Autonomous Driving with Priority-Ordered STL Specifications Under Multimodal Uncertainty
For autonomous vehicle safety, the framework provides a way to prioritize requirements under uncertainty, but results are only in simulation and incremental over existing STL-based planning.
The paper proposes an uncertainty-aware trajectory planning framework for autonomous vehicles that uses lexicographic ordering over Signal Temporal Logic (STL) specifications to handle conflicting objectives under multimodal uncertainty, demonstrating effectiveness in simulation.
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.