Abstract: A method for outputting to a road user at least one, in particular a visual and/or acoustic, warning signal from a vehicle operating fully autonomously. A gesture and/or an acoustic message from at least one vehicle occupant of the vehicle operating fully autonomously is captured first. In addition, a road user in the surroundings of the vehicle is detected. In addition, a viewing direction of the road user, in particular at a time of gesture capture or capture of the acoustic message, is detected. Following this, the warning signal from the vehicle operating fully autonomously is output to the road user depending on the captured gesture of the vehicle occupant and/or the acoustic message and the viewing direction of the road user.

Abstract: A computer-implemented method for verifying a software component of an automated driving function, The method includes: translating the native program code into a model checker representation of the software component to be verified and analyzing the model checker representation of the software component to be verified using a model checking method. The native program code of the software component to be verified is analyzed to identify independent sequences of commands, wherein an independent sequence of commands is a cohesive succession of program commands by which at least two variables are set, and the at least one result of an independent sequence of commands is independent of the order in which its program commands are processed. The variables of the at least one independent sequence of commands of the native program code are then simultaneously set in the model checker representation of the software component to be verified.

Abstract: A method for selecting a driving maneuver to be carried out by an at least semi-autonomously driving vehicle is disclosed. The method includes (i) using measurement data of at least one sensor carried by the vehicle, creating a representation of the situation the vehicle is in, (ii) mapping the representation of the situation to a probability distribution by way of a trained machine learning model, which probability distribution specifies a probability for every driving maneuver from a predefined catalog of available driving maneuvers, with which said driving maneuver is carried out, (iii) selecting a driving maneuver from the probability distribution as the driving maneuver to be carried out, (iv) in addition to using at least one aspect of the situation the vehicle is in, a subset of driving maneuvers which are disallowed in this situation is determined, and (v) this disallowed driving maneuver is prevented from being carried out.

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 method for evaluating a first method for a control of an at least semi-automated mobile platform in surroundings of the mobile platform. The method includes: determining a control action using the first method for a setting of the surroundings; determining a confidence value for the determination of the control action using the first method; determining a representation of the setting of the surroundings of the mobile platform, if the determined confidence value is lower than a trust level, in order to evaluate the first method using this setting.

Abstract: In a method for calculating a gradient of a data-based function model, having one or multiple accumulated data-based partial function models, e.g., Gaussian process models, a model calculation unit is provided, which is designed to calculate function values of the data-based function model having an exponential function, summation functions, and multiplication functions in two loop operations in a hardware-based way, the model calculation unit being used to calculate the gradient of the data-based function model for a desired value of a predefined input variable.

Abstract: A computerized method for creating a function model based on a non-parametric, data-based model, e.g., a Gaussian process model, includes: providing training data including measuring points having one or multiple input variables, the measuring points each being assigned an output value of an output variable; providing a basic function; modifying the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; creating the data-based model based on the modified training data; and providing the function model as a function of the data-based model and the basic function.

Abstract: A model calculation unit for calculating a data-based function model in a control unit is provided, the model calculation unit having a processor core which includes: a multiplication unit for carrying out a multiplication on the hardware side; an addition unit for carrying out an addition on the hardware side; an exponential function unit for calculating an exponential function on the hardware side; a memory in the form of a configuration register for storing hyperparameters and node data of the data-based function model to be calculated; and a logic circuit for controlling, on the hardware side, the calculation sequence in the multiplication unit, the addition unit, the exponential function unit and the memory in order to ascertain the data-based function model.

Abstract: A method for determining a sparse Gaussian process model to be carried out in a solely hardware-based model calculation unit includes: providing supporting point data points, a parameter vector based thereon, and corresponding hyperparameters; determining or providing virtual supporting point data points for the sparse Gaussian process model; and determining a parameter vector Qy* for the sparse Gaussian process model with the aid of a Cholesky decomposition of a covariant matrix KM between the virtual supporting point data points and as a function of the supporting point data points, the parameter vector based thereon, and the corresponding hyperparameters, which define the sparse Gaussian process model.

Abstract: A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points.

