Computer-implemented method, electronic vehicle system and computer program product

Abstract

The invention relates to a computer-implemented method for prioritising reflection points in a point cloud (28) generated by means of an active optical sensor system (10), wherein the point cloud (28) has respective solid angles, respective distances, and respective intensities of the received reflections for the reflection points, wherein the method comprises, for at least one solid angle of the point cloud (28): prioritising a reflection point among reflection points located at the respective solid angle, wherein the respective distances and the respective intensities of the reflection points located at the respective solid angle are used for the prioritisation. The invention further relates to a method for at least partially automatically guiding a vehicle (20), to an electronic vehicle system (30), and to a computer program product.

Description

[0001] COMPUTER-IMPLEMENTED METHOD, ELECTRONIC VEHICLE SYSTEM AND COMPUTER PROGRAM PRODUCT

[0002] Technical area

[0003] The application relates to a computer-implemented method, an electronic vehicle system, and a computer program product. The electronic vehicle system comprises an active optical sensor system, e.g., a lidar system, configured to generate a point cloud.

[0004] background

[0005] Sensor systems for environmental detection can be based on wireless signals such as electromagnetic waves or sound. Accordingly, sound-based ultrasonic sensors and electromagnetic wave-based sensors such as camera sensors, radar sensors, or lidar sensors are known.

[0006] Lidar technology (Lidar stands for Light Detection and Ranging) is an important sensor principle for environmental detection. It is based on optical electromagnetic waves and is used in active optical sensor systems. A Lidar system has an optical transmitter and an optical receiver. The transmitter can emit light, which can be pulsed. In a Lidar system, laser beams in the ultraviolet, visible, or infrared range can be used as light. The receiver can receive the transmitted light as received light after it has been reflected from an object in a detection area in the vicinity of the Lidar system. The received light can be evaluated using the transmitted light by an evaluation device in the Lidar system, e.g. using a time-of-flight method.Time-of-Flight (TOF) can be evaluated, and the spatial position and distance of the objects at which the reflection occurred can be determined. Furthermore, a relative speed can be determined. Reflection or reflected light is understood here to mean any light that is returned and should, in particular, also include light that is returned by scattering or absorption / emission.

[0007] The light reflected in the environment can be detected in the receiving device via receiving sensors. The receiving sensors of lidar systems can have multiple receiving elements, called pixels, for optical-to-electrical conversion. The pixels can be configured to receive optical signals from different receiving angles.

[0008] DE102021112709A1 describes a Lidar laser scanner in which the intensity of the reflected echo signal of a target object is evaluated in order to check the functional reliability of the laser scanner.

[0009] Overview

[0010] A point cloud generated by an active optical sensor system displays the respective solid angles, distances, and intensities of the received reflections for each reflection point. A computer-implemented method for prioritizing reflection points comprises:

[0011] • for at least one solid angle of the point cloud:

[0012] Prioritizing a reflection point among reflection points located at the respective solid angle, whereby the respective distances and the respective intensities of the reflection points located at the respective solid angle are used for the prioritization.

[0013] The point cloud is generated by the active optical sensor system, e.g., a lidar system. The points in the point cloud contain information about the spatial position and distance of the reflection points in the environment. The reflection points are the points at which the light emitted and received by the sensor system was reflected. The point cloud can be understood as a set of points, where each point has corresponding coordinates in a, in particular, 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, i.e., the solid angle, of a light beam reflected at a reflection point and the corresponding time of flight or radial distance measured for this particular point. The solid angle and the respective associated incident light beams can have multiple reflection points in the environment.In addition to spatial information, such as three-dimensional coordinates that include the distance to the point, the point cloud can also store additional information or measurement data for the individual points, such as the intensity of the light received. The intensity is directly related to the energy of the light received.

[0014] When measuring with a lidar sensor, more than one reflection may be obtained from a light beam in a solid angle. In the point cloud, such a solid angle will then have multiple reflection points in the surrounding area, corresponding to the respective incident light beam. These multiple reflection points can also be referred to as echoes.

