensional space, and the at least one further geometric figure includes at least one further curve in three-dimensional space. In particular, each further geometric figure of the at least one further geometric figure includes a corresponding further curve in three-dimensional space.

[0060] This can reduce the required storage space and computing power.

[0061] In particular, the region of the at least one object is an edge of the at least one object and the at least one further region of the at least one object is at least one further edge of the at least one object.

[0062] According to at least one embodiment, the symbolic representation is generated depending on a starting point of the curve and an endpoint b of the curve, as well as depending on the respective starting points of the at least one further curve and the respective endpoints of the at least one further curve.

[0063] Because the type of curve and at least one other curve are defined, the path of each curve or other curve between the start and end points is not arbitrary. Representing the curve based on its start and end points, and that of at least one other curve, thus allows for a compact and simple symbolic representation of the at least one object.

[0064] The fact that the symbolic representation is generated depending on a point can be understood, in particular, to mean that the symbolic representation is generated depending on the coordinates of the corresponding point. The symbolic representation can, for example, include the coordinates of the corresponding point or be calculated based on them.

[0065] In particular, the symbolic representation can generate the coordinates or transformed coordinates of the start and end points of the curve and of at least one other curve in a common coordinate system, or the coordinates of the start and end points of the curve and of at least one other curve in the 2023PF02454

[0066] 12 common coordinate systems. Transformed coordinates are, in particular, coordinates that have been transformed according to a predefined transformation, for example, a linear transformation.

[0067] In some embodiments, the symbolic representation is generated not only depending on the aforementioned starting and ending points, but also depending on an intermediate point of the curve and on respective intermediate points of at least one further curve.

[0068] This allows the course of the curve, or any subsequent curve between the starting and ending points, to be further restricted or, depending on the type of curve, even uniquely defined. This reduces the likelihood of an ambiguous interpretation of the symbolic representation.

[0069] In some embodiments, particularly with a semi-ellipse or semicircle as a curve or further curve, the respective intermediate point can, for example, lie centrally or symmetrically between the starting point and the endpoint. For example, the respective intermediate point can correspond to a vertex of the respective semi-ellipse or semicircle.

[0070] According to at least one embodiment, to generate the symbolic representation, the starting point and the endpoint of the curve, as well as the respective starting points and respective endpoints of at least one further curve, are transformed such that the transformed starting point of the curve and / or the transformed endpoint of the curve lie at predefined positions of a common coordinate system.

[0071] If the symbolic representation includes intermediate points, the intermediate points are also transformed accordingly.

[0072] In other words, a transformation is applied to the aforementioned start and end points, and, if applicable, the intermediate points, which moves the start and end points of the curve to the predefined positions. The remaining transformed start and end points, and any transformed intermediate points, then lie at positions unknown beforehand. Their positions can then be used particularly easily to characterize the area of ​​the at least one object or the curve. 2023PF02454

[0073] 13

[0074] In particular, the reference representations are in analogous form, so that the reference anchor figure, which in this case can also be called the reference anchor curve, has a starting point and an endpoint at the predefined positions of a common coordinate system, thus enabling a simple or direct comparison.

[0075] The transformation may include, in particular, a rotation and / or a translation and / or a scaling.

[0076] In one exemplary embodiment, the transformed starting point or the transformed endpoint of the curve lies at the origin of the common coordinate system.

[0077] According to at least one embodiment, the area of ​​the at least one object, in particular the edge, is approximated by a polyline and the polyline is approximated by the geometric figure, in particular the curve.

[0078] The edge itself can be identified, for example, by applying an edge detection algorithm, especially one that is known per se, such as a threshold-based edge detection algorithm.

[0079] For example, starting from a result of the edge detection algorithm, the polyline that approximates the edge is determined, and then the curve is determined so that it approximates the polyline.

[0080] A polyline can be understood as two or more line segments, each connected at one end. The polyline can, for example, be a polynomial chain, also known as a spline. In some embodiments, the individual line segments can be straight lines, i.e., first-degree polynomial segments or linear polynomial segments. In this case, the polyline can also be called a traverse. The term traverse does not imply, in particular, that the polyline is a closed polyline.

