Method and system for detecting obstacles

Abstract

Method (500) for detecting one or more objects (50) in an environment (80) of a vehicle (100), wherein the environment (80) is bounded by a perimeter (82), the method comprising: Segmenting (502) the environment (80) into a plurality of segments to provide an occupancy grid, such that each segment (220, 230) of the plurality of segments is at least partially bounded by the perimeter (82) of the environment (80), wherein the environment (80) substantially has one of the following shapes: square, rectangle, circle, ellipse, polygon, trapezoid, parallelogram; Detecting (504) one or more detection points (54, 56) based on the one or more objects (50) in the environment (80) of the vehicle (100); and Assigning (508) a state to each of the segments (220, 230) of the plurality of segments based on the one or more detected detection points (54, 56).

Description

The disclosure relates to methods and systems for detecting obstacles. The disclosure relates in particular to methods and systems for detecting static obstacles in the environment of vehicles.Prior ArtVarious methods and systems for detecting obstacles (i.e. generally objects) in the environment of vehicles are known in the prior art. In this case, the environment of a vehicle is detected by means of various sensors and, based on the data supplied by the sensor system, it is determined whether obstacles are present in the environment of the vehicle and, if appropriate, the position thereof is determined. The sensor system used for this purpose typically comprises sensors present in the vehicle, for example ultrasonic sensors (e.g. PDC or parking aid), one or more cameras, radar (e.g. cruise control with a distance-maintaining function) and the like. Conventionally, various sensors are present in a vehicle that are optimized for certain tasks, for example, as regards the sensing range, dynamic aspects and requirements with respect to accuracy, and the like.The detection of obstacles in the vehicle environment is used for various driving assistance systems, for example for collision avoidance (e.g. brake assist, lateral collision avoidance), lane change assist, steering assist and the like.For the detection of static obstacles in the environment of the vehicle, fusion algorithms for the input data of the different sensors are required. To compensate for sensor errors, for example false positive detections (e.g. ghost targets) or false negative detections (e.g. non-detected obstacles) and occlusions (e.g. due to moving vehicles or limitations of the sensor's field of view), tracking (tracking) of the sensor detections of static obstacles is necessary.In order to map the immediate environment around the vehicle, different models are used. One method known in the art for detecting static obstacles is the Occupancy-Grid fusion (OGF). In the OGF, the vehicle environment is divided into rectangular cells. For each cell, a probability of occupancy with respect to static obstacles is calculated in the context of the fusion. The size of the cells determines the accuracy of the environmental representation.S. Thrun and A. Becken, "Integrating grid-based and topological maps for mobile robot navigation," in Proceedings of the Thirtenth National Conference on Artificial Intelligence-Volume 2, Portland, Oregon, 1996, describe research works in the area of mobile robot navigation and essentially two main paradigms for mapping indoor environments: grid-based and topological. While mesh-based techniques produce accurate metric maps, their complexity is often unable to plan efficiently and solve problems in large indoors. Topological maps, on the other hand, can be used much more efficiently, but accurate and consistent topological maps are difficult to learn in large environments. Thrun and Bücken describe an approach that integrates both paradigms. Grid-based maps are learned with artificial neural networks and Bayesian integration. Topological maps are generated as a further superordinate level on the grid-based maps by dividing the latter into coherent regions. The described integrated approaches are not readily applicable to scenarios whose parameters deviate from the described interior environments.With respect to the vehicle application, OGF-based methods have at least the following disadvantages. A representation having high accuracy requires a correspondingly large number of comparatively small cells and thereby causes a high computational effort and places high demands on the available storage capacity. Therefore, efficient detection of static obstacles by means of OGF is often inaccurate, since, due to the method, an increase in efficiency can practically only be achieved by using larger cells, which is at the expense of accuracy.As in the present case in the case of an insert for obstacle detection in vehicles, in many applications a more precise representation of the environment is required in the immediate environment, while a more inaccurate representation is sufficient at a medium to a longer distance. These requirements are typical of the specific application described here and have their correspondence in the available sensor system. Typically, the accuracy of the sensor system used decreases with increasing distance, so that a sufficient or desired accuracy is available in the near range, but not in the far range. These properties cannot be mapped with an OGF, since the cells are stationary. As