Abstract: A computer-implemented method for classification of at least one object in an environment of a vehicle. The method includes: collecting first data from a first sensor within a first data collecting frame; collecting second data from at least a second sensor within a second data collecting frame; determining a first object representation using the first data; determining a second object representation using the second data; updating the first and/or second object representation depending on an arrival of third data from the at least second sensor collected in a third data collecting frame after the first data collecting frame; fusing the first and second representation to determine an updated representation of the object based on the received data; applying the updated representation for training the data-driven model as input data for a data-driven model to obtain output data containing an information about a classification of the detected object.

Abstract: In a method for determining a winding temperature of a winding of an electric motor, a measurement operating state of the electric motor is specified for measuring an electrical winding resistance of the winding, with the measurement operating state being specified by an operating state in which an electric current flowing in the winding is modulated with a modulation frequency. The winding resistance is measured in the measurement operating state of the electric motor, with the winding resistance being calculated from modulations of the electric current and an associated electric voltage that is applied to the winding. The winding temperature is calculated from the measured winding resistance. In an operating state of the electric motor which operating state is different from a measurement operating state, the winding temperature is calculated from an operating parameter of the electric motor using a thermal model.

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: 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: Computer-implemented method for training a machine learning system. The machine learning system is configured to accept a sensor signal as input for anomaly detection and/or for sampling a trajectory of a traffic participant and/or for sampling of sensor signals and/or for determining a value characterizing a likelihood of a sensor signal with respect to a training dataset. The training includes: determining, by an encoder of the machine learning system and based on a training sensor signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space and a second intermediate representation characterizing a variance and/or covariance of the latent distribution; determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution; determining an output signal.

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 controlling a robot device includes (i) receiving, from each sensor of a plurality of sensors, a respective sensor data set from the sensor, (ii) determining, for each object of a set of objects containing at least one object, for each of a plurality of different combinations of the sensor data sets, a position prediction for the object by way of sensor data fusion of the sensor data sets according to the combination of the sensor data sets, (iii) determining, for each object of the set of objects, for each pair of a plurality of pairs of combinations, a distance between the position predictions determined for the object according to the combinations of the pair, (iv) feeding the determined distances to a neural network trained to determine confidence information for the position predictions from distances between position predictions for the pairs of combinations, and (v) controlling the robot device using one or a plurality of the position predictions taking into account the confidence informat

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 computer-implemented method for training a machine learning system. The training includes: determining, by an encoder of the machine learning system and based on a training input signal, a first intermediate representation characterizing a mean of a latent distribution of a latent space and a second intermediate representation characterizing a variance and/or covariance of the latent distribution; determining, based on the first intermediate representation and the second intermediate representation, a plurality of sigma points with respect to the latent distribution; determining an output signal, wherein the output signal is determined by providing a randomly sampled sigma point of the plurality of sigma points to a decoder of the machine learning system; adapting the machine learning system based on a loss value, wherein the loss value characterizes a difference between the training input signal and the output signal.

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.

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 method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Abstract: A method for creating a training dataset, a validation dataset, and/or a test dataset for an AI module from measurement data includes dividing the measurement data into divided portions based on time periods, applying a mathematical function to the divided portions of the measurement data in order to obtain signatures representing the divided portions, determining a measure of a frequency of occurrence of a respective signature of the obtained signatures, and creating the training dataset, the validation dataset, and/or the test dataset from the measurement data based on the determined measure of the frequency.

Abstract: A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.