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

Rudolf ReiterArmin Nurkanovi\'cJonathan FreyMoritz Diehl
Dec 2022
摘要
In recent years, nonlinear model predictive control (NMPC) has beenextensively used for solving automotive motion control and planning tasks. Inorder to formulate the NMPC problem, different coordinate systems can be usedwith different advantages. We propose and compare formulations for the NMPCrelated optimization problem, involving a Cartesian and a Frenet coordinateframe (CCF/ FCF) in a single nonlinear program (NLP). We specify costs andcollision avoidance constraints in the more advantageous coordinate frame,derive appropriate formulations and compare different obstacle constraints.With this approach, we exploit the simpler formulation of opponent vehicleconstraints in the CCF, as well as road aligned costs and constraints relatedto the FCF. Comparisons to other approaches in a simulation framework highlightthe advantages of the proposed approaches.
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