a result, a cell can represent a location that is in the near range at one point in time, but in the far range at another point in time.Embodiments of the presently disclosed methods and systems partially or completely address one or more of the foregoing disadvantages and enable one or more of the following advantages.Methods and systems disclosed herein enable improved detection of obstacles or objects in the environment of vehicles. In particular, the disclosed methods and systems enable simultaneously improved detection of obstacles or objects in the environment of vehicles with regard to efficiency and accuracy. Methods and systems disclosed herein further enable differentiated viewing of objects as a function of distance to the vehicle, such that more proximate objects can be detected more accurately and more remote objects with sufficient precision and high efficiency. Methods and systems disclosed herein further enable efficient detection of all objects based on a relative position of the objects to the vehicle such that objects of primary importance (e.g., objects in front of the vehicle) can be detected accurately and efficiently and objects of secondary importance (e.g., lateral objects or objects in the rear of the vehicle) can be detected with sufficient precision and resource-saving.DE 10 2016 208 634 A1 relates to a method for outputting warning information in a vehicle. The method includes determining a distance between the vehicle and an obstacle in the environment of the vehicle. At least depending on the distance, an output characteristic for the warning information is generated. First warning information including the output characteristic is output via an output device of the vehicle. Second warning information including the output characteristic is output via a lighting device in the interior of the vehicle.DE 10 2013 214 631 A1 relates to a method for efficiently providing occupancy information for the environment of a vehicle, comprising: receiving sensor measurements of the environment of the vehicle; determining the assignments of the environment by obstacles on the basis of the sensor measurements; wherein assignments in a first section of the environment in a first coordinate system, namely a polar coordinate system, are each indicated by an angle indication and a distance indication; wherein assignments in a second section of the environment in a second coordinate system are each indicated by two value indications, wherein the second coordinate system differs from the polar coordinate system.Disclosure of the InventionIt is an object of the present disclosure to provide methods and systems for detecting obstacles in the environment of vehicles that avoid one or more of the aforementioned disadvantages and realize one or more of the aforementioned advantages. It is a further object of the present disclosure to provide vehicles with such systems that avoid one or more of the foregoing disadvantages and realize one or more of the foregoing advantages.This object is achieved by the respective subject matter of the independent claims. Advantageous embodiments are specified in the dependent claims.According to embodiments of the present disclosure, in a first aspect a method for detecting one or more objects in a surroundings of a vehicle is specified, wherein the surroundings are bounded by a scope. The method preferably comprises segmenting the environment into a plurality of segments, such that each segment of the plurality of segments is at least partially bounded by the circumference of the environment, detecting one or more detection points based on the one or more objects in the environment of the vehicle, and assigning a state to each of the segments of the plurality of segments based on the one or more detected detection points.Preferably, in a second aspect according to the preceding aspect, the environment contains an origin, wherein the origin optionally coincides with a position of the vehicle, in particular a position of the center of a rear axle of the vehicle.Preferably, in a third aspect according to the preceding aspect, each segment of a first subset of the plurality of segments is defined starting from the origin in the form of a respective angular opening, wherein the first subset comprises one, a plurality, or all segments of the plurality of segments.Preferably, in a fourth aspect according to the preceding aspect, the segments of the first subset have at least two different angular openings, in particular wherein: segments which extend substantially laterally with respect to the vehicle have a larger angular opening than segments which extend substantially in a longitudinal direction with respect to the vehicle; or segments which extend substantially laterally with respect to the vehicle have a smaller angular opening than segments which extend substantially in a longitudinal direction with respect to the vehicle.Preferably, in a fifth aspect according to either of aspects 3 and 4, the segments of the first subset have an angular opening starting from the origin substantially in the direction of travel of the vehicle.Preferably, in a sixth aspect according to any of the preceding aspects and aspect 2, each segment of a second subset of the plurality of segments is defined in the form of a Cartesian sub-region, wherein the second subset comprises one, a plurality, or all segments of the plurality of segments, optionally based on the first