Abstract: A trajectory planning system for a vehicle includes a sensor system for measuring an actual curvature on the basis of a respective current yaw rate, a memory containing a target trajectory with target curvatures, wherein the trajectory planning system determines target yaw rates from the target curvature, and a steering system that uses steering variables to obtain target curvatures in actual curvatures. The system determines a respective first actual derivative of the measured respective current actual yaw rate over time and a respective first target derivative of the respective current target yaw rate over time. A correlator determines a respective current delay on the basis of the respective first actual derivatives and the respective first target derivatives in a current yaw rate segment, and a parameter estimator recursively estimates the delay between the target yaw rate and the actual yaw rate on the basis of respective current delay inputs.

Abstract: The disclosure relates to the process of ascertaining an input variable of a vehicle actuator using a model-based predictive control. According to one exemplary arrangement, a processor unit is designed to access trajectory information and a state data set, which represents a state of surroundings of a vehicle and/or the state of the vehicle and/or a driving state of the vehicle, by an interface. The processor unit carries out a secondary condition algorithm in order to calculate a secondary condition and an MPC algorithm for a model-based predictive control. By carrying out the secondary condition algorithm, a secondary condition is ascertained for the MPC algorithm on the basis of the trajectory information and on the basis of the state data set. By carrying out the MPC algorithm, an input variable is ascertained for an actuator of the vehicle on the basis of the secondary condition.