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 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 generating labels for a data set. The method includes: providing an unlabeled data set comprising a number of unlabeled data; generating initial labels for the data of the unlabeled data set; providing the initial labels as nth labels where n=1; performing an iterative process, where an nth iteration of the iterative process comprises the following steps for every n=1, 2, 3, . . . N: training a model as an nth trained model using a labeled data set, the labeled data set being given by a combination of the data of the unlabeled data set with the nth labels; predicting nth predicted labels for the unlabeled data of the unlabeled data set by using the nth trained model; determining (n+1)th labels from a set of labels comprising at least the nth predicted labels.

Abstract: A method and a device for generating labeled data, for example training data, in particular for a neural network.

Abstract: A method for training a machine learning model for determining a quality grade of data sets from each of a plurality of sensors. The sensors are configured to generate surroundings representations. The method includes: providing data sets of each of the sensors from corresponding surroundings representations; providing attribute data of ground truth objects of the surroundings representations; determining a quality grade of the respective data set of each of the sensors using a metric, the metric comparing at least one variable, which is determined using the respective data set, with at least one attribute datum of at least one associated ground truth object of the surroundings representation; and training the machine learning model using the data sets of each of the sensors and the respectively assigned determined quality grades.

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.