Method for performing object classification

Abstract

The present invention relates to a method for classifying objects into object classes based on information of at least one ultrasonic sensor of a vehicle.

Description

[Technical Field]

[0001] The present invention relates to a method for classifying objects into object classes. [Background technology]

[0002] It is known to perform height estimation of an object using geometric methods based on information captured by ultrasonic sensors, which makes it possible to distinguish, for example, between a linear object representing a curb and a linear object in the shape of a wall.

[0003] A problem with height estimation is that known methods for estimating height give erroneous results when used on vehicle-shaped objects. Summary of the Invention [Problem to be solved by the invention]

[0004] Starting from this, the object of the present invention is to provide a method which allows reliable classification of objects into predefined object classes. [Means for solving the problem]

[0005] This object is achieved by a method having the features of independent claim 1. Preferred embodiments are the subject of the dependent claims. A system for classifying objects into object classes based on information from at least one ultrasonic sensor of a vehicle is the subject of parallel claim 14, as well as a vehicle equipped with such a system.

[0006] According to a first aspect, a method for classifying objects into object classes based on information from at least one ultrasonic sensor is disclosed, the method comprising the steps of:

[0007] First, a plurality of detections are received from at least one ultrasonic sensor of the vehicle. It goes without saying that the detections may also be obtained by cooperation of a plurality of ultrasonic sensors. Each detection is assigned with position information and direction information. The position information indicates a reflection position from which an ultrasonic signal of the at least one ultrasonic sensor is reflected. The position information particularly preferably includes coordinates indicating the reflection position. The direction information indicates a direction along which the ultrasonic signal propagates between the reflection position and the at least one ultrasonic sensor. The direction information can, for example, be given as an angle, particularly preferably an azimuth angle.

[0008] The received detections are then assigned to clusters, where one cluster contains multiple detections, thus forming a group of multiple detections. One to multiple clusters relate to one object, i.e., detections originating from one object can be assigned to one or multiple clusters.

[0009] After the clusters are formed, information about the statistical distribution of position information and information about the statistical distribution of directional information is calculated for each cluster, based on the position information and directional information of the detections assigned to each cluster.

[0010] Finally, classification of the objects into object classes is performed based on information about the statistical distribution of the position information of the clusters and information about the statistical distribution of the direction information.

[0011] A technical advantage of the disclosed method is that it can use information about the statistical distribution of the position information of a cluster and information about the statistical distribution of the directional information to aid in determining to which object class a cluster, and therefore the objects assigned to that cluster, should be assigned, thereby achieving accurate and efficient object class classification, particularly for the purpose of excluding detections in the cluster assigned to the object class "vehicle" from height estimation.

[0012] According to one embodiment, the position information includes at least first and second coordinates. The information on the statistical distribution of the position information includes information based on a covariance matrix of the first and second coordinates of the position information. By taking into account the covariance matrix of the first and second coordinates of the position information, the distribution of detections in the longitudinal direction and in the transverse direction perpendicular thereto can be used for classifying the object into classes. In particular, the determinant of the covariance matrix of the first and second coordinates of the position information can be used as a judgment criterion. This determinant is larger for two-dimensionally shaped objects (particularly objects with rounded edges and / or corners), such as vehicles, than for linear objects (e.g., curbs and wall objects).

[0013] According to one embodiment, the information about the statistical distribution of the position information comprises eigenvalues ​​of a covariance matrix of the first and second coordinates of the position information, which provide a measure of the spread of the cluster along its major and minor axes, and from which the shape of the object can be estimated.

[0014] According to one embodiment, the information about the statistical distribution of the position information includes a ratio of eigenvalues ​​of the covariance matrix of the first and second coordinates of the position information, which is very different between linear objects and two-dimensionally formed objects and can therefore be frequently adopted as a criterion.

[0015] According to one embodiment, the information about the statistical distribution of the detected directional information includes the variance of the directional information, which is very small for linear objects but very large for rounded objects such as pillars. For a vehicle, the variance of the directional information is between that of linear objects and that of rounded objects because the vehicle contour has flat and rounded surfaces.

[0016] According to one embodiment, the information about the statistical distribution of the directional information of the detections comprises the temporal derivative of the directional information, so that the change in the directional information over time can be used as a criterion for object class classification.

