Patent Publication Number: US-2023146935-A1

Title: Content capture of an environment of a vehicle using a priori confidence levels

Description:
The present invention relates to a method for the content capture of an environment of a vehicle using an artificial intelligence, in particular a neural network, on the basis of a point cloud generated by at least one environment sensor of the vehicle. 
     The present invention furthermore relates to a driving assistance system for a vehicle having at least one environment sensor and a processing unit, which is connected to the at least one environment sensor via a data connection, the driving assistance system being designed to perform the above method. 
     Driving assistance systems for providing assistance when driving a vehicle typically capture an environment of the vehicle by at least one environment sensor in order to provide, for example, sensor information in the form of a point cloud. On the basis thereof, the driving assistance system can perform its desired assistance function. Automatic detection of contents and/or objects using an artificial intelligence (AI), in particular a neural network, is often performed here. The sensor information is processed using the artificial intelligence for the content capture of the environment in order to detect the contents/objects. 
     In real decision-making systems, classifications must not only be accurate but also indicate when they are probably false. If the artificial intelligence is not able to reliably predict, for example, the presence or absence of immediate obstacles in the environment of the vehicle on the basis of sensor information of an environment sensor, the vehicle should rely more heavily on the results of other sensors when braking. In particular, in addition to its object detection, an artificial intelligence should additionally provide a degree of confidence. The degree of confidence, referred to hereinbelow as the confidence level, indicates a probability associated with the predicted class designation of an object. The confidence level should reflect a correctness of a basic probability here. The confidence level is determined, for example, on the basis of the sensor information on which the detection of the objects was based. This determination is in principle inaccurate and furthermore prone to errors. 
     Proceeding from the aforementioned prior art, the object of the invention is thus to specify a method for the content capture of an environment of a vehicle using an artificial intelligence, in particular a neural network, on the basis of a point cloud generated by at least one environment sensor of the vehicle, as well as a driving assistance system for performing the method, which allow improved content capture and in particular allow improved determination of confidence levels for the contents in the environment of the vehicle. 
     The object is achieved according to the invention by the features of the independent claims. Advantageous configurations of the invention are specified in the dependent claims. 
     The invention thus specifies a method for the content capture of an environment of a vehicle using an artificial intelligence, in particular a neural network, on the basis of a point cloud generated by at least one environment sensor of the vehicle, comprising the steps of performing reference measurements by the at least one environment sensor of the vehicle in order to capture reference objects depending on positions in the environment in relation to the at least one environment sensor, generating confidence values depending on positions in the environment in relation to the at least one environment sensor on the basis of the reference measurements by the at least one environment sensor of the vehicle in order to capture the reference objects, training the artificial intelligence for the content capture of the environment of the vehicle on the basis of training point clouds for the at least one environment sensor, capturing the environment of the vehicle by the at least one environment sensor of the vehicle in order to generate the point cloud, and processing the point cloud generated by the at least one environment sensor of the vehicle using the trained artificial intelligence for the content capture of the environment, the method using the generated confidence values depending on the positions in the environment as a priori confidence levels in order to improve the content capture of the environment of the vehicle. 
     The invention furthermore specifies a driving assistance system for a vehicle having at least one environment sensor and a processing unit which is connected to the at least one environment sensor via a data connection, the driving assistance system being designed to perform the above method. 
     The basic concept of the present invention is thus to capture properties of the at least one environment sensor in relation to positions in the environment of the vehicle via the reference measurements and to generate therefrom a priori confidence values on the basis of which the contents of the environment can be reliably captured. As a result, a statement about the reliability of the detection of the object is immediately made possible depending on the respective position of a captured object. In practice, the confidence level for different positions decreases here the further away an object is from the vehicle. Even when objects are the same distance away, differences in the confidence values, for example due to different angular positions in relation to the vehicle, can also result, depending on sensor properties of the at least one environment sensor. 
