Arbitration With Control Barrier Functions for Safe Shared Control

By combining automation accuracy with human adaptability, shared control provides enhanced performance and safety in dynamic, complex environments. Traditional arbitration methods for integrating automation and human inputs often rely on system-specific, parameter-dependent functions that are based on shared control metrics such as trust, workload, or attention. Meanwhile, Control Barrier Functions (CBFs) enforce safety constraints on automated systems but are typically limited to safeguarding plant states. This letter introduces a novel arbitration method based on Control Barrier Functions (CBFs), where shared control metrics such as workload, attention, and trust are expressed as real-time inequality constraints. The resulting quadratic-programming formulation determines the automation assistance input that enforces these constraints while preserving feasibility and safety. This CBF-based arbitration provides a systematic, interpretable, and scalable foundation for safe human-autonomy integration.