Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combinatorial aspects need to be taken into account in the planning framework. We address this problem by proposing a novel planning approach that combines trajectory planning and maneuver reasoning. We define a classification for dynamic obstacles along a reference curve that allows us to extract tactical decision sequences. We separate longitudinal and lateral movement to speed up the optimization-based trajectory planning. To map the set of obtained trajectories to maneuver variants, we define a semantic language to describe them. This allows us to choose an optimal trajectory while also ensuring maneuver consistency over time. We demonstrate the capabilities of our approach for a scenario that is still widely considered to be challenging.
翻译:具有众多移动物剂的城市环境规划可被视为一个组合问题。 当一个自主车辆在前后、右侧或左侧经过一个障碍后, 就可以选择执行多种选项。 这些组合方面需要在规划框架中加以考虑。 我们通过提出将轨迹规划和机动推理结合起来的新规划方法来解决这个问题。 我们沿着一个参考曲线来界定动态障碍的分类, 从而使我们能够提取战术决策序列。 我们将纵向和横向移动区分开来, 以加快优化的轨迹规划。 为了绘制一套已获得的轨迹图以调整变体, 我们定义了一种语义语言来描述这些变体。 这使我们能够选择一种最佳的轨迹, 同时确保时间的操作一致性。 我们展示了我们的方法对于仍然被认为具有挑战性的一种情景的能力。