[0081] For example, the curve can be a smooth curve, meaning it can be differentiable any number of times except for a corresponding starting and ending point. This simplifies processing. On the other hand, the polyline, especially with straight lines, is 2023PF02454

[0082] 14

[0083] Lines as line segments are generally not distinguishable at the connection points. However, a polyline may allow for a more precise approximation of the edge.

[0084] The same applies analogously to the at least one further curve. In particular, for each further curve of the at least one further curve, the respective further area, especially the respective further edge, is approximated by a further polyline, and the further polyline is approximated by the respective further curve.

[0085] According to at least one embodiment, the polyline is determined as a polygonal chain.

[0086] For example, the polyline is defined as a polygonal path and the curve as a semi-ellipse.

[0087] This allows for a computationally efficient yet comparatively accurate approximation of the object.

[0088] The same applies in particular to at least one other curve analogously.

[0089] According to at least one embodiment, the area, in particular the edge, is approximated by two or more line segments and the two or more line segments are approximated by the curve.

[0090] The two or more line segments differ from a polyline in that they are not necessarily connected to each other.

[0091] In particular, the two or more line segments can each be polynomial segments, for example, straight line segments. The above explanations and embodiments regarding the polyline can be applied analogously to corresponding embodiments with two or more line segments.

[0092] The same applies in particular to at least one other curve analogously.

[0093] According to at least one embodiment, the active optical sensor system comprises a two-dimensional detector array with a plurality of detector pixels. The sensor data includes a depth image of the environment generated by the detector array, or the depth image is generated based on the sensor data. The 2023PF02454

[0094] 15

[0095] The depth image comprises a multitude of image pixels, where each image pixel corresponds to one of the detector pixels, and a pixel value of the respective image pixel corresponds to a distance from the detector array determined by the corresponding detector pixel. The symbolic representation, in particular a geometric figure and at least one other geometric figure, is generated based on the depth image.

[0096] The detector pixels are arranged in a detector plane, for example as a multitude of rows and a multitude of columns, which corresponds to the image plane of the depth image. The distance from the detector array can, for example, be a distance perpendicular to the detector plane.

[0097] In some embodiments, filtering of the data generated by the detector array can also be carried out to create the depth image, for example noise filtering and / or filtering to remove certain objects or other components of the data that are not considered relevant.

[0098] If the image plane of the depth image is defined by a transverse direction y and a vertical direction z perpendicular to it, then the depth is defined along a longitudinal direction x perpendicular to both y and z. The rows of the detector array and the depth image are then arranged side by side parallel to the vertical direction z, and the columns of the detector array and the depth image are arranged side by side parallel to the transverse direction y. The three-dimensional space is defined by x, y, and z.

[0099] The geometric figure, for example the curve, and the region, for example the edge, of the at least one object lie in three-dimensional space, and therefore not necessarily in a plane parallel to the xz-plane. This applies analogously to the at least one further geometric figure, for example the at least one further curve, and the at least one further region, for example the at least one further edge, of the at least one object.

[0100] The area, in particular the edge, of the at least one object is defined by the two-dimensional position of the image pixels belonging to the area in the image plane in combination with their pixel values, which correspond to the depth. The geometric figure, in particular the curve, is then determined, for example, such that it approximates the first area. The geometric figure can be determined, for example, by defining a basic shape or a type of geometric figure and adjusting the parameters that remain free so that the geometric figure 2023PF02454

[0101] 16 approximates the region, for example by regression or optimization in a known manner. The region itself can be identified, in particular, by applying an edge detection algorithm, especially one known per se, for example, a threshold-based edge detection algorithm. This applies analogously to the at least one further geometric figure.

[0102] According to at least one embodiment, the curve lies in a first curve plane which forms an angle in the range [0°, 180°[ with the image plane of the depth image.

[0103] In other words, the curve can be considered a plane curve in three-dimensional space. The value of the angle depends on the path of the edge in three-dimensional space.

[0104] In this way, the complexity of the curve and the number of parameters needed to describe the curve remain comparatively low, which further reduces the storage and computing effort.

[0105] Similarly, in some embodiments, each further curve of the at least one further curve lies in a corresponding further first curve plane, which encloses a respective angle in the range [0°, 180°[ with the image plane of the depth image.

