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: A method for automated driving of a vehicle, comprising: provide function modules, wherein the function modules each comprise at least one subfunction for automated driving, and wherein the function modules are each assigned at least one module validity attribute that defines the context of the surroundings in which the particular function module is valid, provide at least one system configuration, wherein the at least one system configuration comprises at least one of the function modules, and wherein the at least one system configuration is assigned at least one configuration validity attribute that defines the context of the surroundings in which the at least one system configuration is valid, select one of the at least one system configurations, wherein the selection depends on a context of the surroundings and the at least one associated configuration validity attribute, control the vehicle, wherein controlling is based on the selected system configuration.

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 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: The present invention relates to a method, a computer program, and an apparatus for training a neural network for semantic image segmentation. The invention further relates to an in-car control unit or a backend system, which make use of such a method or apparatus, and to a vehicle comprising such an in-car control unit. In some embodiments and in a first step, image data of a sequence of image frames are received. Then a frame-based evaluation of semantic segmentation predictions of one or more objects in individual image frames is performed. Furthermore, a sequence-based evaluation of temporal characteristics of semantic segmentation predictions of said one or more objects in at least two image frames is performed. The results of the frame-based evaluation and the sequence-based evaluation are combined.

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 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: 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: 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 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.

Abstract: A method, an apparatus, and a computer-readable storage medium having instructions for cancelling a redundancy of two or more redundant modules. Results of the two or more redundant modules are received; reliabilities of the results are ascertained; and, based on the ascertained reliabilities, an overall result is determined from the results. The overall result is output for further processing.

Abstract: A method for automated driving of a vehicle, comprising: provide function modules, wherein the function modules each comprise at least one subfunction for automated driving, and wherein the function modules are each assigned at least one module validity attribute that defines the context of the surroundings in which the particular function module is valid, provide at least one system configuration, wherein the at least one system configuration comprises at least one of the function modules, and wherein the at least one system configuration is assigned at least one configuration validity attribute that defines the context of the surroundings in which the at least one system configuration is valid, select one of the at least one system configurations, wherein the selection depends on a context of the surroundings and the at least one associated configuration validity attribute, control the vehicle, wherein controlling is based on the selected system configuration.

Abstract: A method, computer program, and devices for the remote control of a transportation vehicle via a mobile device. The method includes determining a proposal for triggering a functionality of the transportation vehicle; outputting the proposal via an output device; detecting a reaction of a user of the mobile device, wherein the reaction consists of an input via the input device or of the absence of an input; and transmitting a triggering signal for the functionality from the mobile device to the transportation vehicle in response to the input being absent within a period of time or in response to the event not corresponding to a termination of the proposal.

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: 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: A method for detecting a distribution shift in a data and/or feature distribution of input data, wherein the method is carried out in at least one mobile device and includes receiving the input data by an input device, performing a function, which is identical according to the objective, on the received input data by a first processing module and at least one second processing module, wherein the first processing module and the at least one second processing module are structurally different than one another, wherein at least the first processing module was created based on machine learning, comparing the results delivered by the processing modules and discovering a distribution shift based on the comparison result by an evaluation device, and providing a candidate signal in response to a distribution shift being discovered, and outputting the provided candidate signal by an output device.