     Calibration of the confidence level—the problem of predicting probability estimates which are representative of the actual correctness probability—is important in many applications, for example for models of deep neural networks. Modern, very deep neural networks are often poorly calibrated and may be reliant on the underlying training data. Factors such as depth, resolution, weight decay and batch normalization (even for the training processes) are important factors which influence the calibration. These problems can be reduced by using the a priori determined confidence values. 
     Calibrated confidence level estimates are important for the interpretability of an artificial intelligence here. As opposed to artificial intelligence, human beings have a natural cognitive intuition for probabilities. Good confidence level estimates constitute valuable additional information in order to establish the trustworthiness of the artificial intelligence with respect to a human user and thus to increase the acceptance thereof—in particular for an artificial intelligence, the classification decisions of which can be difficult to interpret. Good probability estimates can furthermore be used in order, for example, to integrate neural networks into other probabilistic models. 
     The steps of performing reference measurements, of generating confidence values and of training the artificial intelligence are typically performed once for the method. These steps can furthermore be performed once centrally for the purpose of providing a plurality of driving assistance systems such that the confidence values and the trained artificial intelligence can be equally used for the plurality of driving assistance systems. As will be apparent from the following statements, the training of the artificial intelligence can take place in part independently of the confidence values and these steps can thus take place in a different order. 
     The steps of capturing the environment of the vehicle by the at least one environment sensor and of processing the point cloud using the trained artificial intelligence for the content capture of the environment relate to the operation of the vehicle. These steps are therefore each performed individually in each driving assistance system. These steps are furthermore performed repeatedly in the driving assistance system in order to perform continuous content capture of the environment. 
     The reference measurements can in principle be performed on a reference measurement stand as well as in the moving vehicle. The reference object is preferably a predefined object with defined properties such as size and reflectivity and may be dependent on the type of the environment sensor used. 
     The reference measurements can be performed repeatedly until reference objects have been captured for all the positions in the environment. The reference measurements can be performed in such a way here that, for individual reference measurements, a plurality of reference objects are simultaneously positioned at different positions so that they can be considered simultaneously. 
     The positions can be defined, for example, by a raster which is laid over the environment of the vehicle. 
     The confidence values typically indicate true-positive confidence levels for a correct detection of the reference object at the corresponding positions. Alternative definitions for the confidence values are likewise possible. 
     The artificial intelligence can be a neural network, in particular a deep neural network. Alternatively, a support vector machine can also be used as artificial intelligence. The artificial intelligence generates a semantic interpretation of sensor information which represents the environment of the vehicle. The sensor information can comprise the point cloud and in principle any further sensor information, for example images from optical cameras, which can be processed together by the artificial intelligence. On the basis of the sensor information, when processing the point cloud using the trained artificial intelligence, objects such as passenger cars, bicycles or pedestrians, for example, can be detected. The detection can take place here via so-called bounding boxes. 
     The artificial intelligence is thus not limited to using the sensor information of the at least one environment sensor considered here. Through the additional use of the confidence values, however, the functioning of the artificial intelligence with regard to the processing of the sensor information of this at least one environment sensor, and therefore also overall, can be improved. 
     The positions in the environment in relation to the at least one environment sensor can be indicated in different ways for the performing of the reference measurements as well as for the generating of the confidence values. Due to the functioning of the environment sensors considered here, it is advisable, for example, to indicate the positions as angular information (polar angle and optionally azimuth angle) together with an item of distance information. Alternatively, the positions can be indicated in Cartesian coordinates, the at least one environment sensor preferably defining a zero point of the coordinate system. 
     The training point cloud is a point cloud, as is also provided by the at least one environment sensor, which however typically comprises additional training information that is required for the training, for example in the form of annotations. 