[0106] According to at least one embodiment, a second curve plane is constructed that intersects the first curve plane. For each image pixel of the depth image, a further pixel value is determined. This first binary value is determined if a position defined by the respective image pixel in three-dimensional space lies on a first side of the second curve plane, and a second binary value is determined if the position defined by the respective image pixel in three-dimensional space lies on a second side of the second curve plane opposite the first side. Based on these determined further pixel values, a binary image is generated. The curve is referred to as a first curve, and the area of ​​the at least one object is referred to as a first area, or the edge as a first edge.Based on the binary image, a second curve is determined in three-dimensional space, which approximates a second region, in particular a second edge, of the at least one object. The symbolic representation contains the second curve.

[0107] The first binary value is, for example, one and the second binary value is zero, or vice versa. 2023PF02454

[0108] 17

[0109] The second edge can also be interpreted as the contour of the object in the binary image. The second curve is therefore a planar curve in three-dimensional space that lies in the second curve plane. The second curve plane can, for example, be perpendicular to the first curve plane and / or perpendicular to the xy-plane. The second curve plane can also be parallel to the xz-plane.

[0110] The position defined by an image pixel in three-dimensional space is given, in particular, by the position of the image pixel in the image plane of the depth image and the corresponding pixel value of the depth image, i.e., the corresponding depth. For image pixels whose position in three-dimensional space lies exactly on the second curve plane, various predefined procedures can be used. For example, the next pixel value in this case can, by definition, be the first binary value or the second binary value.

[0111] The symbolic representation is generated and stored, for example, in such a way that the first curve and the second curve are assigned to each other. If a further first curve is determined for each additional first edge of the at least one object, then, in various embodiments, a corresponding further second curve can be generated for each additional first curve analogously to the first and second curves.

[0112] By representing at least one object using pairs of curves, the at least one object can be represented more accurately without significantly increasing the required storage and computing effort.

[0113] According to at least one embodiment, the first curve is mirror-symmetric and the second curve plane is constructed as a mirror plane of the first curve.

[0114] The second curve plane therefore passes, in particular, through a midpoint of the first curve. If the first curve is a semi-ellipse, the second curve plane passes, in particular, through a vertex of the semi-ellipse.

[0115] This enables a particularly simple construction of the second curve plane and achieves a particularly meaningful representation for at least one object. 2023PF02454

[0116] 18

[0117] The same applies in some embodiments to the further first curves and the further second curves.

[0118] According to at least one embodiment, the first curve is an elliptical arc, in particular a semi-ellipse. For example, each further first curve is an elliptical arc, in particular a semi-ellipse.

[0119] This also includes, in particular, the limiting cases of a straight line, which corresponds to a semi-ellipse with a first semi-axis length equal to half the length of the line and a second semi-axis length equal to zero, and of a semicircle, which corresponds to a semi-ellipse with two equal semi-axis lengths. This can be interpreted, in particular, as meaning that the first curve is generally assumed to be a semi-ellipse, but when fitting it to the first edge, one of the two limiting cases may prove to be optimal.

[0120] By using an elliptical arc or a semi-ellipse, a good approximation to the first edge can be achieved with a comparatively low complexity of the first curve.

[0121] According to at least one embodiment, the second curve is an elliptical arc, in particular a semi-ellipse. For example, every further second curve is an elliptical arc, in particular a semi-ellipse.

[0122] The statements regarding the first curve apply analogously.

[0123] According to a further aspect of the invention, a method for at least partially automatic vehicle control is described, wherein a computer-implemented method according to the invention is carried out. At least one control signal for at least partially automatic vehicle control is generated depending on a result of the characterization of the first area and / or driver assistance information to support a driver in controlling the vehicle is generated depending on the result of the characterization of the first area.

[0124] The at least one control signal can be provided, for example, to one or more actuators of the vehicle, including, for example, one or more brake actuators and / or one or more steering actuators and / or one or 2023PF02454

[0125] 19. Several of the vehicle's drive motors. One or more actuators can influence the longitudinal and / or lateral steering of the vehicle based on at least one control signal, in order to steer the vehicle at least partially automatically.

[0126] The driver assistance information can be output via a vehicle output device, such as a display and / or an audio output system and / or a haptic output system.

[0127] Further embodiments of the inventive method for at least partially automatic vehicle guidance follow directly from the various configurations of the inventive computer-implemented method for characterizing at least one object, and vice versa. In particular, individual features and corresponding explanations as well as advantages relating to the various embodiments of the inventive computer-implemented method can be transferred analogously to corresponding embodiments of the inventive method for at least partially automatic vehicle guidance.