subset.Preferably, in a seventh aspect according to the preceding aspect, the segments of the second subset have at least two different extents in one dimension.Preferably, in an eighth aspect according to one of the two preceding aspects, the segments of the second subset have a first extent substantially transversely to a direction of travel of the vehicle which is greater than a second extent substantially in a direction of travel of the vehicle.Preferably, in a ninth aspect according to the preceding aspects 3 and 6, the segments of the first subset are defined on one side of the origin 84 and the segments of the second subset are defined on an opposite side of the origin. In particular, the segments of the first subset are defined starting from the origin in the direction of travel of the vehicle.Preferably, in a tenth aspect according to any of the preceding aspects, the method further comprises grouping the one or more detection points into one or more clusters based on a spatial proximity of the one or more detection points. The step of assigning a state to each of the segments of the plurality of segments is additionally or alternatively based on the one or more merged clusters, in particular wherein the merging of the one or more acquisition points into one or more clusters is based on the application of the Kalman filter.Preferably, in an eleventh aspect according to the preceding aspect, the one or more clusters are treated as one or more detection points.Preferably, in a twelfth aspect according to any of the preceding aspects, the state of a segment of the plurality of segments indicates an at least partial overlap of an object with the respective segment, wherein preferably the state includes at least one discrete value or a probability value.Preferably, in a thirteenth aspect according to any of the preceding aspects, the vehicle comprises a sensor system configured to detect the objects in the form of detection points.Preferably, in a fourteenth aspect according to the preceding aspect, the sensor system comprises at least a first sensor and a second sensor, wherein the first and second sensor are configured to detect objects, optionally wherein the first and second sensor are different from one another and / or the first and second sensor are selected from the group comprising ultrasound-based sensors, optical sensors, radar-based sensors, and lidar-based sensors.Preferably, in a fifteenth aspect according to the preceding aspect, detecting the one or more detection points further includes detecting the one or more detection points by means of the sensor system.Preferably, in a sixteenth aspect according to one of the preceding aspects, the environment substantially has one of the following shapes: square, rectangle, circle, ellipse, polygon, trapezoid, parallelogram.According to embodiments of the present disclosure, in a seventeenth aspect, a system for detecting one or more objects in a surroundings of a vehicle is specified. The system comprises a control unit and a sensor system, wherein the control unit is configured to execute the method according to one of the preceding aspects.According to embodiments of the present disclosure, in an eighteenth aspect, a vehicle is provided comprising the system according to the preceding aspect.Brief Description of the DrawingsExemplary embodiments of the disclosure are illustrated in the figures and are described in more detail below. FIG. 1 shows, by way of example, a schematic illustration of a surrounding area of a vehicle and of objects or obstacles present in the environment; FIG. 2 shows a schematic illustration of the application of OGF-based detection of obstacles in the environment of a vehicle; FIG. 3 shows a schematic illustration of the detection of objects in the environment of a vehicle according to embodiments of the present disclosure; FIG. 4 illustrates an example segment-based fusion of objects according to embodiments of the present disclosure; and FIG. 5 shows a flowchart of a method for detecting objects in the environment of a vehicle according to embodiments of the present disclosure.Embodiments of the DisclosureUnless otherwise noted, the same reference numerals are used below for the same and identically acting elements.FIG. 1 shows, by way of example, a schematic illustration of a surrounding field 80 of a vehicle 100 and of objects 50 or obstacles present in the environment 80. The vehicle 100, here illustrated as a passenger car by way of example in a plan view with the direction of travel to the right, is located in a surrounding area 80 present around the vehicle 100. According to embodiments of the present invention, the environment has an extension of up to 400 m length and up to 200 m width, preferably up to 80 m length and up to 60 m width.Typically, a surrounding area 80 is considered, the extent of which in the longitudinal direction, i.e. along a direction of travel of the vehicle 100, is greater than in the direction transverse thereto. Furthermore, the environment in front of the vehicle 100 in the direction of travel may have a greater extent than behind the vehicle 100. The environment 80 preferably has a speed-dependent extent, so that a sufficient preview of at least two seconds, preferably at least three seconds, is made possible.As exemplarily shown in FIG. 1, the environment 80 of the vehicle 100 may include a number of objects 50, which may also be referred to as "obstacles" in the context of this