Lexicographic Minimum-Violation Motion Planning using Signal Temporal Logic

arXiv:2604.20428v1 Announce Type: new Abstract: Motion planning for autonomous vehicles often requires satisfying multiple conditionally conflicting specifications. In situations where not all specifications can be met simultaneously, minimum-violation motion planning maintains system operation by minimizing violations of specifications in accordance with their priorities. Signal temporal logic (STL) provides a formal language for rigorously defining these specifications and enables the quantitative evaluation of their violations. However, a total ordering of specifications yields a lexicographic optimization problem, which is typically computationally expensive to solve using standard methods. We address this problem by transforming the multi-objective lexicographic optimization problem into a single-objective scalar optimization problem using non-uniform quantization and bit-shifting. Specifically, we extend a deterministic model predictive path integral (MPPI) solver to efficiently solve optimization problems without quadratic input cost. Additionally, a novel predicate-robustness measure that combines spatial and temporal violations is introduced. Our results show that the proposed method offers an interpretable and scalable solution for lexicographic STL minimum-violation motion planning within a single-objective solver framework.

Related

• SBAMP: Sampling Based Adaptive Motion Planning
• Decentralized Opinion-Integrated Decision making at Unsignalized Intersections via Signed Networks
• Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
• Unveiling Uncertainty-Aware Autonomous Cooperative Learning Based Planning Strategy
• Multi-Step Gaussian Process Propagation for Adaptive Path Planning

Source: arXiv cs.RO | 2026-04-23