Enhancing Maritime Vessel Collision Avoidance Through Dynamic Predictive Risk Assessment with Federated Learning and HyperScore Evaluation (DPRA-FLHE)

**Abstract:** Current maritime collision avoidance systems (CAS) rely heavily on reactive measures and limited predictive capabilities, contributing to near-miss incidents and posing a significant risk to vessels and personnel. This paper introduces Dynamic Predictive Risk Assessment with Federated Learning and HyperScore Evaluation (DPRA-FLHE), a novel system that leverages real-time sensor data, advanced predictive modeling, and federated learning to dynamically assess collision risks and generate proactive avoidance maneuvers. DPRA-FLHE outperforms traditional CAS by integrating heterogeneous sensor data streams, employing graph neural networks for enhanced vessel trajectory prediction, and utilizing a HyperScore evaluation mechanism to prioritize risk mitigation strategies based on both statistical likelihood and potential impact. This research promises to significantly reduce maritime accidents, improve navigational safety, and enable autonomous collision avoidance capabilities even in complex and dynamic environments.