Abstract: Processing of sensor data from sensor(s). The sensor data are provided as an unordered point cloud. The points of the unordered sequence are then converted into a regular structure using a point-processing neural network and made available for further processing. A transfer device is configured to receive a group of input data elements from the sensors. Each input data element of this group of input data elements includes a point that specifies at least one position. The transfer device also includes a point-processing neural network. This point-processing neural network is configured to map the points of the group of input data elements to a regular output data structure. A processing device is configured to detect an object and/or ascertain properties of an object using the regular output data structure. For the conversion of points of an unordered point cloud to a regular structure, a point-processing neural network is provided.

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 method for determining a surrounding representation of a surrounding of a vehicle includes (i) providing input data, wherein the input data comprises sensor data and feedback data, wherein the sensor data results from a detection of at least one sensor of the vehicle, wherein the sensor data represents a detection of the surrounding of the vehicle, (ii) providing a machine-learning model, wherein the machine-learning model comprises a pre-processing module and at least one task-specific module, (iii) providing the feedback data, wherein the feedback data comprises at least one historical output of the at least one task-specific module and/or at least one historical output of the pre-processing module, wherein the historical output has been determined by the at least one task-specific module and/or the pre-processing module at least one iteration prior to a current iteration, (iv) extracting features from the input data by way of the pre-processing module, and (v) determining, by way of the at least one task

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 method for the chronological correction of multimodal data includes: receiving a first data set from a reference sensor with measurements at different measurement timepoints, receiving a second data set of a second sensor with measurements at different measurement timepoints, each not exactly matching those of the reference sensor, reading the first and the second data sets by a neural network and identifying a respective plurality of feature vectors for the first and second data set at the respective measurement timepoints, merging and comparing the respective feature vectors, which refer to corresponding, not exactly matching measurement timepoints, by the neural network so that parameters of a chronological correction are identified, and identifying a chronological offset between the respective measurement timepoints of the reference sensor and the second sensor, and/or a corrected data set from the second sensor based on the measurement timepoints of the reference sensor.

Abstract: An adapter for an automation field device comprises a first communications interface designed for linking to a HART or 4-20 mA communications loop; a second communications interface designed for linking to a Foundation Fieldbus or Profibus PA Fieldbus based on Manchester Bus Powered transmission technology; an electronics unit with operating software that instructs the electronics unit to convert HART or 4-20 mA telegrams sent by the field device and incoming at the first communications interface into Foundation Fieldbus- or Profibus PA-compatible telegrams and to output same via the second communications interface, and/or to convert Foundation Fieldbus or Profibus PA telegrams incoming at the second communications interface into HART- or 4-20 mA-compatible telegrams and to output same via the first communications interface; and a power supply unit designed to tap electrical energy from the Foundation Fieldbus or Profibus Fieldbus via the second communications interface.

Abstract: A method is for training an object detector configured to detect objects in sensor data of a sensor. The method includes providing first sensor data of the sensor, providing an object representation assigned to the first sensor data, and transmitting the object representation to a sensor model. The method further includes imaging object representations onto the first sensor data of the sensor with the sensor model, assigning the object representation to second sensor data with the sensor model, and training the object detector based on the second sensor data.

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: Learning extraction of movement information from sensor data includes providing a time series of frames of sensor data recorded by physical observation of an object, providing a time series of object boundary boxes each encompassing the object in sensor data frames, supplying the object boundary box at a time t, as well as a history of sensor data from the sensor data time series, and/or a history of object boundary boxes from the time series of object boundary boxes, prior to time t to a trainable machine learning model which predicts an object boundary box for a time t+k, comparing the predicted object boundary box with a comparison box obtained from the time series of object boundary boxes for the time t+k, evaluating a deviation between the predicted object boundary box and the comparison box using a predetermined cost function, and optimizing parameters which characterize the behavior of the model.

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 processing sensor data, wherein the sensor data are present as a point cloud of individual points. The points are projected into at least two rasters with different resolutions. Points from a subregion of the point cloud are projected into a first raster with higher resolution and further points of the point cloud are projected into a second raster with lower resolution. Attributes from the first raster are compressed and arranged in the second raster before the attributes from the second raster are compressed.