[0128] According to another aspect of the invention, a data processing system is provided which is configured to carry out a computer-implemented method according to the invention.

[0129] The terms "data processing system" and "at least one data processing device" may be used interchangeably within the scope of this disclosure. In this disclosure, a data processing device may, for example, be understood as a device with processing circuits for processing data. A data processing device can thus perform arithmetic operations to process data. Indexed access to a data structure, such as a lookup table (LUT) or a database, may also be considered an arithmetic operation. Similarly, data processing that is partially or fully implemented in hardware may be considered an arithmetic operation.

[0130] A data processing device may, in particular, comprise one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example, one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more systems-on-a-chip (SoCs). A data processing device may also include a 2023PF02454

[0131] The data processing equipment may contain 20 or more processors, for example, one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and / or one or more signal processors, in particular one or more digital signal processors (DSPs). The data processing equipment may also include a physical or virtual cluster of computers or other devices of the aforementioned type.

[0132] A data processing device may also include one or more hardware and / or software interfaces, for example for receiving and / or providing data.

[0133] A data processing device may also include one or more storage devices. A storage device may be implemented as volatile memory, such as dynamic random access memory (DRAM) or static random access memory (SRAM), or as non-volatile data storage, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or flash EEPROM, ferroelectric random access memory (FRAM), magnetoresistive random access memory (MRAM), or phase-change random access memory (PCRAM).

[0134] According to a further aspect of the invention, an electronic vehicle guidance system for a vehicle is provided. The electronic vehicle guidance system comprises a data processing system according to the invention and a control system configured to generate at least one control signal for at least partially automatic guidance of the vehicle depending on a result of the characterization of the first area and / or to generate driver assistance information to support a driver of the vehicle in guiding the vehicle depending on the result of the characterization of the first area.

[0135] The control system can be part of the data processing system or part of another data processing system.

[0136] An electronic vehicle guidance system can be understood as an electronic system that is designed to guide a vehicle fully automatically or autonomously, in particular without intervention in a control system by a 2023PF02454

[0137] 21

[0138] A driver is not required. The vehicle automatically performs all necessary functions, such as steering, braking, and / or acceleration maneuvers, monitoring and recording road traffic, and reacting accordingly. In particular, the electronic vehicle control system can implement a fully automatic or fully autonomous driving mode according to Level 5 of the SAE J3016 classification. An electronic vehicle control system can also be understood as an advanced driver assistance system (ADAS), which supports the driver in partially automated or semi-autonomous driving. In particular, the electronic vehicle control system can implement a partially automated or semi-autonomous driving mode according to Levels 1 to 4 of the SAE J3016 classification. Here and in the following, "SAE J3016" refers to the corresponding standard in the April 2021 version.

[0139] At least partially automated vehicle control can therefore include driving the vehicle in accordance with a fully automated or fully autonomous driving mode of Level 5 according to SAE J3016. At least partially automated vehicle control can also include driving the vehicle in accordance with a partially automated or semi-autonomous driving mode according to Levels 1 to 4 of SAE J3016.

[0140] According to at least one embodiment of the electronic vehicle guidance system, this includes the environment sensor system, in particular the active optical sensor system.

[0141] According to at least one embodiment, the environmental sensor system is designed as a lidar sensor system, in particular as a laser scanner.

[0142] According to at least one embodiment, the plurality of detector pixels is arranged corresponding to the plurality of columns and the plurality of rows of the detector array. The total number of rows of the detector array is at least 100, for example 100 to 5,000, and / or the total number of columns of the detector array is at least 100, for example 100 to 5,000.

[0143] Further embodiments of the electronic vehicle guidance system according to the invention follow directly from the various configurations of the computer-implemented method according to the invention and the method according to the invention for at least partially automatic guidance of a vehicle and 2023PF02454

[0144] 22 Conversely. In particular, individual features and corresponding explanations as well as advantages regarding the various embodiments of the computer-implemented method according to the invention and the method according to the invention for at least partially automatic guidance of a vehicle can be transferred analogously to corresponding embodiments of the electronic vehicle guidance system according to the invention. In particular, the electronic vehicle guidance system according to the invention is configured or programmed to carry out a computer-implemented method or method according to the invention. In particular, the electronic vehicle guidance system according to the invention carries out the computer-implemented method or method according to the invention.