     In one advantageous configuration of the invention, the generating of confidence values depending on positions in the environment in relation to the at least one environment sensor on the basis of the reference measurements by the at least one environment sensor of the vehicle in order to capture the reference objects involves generating the confidence values as a function of a frequency of correctly received sensor echoes at the corresponding position for the reference object positioned there. For this purpose, for the reference measurements, the reference objects can be placed in the environment of the vehicle and such a reference scene can be recorded over a relatively long period of time. From a plurality of individual reference measurements, the frequencies for the true-positive and/or false-negative detection of the reference objects, for example, are then ascertained by determining the relative number of measurements in which the point cloud detects or does not detect the reference object. In the case of the lidar-based environment sensor, a single laser point, for example, can in principle be sufficient here in order to capture the reference object at its position. 
     In one advantageous configuration of the invention, the generating of the confidence values is performed as a function of a frequency of correctly received sensor echoes at the corresponding position for the reference object positioned there while taking into account intensities of the received sensor echoes at the corresponding position for a reference object positioned there, and/or the generating of the confidence values is performed as a function of a frequency of correctly received sensor echoes at the corresponding position for the reference object positioned there while taking into account an echo pulse width of the received sensor echoes at the corresponding position for a reference object positioned there. Current versions of the environment sensors considered make it possible not only to determine the distance of the echoes but also to determine the intensity or the echo pulse width (EPW) of the received sensor echoes which can be used when generating the confidence values. An additional information channel, which allows improved generation of the confidence values, is provided. Low values of the measured intensity or EPW tend to be a sign of lower detection reliability, in particular if the intensity or EPW approaches the lower detection threshold or the lower limit of the environment light noise. The echo pulse width specifies a reception duration of the received sensor echo. By comparing with a duration of the emitted signal pulse, a measure of the reception intensity can thus be provided. The echo pulse width thus correlates with the intensity of the received sensor echo. 
     In one advantageous configuration of the invention, the generating of confidence values depending on positions in the environment in relation to the at least one environment sensor on the basis of the reference measurements by the at least one environment sensor of the vehicle in order to capture the reference objects involves generating a confidence map using the generated confidence values. The confidence map reflects the environment of the vehicle and is typically centered on the basis of the position of the vehicle. In a confidence map with true-positive confidence levels, the confidence values decrease the further away the position is from the vehicle. When approaching a distance limit of the at least one environment sensor, the confidence values ultimately approach the value zero. The entries of the confidence map can also have systematic dependencies, for example on an angle of the corresponding position in relation to the vehicle, which can result, for example, due to a reduced capture probability at edges of a field of view of the at least one environment sensor. 
     In one advantageous configuration of the invention, the performing of reference measurements by the at least one environment sensor of the vehicle in order to capture reference objects depending on positions in the environment in relation to the at least one environment sensor involves performing reference measurements for different environment conditions, and the generating of confidence values depending on positions in the environment in relation to the at least one environment sensor on the basis of the reference measurements by the at least one environment sensor of the vehicle in order to capture the reference objects involves generating the confidence values for the different environment conditions and/or operating conditions. The confidence values can thus be generated, for example, as multi-dimensional confidence values for the respective positions and be used depending on the respective current environment conditions and/or operating conditions. The environment conditions can comprise, for example, an environment temperature, an environment brightness, a particular lighting situation such as backlight, precipitation, air humidity or else atmospheric turbulence, to name just a few. The operating conditions can comprise, for example, driving parameters of the vehicle such as its speed, inclination, or others, as well as internal functional parameters of the at least one environment sensor. 
     In one advantageous configuration of the invention, the generating of confidence values depending on positions in the environment in relation to the at least one environment sensor on the basis of the reference measurements by the at least one environment sensor of the vehicle in order to capture the reference objects involves generating the confidence values on the basis of a specification of the at least one environment sensor for different environment conditions and/or operating conditions. Multi-dimensional confidence values for the respective positions can thus also be generated here, the generating taking place, for example, on the basis of a reference measurement or a plurality of reference measurements for an environment condition and/or operating condition. Proceeding therefrom, the confidence values can be determined for the different environment conditions and/or operating conditions on the basis of the specification of the at least one environment sensor, as a result of which the outlay for performing the reference measurements can be reduced. The confidence values can consequently also be generated here, for example, as multi-dimensional confidence values for the respective positions and be used depending on the respective current environment conditions and/or operating conditions. The environment conditions can comprise an environment temperature, an environment brightness, a particular lighting situation such as backlight, precipitation, air humidity or else atmospheric turbulence, to name just a few. The operating conditions can comprise, for example, driving parameters of the vehicle such as its speed, inclination, or others, as well as internal functional parameters of the at least one environment sensor. 
