Patent ID: 12230010

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG.1is a schematic flow chart of an exemplary embodiment of the method100for training a classifier and/or regressor1.

In step110, a set of training samples2ais provided. The training samples2aare labelled with ground truth classification and/or regression scores2b.

In step120, for each training sample2afrom at least a subset of the training samples, a confidence score ∈mis determined. This confidence score ∈ quantifies an uncertainty of the training sample2a, and/or an ease or difficulty of classifying this particular sample2a.

According to block111, the training samples2amay comprise images. According to block121, the confidence score ∈ may then be determined with a predetermined function that characterizes the quality with which the image was acquired.

According to block112, the training samples2amay comprise spectra and/or point clouds obtained by sending out radar, lidar and/or ultrasound radiation and measuring an intensity of the reflected radiation. According to block122, the confidence score ∈ may then be determined based on a measure for one or more of:the signal-to-noise ratio of the radar, lidar and/or ultrasound measurement with which the training sample2awas acquired;interference and/or environmental effects during the radar, lidar and/or ultrasound measurement with which the training sample2awas acquired;ambiguities between different objects when viewed from different angles and distances;occlusion of objects by other objects; anda variability within different instances of one and the same object class.

According to block123, the confidence score ∈ may comprise a contribution ∈Rthat is dependent on a range R between a sensor with which the training sample2awas acquired and at least one object that has reflected radar, lidar and/or ultrasound radiation.

According to block124, the confidence score ∈ may comprise a contribution ∈Pthat is dependent on a power π of reflected radar, lidar and/or ultrasound radiation received from at least one object.

In step130, a largest ground truth classification and/or regression score2bwith respect to one class and/or regression value is reduced by an amount that is dependent on the confidence score E. In step140, this amount is distributed to ground truth classification and/or regression scores2bwith respect to other classes and/or regression values. In this manner, updated ground truth classification and/or regression scores2b* are obtained.

According to block141, the amount by which a largest ground truth classification and/or regression score has been reduced may be distributed to ground truth classification and/or regression scores with respect to other classes and/or regression values according to a given prior distribution over the classes, and/or over regression values.

In step150, the training samples2aare mapped to classification and/or regression scores3by the classifier and/or regressor1.

In step160, a deviation of the classification and/or regression scores (3) from the updated ground truth classification and/or regression scores2b* is rated by means of a predetermined loss function4.

In step170, parameters1athat characterize the behavior of the classifier and/or regressor1are optimized with the objective that, when further training samples2aare supplied to the classifier and/or regressor1, the rating4aby the loss function4is likely to improve. The finally optimized state of the parameters1ais labelled with the reference sign1a*.

According to block125, the confidence score ∈ may be determined (125) using a function whose behavior is characterized by at least one hyperparameter α. According to block171, this hyperparameter α may then be optimized with the objective that, when further training samples2aare supplied to the classifier and/or regressor1, the rating4aby the loss function4is likely to improve. The finally optimizes state of the hyperparameter α is labelled with the reference sign α*.

FIG.2shows exemplary dependencies of average reflected radar power P on range R to an object for different kinds of objects. Data points A relate to a car as the object. Data points B relate to a motorbike as the object. Data points C relate to a bicycle as the object. Data points D relate to a pedestrian as the object. Data points E relate to noise that is measured when no object is present and no power is reflected.

FIG.3shows two reliability diagrams (a) and (b) for different tests of an exemplary classifier1that has been trained according to different strategies. The classification accuracy AC is plotted over the predicted confidence PC of the classification. The dashed line shows the ideal state, namely that the classification is predicted with a confidence score that corresponds exactly to the actual accuracy.

Curve a relates to a training with “one-hot” ground truth classification scores. Curve b relates to a training with updated ground truth classification scores2b* obtained with a sample-dependent confidence score ∈. Curve c relates to a training where the sample-dependent confidence score ∈ comprises a range-dependent contribution ∈R. Curve d relates to a training where the sample-dependent confidence score ∈ comprises a power-dependent contribution ∈P.

FIG.4is a schematic flow chart of an exemplary embodiment of the method200.

In step210, a classifier and/or regressor1is provided. This classifier and/or regressor1is configured to map an input sample2of measurement data to a set of classification and/or regression scores3with respect to classes of a given classification and/or regression values.

In step220, the classifier and/or regressor is trained with the method100described above.

In step230, at least one sample2of measurement data is acquired using at least one sensor5carried by a vehicle50.

In step240, using the trained classifier and/or regressor1*, the at least one sample2of measurement data is mapped to classification and/or regression scores3.

In step250, an actuation signal250ais determined based at least in part on the classification and/or regression scores3.

In step260, the vehicle is actuated with the actuation signal250a.