[0145] According to another aspect of the invention, a computer program with instructions is specified. When the instructions are executed by a data processing system, the instructions cause the data processing system to carry out a computer-implemented method according to the invention.

[0146] The instructions can be provided, for example, as program code. This program code can be provided, for example, as binary code or assembly language, and / or as source code in a programming language such as C, and / or as a program script, such as Python.

[0147] According to a further aspect of the invention, a further computer program with additional commands is specified. When the additional commands are executed by an electronic vehicle guidance system according to the invention, in particular by the data processing system of the electronic vehicle guidance system, the commands cause the electronic vehicle guidance system to carry out a method according to the invention for at least partially automatic guidance of a vehicle.

[0148] The additional instructions can be provided, for example, as program code. This program code can be provided, for example, as binary code or assembly language, and / or as source code in a programming language such as C, and / or as a program script, such as Python.

[0149] According to a further aspect of the invention, a computer-readable storage medium is provided that stores a computer program according to the invention and / or a further computer program according to the invention. 2023PF02454

[0150] 23

[0151] The computer program, the further computer program, and the computer-readable storage medium are each computer program products containing the commands and / or the further commands.

[0152] Further features of the invention are evident from the claims, the figures, and the description of the figures. The features and combinations of features mentioned above in the description, as well as those mentioned below in the description of the figures and / or illustrated in the figures, may be encompassed by the invention not only in the combinations specified, but also in other combinations. In particular, embodiments and combinations of features that do not include all the features of an originally formulated claim may also be encompassed by the invention. Furthermore, embodiments and combinations of features that go beyond or deviate from the combinations of features mentioned in the claims may also include the invention.

[0153] The invention is explained in more detail below with reference to specific exemplary embodiments and corresponding schematic drawings. Identical or functionally equivalent elements in the drawings may be provided with the same reference numerals. The description of identical or functionally equivalent elements is not necessarily repeated with respect to the different figures.

[0154] The figures show,

[0155] Fig. 1 shows a schematic representation of a vehicle with an exemplary embodiment of an electronic vehicle guidance system according to the invention;

[0156] Fig. 2 shows a schematic representation of an active optical sensor system of another exemplary embodiment of an electronic vehicle guidance system according to the invention;

[0157] Fig. 3 shows a schematic representation of a symbolic representation of the environment according to an exemplary embodiment of a device according to the invention 2023PF02454.

[0158] 24 computer-implemented methods for characterizing at least one object;

[0159] Fig. 4 schematically shows a depth image corresponding to a further exemplary embodiment of a computer-implemented method according to the invention for characterizing at least one object;

[0160] Fig. 5 schematically shows a depth image and first curves according to a further exemplary embodiment of a computer-implemented method according to the invention for characterizing at least one object;

[0161] Fig. 6 schematically shows a depth image, a first curve and a second curve according to a further exemplary embodiment of a computer-implemented method according to the invention for characterizing at least one object;

[0162] Fig. 7 schematic curve shapes for use in a further exemplary embodiment of a computer-implemented method according to the invention for characterizing at least one object;

[0163] Fig. 8 schematically shows objects according to a further exemplary embodiment of a computer-implemented method according to the invention for characterizing at least one object; and

[0164] Fig. 9 schematically shows further objects according to a further exemplary embodiment of a computer-implemented method according to the invention for characterizing at least one object.

[0165] Figure 1 schematically depicts a motor vehicle 1 with an exemplary embodiment of an electronic vehicle guidance system 2 according to the invention. The electronic vehicle guidance system 2 comprises a data processing system 4 and an active optical sensor system 3.

[0166] In particular, a computer-implemented method according to the invention for characterizing at least one object 5 can be implemented using the data processing system 4, 2023PF02454

[0167] 25

[0168] 14, 20, 22, 24 in an environment of the active optical sensor system 3, in particular the vehicle 1 .

[0169] Sensor data, generated by the active optical sensor system 3, which represent the environment three-dimensionally, are obtained. Based on this sensor data, a symbolic representation of the environment is generated, containing a geometric figure 16a, 16b, 16c, 16d, 25 that approximates a region of the at least one object 5, 14, 20, 22, 24 in the environment, as well as at least one further geometric figure, each approximating a corresponding further region of the at least one object 5, 14, 20, 22, 24. The region of the at least one object 5, 14, 20, 22, 24 is characterized by comparing the symbolic representation of the environment with a plurality of predefined reference representations.

