Abstract: A method and a device for classifying sensor data are proposed, wherein the method comprises providing a respective averaged feature vector for a multiplicity of classes wherein the method further comprises the following steps by means of a classifier, wherein the classifier comprises at least one neural network trained on the basis of training data: determining a feature vector on the basis of sensor data, respective determining of a cosine similarity between the feature vector and a respective averaged feature vector for the multiplicity of classes, comparing the respective cosine similarity with a threshold value established for each class beforehand, detecting a scenario not represented by the multiplicity of classes if the threshold values are not reached for all classes, and wherein the device comprises a classifier and is designed for carrying out the method.

Abstract: A method for classifying targets is proposed, which comprises the extraction of features from measurement data of one or several receiving elements of a sensor by means of a neuronal network or by means of a Gaussian Mixture Model, wherein the respective measurement data of the at least one receiving element of the sensor involve at least one section of a photon histogram, and wherein the neuronal network involves a fully connected neuronal network or a convolutional neuronal network.

Abstract: A method and a device for classifying sensor data are proposed, wherein the method comprises providing a respective averaged feature vector for a multiplicity of classes wherein the method further comprises the following steps by means of a classifier, wherein the classifier comprises at least one neural network trained on the basis of training data: determining a feature vector on the basis of sensor data, respective determining of a cosine similarity between the feature vector and a respective averaged feature vector for the multiplicity of classes, comparing the respective cosine similarity with a threshold value established for each class beforehand, and detecting a scenario not represented by the multiplicity of classes if the threshold values are not reached for all classes, and wherein the device comprises a classifier and is designed for carrying out the method.

Abstract: A method for classifying targets is proposed, which comprises the extraction of features from measurement data of one or several receiving elements of a sensor by means of a neuronal network or by means of a Gaussian Mixture Model, wherein the respective measurement data of the at least one receiving element of the sensor involve at least one section of a photon histogram, and wherein the neuronal network involves a fully connected neuronal network or a convolutional neuronal network.

Abstract: A method for classifying objects which comprises a provision of measuring data from a sensor for a feature extraction unit as well as extraction of modality-independent features from the measuring data by means of the feature extraction unit, wherein the modality-independent features are independent of a sensor modality of the sensor, so that a conclusion to the sensor modality of the sensor is not possible from the modality-independent features.

Abstract: A method for classifying objects which comprises a provision of measuring data from a sensor for a feature extraction unit as well as extraction of modality-independent features from the measuring data by means of the feature extraction unit, wherein the modality-independent features are independent of a sensor modality of the sensor, so that a conclusion to the sensor modality of the sensor is not possible from the modality-independent features.