自動運転のための最適化ベース運動プリミティブオートマタ
Optimization-based motion primitive automata for autonomous driving
非線形力学系をオートマタに符号化するプリミティブベース手法において、多目的最適化で得たパレート最適運動プリミティブを優先基準に応じて再構成できる万能オートマタを提案し、複数車両の計画シナリオで検証した。
著者: Matheus V. A. Pedrosa, Patrick Scheffe, Bassam Alrifaee, Kathrin Flaßkamp
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments.