[0015] For algorithms that use the point cloud, e.g., for object detection, prioritizing these echoes is useful. For example, it is advantageous to identify relevant, such as critical, echoes and other less relevant echoes. For these algorithms, this can mean that there are echoes that correspond to real or critical objects in the environment. Such real or critical objects can be important, for example, for algorithms used for autonomous or semi-autonomous driving, as the clearance of the ego vehicle depends on them. Other, less relevant targets may be less critical for the algorithm, e.g., noise, sensor contamination, rain, spray, real obstacles beyond the relevant object, or similar.

[0016] Using the described algorithm, it is possible to appropriately prioritize the echoes, which can be particularly advantageous for further processing, e.g., in the vehicle.

[0017] The three-dimensional coordinate system of the point cloud can be a three-dimensional polar coordinate system. However, the information can also be specified in Cartesian coordinates for each of the points. A conversion can then be performed to determine the solid angle.

[0018] The described method offers the advantage of being based on at least two criteria: the radial distance, from which one can deduce how close the reflection is. The energy / intensity received with the light, from which one can deduce how certain it is that a reflection was actually received.

[0019] In one embodiment of the method, the reflection points located at the respective solid angle are sorted according to their respective distances. A first assessment value is assigned depending on the position in the sorted sequence. For example, the first assessment value may be smaller for closer echoes than for more distant ones. Thus, for each solid angle, a sequence of echoes with corresponding first assessment values ​​is obtained.

[0020] In one embodiment of the method, the reflection points located at the respective solid angle are sorted according to their respective intensities of the received reflections. A second assessment value is assigned depending on the position in the sorted sequence. The second assessment value can, for example, be smaller for echoes with higher energy or intensity than for those with lower energy or intensity. Thus, a sequence of echoes with corresponding second assessment values ​​is obtained for each solid angle.

[0021] In a next step of the method, the first and second assessment values ​​for the respective reflection points of a solid angle can then be added to form a respective sum. Prioritization is then performed using the sum. For the previously described example, in which the first assessment value is smaller for closer echoes than for more distant ones, and the second assessment value is smaller for echoes with higher energy or intensity than for those with lower energy or intensity, the echo with the smallest sum can be prioritized.

[0022] In one embodiment of the method, a distance threshold is used for prioritization to determine whether the respective reflection point is located in a near field or a far field of the point cloud. Near field can be understood, for example, as a distance range up to 50 m, in particular up to 25 m. The distance threshold is then, for example, 50 m or 25 m. The far field then comprises the distances that exceed the distance threshold. Prioritization can then subsequently take place when the respective sums of the first and second assessment values ​​of reflection points of a solid angle are equal, using the information about whether the reflection point is located in the near field or the far field. For example, the echo located in the near field can be prioritized.

[0023] In one embodiment of the method, reflection points within a solid angle that are not prioritized are marked as non-prioritized. The information about these echoes can thus be retained in the point cloud, but can be marked in such a way that, during subsequent use of the point cloud data, it is clear that the respective echoes are associated with less relevant objects.

[0024] In one embodiment of the method, the assignment to a relevant object is determined for each prioritized reflection point using the distance and intensity of the received reflection. This means that the relevant object can be detected for a prioritized reflection. Relevant objects can, in particular, be other road users.

[0025] Prioritized reflection points that are not assigned to a relevant object can be marked as such. Prioritized echoes that are not assigned to a relevant object can include, for example, optical artifacts such as blooming effects. Artifacts such as blooming are actually undesirable. Blooming includes, for example, highly reflective pseudo-echoes. However, such artifacts can still be meaningful under certain conditions. Especially for algorithms such as post-processing algorithms that may process the data later, it could be helpful to have these artifacts available in order to extract information from them. Therefore, it is advantageous to mark such echoes as prioritized but not assigned to a relevant object.

[0026] In a method for at least partially automatically guiding a vehicle, a point cloud is generated using an active optical sensor system of the vehicle. The point cloud contains the respective solid angles, respective distances, and respective intensities of the received reflections for reflection points in the vehicle's surroundings. The described computer-implemented method for prioritizing reflection points is carried out using at least one computing unit of the vehicle. The vehicle is guided at least partially automatically depending on the result of the prioritization.

[0027] In one embodiment of the method, the assignment to a relevant object is determined for a respective prioritized reflection point using the distance and the intensity of the received reflection, wherein the relevant objects are relevant for the at least partially automatic driving of the vehicle.

