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: Using machine learning for sedimentary facies prediction by using one or more logs acquired in a borehole. This includes performing a petrophysical clustering of borehole depths wherein the depths of the borehole are gathered into clusters based on similarities in the one or more logs. Also performed is a log inclusion optimization, including a selection of one or more parameters of the petrophysical clustering, wherein the one or more parameters include a number and/or type of considered logs among the one or more logs and/or a clustering method. Also performed is a classification of the clusters into core depositional facies using one or more predetermined rules.

Abstract: A method, system and computer program for processing image data, to a vehicle comprising such a system, and to a method, system and computer program for generating a filter. Image data processing may include obtaining the image data, and applying a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The filtered image data is processed using a machine-learning model.

Abstract: The invention relates to a method for updating a control model for automatic control of at least one mobile unit. A central control unit generates a detection task and transmits same to the mobile unit. The mobile unit comprises sensors, and the detection task comprises conditions for detecting sensor data sets by means of the sensors. The mobile unit detects the sensor data sets by means of the sensors using the detection task, generates transmission data using the detected sensor data sets, and transmits the transmission data to the central control unit. The central control unit receives the transmission data and generates an updated control model using the received transmission data. The system according to the invention for updating a control model for automatic control of at least one mobile unit comprises a central control unit by means of which a detection task may be generated and transmitted to the mobile unit.

Abstract: The disclosure relates to a method for making sensor data more robust to adversarial perturbations, wherein sensor data are obtained from at least two sensors, wherein the sensor data obtained from the at least two sensors are replaced in each case piecewise by means of quilting, wherein the piecewise replacement is carried out in such a way that the respectively replaced sensor data from different sensors are plausible relative to one another, and wherein the sensor data replaced piecewise are output.

Abstract: Systems and methods for imaging properties of subterranean formations (136) in a wellbore (106) include a formation sensor (120, 200) for collecting currents (304A, 304B) injected into the subterranean formations (139) and a formation imaging unit (118). The formation imaging unit (118) includes a current management unit for collecting data from the currents injected into the subterranean formations (136) and a formation data unit (116) for determining at least one formation parameter from the collected data. The formation imaging unit (118) also includes an inversion unit for determining at least one formation property by inverting the at least one formation parameter. The inversion unit is suitable for generating an inverted standoff image and an inverted permittivity image for comparison with a composite image of the formation imaging unit.

Abstract: A method, a computer program with instructions, and an apparatus for configuring a control system for an at least partially autonomous transportation vehicle. Data relating to the driving situation are captured, selection criteria are then determined from the available data, two or more AI modules are selected from a library of AI modules during driving based on selection criteria, and combined execution of the selected two or more AI modules by the control system is initiated.

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 abstracting a data record, wherein the data record is provided for machine learning at least one function, comprising the following steps: Training a complex neural network structure comprising different neural networks in the at least one function by way of machine learning based on the data record by means of a machine learning control apparatus, wherein the neural networks and the complex neural network structure are optimized with respect to maximum representativity of the data record, providing the trained complex neural network structure in the form of a data-record-specific knowledge module so that knowledge contained in the data record can be further used in a manner compliant with data protection. The invention further relates to an associated device.

Abstract: The monitoring of an AI module of a vehicle driving function, the monitoring being performed using a generative-discriminative situation evaluation which represents a control mechanism for the AI modules along the processing chain of the driving function. The control mechanism is in the form of a monitoring unit for monitoring input and output data of a control module, in the form of an AI module, for semi-automatic or automatic driving.

Abstract: The present invention relates to a method for processing input data provided by a sensor system of a motor vehicle, and also relates to a classifier provided using such a method. In a first step, an AI module to be classified is selected. In addition, a suitable test data set is selected. The AI module is then applied to data points of the test data set. Associated ground truths and contextual parameters are known for the data points. On the basis of the outputs of the AI module, a functional quality is then determined for each of the data points. Finally, a classifier for the AI module is created, which outputs a functional quality for given contextual parameters.

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: Method for automatically executing a vehicle function of a vehicle based on spatially resolved raw sensor data for environment perception generated by at least one sensor for environment detection of the vehicle, comprising: receiving spatially resolved raw sensor data generated by the at least one sensor of the vehicle; processing sensor data which are characteristic of the spatially resolved raw sensor data by a processor, the processor determining at least one region of interest of the sensor data and at least one class for classifying the region of interest; processing the sensor data by an evaluation circuit based on the determined region of interest and hereby determining at least one local quality parameter which is characteristic for the quality of the sensor data with respect to at least a section of the region of interest; executing the vehicle function in dependence on the local quality parameter.

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.

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 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 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 present disclosure relates to a method for generating control signals to assist occupants in a vehicle, wherein a context of the vehicle is determined, a rule of a rule-based data system is selected depending on the determined context, wherein the rule-based data system comprises a plurality of rules, wherein each rule has a condition part and a result part, wherein the condition part comprises conditions for the context of the vehicle, a confidence value associated with the selected rule is determined, wherein the confidence value indicates the probability with which the result of the rule corresponds with the preference of the user, a result of the selected rule is generated, a control signal is generated and output depending on the generated rule result, wherein the control signal automates a vehicle function with a degree of automation, wherein the degree of automation depends on the confidence value of the selected rule. The disclosure likewise relates to a device for executing this method.

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 operating a machine learning model, comprising the following steps during a training phase: receiving selected multidimensional training data; selecting subsets from the received training data; generating a training data set, wherein the training data set includes data set elements which are generated on the basis of the selected subsets; training the machine learning model using the training data set; and/or comprising the following steps during an inference phase: receiving sensor data of at least one sensor; selecting subsets from the received sensor data; generating a data stack, wherein the data stack includes the respective selected subsets as stack elements; applying the, or a, machine learning model trained according to the steps of the training phase to every stack element of the data stack, wherein the application occurs simultaneously, and deducing an inference result; and outputting the inference result.