Abstract: A method for operating a control unit, the control unit including a software-controlled main processing unit, a strictly hardware-based model calculation unit for calculating an algorithm, for carrying out a Bayesian regression method, based on configuration data, and a memory unit, a model memory area being defined in the memory unit to which a configuration register block for providing the configuration data in the model calculation unit is assigned, a calculation start-configuration register being assigned the highest address in the configuration register block into which configuration data are written, the writing into of which starts the calculation in the model calculation unit, the configuration data being written in a memory area of the memory unit from the model memory area into the configuration register block with an incremental copying process, the addresses being copied in the incremental copying process in ascending order.

Abstract: A method for generating a data-based function model includes: providing a first data-based partial model ascertained from a first training data record; providing at least one additional training data record; and performing the following steps for the at least one additional training data record: ascertaining a difference training data record having training data which correspond to the differences between the output values of the relevant additional training data record and the function value of the sum of the partial function values (ffirst_partial_model(x) fsecond_partial_model(x)) of the first data-based partial model and previously ascertained data-based partial model(s) at each of the measuring points of the relevant training data record; ascertaining an additional data-based partial model from the difference training data record; and forming a sum (f(x)) from the first and the additional data-based partial models.

Abstract: A method for determining a sparse Gaussian process model to be carried out in a solely hardware-based model calculation unit includes: providing supporting point data points, a parameter vector based thereon, and corresponding hyperparameters; determining or providing virtual supporting point data points for the sparse Gaussian process model; and determining a parameter vector Qy* for the sparse Gaussian process model with the aid of a Cholesky decomposition of a covariant matrix KM between the virtual supporting point data points and as a function of the supporting point data points, the parameter vector based thereon, and the corresponding hyperparameters, which define the sparse Gaussian process model.

Abstract: In a method for calculating a gradient of a data-based function model, having one or multiple accumulated data-based partial function models, e.g., Gaussian process models, a model calculation unit is provided, which is designed to calculate function values of the data-based function model having an exponential function, summation functions, and multiplication functions in two loop operations in a hardware-based way, the model calculation unit being used to calculate the gradient of the data-based function model for a desired value of a predefined input variable.

Abstract: A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points.

Abstract: A method for operating a control unit, the control unit including a software-controlled main processing unit, a strictly hardware-based model calculation unit for calculating an algorithm, for carrying out a Bayesian regression method, based on configuration data, and a memory unit, a model memory area being defined in the memory unit to which a configuration register block for providing the configuration data in the model calculation unit is assigned, a calculation start-configuration register being assigned the highest address in the configuration register block into which configuration data are written, the writing into of which starts the calculation in the model calculation unit, the configuration data being written in a memory area of the memory unit from the model memory area into the configuration register block with an incremental copying process, the addresses being copied in the incremental copying process in ascending order.

Abstract: A method for post-adaption of an at least partially data-based function model which corresponds to a sum of a basis function model, e.g., a data-based basis function model, and an additive fault model, includes: providing the basis function model; recording training data; ascertaining the data-based additive fault model based on difference training data which represent differences between the measured values of the training data and the function values of the data-based basis function model at the measuring points of the training data; and modifying the training data and/or the additive fault model so that function values of the data-based function model remain within a predefined adaption range.

Abstract: A model calculation unit for calculating a data-based function model in a control unit is provided, the model calculation unit having a processor core which includes: a multiplication unit for carrying out a multiplication on the hardware side; an addition unit for carrying out an addition on the hardware side; an exponential function unit for calculating an exponential function on the hardware side; a memory in the form of a configuration register for storing hyperparameters and node data of the data-based function model to be calculated; and a logic circuit for controlling, on the hardware side, the calculation sequence in the multiplication unit, the addition unit, the exponential function unit and the memory in order to ascertain the data-based function model.

Abstract: A method for ascertaining a nonparametric, data-based function model, in particular a Gaussian process model, using provided training data, the training data including a number of measuring points which are defined by one or multiple input variables and which each have assigned output values of at least one output variable, including: selecting one or multiple of the measuring points as certain measuring points or adding one or multiple additional measuring points to the training data as certain measuring points; assigning a measuring uncertainty value of essentially zero to the certain measuring points; and ascertaining the nonparametric, data-based function model according to an algorithm which is dependent on the certain measuring points of the modified training data and the measuring uncertainty values assigned in each case.