[0170] An exemplary embodiment of the active optical sensor system 3 is shown schematically in Fig. 2. The active optical sensor system 3 comprises a housing 7 and an emitter unit 9 for emitting light 6a into an external environment of the active optical sensor system 3. The emitted light 6a passes, for example, through a window 8 of the housing 7. The active optical sensor system 3 has a control unit 11, which is configured to control a deflection device 18, for example, a rotatable mirror, of the active optical sensor system 3, to deflect the light 6a in different directions, and thereby to scan the environment of the active optical sensor system 3.

[0171] The active optical sensor system 3 has a detector unit with a two-dimensional detector array 10, which comprises a multitude of detector pixels arranged, for example, in a multitude of rows and a multitude of columns. If components 6b of the emitted light 6a are reflected by an object 5 in the vicinity, these components 6b can be reflected back towards the active optical sensor system 3 and, for example, re-enter through the window 8, where they are directed by the deflection device 18 onto the detector array 10 and detected by it. Depending on the corresponding detector signals, the control unit 11 can then calculate a radial distance of the object 5 from the active optical sensor system 3, for example, by measuring the time of flight.

[0172] The active optical sensor system 3 is specifically designed as a lidar sensor system in the form of a laser scanner. The emitter unit 9 therefore comprises one or more laser light sources, in particular laser diodes, for generating the light 6a. The 2023PF02454

[0173] 26

[0174] Each detector pixel has one or more photodetectors, which can be configured, for example, as photodiodes, avalanche photodiodes (APDs), or single-photon avalanche photodiodes (SPADs). Possible optical components in the optical path are represented in Fig. 2 by a lens 12.

[0175] In some embodiments of the computer-implemented method according to the invention, the data processing system 4 receives a depth image 13 of the environment generated by the detector array 10, containing a plurality of image pixels, wherein each image pixel corresponds to one of the detector pixels and a pixel value of the respective image pixel corresponds to a depth determined by the corresponding detector pixel in a direction perpendicular to the detector array 10. In Fig. 4, the depth image 13 is schematically represented as a binary image for the sake of simplicity, with the hatched area corresponding to a background 15, particularly at infinity, and the unhatched area to an object 14. In the general case, the depth image 13 would be a monochromatic image, for example, a grayscale image.An image plane of the depth image 13 is spanned by a transverse direction y and a vertical direction z; a longitudinal direction x is oriented perpendicular to the transverse direction y and the vertical direction z. In the simplified example of Fig. 4, the object 14 has a flat rectangular surface on the side facing the active optical sensor system 3.

[0176] Based on the depth image 13, a first curve 16a, 16b, 16c, 16d, 25 is determined in three-dimensional space, which approximates a first edge of the object 5, 14, 20, 22, 24. This is shown schematically in Fig. 5 for the depth image 13 from Fig. 4 for four first curves 16a, 16b, 16c, 16d. In this simplified example, the first curves 16a, 16b, 16c, 16d lie in the yz-plane, which is not necessarily the case in general. In general, the first curve 16a, 16b, 16c, 16d, 25 lies, for example, in a first curve plane that forms an angle in the range [0, 180°] with an image plane of the depth image 13.

[0177] As schematically shown in Fig. 3, the symbolic representation can, for example, depend on a starting point a and an endpoint b of the curve, as well as on respective starting points c and respective endpoints d of at least one further curve.

[0178] On the left in Fig. 3 are the curve and another curve in the original coordinate system 27, for example a coordinate system of the active optical 2023PF02454

[0179] 27

[0180] The sensor system 3 and the depth image 13 are shown and, on the right, in a common coordinate system 28, in which the reference representations are also given. To generate the symbolic representation, the starting point a and the endpoint b of the curve, as well as the respective starting points c and endpoints d of at least one further curve, are transformed such that the transformed starting point a' of the curve and the transformed endpoint b' of the curve lie at predefined positions in the common coordinate system 28. The positions of the transformed starting and endpoints c', d' of the at least one further curve result from the transformation.