Abstract: A method for monitoring surroundings of a first sensor system. The method includes: providing a temporal sequence of data of the first sensor system for monitoring the surroundings; generating an input tensor including the temporal sequence of data of the first sensor system, for a trained neural network; the neural network being configured and trained to identify, on the basis of the input tensor, at least one subregion of the surroundings, in order to improve the monitoring of the surroundings with the aid of a second sensor system; generating a control signal for the second sensor system with the aid of an output signal of the trained neural network, in order to improve the monitoring of the surroundings in the at least one subregion.

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.

Abstract: An adapter for an automation field device comprises a first communications interface designed for linking to a HART or 4-20 mA communications loop; a second communications interface designed for linking to a Foundation Fieldbus or Profibus PA Fieldbus based on Manchester Bus Powered transmission technology; an electronics unit with operating software that instructs the electronics unit to convert HART or 4-20 mA telegrams sent by the field device and incoming at the first communications interface into Foundation Fieldbus- or Profibus PA-compatible telegrams and to output same via the second communications interface, and/or to convert Foundation Fieldbus or Profibus PA telegrams incoming at the second communications interface into HART- or 4-20 mA-compatible telegrams and to output same via the first communications interface; and a power supply unit designed to tap electrical energy from the Foundation Fieldbus or Profibus Fieldbus via the second communications interface.

Abstract: A method for object detection of an object based on measurement data from at least one point-based sensor capturing the object. The measurement data, which are based on a point cloud having a plurality of points and associated features, are processed in that, in a point-based first processing step having at least one processing level, the input-side features of the point cloud are realized as learned features, and are enriched at least by information about relationships between the points, and in a grid-based second processing step having at least one processing level, the learned features are then transferred onto a model grid having a plurality of grid cells, and cell-related output data are then generated. An image detection device, a computer program, and a storage unit, are also described.

Abstract: A radar-based environmental detection system for motor vehicles. The system includes at least one radar sensor for providing location data regarding objects in the environment of the motor vehicle, and including a neural network for converting the location data into an environmental model which represents spatio-temporal object data of the objects. The neural network is conditioned to give priority to outputting environmental models in which at least one predetermined physical relationship between the location data and the object data is satisfied.

Abstract: A method for the chronological correction of multimodal data includes: receiving a first data set from a reference sensor with measurements at different measurement timepoints, receiving a second data set of a second sensor with measurements at different measurement timepoints, each not exactly matching those of the reference sensor, reading the first and the second data sets by a neural network and identifying a respective plurality of feature vectors for the first and second data set at the respective measurement timepoints, merging and comparing the respective feature vectors, which refer to corresponding, not exactly matching measurement timepoints, by the neural network so that parameters of a chronological correction are identified, and identifying a chronological offset between the respective measurement timepoints of the reference sensor and the second sensor, and/or a corrected data set from the second sensor based on the measurement timepoints of the reference sensor.

Abstract: Learning extraction of movement information from sensor data includes providing a time series of frames of sensor data recorded by physical observation of an object, providing a time series of object boundary boxes each encompassing the object in sensor data frames, supplying the object boundary box at a time t, as well as a history of sensor data from the sensor data time series, and/or a history of object boundary boxes from the time series of object boundary boxes, prior to time t to a trainable machine learning model which predicts an object boundary box for a time t+k, comparing the predicted object boundary box with a comparison box obtained from the time series of object boundary boxes for the time t+k, evaluating a deviation between the predicted object boundary box and the comparison box using a predetermined cost function, and optimizing parameters which characterize the behavior of the model.

Abstract: A method for monitoring surroundings of a first sensor system. The method includes: providing a temporal sequence of data of the first sensor system for monitoring the surroundings; generating an input tensor including the temporal sequence of data of the first sensor system, for a trained neural network; the neural network being configured and trained to identify, on the basis of the input tensor, at least one subregion of the surroundings, in order to improve the monitoring of the surroundings with the aid of a second sensor system; generating a control signal for the second sensor system with the aid of an output signal of the trained neural network, in order to improve the monitoring of the surroundings in the at least one subregion.