In this paper, we introduce a deterministic operating methodology based on finite-state automata to employ multi-objective Nonlinear Model Predictive Control (NMPC) in autonomous driving applications. We begin with discussing the system’s dynamical behavior and the proposed constraints to guarantee safe driving. Then, we examine a typical urban scenario and dissect it into a set of interacting sequences, so that we develop and fine-tune separate MPC-based controllers for each of these sequences. Finally, we introduce a Finite-State Machine (FSM) that analyzes the current driving situation and accordingly selects the appropriate controller to compute the optimal control action. This approach is numerically simulated and tested with the software OCPID-DAE1 and results show its success in accordance with multi-objective NMPC.     «

In this paper, we introduce a deterministic operating methodology based on finite-state automata to employ multi-objective Nonlinear Model Predictive Control (NMPC) in autonomous driving applications. We begin with discussing the system’s dynamical behavior and the proposed constraints to guarantee safe driving. Then, we examine a typical urban scenario and dissect it into a set of interacting sequences, so that we develop and fine-tune separate MPC-based controllers for each of these sequences....     »