[0181] In the example of Fig. 3, the transformation is chosen such that the transformed starting point a' of the curve lies at the origin (0, 0, 0) of the common coordinate system 28 and the transformed endpoint b' of the curve is, for example, at (1 , 0, 0).

[0182] As shown schematically in Fig. 8, it is also possible to distinguish between edges 21, 23 of different objects 20, 22 based on the depth image 13, although it may happen that these cannot be distinguished in the image plane of the depth image 13 if the objects 20, 22 partially overlap or are directly adjacent to each other.

[0183] The first curves 16a, 16b, 16c, 16d, 25 are defined, for example, as respective semi-ellipses 19, which also includes the limiting case of straight lines, as schematically shown in Fig. 7 and in the simplified example of Fig. 5. The limiting case of a semicircle is also included, as shown in the example of Fig. 9 for a spherical object 24 and the corresponding first curve 25.

[0184] The symbolic representation is generated depending on the first curve 16a, 16b, 16c, 16d, 25. In preferred embodiments, a corresponding second curve 17, 26 is generated for each first curve 16a, 16b, 16c, 16d, 25, as shown in Fig. 9 for the first curve 25 and in the simplified example of Fig. 6 for the first curve 16a. The second curve 17, 26 can also be defined as a semi-ellipse. The symbolic representation is generated depending on the second curve 17, 26.

[0185] To determine the second curve 17, 26, a second curve plane is constructed which intersects the first curve plane. For example, the second curve plane is 2023PF02454.

[0186] 28 perpendicular to the first curve plane. For example, the second curve plane is a mirror plane with respect to a mirror symmetry of the first curve 16a, 16b, 16c, 16d, 25.

[0187] The second curve plane divides three-dimensional space into two halves, defined by a region on the first side of the second curve plane and a region on the opposite side of the second curve plane. For each image pixel of the depth image 13, for example, an additional pixel value is determined. This additional pixel value is a predetermined first binary value if a position defined by the respective image pixel in three-dimensional space lies on the first side of the second curve plane, and a predetermined second binary value if the position defined by the respective image pixel in three-dimensional space lies on the second side of the second curve plane. Based on these determined additional pixel values, a binary image is generated. Based on this binary image, the second curve 17, 26 is determined, which approximates a second edge of the object 5, 14, 20, 22, 24.

[0188] In some embodiments, the present invention provides a universal tool for solving various tasks of classifying objects such as vehicles or pedestrians or landmarks, traffic signs or guardrails.

[0189] In some embodiments, reference representations are each depicted as a series of feature combinations belonging to at least one object. Each feature combination can be stored as a mathematical representation vector with the required invariant properties. In the data to be analyzed, all possible feature combinations can be compared with all stored combinations with respect to their geometry, allowing combinations to only partially match.

[0190] Various, possibly task-specific, representation variants are possible for the symbolic representation, which, depending on the requirements, may be invariant with respect to rotations and / or scalings and / or translations.

Claims

2023PF02454 29 Patent claims 1. Computer-implemented method for characterizing at least one object (5, 14, 20, 22, 24) in the environment of an environmental sensor system (3), wherein sensor data generated by the environmental sensor system (3), representing the environment in three dimensions, are obtained; based on the sensor data, a symbolic representation of the environment is generated, which contains a geometric figure (16a, 16b, 16c, 16d, 25) that approximates a region of the at least one object (5, 14, 20, 22, 24) in the environment, and at least one further geometric figure, each approximating a corresponding further region of the at least one object (5, 14, 20, 22, 24); the region of the at least one object (5, 14, 20, 22, 24) is characterized by comparing the symbolic representation of the environment with a plurality of predefined reference representations.

2. Computer-implemented method according to claim 1, wherein each of the plurality of predefined reference representations is assigned an object class, and characterizing the first area includes determining a subset of the plurality of predefined reference representations such that the symbolic representation matches each reference representation of the subset to a predefined tolerance; and assigning the object classes of the reference representations of the subset to the first area.

3. Computer-implemented method according to one of the preceding claims, wherein the geometric figure (16a, 16b, 16c, 16d, 25) includes a curve in three-dimensional space and the at least one further geometric figure includes at least one further curve in three-dimensional space. 2023PF02454 30 4. Computer-implemented method according to claim 3, wherein the symbolic representation is generated depending on a starting point (a) and an end point (b) of the curve as well as depending on respective starting points (c) and respective end points (d) of the at least one further curve.