disclosure. Objects 50 represent regions of the environment 80 that cannot or should not be used by the vehicle 100. Furthermore, the objects 50 can have different dimensions or shapes and / or be located at different positions. Examples of objects 50 or obstacles can be other road users, in particular stationary traffic, structural limitations (e.g. curbstones, sidewalks, guardrails) or other limitations of the travel path.In FIG. 1, the environment 80 is shown in the form of a rectangle (cf. circumference 82). However, the environment 80 may take any suitable shape and size suitable for a representation thereof, such as square, elliptical, circular, polygonal, or the like. Perimeter 82 is configured to confine environment 80. Furthermore, the environment 80 can be adapted to a detection range of the sensor system. Preferably, the environment 80 corresponds to a shape and size of the area that can be detected by the sensor system (not shown in FIG. 1 ) installed in the vehicle 100. Further, the vehicle 100 may include a control unit 120 in data communication with the sensor system of the vehicle, configured to execute steps of the method 500.FIG. 2 shows a schematic illustration of the application of an OGF-based detection of obstacles 50 in the environment 80 of a vehicle 100 according to the prior art. For the sake of simplicity, the same objects 50 are shown in FIG. 2 in relation to the vehicle 100, as in FIG. 1. FIG. 2 moreover shows a grid structure 60 laid over the environment 80, by means of which grid structure an exemplary classification of the environment 80 into cells 62, 64 is carried out. Here, cells 64 represented by hatching mark the partial regions of the grid structure 60 which at least partially contain an object 50. In contrast, cells 62 marked as "free" are shown without hatching.It can be seen clearly in FIG. 2 that the size of the cells 62, 64 is essential for the detection of the objects 50 in several respects. A cell 64 may be marked as busy based on grid structure 60 when it covers at least partially with an object 50. In the illustrated example, therefore, the group 66 of cells 64 may be marked as occupied, although the effective (lateral) distance of the object 50 detected by the group 66 from the vehicle 100 is substantially greater than the distance of the group 66. In some cases, grid-based methods also use probabilities or "fuzzy" values, so that one or more cells can also be marked in such a way that the probability of an occupancy is detected (e.g. 80% or 30%) or a corresponding value is used (e.g. 0.8 or 0.3), instead of a discrete assessment (e.g. "occupied" or "unoccupied"). Such aspects do not change in the fundamental circumstances, for example with respect to the cell size.Furthermore, a precise determination of an effective size of an object 50 or conclusions about its or its shape, as illustrated in FIG. 2, are likewise dependent on a suitable (small) cell size. For example, groups 66 and 67 of cells 64 contain (in terms of group size) relatively small objects 50, while group 68 contains not only one object 50, but two of them. Conclusions about the size, shape, or number of objects in a respective, contiguous group 66, 67, 68 of cells 64 are accordingly only possible to a limited extent or with relative imprecision on the basis of the grid structure shown.As already described, a smaller cell size requires correspondingly more resources for the acquisition or processing of the object data, so that a higher accuracy is typically associated with disadvantages with regard to efficiency or resource requirements.FIG. 3 shows a schematic illustration of the detection of objects 50 in the environment 80 of a vehicle 100 according to embodiments of the present disclosure. Embodiments of the present disclosure are based on a fusion of the properties of static objects 50 (or obstacles) in a vehicle-fixed, segment-based representation. An exemplary vehicle-fixed, segment-based representation is illustrated in FIG. 3. The environment 80 of the vehicle 100 is bounded by the perimeter 82. For purposes of illustration, the environment 80 in FIG. 3, analogously to that shown in FIG. 1, is likewise illustrated in the form of a rectangle, without the environment 80 having been fixed to such a shape or size (see above).The segment-based representation may consist of Cartesian or polar or mixed segments. In FIG. 3, a representation based on mixed segments 220, 230 is shown. The origin 84 of the coordinate network can be placed substantially at the center point of the rear axle of the vehicle 100, as is illustrated in FIG. 3, in order to define the representation in a vehicle-fixed manner. According to the disclosure, however, other definitions or relative positionings are possible.When various components or concepts are spatially set with respect to the vehicle 100, this occurs relative to a longitudinal axis 83 of the vehicle 100 that extends forward along or parallel to an assumed direction of travel. In FIGS. 1-3, the assumed direction of travel of the vehicle 100 is forward to the right, with the longitudinal axis 83 being shown in FIG. 3. Accordingly, a transverse axis of the vehicle is to be understood as being perpendicular to the longitudinal axis 83. Thus, for example, object 50- 2 is located laterally or transversely to vehicle 100, and object 50- 6 is located essentially in front of vehicle 100 in the direction of travel.Starting from the origin 84 of the coordinate