Online Bayesian Learning and Classification of Ship-to-Ship Interactions for Port Safety

Interaction analysis of ships mooring and maneuvering in harbors is pursued in this paper by using Bayesian probabilistic models. A number of ship-to-ship interactions are deduced from the navigation rules in port areas, and then used to train different Event-based Dynamic Bayesian Networks (E-DBNs). When data of two interacting ships are injected into the network, inference is performed in order to verify if the interaction between the vessels is known or not, and in the latter case actions to preserve the port safety can be taken. Results are drawn in the final part of the paper by including into the networks data provided by a simulator of realistic trajectories relative to an existing port.