5. Computer-implemented method according to claim 4, wherein, for generating the symbolic representation, the starting point (a) and the endpoint (b) of the curve, as well as the respective starting points (c) and respective endpoints (d) of the at least one further curve, are transformed such that the transformed starting point (a') of the curve and / or the transformed endpoint (b') of the curve lie at predefined positions of a common coordinate system (28).

6. Computer-implemented method according to any one of claims 3 to 5, wherein the area is approximated by a polyline and the polyline is approximated by the curve; or the area is approximated by two or more line segments and the two or more line segments are approximated by the curve.

7. Computer-implemented method according to one of the preceding claims, wherein the environmental sensor system (3) comprises a two-dimensional detector array (10) with a plurality of detector pixels, and wherein the sensor data includes a depth image (13) of the environment generated by means of the detector array (10), or the depth image (13) is generated depending on the sensor data; the depth image (13) comprises a plurality of image pixels, each image pixel corresponding to one of the detector pixels, and a pixel value of the respective image pixel corresponds to a distance from the detector array (10) determined by means of the corresponding detector pixel; and the symbolic representation is generated based on the depth image (13).

8. Computer-implemented method according to claim 7 and one of claims 3 to 6, wherein the curve lies in a first curve plane which encloses an angle in the range [0, 180°] with an image plane of the depth image (13).

9. Computer-implemented method according to claim 8, wherein 2023PF02454 31 a second curve plane is constructed which intersects the first curve plane; for each image pixel of the depth image (13) a further pixel value is determined, which is a predetermined first binary value if a position defined by the respective image pixel in three-dimensional space lies on a first side of the second curve plane, and a predetermined second binary value if the position defined by the respective image pixel in three-dimensional space lies on a second side of the second curve plane opposite the first side; based on the determined further pixel values ​​a binary image is generated; the curve is a first curve, the region of the at least one object (5, 14, 20, 22, 24) is a first region and based on the binary image a second curve (17, 26) is determined in three-dimensional space which approximates a second region of the at least one object (5, 14, 20, 22, 24);and the symbolic representation contains the second curve (17, 26).

10. Computer-implemented method according to claim 9, wherein the first curve is mirror-symmetric and the second curve plane is constructed as a mirror plane of the first curve.

11. Computer-implemented method according to one of claims 9 or 10, wherein the first curve is an elliptical arc or a semi-ellipse (19) and / or the second curve (17, 26) is an elliptical arc or a semi-ellipse (19).

12. Method for at least partially automatic guidance of a vehicle (1), wherein a computer-implemented method according to any of the preceding claims is carried out and at least one control signal for at least partially automatic guidance of the vehicle (1) is generated depending on a result of the characterization of the first area; and / or Driver assistance information to support a driver of the vehicle (1 ) in controlling the vehicle (1 ) is generated depending on the result of the characterization of the first area.

13. Data processing system (4) configured to carry out a computer-implemented method according to any one of claims 1 to 11. 2023PF02454 32 14. Electronic vehicle guidance system (2) for a vehicle (1), comprising a data processing system (4) according to claim 13 and a control system configured to generate at least one control signal for at least partially automatic guidance of the vehicle (1) depending on a result of the characterization of the first area; and / or To generate driver assistance information to support the driver of the vehicle (1) in controlling the motor vehicle depending on the result of the characterization of the first area.

15. Electronic vehicle guidance system (2) according to claim 15, comprising the environment sensor system (3).

16. Electronic vehicle guidance system (2) according to claim 15, wherein the environment sensor system (3) is designed as a lidar sensor system.

17. Electronic vehicle guidance system (2) according to one of claims 15 or 16, wherein the environment sensor system (3) comprises a two-dimensional detector array (10) with a plurality of detector pixels; the plurality of detector pixels is arranged according to a plurality of columns and rows of the detector array (10); and a total number of rows of the detector array (10) is at least 100 and / or a total number of columns of the detector array (10) is at least 100.

18. computer program product Commands which, when executed by a data processing system (4), cause the data processing system (4) to perform a computer-implemented method according to any one of claims 1 to 11; and / or further commands which, when executed by an electronic vehicle guidance system (2) according to any one of claims 14 to 17, cause the electronic vehicle guidance system (2) to perform a method according to claim 12.

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