[0017] According to one embodiment, the information about the statistical distribution of the direction information of the detections comprises a time derivative of the direction information filtered by a filter function, which can particularly preferably be a filter for filtering statistical outliers, so that the change in the filtered direction information over time can be used as a criterion for object class classification.

[0018] According to one embodiment, the classification is performed based on at least a first and a second threshold, where the first threshold is a threshold for information on the statistical distribution of the position information and the second threshold is a threshold for information on the statistical distribution of the direction information of the detection, where it is clear that two or more decision rules based on these thresholds may be used as decision criteria for object class classification.

[0019] According to one embodiment, the first and second thresholds are determined by training data, which includes object information and label information assigned to the object information, and the label information indicates the object class to which each object belongs. Using this training data, a decision rule can be created regarding which thresholds of information on the statistical distribution of position information and information on the statistical distribution of direction information are used to classify objects into each object class.

[0020] According to one embodiment, classification is performed using decision trees or random forests, which allows object class classification with little computational effort.

[0021] According to some other embodiments, a neural network is used for classification, which is pre-trained with training data that includes label information for each object class.

[0022] According to one other embodiment, a classification into object classes "vehicle" and "non-vehicle" is performed, which allows objects that are vehicles to be distinguished from objects of other object classes, for example "wall" or "curb".

[0023] According to another embodiment, depending on the classification result of the object, height estimation of the object is performed or not, in particular, height estimation is not performed for detections related to the object class "vehicle", thereby minimizing the computational load and error rate during height estimation.

[0024] According to a further aspect, a system for classifying objects into object classes based on information from at least one ultrasonic sensor of a vehicle is disclosed. Has a computing unit , the following steps 、 (a) receiving a plurality of detections of at least one ultrasonic sensor of the vehicle; And Each detection requires location and direction information. Togawari It is assigned , position Location information , small At least one ultrasonic sensor's ultrasonic signal is reflected from a reflection point. ,direction Directional information Show the direction , Along this direction, the ultrasound signal Between the reflection point and at least one ultrasonic sensor in propagation to, step, (b) forming clusters of detections based on the received detections; And ,One cluster contains multiple detections. including, steps, (c)Information about the statistical distribution of the location information and the statistical distribution of the direction information of the detections assigned to each cluster and Step of calculating 、 (d) Cluster objects based on information about the statistical distribution of location information and information about the statistical distribution of direction information. of Object Classification Steps 、 A system configured to perform .

[0025] According to another further aspect, a vehicle is disclosed that includes a system for classifying objects into object classes.

[0026] In the present invention, expressions such as "approximately," "substantially," or "approximately" are interpreted as meaning an error within + / - 10%, preferably + / - 5%, from the exact value, and / or an error that is insignificant to the function.

[0027] The developments, advantages and scope of application of the invention are shown in the following description with reference to examples and drawings, in which all described and / or shown features are subject of the invention, individually and in any combination, independently of the respective claims or their references, the contents of which are also incorporated into the description.

[0028] The present invention will now be described in detail with reference to the drawings and examples. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 illustrates a schematic diagram of a vehicle equipped with ultrasonic sensors, including ultrasonic sensors distributed around the vehicle, and a computing unit for evaluating the information provided by the ultrasonic sensors; [Figure 2]Figure 2 illustrates a graph showing the position information of the detection in a longitudinal parking situation between two vehicles; [Figure 3] FIG. 3 illustrates a graph showing the position information and partly the directional information of the detection in a longitudinal parking situation according to FIG. 2; [Figure 4] FIG. 4 illustrates multiple clusters determined based on the detections shown in FIGS. 2 and 3; [Figure 5] Figure 5 shows an example of a decision tree for classifying objects into object classes; and [Figure 6] FIG. 6 illustrates a block diagram that specifies the steps of a method for classifying objects into object classes. DETAILED DESCRIPTION OF THE INVENTION

[0030] 1 illustrates very diagrammatically a vehicle 1. The vehicle 1 is equipped with a number of ultrasonic sensors 2 with which perimeter acquisition is performed.

[0031] The ultrasonic sensor 2 is connected to at least one computing unit 3 with which the method for classifying an object O into an object class, described below, is implemented.