[0028] An electronic vehicle system comprises a computing unit and an active optical sensor system. The optical sensor system is configured to generate a point cloud, wherein the point cloud has respective solid angles, respective distances, and respective intensities of the received reflections for reflection points in an environment of the vehicle. The computing unit is configured to prioritize a reflection point, for at least one solid angle of the point cloud, among the reflection points located at the at least one solid angle, wherein the distances and intensities of the reflection points located at the at least one solid angle are used for the prioritization.

[0029] In one embodiment, the electronic vehicle system is configured to guide the vehicle at least partially automatically depending on a result of the prioritization.

[0030] A computer program product comprises instructions which, when executed by a data processing device, cause the data processing device to carry out the described computer-implemented method, or, when executed by the electronic vehicle system, cause the electronic vehicle system to carry out the described method for at least partially automatically driving a vehicle.

[0031] Fiourenliste

[0032] In the following, exemplary embodiments of this application are further explained and described with reference to the figures. Fig. 1 shows a schematic representation of a vehicle with an electronic vehicle system,

[0033] Fig. 2 schematically shows an electronic vehicle system with an active optical sensor system and a computing unit,

[0034] Fig. 3 shows schematically an environment detected by the sensor system,

[0035] Fig. 4 schematically shows a method for prioritizing reflection points in a point cloud,

[0036] Fign. 5 + 6 schematic tables to illustrate the procedure.

[0037] The same reference numerals are used throughout the figures to refer to identical or similar elements. Representations in the figures may not be to scale.

[0038] Figure description

[0039] Figure 1 schematically illustrates a vehicle 20, for example, a passenger car. The vehicle 20 has an active optical sensor system 10, e.g., a lidar system. The optical sensor system 10 is arranged in a front region of the vehicle 20, and the surroundings 22 detected by it are located in front of the vehicle 20 in the direction of travel.

[0040] The active optical sensor system 10 comprises an optical transmitting device 12, an optical receiving device 14, an optical deflecting device 16, and an evaluation device 18. The evaluation device 18 may comprise a processor, an FPGA, or the like for processing data.

[0041] The optical transmitting device 12 emits light L. It has a light source for emitting, for example, laser light. The optical receiving device 14 receives the light L reflected in the surroundings 22.

[0042] Optionally, the optical transmitting device 12 can emit the light L in pulses. The pulsed light L has short periods of time in which the light L is transmitted. This can be referred to as a pulse. Between the pulses, no light L is transmitted by the optical transmitting device 12. The reflections in the surroundings 22 of the pulses are then received by the optical receiving device 14. Preferably, the optical receiving device 14 comprises an optoelectronic detector, for example a point sensor, line sensor, or area sensor, in particular an avalanche photodiode, a photodiode cell, a CCD sensor, an active pixel sensor, for example a CMOS sensor, or the like. The optoelectronic detector can receive light L, in particular laser light, and convert it into electrical signals. The electrical signals can be processed by the evaluation device 18.

[0043] The optical deflection device 16 is configured to deflect the light L transmitted by the optical transmitting device 12 into the surroundings 22 and to deflect the light L reflected from the surroundings 22 to the optical receiving device 14. The deflection device 16 can be controlled such that the light L performs a scanning movement 26 across the surroundings 22. The deflection device 16 can, for example, comprise a rotating mirror device that performs a rotating movement to deflect the light L such that the scanning movement 26 is performed by the light L. During the rotating movement, the angular position of the deflection device 16 is changed.

[0044] The evaluation device 18 is configured to control the transmission of the light L depending on the angular position of the deflection device 16. The evaluation unit 18 is further configured to evaluate the transmitted and received light L. The evaluation data thus generated can be transmitted from the evaluation device 18 to a computing unit 24 of the vehicle 20.

[0045] A point cloud 28 can be generated from the evaluation data generated by the evaluation, in which information is specified for the individual points of the point cloud 28 that depends on the evaluation data, such as distance information. The point cloud 28 can be used, for example, to detect objects 001, 002, 003, 004, R01, R02, to determine the distance to objects 001, 002, 003, 004, R01, R02 and / or to perform further evaluations. The point cloud 28 can be generated, for example, in the optical sensor system 10 from the evaluation data, or the point cloud 28 can be generated, for example, in the computing unit 24 from the raw data of the optical sensor system 10. The computing unit 24 can, for example, be designed as a central vehicle computer of the vehicle 20, in which data from several sensor systems of the vehicle 20 can be received, evaluated and / or further processed.The computing unit 24 can be used, for example, to implement autonomous or semi-autonomous driving functions.

