Safe and explainable motion planning remains a central challenge in autonomous driving. While rule-based planners offer predictable and explainable behavior, they often fail to grasp the complexity and uncertainty of real-world traffic. Conversely, learned planners exhibit strong adaptability but suffer from reduced transparency and occasional safety violations. We introduce Mosaic, an extensible framework for structured decision-making that integrates both paradigms through arbitration graphs. By decoupling trajectory verification and scoring from the generation of trajectories by individual planners, every decision becomes transparent and traceable. Trajectory verification at a higher level introduces redundancy between the planners, limiting emergency braking to the rare case where all planners fail to produce a valid trajectory. Through unified scoring and optimal trajectory selection, rule-based and learned planners with complementary strengths and weaknesses can be combined to yield the best of both worlds. In experimental evaluation on nuPlan, Mosaic achieves 95.48 CLS-NR and 93.98 CLS-R on the Val14 closed-loop benchmark, setting a new state of the art, while reducing at-fault collisions by 30% compared to either planner in isolation. On the interPlan benchmark, focused on highly interactive and difficult scenarios, Mosaic scores 54.30 CLS-R, outperforming its best constituent planner by 23.3% - all without retraining or requiring additional data. The code is available at github.com/KIT-MRT/mosaic.

翻译：安全且可解释的运动规划仍然是自动驾驶领域的核心挑战。基于规则的规划器能够提供可预测且可解释的行为，但往往难以应对真实交通场景的复杂性和不确定性。相反，学习型规划器展现出强大的适应性，但存在透明度降低和偶尔违反安全性的问题。我们提出Mosaic，一种通过仲裁图整合这两种范式的结构化决策可扩展框架。通过将轨迹验证与评分从各规划器的轨迹生成过程中解耦，每个决策都变得透明且可追溯。在更高层级进行的轨迹验证引入了规划器间的冗余，使得紧急制动仅在所有规划器均无法生成有效轨迹的罕见情况下触发。通过统一评分与最优轨迹选择，具有互补优势与劣势的基于规则和学习型规划器可以被组合起来，实现两全其美的效果。在nuPlan上的实验评估中，Mosaic在Val14闭环基准上取得了95.48 CLS-NR和93.98 CLS-R的成绩，创下新的最优水平，同时与任一单独规划器相比，将责任碰撞减少了30%。在专注于高交互性和高难度场景的interPlan基准上，Mosaic取得了54.30 CLS-R的成绩，比其最佳组成规划器高出23.3%——所有这些均无需重新训练或额外数据。代码已开源在github.com/KIT-MRT/mosaic。