[0032] 2 shows a graph with a number of detections captured by the ultrasonic sensors 2 of the vehicle 1 and assigned to a number of objects O. The detections relate, for example, to a longitudinal parking situation in which a longitudinal parking space is formed between two vehicles. The linearly arranged detections between the two vehicles relate, for example, to a curb that laterally delimits the parking space.

[0033] In this case, one detection is represented by one point. The reflection position at which the detection is formed by reflection on a surrounding object is indicated by position information. The position information can include two coordinates by which the reflection position is defined on a horizontal level plane. These are particularly preferably x- and y-coordinates plotted on the respective graph axes in the graph of FIG. 2. In other words, the position information is indicated in a Cartesian coordinate system. Alternatively, the position information can be indicated in a cylindrical or spherical coordinate system.

[0034] The statistical distribution of the position information is used to classify the objects.

[0035] If available, the position information may also include information about the height of the object region where the reflection is formed. In other words, the position information may indicate the reflection position in three-dimensional space. Information about the third dimension (i.e., height) may also be used to classify the object.

[0036] Directional information is also determined for each detection. The directional information indicates from which direction the detection was received. The directional information can be, for example, an angle subtended on a horizontal level plane indicating the direction of a connecting line connecting the reflection position to the sensor position of the ultrasonic sensor from which the reflected ultrasonic signal was emitted and / or received. The angle can be measured, for example, relative to a coordinate axis, e.g., the x-axis. Thus, the directional information can include, for example, the angle between the x-axis and the connecting line connecting the reflection position to the sensor position of the ultrasonic sensor.

[0037] Figure 3 illustrates a graph similar to Figure 2, but with each detection assigned to the vehicle on the left additionally assigned directional information. The directional information is indicated by a line. These lines indicate the direction from which the detection was determined. In other words, the lines indicate the direction in which the ultrasonic sensor 2 was located when the ultrasonic signal was transmitted and / or received. It should be noted that due to the high propagation speed and the resulting short runtime of the ultrasonic signal between transmission and reception, it can be approximately assumed that the ultrasonic sensor 2 was in the same position when it transmitted the ultrasonic signal and when it received the reflected portion of the ultrasonic signal.

[0038] To assign the detections to an object, at least one cluster C is formed from the captured detections. To form the clusters, known clustering algorithms can be used, such as density-based clustering or partitioned clustering, but preferably the k-means clustering algorithm or the DBSCAN algorithm can be used to form the clusters.

[0039] Figure 4 illustrates a number of clusters C determined based on the detections shown in Figures 2 and 3. The ellipses shown in Figure 4 illustrate the covariance matrix of the x and y coordinates of each detection assigned to each cluster C. A cluster C relates to a group of positionally related detections, and preferably indicates how each group of detections is arranged in a horizontal level plane (i.e., the x and y plane).

[0040] It is preferable to predefine a threshold for the number of detections, above which the number of detections forming a cluster must exceed, in order to avoid clusters being formed from a small number of detections.

[0041] After the clusters are formed, it is possible to determine, for each cluster, information about the statistical distribution of the position information of the detections and information about the statistical distribution of the directional information.

[0042] It is particularly preferred that the mean value of the position information is calculated for each cluster C. This mean value indicates the center of the cluster. This can be determined, for example, by averaging the x- and y-coordinates of the detections of one cluster. In addition, the covariance of the x- and y-coordinates of the detections assigned to each cluster can also be calculated. In other words, a two-dimensional Gaussian distribution of the x- and y-coordinates of the detections is calculated.

[0043] The information about the statistical distribution of the direction information of the detection of a cluster may include the mean value and variance of the direction information of the detection of a cluster, where the mean value and variance may be calculated directly, or the direction information may be filtered, for example, using a smoothing filter or filtered to remove statistical outliers, before the mean value and variance are calculated.

[0044] From the information about the statistical distribution of the position information and the information about the statistical distribution of the direction information, further information or values ​​can be calculated that can be used to classify the objects.

[0045] For example, the determinant of the covariance matrix of the first and second coordinates of the position information can be calculated, which is larger for multi-dimensionally defined objects than for linear objects, i.e., the determinant of the covariance matrix can be used to distinguish between multi-dimensionally defined objects and linear objects.