[0046] The optical sensor system 10 can, for example, be mounted or integrated on the front of the vehicle 20. Optical sensor systems 10 for other parts of the vehicle 20 are also possible, e.g., for surround-view functions, such as on the sides and / or rear of the vehicle 20. It is also possible to arrange optical sensor systems 10, such as lidar sensors, and / or other sensor systems such as radar, ultrasound, etc., on the vehicle 20, in particular also in corner areas of the vehicle 20.

[0047] The optical sensor system 10 can be used to detect stationary or moving objects OO1, 002, 003, 004, RO1, RO2 in the environment 22. Such objects 001, 002, 003, 004, RO1, RO2 can include objects such as vehicles, people, animals, plants, obstacles, road surface irregularities, in particular potholes or stones, road markings, traffic signs, open spaces, in particular parking spaces, precipitation, or the like.

[0048] Figure 2 shows a schematic block diagram of the electronic vehicle system 30 with an embodiment of the optical sensor system 10. The optical sensor system 10 has the optical transmitting device 12, the optical receiving device 14, the deflection device 16 and the evaluation device 18.

[0049] The deflection device 16 has a rotatable mirror. The mirror of the deflection device 16 can be a double-sided mirror, which offers a surface on both sides for the optical deflection of the light L. The transmitted and received light L is evaluated in the evaluation unit 18. For example, distance data to the reflection points can be generated using direct or indirect time-of-flight measurements. In addition, it is possible to evaluate the intensity of the received light and store it together with the solid angle of the reflection point and its distance in the point cloud 28. Figure 2 also shows the computing unit 24, which is arranged in the vehicle 20. The computing unit 24 on board the vehicle 20 is connected to the optical sensor system 10 via a communication interface. The communication interface can be used, for example, to transmit the evaluation data of the sensor system 10 to the computing unit 24 of the vehicle 20.It is also possible for the evaluation device 18 to generate the point cloud 28 and for this to then be transmitted from the evaluation device 18 to the computing unit 24 via the communication interface.

[0050] The point cloud 28 comprises the collection of measurement data for the various reflection points in the environment. For example, each pulse of light L can provide a measurement value for one or more reflection points during evaluation. In a scanning optical sensor system 10, the light L performs the scanning movement 26 and thus successively scans points in the environment 22 with a changing solid angle. During scanning, the measurement data of the point cloud 28 can be obtained as evaluation data. When the point cloud 28 is generated in the computing unit 24, the measurement data can be assigned to the spatial points.

[0051] Figure 3 schematically shows an environment 22 detected by the sensor system 10. Rays L1, L2, L3, L4, L5, L6, L7, L8 are schematically shown in different solid angles of the light L.

[0052] During measurements with the optical sensor system 10, more than one reflection may be received from a light beam L1, L2, L3, L4, L5, L6, L7, L8 of a solid angle. In the point cloud 28, such a solid angle then exhibits multiple reflection points in the environment 22 corresponding to the respective associated incident light beam L1, L2, L3, L4, L5, L6, L7, L8. These multiple reflection points can also be referred to as echoes.

[0053] In the illustrated embodiment, a tree OO1 is located on the light beam Ll as a reflection point or echo.

[0054] On the light beam L.2, the first relevant object RO1 and the tree OO1 are reflected points or echoes. On the light beam L.3, the reflection point or echo is an object 002, such as rain, fog, or spray.

[0055] On the light beam L.4 there are reflection points or echoes of object 002 such as rain, fog, spray, as well as the first relevant object R01.

[0056] The relevant object R01 is located on the light beam L.5 as a reflection point or echo.

[0057] On the light beam L.6 there is an artifact Blooming BLO as a reflection point or echo.

[0058] On the light beam L.7, the artifact Blooming BLO and a second relevant object R02 are located as reflection points or echoes.