network, the environment 80 is divided or segmented into polar segments 220 in the direction of travel (to the right in FIG. 3 ), so that each segment 220 is defined by an angle located at the origin (and accordingly an angle opening) and the circumference 82 of the environment 80. In this case, as illustrated in FIG. 3, different segments 220 can be defined on the basis of angles or angular openings of different sizes. For example, segments 220 that substantially cover the surroundings transversely of vehicle 100 (or laterally to the direction of travel) have larger angles than those segments 220 that substantially cover surroundings 80 in the direction of travel. In the example illustrated in FIG. 3, a more precise resolution in the direction of travel is achieved by the laterally-longitudinally different segmentation (larger angles transversely, smaller angles in the longitudinal direction), while a lower resolution is applied transversely. In other embodiments, for example if a different prioritization of the detection accuracy is desired, the segmentation can be adapted accordingly. In examples in which the acquisition is to take place transversely with higher resolution, the segmentation can have transversely smaller opening angles (or narrower segments).Furthermore, starting from the origin 84 of the coordinate network, the environment 80 is segmented counter to the direction of travel (to the left of the vehicle 100 in FIG. 3 ) into Cartesian segments 230, so that each segment 230 is defined by a rectangle bounded on one side by the axis 83 (extending through the origin 84 and parallel to the direction of travel) and on the other side by the circumference 82. A width of the (rectangular) segments 230 may be appropriately defined by a predetermined value.Segmenting the environment 80 by different segments 220, 230 (e.g. polar and Cartesian) can allow adaptation to different detection modalities depending on the specific application. For example, the detection of objects 50 in the environment 80 of the vehicle 100 in the direction of travel may have a greater accuracy and range than the detection of objects 50 in the environment 80 of the vehicle 100 counter to the direction of travel (e.g., behind the vehicle) or laterally of the vehicle 100.FIG. 3 shows an exemplary segmentation for the purpose of illustrating embodiments according to the disclosure. In other embodiments, other segmentations may be used, for example based only on polar or only on Cartesian coordinates, and based on mixed coordinates, deviating from that shown in FIG. 3.Generally, a segment 220, 230 may include none, one, or more objects 50. In FIG. 3, segments 220, 230 that contain one or more objects 50 are designated as segments 220' and 230', respectively. The area represented by a segment 220, 230 is bounded on at least one side by the circumference 82 of the surroundings 80. A polar representation in particular maps the property that the accuracy decreases with distance. This is due to the fact that the polar representation, i.e. the radiation-based segmentation starting from the origin 84, covers an ever greater area with increasing distance from the origin 84, whereas comparatively small sections, and thus areas, are considered proximal to the origin 84.Based on the sensor system of the vehicle 100, i.e. based on the signals of one or more sensors, none, one or more detection points 54, 56 are detected in a segment. When using a plurality of sensors, different fields of view or detection are typically present, which allow a more reliable or reliable detection of objects 50. In this case, objects 50 which cannot be detected by a sensor or can only be detected poorly (for example on the basis of a restricted detection range, the type of detection and / or disturbances) can often be reliably detected by another sensor. In the detection, detection points are registered which can be locally arranged in the coordinate system.The sensor system of the vehicle 100 preferably comprises one or more sensors selected from the group comprising ultrasonic-based sensors, lidar sensors, optical sensors and radar-based sensors.In each time step, obstacle points lying close to one another can be associated together cyclically and fused with respect to their properties (e.g. position, probability of existence, height, etc.). The result of this fusion is stored in the described representation and tracked or tracked over time by means of the vehicle movement (cf. Engl. "Tracking", generally track, track). The results of fusion and tracking serve as further obstacle points in the following time steps in addition to new sensor measurements.The tracking or tracking describes a continuation of the already detected objects 50 or the detection points 54, 56 based on a change in position of the vehicle. In this case, a relative movement of the vehicle (e.g. based on dead reckoning or odometry sensor system, or GPS coordinates) is correspondingly mapped in the representation.FIG. 4 illustrates an example segment-based fusion of objects 54- 1, 54- 2, 54- 3, 54- 4, 54- 5, in accordance with embodiments of the present disclosure. FIG. 4 shows a segment 220' with the exemplary recognition of five detection points 54- 1, 54- 2, 54- 3, 54- 4 and 54- 5. Preferably, one or more of the detection points are detected based on signals from different sensors. The diamonds identify the detected object positions approximately as detection points and the respective