     In one advantageous configuration of the invention, the training of the artificial intelligence for the content capture of the environment of the vehicle on the basis of training point clouds for the at least one environment sensor is performed using the generated confidence values depending on the positions in the environment as a priori confidence levels in order to improve the content capture of the environment of the vehicle. The generated confidence values can thus be used as an additional feature input channel for the training of the artificial intelligence in order to thereby improve the artificial intelligence and consequently achieve an improvement in the content capture of the environment. 
     In one advantageous configuration of the invention, the processing of the point cloud generated by the at least one environment sensor of the vehicle using the trained artificial intelligence for the content capture of the environment is performed using the generated confidence values depending on the positions in the environment as a priori confidence levels in order to improve the content capture of the environment of the vehicle. The confidence values are thus used directly for processing the point cloud generated by the at least one environment sensor of the vehicle. This can take place by using the generated confidence values as an additional feature input channel for the artificial intelligence in order to improve the content capture of the environment. Alternatively, the generated confidence values can be applied in the post-processing of outputs from the artificial intelligence in order to achieve an improvement in the content capture of the environment. For this purpose, simple scaling of object confidence levels which are output by the artificial intelligence for captured contents/objects can be performed, for example. 
     In one advantageous configuration of the invention, the processing of the point cloud generated by the at least one environment sensor of the vehicle using the trained artificial intelligence for the content capture of the environment involves outputting object confidence levels of captured objects in the environment of the vehicle, the object confidence levels being ascertained while taking into account the positions thereof in the environment of the vehicle and the generated confidence values for these positions in the environment. Contents or objects captured by the artificial intelligence can thus be assigned object confidence levels using the corresponding confidence values. For example, the corresponding confidence value can be used directly as the object confidence level. Alternatively, the artificial intelligence can output for detected contents/objects object confidence levels, which are adjusted depending on the positions of the captured contents/objects on the basis of the respective confidence values. The artificial intelligence can determine the object confidence levels for the detected contents/objects in different ways. The object confidence levels can be determined, for example, on the basis of matching captured objects with different object classes. When using bounding boxes, a distribution of points of the point cloud in the corresponding bounding box can be taken into account. Additionally or alternatively, intensities of points of the point cloud can be taken into account. In particular, when using bounding boxes, a distribution of intensities of the points of the point cloud in the corresponding bounding box can be taken into account. 
     In one advantageous configuration of the invention, the at least one environment sensor is designed as a lidar-based environment sensor or as a radar sensor. Both lidar-based environment sensors and radar sensors are suitable for providing a point cloud of the environment of the vehicle with a plurality of environment points. Each of the environment points is defined by its angular position in relation to the environment sensor and an associated distance value. The environment points thus indicate positions of the objects and structures in the environment of the vehicle. As a result, a geometric structure of the environment can be ascertained reliably and with high accuracy. Lidar-based environment sensors emit, for example, discrete laser pulses at an angular distance of about 0.1 degrees in the horizontal direction. Reflections of the emitted laser pulses are received by the lidar-based environment sensor and the corresponding distance value can be determined from a duration between emitting the laser pulse and receiving the associated reflection. The lidar-based environment sensor can emit the laser pulses in one or more scanning planes, the angular distance in the vertical direction being greater than in the horizontal direction when used on vehicles. The details regarding angular distances in the horizontal and vertical direction as well as a total number of scanning planes are dependent on the lidar-based environment sensor used in each case. The functioning of radar sensors is in principle similar and, as opposed to the lidar-based environment sensors is based on emitted radio signals. 