[0046] Furthermore, the eigenvalues ​​of the covariance matrix of the first and second coordinates of the position information can be calculated. In particular, the first and second eigenvalues ​​can be obtained. These eigenvalues ​​indicate the extent of cluster C along the major axis and the minor axis perpendicular to the major axis. It is particularly preferable that these eigenvalues ​​provide the lengths of the major and minor axes of an ellipse that can be used to reproduce the position and orientation of cluster C. The ellipse assigned to cluster C is depicted in FIG. 4.

[0047] Furthermore, a quotient can be calculated from the first and second eigenvalues ​​of the covariance matrix of the first and second coordinates of the position information. The ratio of the eigenvalues ​​provides indirect evidence of whether it is a linear object, because the ratio of the eigenvalues ​​of a linear object is significantly different from that of a two-dimensionally formed object.

[0048] The variance of the directional information of multiple detections in a cluster also provides indirect evidence of whether the object is linear or two-dimensional. For example, the variance of the directional information is very small for linear objects, but very large for rounded objects such as pillars. Because vehicles have both linear vehicle regions and diffusely reflective regions such as rearview mirrors and door handles, the variance of the directional information for vehicles is between that of linear and round objects.

[0049] Additionally, the change in the statistical characteristics of the directional information of the detections or in the temporal derivative of the filtered directional information of the detections, i.e., the mean, variance, etc., can be determined.

[0050] Preferably, the information on the statistical distribution of the location information and the information on the statistical distribution of the direction information are determined recursively, i.e., the formation of the cluster C is updated each time one or more detections are received, thereby obtaining updated clusters. The information on the statistical distribution of the location information and the information on the statistical distribution of the direction information of the detections assigned to a cluster are also updated after the cluster is updated, i.e., are calculated again based on the detections newly added to the cluster.

[0051] The information about the statistical distribution of the position information and the information about the statistical distribution of the directional information of the detections can then be used to create decision rules, based on which classification of the objects into object classes is performed.

[0052] To create the decision rules, training data can be used. The training data includes information about objects and label information assigned to the objects. The label information defines which object class each object should be assigned to. The decision rules are particularly preferably threshold values. The threshold magnitude can be determined based on training data.

[0053] The threshold can be assigned to specific information about the statistical distribution of position information or specific information about the statistical distribution of directional information, and can indicate, in particular, that information below the threshold is classified into a first object class and information above the threshold is classified into a second object class.

[0054] To classify objects, decision trees or random forests can be used.

[0055] The structure and decision rules of a decision tree or randomized decision forest can be determined by training data.

[0056] 5 illustrates a decision tree, which is illustrated as a method for performing classification into object classes "curb", "wall" and "vehicle". Particularly preferably, this decision tree is used for classification into object classes "vehicle" and "non-vehicle".

[0057] The decision rules of the decision tree are based on information about the statistical distribution of the position information and information about the statistical distribution of the directional information. As the information about the statistical distribution of the position information, the quotient of the first and second eigenvalues ​​of the covariance matrix of the first and second coordinates of the position information, i.e., the ratio of the eigenvalues ​​of both covariance matrices, is used. As the information about the statistical distribution of the directional information, the variance of the directional information is used. Based on the information about the statistical distribution of the position information, the information about the statistical distribution of the directional information, and thresholds assigned to these pieces of information, it is possible to assign each detection to a cluster C of one object class. This makes it possible to determine, in particular, whether the recognized detection of cluster C relates to the object class "vehicle".

[0058] According to the decision tree in Figure 5, if the variance of the directional information is greater than 0.00665 and the ratio of the eigenvalues ​​is greater than 0.0012, it can be assumed with 99% certainty that the detection of the cluster is a vehicle.

[0059] After classifying the objects or the clusters to which they are assigned into object classes, it is possible to selectively implement a height estimation algorithm based on information from ultrasonic sensors, in particular, detections assigned to the cluster of object class "vehicle" are excluded from the height estimation to avoid erroneous estimations.

[0060] FIG. 6 shows, in its schematic representation, the steps of the method according to the invention for classifying objects into object classes using ultrasonic sensor means of a vehicle.

[0061] First, a plurality of detections from at least one ultrasonic sensor of the vehicle are received (S10). Each detection is associated with position information and direction information. The position information indicates a reflection location from which an ultrasonic signal of the at least one ultrasonic sensor is reflected. The direction information indicates a direction along which the ultrasonic signal propagates between the reflection location and the at least one ultrasonic sensor.

[0062] Subsequently, clusters of detections are formed based on the received detections, with one cluster containing multiple detections (S11).

