Abstract: Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.

Abstract: A computer-implemented method for generating reliability indication data of a computer vision model. The method includes: obtaining visual data including an input image or sequence representing an observed scene, the visual data being characterizable by a first set of visual parameters; analysing the observed scene in the visual data using a computer vision reliability model sensitive to a second set of visual parameters, the second set of visual parameters includes a subset of the first set of visual parameters, and is obtained from the first set of visual parameters according to a sensitivity analysis applied to a plurality of parameters in the first set of visual parameters, the sensitivity analysis is performed during an offline training phase of the computer vision reliability model; generating reliability indication data of the observed scene using the analysis of the observed scene; and outputting the reliability indication data of the computer vision model.

Abstract: A computer-implemented method for verifying at least one software component of an automated driving function. The method includes the following steps: providing an environment model that limits the state space of the software component to be verified by way of predefinable boundary conditions, wherein the environment model is provided in the form of a native environment model program code; translating the native program code of the software component to be verified and the environment model program code, wherein a model checker representation limited by the boundary conditions of the environment model and intended for the software component to be verified is generated; and verifying the model checker representation using a model checking method.

Abstract: A computer-implemented system for monitoring the functionality of an automated driving function of a vehicle using sensor information from at least one sensor includes a software model of the automated driving function, a sensor performance model for the at least one sensor, a sensor monitoring module, which determines performance parameters and monitors the performance of the at least one sensor, an update module for updating the at least one sensor performance model based on the performance parameters determined, and a model checking module for analyzing an overall model comprising a combination of the software model and the at least one sensor performance model.

Abstract: A computer-implemented method for verifying at least one software component of an automated driving function. The software component to be verified includes at least one function which uses sensor information from at least one sensor. The method includes: a. providing a model for the software component to be verified, b. providing at least one sensor performance model for the at least one sensor, c. generating an overall model, in the process of which the at least one sensor performance model is combined with the model of the software component to be verified, d. analyzing the overall model using a model checking method.

Abstract: A computer-implemented method for providing a digital road map for testing an at least partially automated vehicle system. the method includes: accessing a database in which are stored permissible characteristics of the road properties for a multitude of road properties; creating at least one road map section by one of the possible characteristics being selected for the road map section for the first of the multitude of road properties, in each particular case in automated fashion from the database; providing the digital road map, the digital road map including the at least one road map section.

Abstract: A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

Abstract: A computer-implemented method for detecting an obstacle on a route ahead of a first vehicle. In the method, information on a second vehicle driving ahead on the route is recorded in the first vehicle by at least one sensor of the first vehicle. In the first vehicle, depending on the recorded information, a computer detects an avoidance maneuver of the second vehicle due to an obstacle or detects that the second vehicle has driven over an obstacle. An obstacle is detected on the route depending on the detected avoidance maneuver or the detection that the vehicle has driven over an obstacle. A measure for protecting the vehicle and/or the obstacle is initiated depending on the detected obstacle.

Abstract: A computer-implemented method for testing conformance between images generated by a synthetic image generator and images obtained from authentic visual data. A conformance test result results from comparing results of global sensitivity analyses used to rank the effect of visual parameters on the computer vision model both for synthetic and authentic visual data.

Abstract: A computer-implemented method for analysing the relevance of visual parameters for training a computer vision model. Upon adjusting the set of visual parameters to increase their relevance a new set of visual data and corresponding groundtruth results that can be used in (re)training and/or testing the computer vision model.

Abstract: A method or testing a system. Input parameters of the system are divided into a first group and a second group. Using a first method, a first selection is made from among the input parameter assignments of the first group. Using a second method, a second selection is made from among the input parameter assignments of the second group. A characteristic value is calculated from the second selection. The first selection is adapted depending on the characteristic value.

Abstract: Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.

Abstract: Facilitating the description or configuration of a computer vision model by generating a data structure comprising a plurality of language entities defining a semantic mapping of visual parameters to a visual parameter space based on a sensitivity analysis of the computer vision model.

Abstract: A computer-implemented method for training a computer vision model to characterise elements of observed scenes parameterized using visual parameters. During the iterative training of the computer vision model, the latent variables of the computer vision model are altered based upon a (global) sensitivity analysis used to rank the effect of visual parameters on the computer vision model.

Abstract: Modifying a visual parameter specification characterising the operational design domain of the computer vision model by improving the visual parameter specification according to a sensitivity analysis of the computer vision model.

Abstract: A method for controlling a vehicle. In the method, data of a digital road map are read in, zones are determined for the digital road map, and possible sequences of trips along a road of the digital road map are ascertained as a function of the determined zones. Furthermore, it is ascertained, as a function of sensor data and/or current driving data of the vehicle, whether a current or predicted traffic situation is outside the possible sequences or corresponds to a possible sequence that is determined as being outside an intended operating range. If the current or predicted traffic situation is outside the possible sequences or corresponds to the possible sequence outside the intended operating range, a measure is determined and the vehicle is controlled as a function of the measure that is taken.

Abstract: A device and a computer-implemented method for machine learning. First input data are provided which encompass information concerning dimensions and options for the machine learning. At least one of the options is associated with at least one of the dimensions as a function of information concerning the dimensions and options for at least one test case for the machine learning. A combination of options for a subset of the dimensions that is lacking in the set of test cases is determined, and a test case is determined for this combination.

Abstract: A computer-implemented method for testing a vehicle system. In the method, data of a digital road map are read in, zones are determined for the digital road map, possible sequences of drives along a road of the digital road map are ascertained as a function of the determined zones, a behavior of the vehicle or of a vehicle system of the vehicle is ascertained in a simulation for at least one of the possible sequences, and it is determined as a function of a comparison of the ascertained behavior with at least one predetermined requirement whether the vehicle system exhibits an error or a weakness.

Abstract: A method or testing a system. Input parameters of the system are divided into a first group and a second group. Using a first method, a first selection is made from among the input parameter assignments of the first group. Using a second method, a second selection is made from among the input parameter assignments of the second group. A characteristic value is calculated from the second selection. The first selection is adapted depending on the characteristic value.

Abstract: A computer-implemented method for providing a digital road map for testing an at least partially automated vehicle system. the method includes: accessing a database in which are stored permissible characteristics of the road properties for a multitude of road properties; creating at least one road map section by one of the possible characteristics being selected for the road map section for the first of the multitude of road properties, in each particular case in automated fashion from the database; providing the digital road map, the digital road map including the at least one road map section.