Abstract: A computer-implemented method for validating a behavior planner for an automated vehicle. The behavior planner has a database having previously defined partial situations and an evaluation model for each partial situation to break down a given situation into partial situations of the database and, based on the associated evaluation models, to determine boundary conditions for permissible behavior options of the vehicle in the given situation, as a combination of boundary conditions of the individual partial situations. The evaluation models of the partial situations are made available in a declarative program representation which enables the determination of formally explorable boundary conditions for permissible behavior options of the vehicle in the particular partial situation. A test situation is predefined as a composition of selected partial situations.

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 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 method for optimized training of a machine learning algorithm. The method includes: providing a domain model that has domain parameters and/or domain values for at least one domain; providing a data model that has a training data set; removing and/or hiding and/or modifying at least one training datum from the training data set depending on at least one domain parameter and/or domain value to provide a reduced training data set; training a neural network based on the reduced training data set to determine a model performance depending on the reduced data set; comparing the determined model performance with a model performance and determining a model performance deviation depending on the reduced data set; selecting training data from the training data set depending on the model performance deviation; training the machine learning algorithm based on the selected training data; and providing the trained machine learning algorithm.

Abstract: A method for optimized training of a machine learning algorithm. The method includes: providing a domain model that has domain parameters and/or domain values for at least one domain; providing a data model that has a training data set including training data for the at least one domain; removing/hiding/modifying at least one training datum from the training data set depending on at least one domain parameter and/or domain value to provide a reduced training data set; training a neural network based on the reduced training data set to determine a model performance depending on the reduced data set; comparing the determined model performance with a model performance associated with the training data set; selecting training data from the training data set depending on the comparison of the model performances; training the machine learning algorithm based on the selected training data; and providing the trained machine learning algorithm.

Abstract: A computer-implemented method and a corresponding system for planning the behavior of a vehicle using a predefined set of rules with prioritized rules for assessing possible behaviors of the vehicle in a given situation. For this purpose, situation-specific information is aggregated, and an environmental model of the given situation is generated based on the aggregated situation-specific information. The set of rules includes a decision-making process structure, which represents the prioritization of the individual rules of the set of rules. On the basis of the environmental mode, boundary conditions for the possible behaviors of the vehicle are determined. The latter are then prioritized by applying the decision-making process structure to the respective boundary conditions.

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 computer-implemented method for the behavior planning of an at least partially automated EGO vehicle. The method uses a database of pre-defined partial situations and an evaluation model for each partial situation, as well as a pre-defined rule set for evaluating possible behaviors of the EGO vehicle in a given situation. The EGO vehicle performs: aggregating situation-specific information; generating an environment model of the given situation based on the situation-specific information; analyzing the environment model to identify at least one partial situation in the database; generating at least one instance for each identified partial situation; analyzing all generated instances by using the evaluation model of the respectively underlying partial situation to determine boundary conditions for the possible behaviors of the EGO vehicle in the given situation; prioritizing the possible behaviors of the EGO vehicle based on the boundary conditions determined in this way in conjunction with the rule set.

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: 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.