     The invention is explained in more detail below with reference to the attached drawing and on the basis of preferred embodiments. The features shown may each represent an aspect of the invention both individually and in combination. Features of different exemplary embodiments may be transferred from one exemplary embodiment to another. 
    
    
     
       In the figures: 
         FIG.  1    shows a schematic view of a vehicle with a driving assistance system according to a first, preferred embodiment, the driving assistance system having a lidar-based environment sensor and a processing unit, which are connected to each other via a data connection, 
         FIG.  2    shows an illustration of an exemplary point cloud, which was generated by the lidar-based environment sensor of the driving assistance system from  FIG.  1   , with detectable objects therein, 
         FIG.  3    shows an illustration of a confidence map with confidence values for the environment of the vehicle and the point cloud from  FIG.  2    as an overlay, 
         FIG.  4    shows an exemplary, schematic illustration of a deep neural network for processing the point cloud generated by the environment sensor of the driving assistance system from  FIG.  1   , and 
         FIG.  5    shows a flowchart of a method for the content capture of an environment of the vehicle from  FIG.  1   . 
         FIG.  1    shows a vehicle  10  with a driving assistance system  12  according to a first, preferred embodiment. 
     
    
    
     The driving assistance system  12  can be designed to provide any assistance functions for the driving of the vehicle  10  or with the vehicle  10 . Driver assistance systems which assist a human driver when driving the vehicle  10 , as well as providing functionalities for performing autonomous or semi-autonomous driving functions, may be involved here. Various driver assistance systems are known, for example, under the term ADAS (Advanced Driver Assistance Systems). 
     The driving assistance system  12  comprises an environment sensor  14 , which is designed here as a lidar-based environment sensor  14 . The driving assistance system  12  further comprises a processing unit  16  and a data connection  18 , via which the lidar-based environment sensor  14  and the processing unit  16  are connected to each other. 
     The lidar-based environment sensor  14  is designed to capture an environment  20  of the vehicle  10 . The environment  20  of the vehicle  10  relates here to an area captured by the lidar-based environment sensor  14 . 
     The environment  20  is captured as a point cloud  22  with a plurality of environment points  24 , as illustrated in  FIG.  2    for example. The environment points  24  are generated by laser pulses being emitted and reflections of the emitted laser pulses being received, as a result of which a distance value can be determined from the resulting duration. Each of the environment points  24  is defined by its angular position in relation to the lidar-based environment sensor  14  and the associated distance value. Additionally, an intensity of the received reflections is captured. 
     The processing unit  16  comprises a processor and a memory in order to execute a program for performing an assistance function of the driving assistance system  12 , and also in order to perform the method described below. The processing unit  16  receives and processes point clouds  22  provided by the lidar-based environment sensor  14 . Such processing units  16  are known in the automotive sector as electronic control units (ECUs). 
     The data connection  18  is designed, for example, in the manner of a bus system customary in the automotive sector. Various bus systems such as CAN, FlexRay, LON or others are known in this context. 
     With reference to  FIGS.  2  to  4   , a method for the content capture of the environment  20  of the vehicle  10  using an artificial intelligence, which is designed here as a neural network  26 , on the basis of the point clouds  22  generated by the lidar-based environment sensor  14  is described below.  FIG.  5    shows a flowchart of the method. 
     The neural network  26  is illustrated by way of example and in a greatly simplified form in  FIG.  4    and is designed here as a deep neural network  26 . The neural network  26  comprises an input layer  28  and an output layer  30 , between which two processing layers  32  are arranged here by way of example. Likewise by way of example, the input layer  28  comprises two input nodes  34 , the output layer  30  comprises three output nodes  36  and the processing layers  32  each comprise four processing nodes  38 , which are fully interconnected between the individual layers. 
     The method starts in step S 100  with performing reference measurements by the lidar-based environment sensor  14  of the vehicle  10  in order to capture reference objects depending on positions in the environment  20  in relation to the lidar-based environment sensor  14 . 
