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 tracking an object using an environment sensor. The object is represented by an object status. The method includes detecting a sensor value of the environment sensor, predicting a future object status of the object, and updating the object status using a Bayesian filter. The updating includes using an artificial intelligence module (“AI module”). The AI module is trained such that the detected sensor value is associated with the object and the object status of the object is updated based on the predicted future object status of the object and the detected sensor value.

Abstract: A method for generating a trained convolutional neural network including at least one invariant integration layer for classifying objects of a digital image of the surroundings of a mobile platform including a plurality of training cycles. Each training cycle includes: providing a digital image of the surroundings of a mobile platform including at least one object; providing a reference image associated with the digital image, the at least one object being labeled in the reference image; providing the digital image as an input signal of the convolutional neural network including at least one invariant integration layer; and adapting the convolutional neural network including at least one invariant integration layer in order to minimize a deviation of the classification from the particular associated reference image upon the classification of the at least one object of the digital image.

Abstract: A method, for example a computer-implemented method, for processing data associated with a, for example artificial, for example deep, neural network, for example, convolutional neural network (CNN). The method includes: representing at least one filter of the neural network based on at least one filter dictionary, and, optionally, processing input data, and/or data that can be derived or are derived from input data, by using the at least one filter.

Abstract: A method for simplifying an artificial neural network (ANN) whose behavior is characterized by trainable parameters. In the method: a first assessment criterion is provided, which maps simplified configurations of the ANN on predictions for the performance of the ANN in the particular configuration; a second assessment criterion is provided, which also maps simplified configurations of the ANN on predictions for the performance of the ANN in the particular configuration, this second assessment criterion being at least partially complementary to the first assessment criterion; a simplified configuration of the ANN is optimized with the goal that this simplified configuration is mapped both by the first assessment criterion and also by the second assessment criterion, and/or by an overall assessment criterion resulting from a combination of both assessment criteria, on the best possible prediction for the performance of the ANN.

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 training an artificial neural network (ANN) whose behavior is characterized by trainable parameters. In the method, the parameters are initialized. Training data are provided which are labeled with target outputs onto which the ANN is to map the training data in each case. The training data are supplied to the ANN and mapped onto outputs by the ANN. The matching of the outputs with the learning outputs is assessed according to a predefined cost function. Based on a predefined criterion, at least one first subset of parameters to be trained and one second subset of parameters to be retained are selected from the set of parameters. The parameters to be trained are optimized. The parameters to be retained are in each case left at their initialized values or at a value already obtained during the optimization.

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.