Abstract: The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

Abstract: The invention relates to a method for generating disturbed input data for a neural network for analyzing sensor data, in particular digital images, of a driver assistance system, in which a first metric is defined which indicates how the magnitude of a change in sensor data is measured, a second metric is defined which indicates where a disturbance of sensor data is directed, an optimization problem is generated from a combination of the first metric and second metric, the optimization problem is solved by means of at least one solution algorithm, wherein the solution indicates a target disturbance of the input data, and disturbed input data is generated from sensor data for the neural network by means of the target disturbance.

Abstract: The invention relates to a method for assessing a function-specific robustness of a neural network, comprising the following steps: providing the neural network, wherein the neural network is/has been trained on the basis of a training data set including training data; generating at least one changed training data set by manipulating the training data set, wherein the training data is changed while maintaining semantically meaningful content; determining at least one activation differential between an activation of the neural network via the training data of the original training data set and an activation via the respective corresponding training data of the at least one changed training data set; and providing the determined at least one activation differential. The invention also relates to a device, a computer program product and a computer-readable storage medium.

Abstract: A method for operating a driver assistance system of a vehicle is disclosed, wherein sensor data are recorded from the surroundings of the vehicle, the recorded sensor data are verified, the verified sensor data are analyzed by a neural network and analyzed sensor data are generated. Based on the analyzed sensor data, control data are generated for controlling the vehicle. During verification of the sensor data, at least first sensor data, which were recorded at a first, earlier point in time, are compared with second sensor data, which were recorded at a second, later point in time, the result of the comparison is cross-checked with a database in which data on perturbations to input data of a neural network are stored, wherein it is checked whether the second sensor data were generated at least in part by a perturbation to the first sensor data that is stored in the database.

Abstract: The invention relates to a method for operating a deep neural network, wherein the deep neural network is operated with multiple layers between an input layer and an output layer, and wherein, in addition, at least one classic filter is used in the deep neural network between the input and the output layer. The invention also relates to a device for data processing and to a computer-readable storage medium.

Abstract: The invention relates to a method for making a neural network more robust in a function-specific manner, comprising the following steps: providing the neural network, wherein the neural network is/has been trained on the basis of a training data set including training data; generating at least one changed training data set by manipulating the training data set, wherein the training data is changed while maintaining semantically meaningful content; changing parameters and/or an architecture of the neural network according to a comparison result of a comparison between an application of the original training data set and the at least one changed training data set on the trained neural network; training the changed neural network on the basis of the training data set and at least one part of the at least one changed training data set. The invention also relates to a device, to a computer program product, and to a computer-readable storage medium.

Abstract: The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

Abstract: The present invention relates to an object classification method, comprising: classifying an object based on sensor data from a sensor, wherein the classification is based on a training of an artificial intelligence, wherein the training comprises: obtaining first sensor data which are indicative of the object; obtaining second sensor data which are indicative of the object, wherein a partial symmetry exists between the first and second sensor data; detecting the partial symmetry; and creating an object class based on the detected partial symmetry.