     The reference measurements are performed on a reference measurement stand. A predefined object with defined properties such as size and reflectivity is used as the reference object. The reference measurements are performed repeatedly until reference objects have been captured for all the positions in the environment  20 . The reference measurements can be performed in such a way here that, for individual reference measurements, a plurality of reference objects are simultaneously positioned at different positions so that they can be considered simultaneously. In this exemplary embodiment, the positions are defined by a regular raster which is laid over the environment  20  of the vehicle  10 . 
     The reference measurements are performed over a relatively long period of time here in order to obtain a plurality of individual reference measurements for each position of the reference object. 
     In an alternative exemplary embodiment, separate reference measurements are performed for different environment conditions. The environment conditions can comprise, for example, an environment temperature, an environment brightness, a particular lighting situation such as backlight, precipitation, air humidity or else atmospheric turbulence, to name just a few. The operating conditions can comprise, for example, driving parameters of the vehicle  10  such as its speed, inclination, or others, as well as internal functional parameters of the lidar-based environment sensor  14 . 
     Step S 110  relates to generating confidence values  40  depending on positions in the environment  20  in relation to the lidar-based environment sensor  14  on the basis of the reference measurements by the lidar-based environment sensor  14  of the vehicle  10  in order to capture the reference objects. 
     In this exemplary embodiment, the confidence values  40  indicate true-positive confidence levels for a correct detection of the reference object at the corresponding positions. 
     In detail, the generating of the confidence values  40  is performed as a function of a frequency of correctly received sensor echoes at the corresponding position for the reference object positioned there while additionally taking into account intensities of the received sensor echoes at the corresponding position for a reference object positioned there. From a plurality of the individual reference measurements, the frequencies for the true-positive and/or false-negative detection of the reference objects, for example, are then ascertained at the respective position by determining the relative number of measurements in which the point cloud  22  detects or does not detect the reference object at this position. A single environment point  24  can in principle be sufficient here to capture the reference object at its position. 
     Furthermore, a confidence map  42  is generated on the basis of the generated confidence values  40 . The confidence map  42  is illustrated together with the point cloud  22  in  FIG.  3    and is centered on the basis of the position of the vehicle  10 . As can be seen in  FIG.  3   , the confidence values  40  decrease the further away the position is from the vehicle  10 . Moreover, when positions are the same distance away, the confidence values  40  decrease depending on an angle with respect to a longitudinal direction of the vehicle  10 . 
     In the alternative exemplary embodiment specified with regard to step S 100 , the confidence values  40  are generated for the different environment conditions and/or operating conditions. The confidence values  40  are generated, for example, as multi-dimensional confidence values  40  for the respective positions on the basis of the reference measurements for the different environment conditions and/or operating conditions and are used in the vehicle  10  depending on the respective current environment conditions and/or operating conditions. 
     In a further alternative embodiment, the reference measurements in step S 100  are performed only for one environment condition and/or operating condition. In this case, the confidence values  40  for the different environment conditions and/or operating conditions can be generated on the basis of a specification of the lidar-based environment sensor  14  for different environment conditions and/or operating conditions. Multi-dimensional confidence values  40  can thus also be generated here for the respective positions. 
     Step S 120  relates to training the neural network  26  for the content capture of the environment  20  of the vehicle  10  on the basis of training point clouds for the lidar-based environment sensor  14 . 
     The training point cloud is a point cloud  22 , as is also provided by the lidar-based environment sensor  14 , which however typically comprises additional training information that is required for the training, for example in the form of annotations. The training point cloud can be supplied to the neural network  26 , for example, at one of the input nodes  34  of the input layer  28 . 