[0059] On the light beam L.8 there are reflection points or echoes of a reflecting floor 003, as well as the second relevant object R02 via the double reflection on the reflecting floor 003.

[0060] For all of the light beams L.1-L.8 mentioned, it is possible that, for example, dirt on a cover plate of the sensor system 10 is perceived as an echo or reflection point.

[0061] The recorded reflection points can be stored in the point cloud 28 with information on their distance and the intensity of the received light.

[0062] For further processing of the data from point cloud 28, it is advantageous to evaluate the multiple reflection points over a solid angle. It is advantageous to identify the relevant objects R01, R02 in point cloud 28.

[0063] Figure 4 schematically shows a method for prioritizing reflection points in a point cloud 28.

[0064] For object detection algorithms, such as those used for autonomous or semi-autonomous driving, it is advantageous to prioritize and select one echo from the multiple echoes on a solid angle.

[0065] The prioritization process uses two criteria:

[0066] 1) the radial distance

[0067] 2) the intensity or energy of the received light The order of the objects 001, 002, 003, 004, R01, R02 shown in Figure 3 sorted by radial distance from near to far is the following (list without the dirt on the cover plate 004):

[0068] 1) Noise, rain, fog, spray 002

[0069] 2) reflective ground 003 (e.g. puddle)

[0070] 3) first relevant object R01

[0071] 4) Artifact Blooming BLO

[0072] 5) second relevant object R02

[0073] 6) Tree 001 (or bush)

[0074] The intensity or energy of the received light L is a measure of how reflective the surface was at which the light L was reflected. At high reflectivity, a large portion of the energy is reflected and can thus be received again by the receiving sensor of the receiving device 14.

[0075] The order of the objects shown in Figure 3 sorted from high intensity to low intensity is as follows (list without the protection on cover plate 004):

[0076] 1) reflective ground 003 (e.g. puddle)

[0077] 2) Tree 001 (or bush)

[0078] 3) first relevant object R01

[0079] 4) Artifact Blooming BLO

[0080] 5) second relevant object R02

[0081] 6) Noise, rain, fog, spray 002

[0082] The procedure for prioritizing echoes on a solid angle is as follows:

[0083] 400: Determine the first assessment value based on the displayed radial distance sorting,

[0084] 402: Determining the second assessment value based on the intensity or energy sorting,

[0085] 404: Determine the sum of the first and second assessment values, 406: If there are several echoes on the solid angle and the echoes of a solid angle have different sums of the first and second assessment values:

[0086] 408: Carrying out prioritization based on totals

[0087] 406: If there is more than one echo with the same sum of the first and second assessment values ​​for a solid angle:

[0088] 410: Determine whether the nearest echo is in the near range,

[0089] If Yes: 412: Perform intensity-based prioritization to suppress ambient noise,

[0090] If No: 414: Perform prioritization based on radial distance.

[0091] Figures 5 and 6 show schematic tables illustrating an embodiment of the method. The embodiment is illustrated without the dirt on the cover plate of the optical sensor system 10.

[0092] Figure 5 shows, in a table, the nearest object according to its radial distance for each light ray L.1-L.8 in each row in the left column. The middle column shows the farthest object according to its radial distance for each light ray L.1-L.8. The right column shows the object for each light ray L.1-L.8 that has the highest energy content (or the highest intensity) upon reception of the reflection.

[0093] Those objects that are not to be prioritized are outlined. For light beam L.2, this is the tree OO1. For light beam L.4, this is the rain / fog / spray 002. For light beam L.8, this is the second relevant object, which appears via the secondary reflection on the reflective floor 003. The Blooming BLO artifact is also outlined. It is not a relevant object but remains in the dataset because it may contain additional information that could be useful for further processing.

[0094] Figure 6 shows exemplary assignments of assessment values ​​to objects 001, 002, 003, RO1, RO2 on the individual light beams L1-L8 according to the method. When assigning the first assessment value in step 400, the nearest object is assigned the value 1, and more distant objects are assigned a value increased by 1.

[0095] When assigning the second assessment value in step 402, the object with the highest energy (or intensity) is assigned the value 1 and objects with lower energy (or intensity) are assigned a value increased by 1.

