Minimum-Violation Planning for Autonomous Systems: Theoretical and Practical Considerations
This work addresses safety-critical planning for autonomous systems like vehicles, though it appears incremental in combining existing concepts of temporal logic and sampling-based methods.
The paper tackles the problem of computing optimal trajectories for autonomous systems subject to conflicting rules by introducing prioritized safety specifications and developing an efficient sampling-based approach with asymptotic optimality guarantees, demonstrating its application in autonomous vehicles with simulation results for overtaking scenarios.
This paper considers the problem of computing an optimal trajectory for an autonomous system that is subject to a set of potentially conflicting rules. First, we introduce the concept of prioritized safety specifications, where each rule is expressed as a temporal logic formula with its associated weight and priority. The optimality is defined based on the violation of such prioritized safety specifications. We then introduce a class of temporal logic formulas called $\textrm{si-FLTL}_{\mathsf{G_X}}$ and develop an efficient, incremental sampling-based approach to solve this minimum-violation planning problem with guarantees on asymptotic optimality. We illustrate the application of the proposed approach in autonomous vehicles, showing that $\textrm{si-FLTL}_{\mathsf{G_X}}$ formulas are sufficiently expressive to describe many traffic rules. Finally, we discuss practical considerations and present simulation results for a vehicle overtaking scenario.