Abstract: The disclosure relates to a method for pedestrian detection in a processor circuit of a motor vehicle, wherein an image data set describing an image of an environment of the motor vehicle is received from an environment sensor, and a machine learning model (ML model) is used to determine bounding boxes with potential images of pedestrians using the image data set, and from image data of the image data set the at least one ML model extracts feature data of image features and a detection of a completely or partially depicted pedestrian is carried out within a bounding box using the image features contained therein by way of a classifier of the at least one ML model, and the bounding box depicting the pedestrian is identified by a detection signal as the result of the detection.

Abstract: The disclosure relates to a method for controlling a driver assistance system during operation of a, especially partially automated, fully automated or autonomous, vehicle, wherein the driver assistance system comprises: a, for example at least one, sensor for observing an environment of the vehicle and an electronic control unit using a, especially at least one, neural network for analyzing sensor data of the sensor and providing perception tasks based on the analyzed sensor data, the method comprising: providing a data set of the sensor data by the sensor) in a spatial domain; transforming the data set of the sensor data by the electronic control unit using frequency analysis into a frequency spectrum in a frequency domain; and analyzing the frequency spectrum of the data set in order to detect an adversarially attacked data set, for example before analyzing the sensor data for providing perception tasks.

Abstract: The disclosure relates to methods for compressing a neural network, wherein members of a vehicle fleet locally execute the neural network and during at least one inference phase each determine a selection of elements of the neural network that should be pruned, wherein the members of the fleet transmit the respective determined selection to a central server, wherein the central server merges the respective transmitted selections and generates a merged selection, and wherein the central server prunes the neural network on the basis of the merged selection.