[0096] For light beam L1, the first assessment value for tree 001 is 1 (step 400, first column). The second assessment value (step 402, second column) for tree OO1 is also 1. The sum of the assessment values ​​for tree 001 is 2. The prioritized object is tree 001 (step 408).

[0097] For light beam L.2, the first assessment value for tree 001 is 2 (step 400, first column) and the first assessment value for the first relevant object RO1 is 1 (step 400, first column). The second assessment value (step 402, second column) for tree 001 is 1 and the second assessment value for the first relevant object RO1 is 2 (step 402, second column). The sum of the assessment values ​​for tree 001 is 3 (step 404, third column). The sum of the assessment values ​​for the first relevant object RO1 is also 3 (step 404, third column). Neither of the two objects is in the near field. The prioritized object is the first relevant object RO1 because its radial distance is shorter (step 414).

[0098] For light beam L.3, the first assessment value for the object Rain / Fog / Spray 002 is 1 (step 400, first column). The second assessment value (step 402, second column) for the object Rain / Fog / Spray 002 is also 1. The sum of the assessment values ​​for the object Rain / Fog / Spray 002 is 2. The prioritized object is the object Rain / Fog / Spray 002 (step 408).

[0099] For the light beam L.4, the first assessment value for the object rain / fog / spray 002 is 1 (step 400, first column) and the first assessment value for the first relevant object RO1 is 2 (step 400, first column). The second assessment value (step 402, second column) for the object rain / fog / spray 002 is 2 and the second assessment value for the first relevant object RO1 is 1 (step 402, second column). The sum of the assessment values ​​for the object rain / fog / spray 002 is 3 (step 404, third column). The sum of the assessment values ​​for the first relevant object RO1 is also 3 (step 404, third column). The object rain / fog / spray 002 is in the near field. The prioritized object is the first relevant object RO1 because the intensity / energy is greater upon reception (step 412).

[0100] For light beam L.5, the first assessment value for the first relevant object RO1 is 1 (step 400, first column). The second assessment value (step 402, second column) for the first relevant object RO1 is also 1. The sum of the assessment values ​​for the first relevant object RO1 is 2. The prioritized object is the first relevant object RO1 (step 408).

[0101] For light beam L.6, the first assessment value for the Blooming BLO 1 artifact is 1 (step 400, first column). The second assessment value (step 402, second column) for the Blooming BLO 1 artifact is also 1. The sum of the assessment values ​​for the Blooming BLO 1 artifact is 2. The prioritized object is the Blooming BLO 1 artifact (step 408).

[0102] For the light beam L.7, the first assessment value for the Blooming BLO artifact is 1 (step 400, first column) and the first assessment value for the second relevant object RO2 is 2 (step 400, first column). The second assessment value (step 402, second column) for the Blooming BLO artifact is 1 and the second assessment value for the second relevant object RO2 is 2 (step 402, second column). The sum of the assessment values ​​for the Blooming BLO artifact is 2 (step 404, third column). The sum of the assessment values ​​for the second relevant object RO2 is 4 (step 404, third column). The prioritized object is the Blooming BLO artifact because the sum of its assessment values ​​is lower (step 408).

[0103] For light beam L.8, the first assessment value for the reflective road 003 is 1 (step 400, first column) and the first assessment value for the second relevant object RO2 is 2 (step 400, first column). The second assessment value (step 402, second column) for the reflective road 003 is 1 and the second assessment value for the second relevant object RO2 is 2 (step 402, second column). The sum of the assessment values ​​for the reflective road 003 is 2 (step 404, third column). The sum of the assessment values ​​for the second relevant object RO2 is 4 (step 404, third column). The prioritized object is the reflective road 003 because the sum of their assessment values ​​is lower (step 408).

[0104] The described method offers the advantage of being based on at least two criteria: the radial distance, from which one can deduce how close the reflection is. The energy / intensity received with the light, from which one can deduce how certain it is that a reflection was actually received.

[0105] In addition, the method can be used to intercept non-real echoes, e.g., due to multiple reflections, or when relevant objects are less reflective than other objects.

[0106] The proposed prioritization using the assessment values ​​has the advantage that the assessment values ​​based on the two criteria are easy to calculate, robust, and can be implemented with little computational effort.

[0107] The described prioritization procedure is therefore particularly well suited for applications of autonomous or semi-autonomous driving.