ellipses correspond to a two-dimensional position uncertainty (variance). Depending on the sensor system, a different variance can be assumed, or an estimated variance can be provided by the respective sensor for each detection.Beginning with the closest object 54- 1, a cluster of objects is created by grouping all objects within the two-dimensional positional uncertainty of object 54- 1. The cluster with the objects 54- 1, 54- 2 and 54- 3 is produced. No further objects can be assigned to the objects 54- 4 and 54- 5, respectively. Therefore, these each form their own cluster. Within a cluster, the position is fused, for example, by means of Kalman filters, and the probability of existence is fused by means of Bayes or Dempster-Shafer.FIG. 5 shows a flowchart of a method 500 for detecting objects 50 in the environment 50 of a vehicle 100, according to embodiments of the present disclosure. The method 500 begins at step 501.In step 502, the environment 80 is divided or segmented into a plurality of segments, such that each segment 220, 230 of the plurality of segments is at least partially bounded by the perimeter 82 of the environment 80. This means (cf. FIG. 3 ) that each of the segments is at least partially delimited by the circumference 82, and therefore the environment is covered over its full circumference by the segments. In other words, the sum of all segments 220, 230 corresponds to surroundings 80, and the surfaces are identical or congruent. Furthermore, each segment has "contact" to the periphery 82 or to the edge of the environment, so that no segment is arranged in an insulated manner within the environment 80 or is separated from the periphery 82. In other words, at least a portion of the perimeter of each segment 220, 230 coincides with a portion of the perimeter 82 of the environment 80.In step 504, one or more detection points 54, 56 are detected based on the one or more objects 50 in the environment 80 of the vehicle 100. Here, based on the sensor system of the vehicle 100, detection points of the object(s) are detected as points (e.g. coordinates, position information), preferably relative to the vehicle 100 or in another suitable frame of reference. The detection points 54, 56 detected in this way accordingly mark locations in the environment 80 of the vehicle 100 at which an object 50 or a partial region of the object has been detected. As can be seen in FIG. 3, a plurality of detection points 54, 56 can be detected for one object each, wherein an object 50 can be detected more accurately the more detection points 54, 56 are detected and if various sensors (e.g. optical, ultrasound-based) are used for detection, so that sensor-related or technical influences (e.g. fields of view or detection, resolution, range, accuracy) are minimized.Optionally, in step 506, one or more detection points 54, 56 are clustered based on a spatial proximity of the points to each other. As described with reference to FIG. 4, position uncertainties which may be present can be reduced or avoided in this way, so that objects 50 can be detected with an improved accuracy on the basis of the resulting clusters of the detection points.In step 508, each of the segments 220, 230 of the plurality of segments is associated with a state based on the one or more detection points 54, 56 and / or the detected clusters. If no clusters have been formed, step 508 is based on the detected detection points 54, 56. Alternatively, step 508 can be based additionally or alternatively on the detected clusters, with the aim of enabling the highest possible detection accuracy and of correspondingly providing segments with a state. The state in particular indicates a relation of the segment with one or more obstacles. According to embodiments of the present disclosure, the state may assume a discrete value (e.g. "occupied" or "unoccupied", or suitable representations such as "0" or "1") or a flowing value (e.g. values expressing an occupancy probability such as "30%" or "80%", or suitable representations such as "0.3" or "0.8"; or other suitable values, e.g. discrete levels of occupancy, e.g. "strong", "medium", "weak").If a vehicle is mentioned here, this is preferably a multi-lane motor vehicle (passenger car, truck, transporter). This results in several advantages that are explicitly described within the scope of this document as well as several further advantages that can be understood by the person skilled in the art.Although the invention has been illustrated and explained in more detail by preferred exemplary embodiments, the invention is not restricted by the disclosed examples and other variations can be derived therefrom by the person skilled in the art without departing from the scope of protection of the invention. It is therefore clear that a large number of possible variations exist. It is also clear that embodiments mentioned by way of example represent only examples which are not to be understood in any way as limiting, for example, the scope of protection, the possible applications or the configuration of the invention. Rather, the preceding description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, wherein the person skilled in the art, having knowledge of the disclosed inventive idea, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without departing from the scope of protection defined by the claims and their legal equivalents, such as, for example, further explanations in the description.

