Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPC

In recent years, nonlinear model predictive control has been extensively used for solving automotive motion and planning tasks. order to formulate the problem, different coordinate systems can be with advantages. We propose compare formulations MPC related optimization involving a Cartesian Frenet frame in single program. specify costs collision avoidance constraints more advantageous frame, derive appropriate obstacle constraints. With this approach, we exploit simpler formulation of opponent vehicle as well road-aligned frame. Comparisons other approaches simulation framework highlight advantages proposed methods.