[0063] For the clusters, information on the statistical distribution of the position information and information on the statistical distribution of the direction information of the detections assigned to each cluster are then calculated (S12).

[0064] Finally, the clusters assigned to an object are classified into object classes based on information about the statistical distribution of the position information and information about the statistical distribution of the direction information (S13).

[0065] The present invention has been described by way of the above examples, but it will be appreciated that numerous modifications and variations can be made thereto without departing from the scope of the claims set forth below. The present application relates to the invention described in the claims, but also includes the following as other aspects. 1. A method for classifying objects (O) into object classes based on information from at least one ultrasonic sensor (2) of a vehicle (1), comprising: 、 The method comprises: The following steps 、 a) receiving a plurality of detections of at least one ultrasonic sensor (2) of the vehicle (1); (S10) Each detection requires location and direction information. Togawari It is assigned , position Location information is The aforementionedAt least one ultrasonic sensor (2) indicates a reflection point where an ultrasonic signal is reflected. ,direction Directional information Show the direction , Along this direction, the ultrasound signal Reflection points and The aforementioned Between at least one ultrasonic sensor (2) in propagation do, step (S10) 、 b) forming clusters of detections (C) based on the received detections; (S11) ,One cluster (C) contains multiple detections. Including, Step (S11) 、 c) Step (S12) of calculating information on the statistical distribution of the position information of the detections assigned to each cluster (C) and information on the statistical distribution of the direction information. 、 d) Object (O) based on information about the statistical distribution of the location information of the cluster (C) and information about the statistical distribution of the direction information of A step of classifying into object classes (S13) 、 A method comprising: . 2. The location information includes at least one first coordinate and one second coordinate. Includes, rank The information on the statistical distribution of location information is based on the covariance matrix of the first and second coordinates of the location information. include 2. The method according to claim 1, 3. rank The information about the statistical distribution of the location information is used to determine the eigenvalues ​​of the covariance matrix of the first and second coordinates of the location information. include 3. The method according to claim 2, wherein 4. rank The information about the statistical distribution of the location information is the ratio of the eigenvalues ​​of the covariance matrix of the first and second coordinates of the location information. include 4. The method according to claim 3, 5. Information about the statistical distribution of the direction information of the detection is used to estimate the dispersion of the direction information. include 3. The method according to any one of the preceding claims, characterized in that 6. Information about the statistical distribution of the direction information of the detection is used to calculate the time derivative of the direction information. include 3. The method according to claim 1, wherein the 7. The information about the statistical distribution of the direction information of the detection is expressed as the time derivative of the direction information filtered by the filter function. include 3. The method according to claim 1, wherein the 8. The aforementioned The classification is performed based on at least a first and a second threshold (s1, s2). R, Any one of the above methods, characterized in that the first threshold (s1) is a threshold for information regarding the statistical distribution of position information, and the second threshold (s2) is a threshold for information regarding the statistical distribution of detection direction information. 9. No. 9. The method according to claim 8, wherein the first and second thresholds (s1, s2) are determined by training data having label information for each object class. 10. The aforementioned A method according to any one of the preceding claims, characterized in that the classification is performed using a decision tree (E) or a random decision forest. 11. The aforementioned For classification, neural networks are used. The aforementioned 10. The method according to any one of the preceding claims, wherein the neural network is trained with training data having label information for each object class. 12. 10. The method according to any one of the preceding claims, characterized in that a classification into object classes "vehicle" and "non-vehicle" is performed. 13. 10. The method according to any one of the preceding claims, characterized in that depending on the classification result of the object (O), a height estimation of the object (O) is or is not performed. 14. A system for classifying objects (O) into object classes based on information from at least one ultrasonic sensor (2) of a vehicle (1), 、 The system It has a calculation unit (3) , the calculation unit (3) The following steps 、 e) receiving a plurality of detections of at least one ultrasonic sensor (2) of the vehicle (1); And Each detection requires location and direction information. Togawari It is assigned , position Location information is The aforementioned At least one ultrasonic sensor (2) indicates a reflection point where an ultrasonic signal is reflected. ,direction Directional information Show the direction , Along this direction, the ultrasound signal Reflection points and The aforementioned Between at least one ultrasonic sensor (2) in propagation to, step, f) forming clusters of detections (C) based on the received detections; And ,One cluster (C) contains multiple detections. including, steps, g) Information about the statistical distribution of the location information and the statistical distribution of the direction information of the detections assigned to each cluster (C) and calculating the h) Object (O) based on information about the statistical distribution of location information of cluster (C) and information about the statistical distribution of direction information of Object Classification Steps 、 A system configured to perform . 15. The system according to claim 14 include vehicle. [Explanation of symbols]