[0108] Reference sign

[0109] 10 optical sensor system

[0110] 12 optical transmitter

[0111] 14 optical receiving device

[0112] 16 optical deflection device

[0113] 18 Evaluation device

[0114] 20 vehicles

[0115] 22 Surroundings

[0116] 24 computing unit

[0117] 26 scanning movement

[0118] 28 point cloud

[0119] 30 electronic vehicle system

[0120] BLO Blooming

[0121] L, Ll - L.8 Light rays in the solid angle

[0122] RO1, RO2 relevant objects

[0123] 001 Tree

[0124] 002 Noise / Rain / Fog / Spray

[0125] 003 reflective floor

[0126] 004 Dirt Disc

[0127] 400-414 procedural steps

Claims

CLAIMS 1. Computer-implemented method for prioritizing reflection points in a point cloud (28) generated by means of an active optical sensor system (10), wherein the point cloud (28) has respective solid angles, respective distances, and respective intensities of the received reflections for the reflection points, the method comprising: for at least one solid angle of the point cloud (28): prioritizing a reflection point among reflection points located on the respective solid angle, wherein the respective distances and the respective intensities of the reflection points located on the respective solid angle are used for the prioritization.

2. Method according to claim 1, wherein the reflection points located on the respective solid angle are sorted with regard to their respective distances and a first assessment value is assigned depending on the position in the sequence.

3. Method according to claim 1 or 2, wherein the reflection points located on the respective solid angle are sorted with regard to their respective intensities of the received reflections and a second assessment value is assigned depending on the position in the sequence.

4. The method according to claim 3, wherein the first and second assessment values ​​for the respective reflection points of a solid angle are added to a respective sum and the prioritization is performed using the sum.

5. Method according to one of the preceding claims, wherein for the prioritization it is determined by means of a distance threshold value whether the respective reflection point is located in a near field or a far field of the point cloud (28).

6. The method according to claim 5, wherein the prioritization is carried out for respective equal sums of reflection points of a solid angle using the information about whether the reflection point is located in the near field or in the far field.

7. Method according to one of the preceding claims, wherein reflection points of a solid angle which are not prioritized are marked as non-prioritized.

8. Method according to one of the preceding claims, wherein the assignment to a relevant object (RO1, RO2) is determined for a respective prioritized reflection point using the distance and the intensity of the received reflection.

9. The method according to claim 8, wherein prioritized reflection points which are not assigned to a relevant object are marked as such.

10. A method for at least partially automatically guiding a vehicle (20), wherein a point cloud (28) is generated by means of an active optical sensor system (10) of the vehicle (20), wherein the point cloud (28) has respective solid angles, respective distances, and respective intensities of the received reflections for reflection points in an environment of the vehicle (20), wherein a computer-implemented method for prioritizing reflection points according to one of the preceding claims is carried out by means of at least one computing unit (24) of the vehicle (20), and the vehicle (20) is guided at least partially automatically depending on a result of the prioritization.

11. The method according to claim 10, wherein the assignment to a relevant object (RO1, RO2) is determined for a respective prioritized reflection point using the distance and the intensity of the received reflection, wherein the relevant objects (RO1, RO2) are relevant for the at least partially automatic driving of the vehicle (20).

12. An electronic vehicle system (30), comprising a computing unit (24) and an active optical sensor system (10) configured to generate a point cloud (28), wherein the point cloud (28) has respective solid angles, respective distances, and respective intensities of the received reflections for reflection points in an environment of the vehicle (20), wherein the computing unit (24) is configured to prioritize a reflection point, for at least one solid angle of the point cloud (28), from among the reflection points located on the at least one solid angle, wherein the distances and the intensities of the reflection points located on the at least one solid angle are used for the prioritization.

13. Vehicle system according to claim 12, wherein the vehicle system (30) is configured to guide the vehicle (20) at least partially automatically depending on a result of the prioritization.

14. A computer program product comprising instructions which, when executed by a data processing device, cause the data processing device to perform a computer-implemented method according to any one of claims 1 to 9, or, when executed by an electronic vehicle system (30) according to claim 13, cause the electronic vehicle system (30) to perform a method according to claim 10 or 11.

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