论文标题
与自动驾驶的组合推理的时空运动计划
Spatiotemporal motion planning with combinatorial reasoning for autonomous driving
论文作者
论文摘要
具有许多移动代理的城市环境的运动计划可以看作是组合问题。通过在左右之后，左右左右的障碍物，自动驾驶汽车可以选择执行多个选项。这些组合方面需要在计划框架中考虑到。我们通过提出一种结合轨迹计划和操纵推理的新型计划方法来解决这个问题。我们沿参考曲线定义了动态障碍物的分类，使我们能够提取战术决策序列。我们将纵向和横向运动分开，以加快基于优化的轨迹计划。为了将一组获得的轨迹映射到操纵变体中，我们定义了一种语义来描述它们。这使我们可以选择最佳轨迹，同时还可以确保随着时间的流逝操纵一致性。我们证明了我们的方法的能力，即仍被广泛认为是具有挑战性的场景。
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.
