自动驾驶车辆必须规划满足多重要求的轨迹，包括安全性、乘客舒适性以及交通规则遵从性。然而，在安全关键场景中，并非总能同时满足所有要求，因而需依据重要性对各项要求进行优先级排序。与此同时，在此类安全关键场景中，周围交通参与者（如其他车辆与行人）的轨迹预测所固有的不确定性亦须被显式建模。本文提出一种不确定性感知的轨迹规划框架，该框架引入预定义的字典序排列以对信号时序逻辑（STL）规范进行优先级排序，并确保该排序在不确定性存在下依然有效。我们基于模型预测路径积分（MPPI）控制实现该框架，并在仿真场景中验证其有效性；结果表明，本框架可在真实多模态不确定性条件下高效处理相互冲突的目标。

Autonomous vehicles must plan trajectories that satisfy a multitude of requirements on safety, passenger comfort, and compliance with traffic rules. However, in safety-critical scenarios, it is not always possible to satisfy all requirements simultaneously, necessitating their prioritization based on importance. At the same time, in these safety-critical scenarios, the uncertainty in trajectory predictions of the surrounding traffic, such as other vehicles and pedestrians, should be explicitly accounted for. In this work, we propose an uncertainty-aware trajectory planning framework that incorporates a predefined lexicographic ordering over Signal Temporal Logic (STL) specifications that stays valid under uncertainty. We implement this formulation with Model Predictive Path Integral (MPPI) control and we demonstrate the effectiveness of our method on simulation scenarios, showing that our framework efficiently handles conflicting objectives under realistic multi-modal uncertainty.