融合上下文信息的鲁棒避障规划，提升复杂场景下安全运动决策能力。
Distributionally Robust Safe Motion Planning with Contextual Information
- 用核方法建模障碍物轨迹与自身运动的条件分布
- 在希尔伯特空间中定义分布模糊集，实现鲁棒约束
- 适用于自动驾驶等需考虑环境动态的高安全需求场景
本文提出一种融合上下文信息的分布鲁棒避障方法。通过条件核均值嵌入算子，将障碍物未来轨迹在给定自车运动下的条件分布映射至再生核希尔伯特空间（RKHS）。基于历史数据学习的条件均值嵌入经验估计，构建一个包含所有在该空间中距离不超过阈值的分布的模糊集。据此设计分布鲁棒的碰撞避免约束，并融入基于滚动时域的自车运动规划框架。仿真结果表明，在多个复杂场景中，该方法相比未考虑上下文信息和/或分布鲁棒性的方法，具有更高的避障成功率。
原文摘要 · Abstract (English)
We present a distributionally robust approach for collision avoidance by incorporating contextual information. Specifically, we embed the conditional distribution of future trajectory of the obstacle conditioned on the motion of the ego agent in a reproducing kernel Hilbert space (RKHS) via the conditional kernel mean embedding operator. Then, we define an ambiguity set containing all distributions whose embedding in the RKHS is within a certain distance from the empirical estimate of conditional mean embedding learnt from past data. Consequently, a distributionally robust collision avoidance constraint is formulated, and included in the receding horizon based motion planning formulation of the ego agent. Simulation results show that the proposed approach is more successful in avoiding collision compared to approaches that do not include contextual information and/or distributional robustness in their formulation in several challenging scenarios.
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