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 control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

Abstract: A method is provided for populating a function for a control unit with data, in which method measurements are performed on a system at different measuring points on a test stand, and a global data-based model is set up based on the obtained measured values, and virtual measurements which simulate real measurements on the test stand are carried out on the global data-based model, and uncertainties for virtual measured values of the virtual measurements are determined from the global data-based model, the uncertainties of the virtual measured values being taken into account when populating the function for the control unit with data.

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 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 system for determining the distance from and the direction to an object includes an emitter and at least two receiver elements for receiving a signal which is transmitted by the emitter and reflected by the object. The receiver elements are arranged as a linear array, as two linear arrays situated at an angle to one another, as an array which surrounds the emitter and forms a circle, or as a two-dimensional array. The diameter of the array may be greater than one-half the wavelength of the signal, and the receiver elements each have an individual surface area whose height or diameter corresponds at most to one-half the wavelength of the signal, and the emitter has a height or a diameter which is greater than one-half the wavelength of the signal.

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 is provided for populating a function for a control unit with data, in which method measurements are performed on a system at different measuring points on a test stand, and a global data-based model is set up based on the obtained measured values, and virtual measurements which simulate real measurements on the test stand are carried out on the global data-based model, and uncertainties for virtual measured values of the virtual measurements are determined from the global data-based model, the uncertainties of the virtual measured values being taken into account when populating the function for the control unit with data.

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 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.

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 microcontroller having a computing unit and a logic circuit. The microcontroller carries out computations for a regulation or control in a vehicle. The computing unit is connected to the logic circuit, and the logic circuit has an arrangement for computing an exponential function and is configurable.

Abstract: A control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

Abstract: A system for determining the distance from and the direction to an object includes an emitter and at least two receiver elements for receiving a signal which is transmitted by the emitter and reflected by the object. The receiver elements are arranged as a linear array, as two linear arrays situated at an angle to one another, as an array which surrounds the emitter and forms a circle, or as a two-dimensional array. The diameter of the array may be greater than one-half the wavelength of the signal, and the receiver elements each have an individual surface area whose height or diameter corresponds at most to one-half the wavelength of the signal, and the emitter has a height or a diameter which is greater than one-half the wavelength of the signal.

Abstract: A microcontroller having a computing unit and a logic circuit. The microcontroller carries out computations for a regulation or control in a vehicle. The computing unit is connected to the logic circuit, and the logic circuit has an arrangement for computing an exponential function and is configurable.