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.

翻译：自动驾驶汽车的轨迹规划可借助基元方法实现，该方法将非线性动力系统行为编码为自动机。本文聚焦于最优轨迹规划。由于通常需要考虑多个准则，因此必须求解多目标优化问题。针对由此产生的帕累托最优运动基元，我们引入了一种通用自动机，该自动机可根据规划过程中的优先准则进行约简或重构。我们通过仿真和实验室实验对相应的多车辆规划场景进行了评估。