Abstract: A method for detecting objects in a radar system. The method includes: providing radar measurement values as a list of points, wherein each point comprises associated features; creating object hypotheses based on the radar measurement values by means of a trained artificial neural network, comprising the steps of: creating a feature map based on the radar measurement values, creating a probability distribution over the feature map, wherein each probability maximum corresponds to the most probable keypoint of an object, regression for each keypoint, filtering the probability maxima, and outputting an object hypothesis for each keypoint. A radar system for performing such a method is also described.

Abstract: A system for detecting objects based on data from at least one environmental sensor. An encoder is configured to process encoder input data that specify data from the at least one environmental sensor, wherein a representation is generated from the encoder input data, which representation represents information about located objects that is contained in the encoder input data. An object detection model is configured to process the generated representation as model input data and to generate object hypotheses from the representation generated by the encoder. The object hypotheses in each case include an object position and/or object features, wherein the object features comprise at least one of a bounding box and a classification.

Abstract: A system, a device, and a method for detecting objects by separately processing point clouds including information about the surroundings of the system and/or the device. The method includes: separating a point cloud into a plurality of point clouds according to one or more features of a plurality of features of each point of the point cloud; preprocessing the plurality of point clouds in a plurality of input paths of a network corresponding to the respective point cloud; fusing the output data of the plurality of input paths of the network; and further processing the fused output data in the network to detect the objects.

Abstract: A device for evaluating sensor data of a sensor device. The device includes an interface that receives the sensor data from the sensor device. A computing device provides input data to an artificial neural network based on the received sensor data and the artificial neural network uses the input data to output output data for evaluating the sensor data. The computing device adjusts parameters of the artificial neural network using a memory state, wherein the memory state depends on latent features of the artificial neural network relating to a previous calculation step.

Abstract: A method for sensor data processing of sensor data of a surroundings sensor. The method includes providing the sensor data of the surroundings sensor acquiring at least one target object in the surroundings of the sensor, calculating a point cloud including a spatial distribution of a plurality of points assigned to respective reflections of the target object from the sensor data, inputting the points and the information sections of the sensor data respectively assigned to them into a trained processing model, calculating feature vectors assigned the points by the processing model depending on the input, outputting the points and the assigned feature vectors for further processing by a further processing model. A processing device is also described.

Abstract: A method for processing measurement data from a surveillance of an area into classification scores with respect to a given classification and/or properties of objects in the area. The method includes: providing a point cloud of measurement data that assigns, to each of a plurality of points in space, measurement values of at least one quantity; providing an input grid with a plurality of cells; assigning, based on the point cloud, values of the at least one measurement quantity, and/or of at least one work product derived therefrom, to each cell of the input grid; and processing, by a task neural network, the input grid into an output grid whose cells carry classification scores and/or properties of objects as values.