Volkswagen Ag

Kristof Van Ende

Jonas Kaste

Jens Hoedt

A method for estimating a steering wheel torque for a mechanical feedback at a steering wheel of a steering system of a motor vehicle is disclosed, comprising: receiving and/or detecting at least one current measurement value of at least one state variable of the motor vehicle by suitable input means; estimating the current steering wheel torque by means of a controller; outputting the estimated steering wheel torque as a steering wheel torque signal, wherein the estimation is carried out by means of a parameterizable steering model, wherein current measurement values of at least a subset of the at least one state variable are fed as input data to the parameterizable steering model for this purpose, and wherein nonlinearities of the steering system are estimated based on the current measurement values of at least the subset of the at least one state variable using a method of artificial intelligence.

Method and device for estimating a steering wheel torque for a mechanical feedback at a steering wheel of a steering system of a motor vehicle

The invention relates to a method for preparing or generating a training data set for machine learning to operate a vehicle component. Provided multidimensional data points are divided up in a first step by dividing up the plurality of data points into multidimensional clusters by using a cluster algorithm. Then a training data set is generated by selecting data points from the basic training data set. The selection comprises determining a smallest cluster among the plurality of clusters with the lowest number of data points. Furthermore, at least one subset of the data points of the smallest cluster is provided for the training data set. In another step, a subset of data points is selected from each of the other clusters for the training data set, wherein the number of selected data points of each other cluster corresponds to the number of selected data points of the smallest cluster.

Computer-implemented method for machine learning for operating a vehicle component, and method for operating a vehicle component

A method and a device for estimating coefficients of friction of a wheel of a vehicle with respect to an underlying surface including decomposing a supplied trajectory into individual curve segments, estimating a lateral force and a slip angle for a front axle of the vehicle, assigning respectively estimated lateral forces and slip angles relating to the associated individual curve segments and storing these value pairs in a memory, estimating a tire characteristic curve for each of the curve segments based on the value pairs stored for the respective curve segment in the memory, estimating a coefficient of friction for each curve segment based on the respectively estimated tire characteristic curve, and storing the estimated coefficients of friction relating to the respectively associated curve segments in a coefficient of friction map.

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Braunschweig, Germany

Kristof Van Ende