[0066] 1 vehicle 2 Ultrasonic sensors 3 arithmetic units C Cluster E. Decision Tree O Object s1 First threshold s2 second threshold

Claims

1. 1. A method for classifying objects (O) into object classes based on information from at least one ultrasonic sensor (2) of a vehicle (1), comprising: The method comprises the steps of: a) receiving (S10) a plurality of detections of at least one ultrasonic sensor (2) of the vehicle (1), each detection being assigned position information and direction information, the position information indicating a reflection point from which the ultrasonic signal of the at least one ultrasonic sensor (2) is reflected, and the direction information indicating a direction along which the ultrasonic signal propagates between the reflection point and the at least one ultrasonic sensor (2); b) forming clusters (C) of detections based on the received detections (S11), where one cluster (C) includes a plurality of detections; c) calculating information on the statistical distribution of the position information and the statistical distribution of the direction information of the detections assigned to each cluster (C) (S12); d) a step (S13) of classifying the objects (O) into object classes based on information about the statistical distribution of the position information of the clusters (C) and information about the statistical distribution of the direction information; A method comprising:

2. 2. The method of claim 1, wherein the location information includes at least one first and one second coordinate, and the information about the statistical distribution of the location information includes information based on a covariance matrix of the first and second coordinates of the location information.

3. The method described in claim 2, characterized in that the information regarding the statistical distribution of the location information includes eigenvalues ​​of the covariance matrix of the first and second coordinates of the location information.

4. The method described in claim 3, characterized in that the information regarding the statistical distribution of the location information includes a ratio of the eigenvalues ​​of the covariance matrix of the first and second coordinates of the location information.

5. 5. The method according to claim 1, wherein the information about the statistical distribution of the direction information of the detections comprises the variance of the direction information.

6. 5. The method according to claim 1, wherein the information about the statistical distribution of the direction information of the detections comprises the time derivative of the direction information.

7. 5. The method according to claim 1, wherein the information about the statistical distribution of the direction information of the detections comprises the time derivative of the direction information filtered by a filter function.

8. A method according to any one of claims 1 to 4, characterized in that the classification is performed based on at least first and second thresholds (s1, s2), the first threshold (s1) being a threshold for information relating to the statistical distribution of position information, and the second threshold (s2) being a threshold for information relating to the statistical distribution of detection direction information.

9. The method of claim 8, wherein the first and second thresholds (s1, s2) are determined by training data having label information for each object class.

10. A method according to any one of claims 1 to 4, characterized in that the classification is performed using a decision tree (E) or a random decision forest.

11. A method according to any one of claims 1 to 4, characterized in that a neural network is used for the classification, and the neural network is trained using training data having label information for each object class.

12. 5. The method according to claim 1, wherein a classification into object classes "vehicle" and "non-vehicle" is performed.

13. 5. The method according to claim 1, wherein depending on the classification result of the object (O), a height estimation of the object (O) is or is not performed.

14. A system for classifying objects (O) into object classes based on information from at least one ultrasonic sensor (2) of a vehicle (1), comprising: The system comprises a calculation unit (3), which performs the following steps: e) receiving a plurality of detections of at least one ultrasonic sensor (2) of the vehicle (1), each detection being assigned position information and direction information, the position information indicating a reflection point from which the ultrasonic signal of said at least one ultrasonic sensor (2) is reflected, and the direction information indicating a direction along which the ultrasonic signal propagates between the reflection point and said at least one ultrasonic sensor (2); f) forming clusters (C) of detections based on the received detections, where one cluster (C) includes a plurality of detections; g) calculating information about the statistical distribution of the position information and the statistical distribution of the direction information of the detections assigned to each cluster (C); h) classifying the objects (O) into object classes based on information about the statistical distribution of the location information and information about the statistical distribution of the direction information of the clusters (C); A system configured to perform the

15. A vehicle including the system of claim 14.

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