针对自动驾驶多模态不确定性，提出分枝随机MPC实现安全高效路径规划。
Branch-Stochastic Model Predictive Control for Motion Planning under Multi-Modal Uncertainty with Scenario Clustering
- 结合分枝结构与随机MPC，为不同驾驶意图生成差异化轨迹。
- 通过场景聚类降低计算开销，实现在高速复杂场景下的实时性能。
- 自适应分枝时机推迟决策，有效缓解意图不确定性带来的保守性。
自动驾驶中的运动规划需应对周围车辆意图与轨迹的多模态不确定性。以最坏情况处理不确定性虽能保证鲁棒性，但过于保守。随机模型预测控制（SMPC）通过概率约束降低轨迹层面的保守性，但在意图不确定性上仍显保守，因约束需对所有可能意图成立。本文提出一种新颖的SMPC与分枝结构结合方法，使规划器能为不同可能意图生成独立轨迹，同时在轨迹不确定性下保持安全。引入一种新的场景聚类方法，基于高层决策相似性合并预测场景，确保实时可计算性。此外，自适应分枝时间计算将计划分离的决策推迟至意图不确定性显著降低时。在复杂高速场景的仿真测试表明，该方法提升了安全性，降低了保守性，并实现了实时计算性能。
原文摘要 · Abstract (English)
Motion planning for autonomous driving must account for multi-modal uncertainty in both the intentions and trajectories of surrounding vehicles. Handling uncertainty in a worst-case manner guarantees robustness but often leads to excessive conservatism. Stochastic Model Predictive Control (SMPC) reduces trajectory-level conservatism through chance constraints, yet remains conservative with respect to intention uncertainty since constraints must hold across all intentions. We present a novel combination of SMPC and the branching structure, enabling the planner to generate distinct trajectories for different possible intentions while maintaining safety under trajectory uncertainty. A novel scenario clustering is proposed to merge prediction scenarios based on high-level decision similarity, thereby ensuring real-time tractability. Furthermore, an adaptive branching-time computation postpones commitment to separate plans until intention uncertainty is sufficiently reduced. Simulation studies in challenging highway scenarios demonstrate that the proposed method improves safety, reduces conservatism, and achieves real-time computational performance.
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