Abstract: The present disclosure relates to a method for trajectory planning for a vehicle. The method includes obtaining a reference trajectory over a finite time horizon, where the reference trajectory includes a speed reference over time for the finite time horizon. Further, the method includes determining a back-up stop trajectory within the finite time horizon. The back-up stop trajectory has a starting state and terminating in a final state, where the final state is defined as a safe state. The method further includes forming a terminal set of states within the finite time horizon based on at least one predefined constraint, wherein the terminal set of states includes a terminal state that corresponds to the starting state for the back-up stop trajectory. Moreover, the method includes generating a nominal trajectory for at least a portion of the finite time horizon based on a constraint controlled technique.

Abstract: The present disclosure relates to a method for trajectory planning for a vehicle. The method includes obtaining a reference trajectory over a finite time horizon, where the reference trajectory includes a speed reference over time for the finite time horizon. Further, the method includes determining a back-up stop trajectory within the finite time horizon. The back-up stop trajectory has a starting state and terminating in a final state, where the final state is defined as a safe state. The method further includes forming a terminal set of states within the finite time horizon based on at least one predefined constraint, wherein the terminal set of states includes a terminal state that corresponds to the starting state for the back-up stop trajectory. Moreover, the method includes generating a nominal trajectory for at least a portion of the finite time horizon based on a constraint controlled technique.

Abstract: A control device that generates manoeuvring decisions for an ego-vehicle in a traffic scenario is provided. The control device includes a first module including a trained self-learning model, the first module being configured to receive data including information about a surrounding environment of the ego-vehicle determine, using the trained self-learning model, an action to be executed by the ego-vehicle based on the received data. The control device includes a second module configured to receive the determined action, receive data including information about the surrounding environment of the ego-vehicle during a finite time horizon, predict an environmental state for a first time period of the finite time horizon, determine a trajectory for the ego-vehicle based on the received action for the finite time horizon and on the predicted environmental state for the first time period, send a signal in order to control the ego-vehicle according to the determined trajectory.