Abstract: The disclosure relates to a method for planning a future trajectory of an autonomously or semi-autonomously driving vehicle, wherein sensor data are detected by means of at least one sensor of the vehicle, wherein an optimum trajectory for the vehicle is determined for an environmental status derived from the detected sensor data, wherein possible future trajectories of the vehicle are generated to this end and evaluated by means of a reward function, wherein in so doing, a behavior of the vehicle, a static environment and a behavior of other road users are taken into consideration, wherein an influence exerted by the behavior of the vehicle on the other road users is additionally taken into consideration in the reward function, and wherein the determined optimum trajectory is provided for execution.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.