Claims

Method (500) for detecting one or more objects (50) in a surroundings (80) of a vehicle (100), wherein the surroundings (80) are bounded by a circumference (82), the method comprising: segmenting (502) the surroundings (80) into a plurality of segments for providing an occupancy grid, such that each segment (220, 230) of the plurality of segments is bounded at least partially by the circumference (82) of the surroundings (80), wherein the surroundings (80) substantially have one of the following shapes: square, rectangle, circle, ellipse, polygon, trapezoid, parallelogram; detecting (504) one or more detection points (54, 56) based on the one or more objects (50) in the surroundings (80) of the vehicle (100); and assigning (508) a state to each of the segments (220, 230) of the plurality of segments based on the one or more detected detection points (54, 56).Method according to the preceding claim, wherein the environment (80) includes an origin (84), the origin (84) optionally coinciding with a position of the vehicle (100), in particular a position of the centre of a rear axle of the vehicle (100).The method according to the preceding claim, wherein - each segment (220) of a first subset of the plurality of segments is defined starting from the origin (84) in the form of a respective angular opening, wherein the first subset comprises one, several, or all segments (220) of the plurality of segments; further preferably wherein - the segments (220) of the first subset have at least two different angular openings, in particular wherein segments (220) extending substantially laterally to the vehicle (100) have a larger angular opening than segments (220) extending substantially in a longitudinal direction to the vehicle (100); or segments (220) extending substantially laterally to the vehicle (100) have a smaller angular opening than segments (220) extending substantially in a longitudinal direction to the vehicle (100); and / or wherein - the segments (220) of the first subset have an angular opening substantially in the direction of travel of the vehicle (100) starting from the origin (84).The method of claim 2 and claim 3, wherein: - each segment (230) of a second subset of the plurality of segments is defined in the form of a Cartesian sub-region, the second subset comprising, optionally based on the first subset, one, more, or all segments (220) of the plurality of segments; further preferably wherein - the segments (230) of the second subset have at least two different extents in one dimension; and / or wherein - the segments (230) of the second subset have a first extent substantially transverse to a direction of travel of the vehicle (100) that is greater than a second extent substantially in a direction of travel of the vehicle (100).Method according to the preceding claims 3 and 4, wherein the segments (220) of the first subset are defined on one side of the origin (84) and the segments (230) of the second subset are defined on an opposite side of the origin (84); in particular wherein the segments (220) of the first subset are defined starting from the origin (84) in the direction of travel of the vehicle (100).The method of any preceding claim, further comprising aggregating (506) the one or more acquisition points (54, 56) into one or more clusters based on a spatial proximity of the one or more acquisition points (54, 56), and wherein the step of associating (508) a state with each of the segments (220, 230) of the plurality of segments is additionally or alternatively based on the one or more aggregated clusters, in particular wherein aggregating (506) the one or more acquisition points (54, 56) into one or more clusters is based on application of the Kalman filter; preferably wherein the one or more clusters are treated as one or more acquisition points (54, 56).The method according to any one of the preceding claims, wherein - the state of a segment (220, 230) of the plurality of segments indicates an at least partial overlap of an object (50) with the respective segment (220, 230), wherein preferably the state includes at least one discrete value or a probability value; and / or wherein - the vehicle (100) comprises a sensor system configured to detect the objects (50) in the form of detection points (54, 56); further preferably wherein the sensor system comprises at least a first sensor and a second sensor, and wherein the first and second sensors are configured to detect objects (50), optionally wherein: the first and second sensors are different from each other; and / or wherein the first and second sensors are selected from the group comprising ultrasound-based sensors, optical sensors, radar-based sensors, lidar-based sensors; and / or wherein - detecting the one or more detection points (54, 56) includes detecting the one or more detection points (54, 56) by means of the sensor system.System for detecting one or more objects (50) in a surrounding area (80) of a vehicle (100), the system comprising a control unit (120) and a sensor system, wherein the control unit is configured to execute the method (500) according to one of the preceding claims.A vehicle (100) comprising the system of the preceding claim.

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