     In this exemplary embodiment, the training of the neural network  26  for the content capture of the environment  20  of the vehicle  10  on the basis of training point clouds for the lidar-based environment sensor  14  is performed using the generated confidence values  40  depending on the positions in the environment  20  as a priori confidence levels in order to improve the content capture of the environment  20  of the vehicle  10 . The generated confidence values  40  are supplied to the neural network  26  at the other input node  34  of the input layer  28  as an additional feature input channel for the training of the neural network  26 . 
     On the basis of the training, the neural network  26  can capture objects  44  from the point cloud  22  and detect the objects  44 . In  FIGS.  2  and  3   , other vehicles are detected and illustrated as such objects  44  by way of example. 
     Step S 130  relates to capturing the environment  20  of the vehicle  10  by the lidar-based environment sensor  14  of the vehicle  10  in order to generate the point cloud  22 . 
     Generating point clouds  22  by the lidar-based environment sensor  14  is known as such and is therefore not explained any further. 
     The point cloud  22  is transmitted via the data connection  18  to the processing unit  16  for further processing. 
     Step S 140  relates to processing the point cloud  22  generated by the lidar-based environment sensor  14  of the vehicle  10  using the trained neural network  26  for the content capture of the environment  20 . 
     For the processing of the point cloud  22  generated by the lidar-based environment sensor  14  of the vehicle  10 , the point cloud is supplied to the trained neural network  26  at one of the input nodes  34  of the input layer  28 . 
     Additionally, any further sensor information, for example images from optical cameras, can in principle be supplied to the neural network  26  and processed together with the point cloud  22 . 
     The processing of the point cloud  22  generated by the lidar-based environment sensor  14  of the vehicle  10  using the trained neural network  26  for the content capture of the environment  20  is performed here using the generated confidence values  40  of the confidence map  42  depending on the positions in the environment  20  as a priori confidence levels in order to improve the content capture of the environment  20  of the vehicle  10 . 
     The generated confidence values  40  depending on the positions in the environment  20  can be used here as a priori confidence levels in order to improve the content capture of the environment  20  of the vehicle  10 . The generated confidence values  40  are thus supplied to the neural network  26  at the other input node  34  of the input layer  28  as an additional feature input channel. 
     Alternatively or additionally, the generated confidence values  40  can be applied in the post-processing of outputs from the neural network  26  in order to achieve an improvement in the content capture of the environment. For this purpose, for example, scaling of object confidence levels which are output by the neural network  26  for captured objects  44  can be performed. For example, the object confidence levels output by the neural network  26  for captured objects  44  can be multiplied by the confidence values  40  at the positions of the captured objects  44 . The neural network  26  can determine the object confidence levels for the detected objects  44 , for example on the basis of matching captured objects  44  with different object classes. When using bounding boxes, a distribution of environment points  24  of the point cloud  22  in the corresponding bounding box can be taken into account. Additionally or alternatively, intensities of environment points  24  of the point cloud  22  can be taken into account. In particular, when using bounding boxes, a distribution of intensities of the environment points  24  of the point cloud  22  in the corresponding bounding box can be taken into account. 
     Further alternatively or additionally, the processing of the point cloud  22  generated by the lidar-based environment sensor  14  of the vehicle  10  using the trained neural network  26  for the content capture of the environment  20  can involve outputting object confidence levels of captured objects  44  in the environment  20  of the vehicle  10 , the object confidence levels being ascertained while taking into account positions of the objects  44  in the environment  20  of the vehicle  10  and the generated confidence values  40  for these positions in the environment  20 . Object confidence levels are thus assigned to the objects  44  captured by the neural network  26  on the basis of the corresponding confidence values  40 . 
     LIST OF REFERENCE SIGNS 
     
         
           10  Vehicle 
           12  Driving assistance system 
           14  Environment sensor 
           16  Processing unit 
           18  Data connection 
           20  Environment 
           22  Point cloud 
           24  Environment point 
           26  Neural network, artificial intelligence 
           28  Input layer 
           30  Output layer 
           32  Processing layer 
           34  Input node 
           36  Output node 
           38  Processing node 
           40  Confidence value 
           42  Confidence map 
           44  Object