Bridging Symmetric Dynamics and Asymmetric Semantic Objectives: Runtime-Assured Predictive Safety Control for Autonomous Surface Vehicles

In maritime navigation, vessel dynamics and open-water environments often exhibit inherent symmetries, whereas control objectives, particularly collision avoidance and COLREGs compliance, are strictly asymmetric, specifying unique responsibilities (e.g., give-way versus stand-on) and distinct desired trajectories. This paper proposes a runtime-assured, dual-envelope predictive safety-control framework for autonomous surface vehicles (ASVs) that directly addresses the symmetry and asymmetry in complex encounters. To manage asymmetric semantic objectives, a large language model (LLM) serves as a semantic-governance module, generating structured COLREGs labels (encounter type, vessel responsibility, and maneuver tendency). These semantic outputs are strictly validated before entering the control stack. In parallel, to break the dangerous symmetry of collision risks, vessel-motion prediction and uncertainty inflation construct a physical safety envelope. A deterministic MPC-CBF safety filter then computes admissible control commands, balancing the symmetric homogeneous tracking dynamics with asymmetric collision-avoidance constraints. A runtime assurance monitor supervises semantic validity and solver latency, preventing unsafe decisions. Simulation results demonstrate that the proposed intelligent decision-making and control scheme significantly improves rule-aware collision avoidance while reducing excessive conservatism, providing a profound perspective for viewing maritime safety through symmetric and asymmetric control theory.