Human-like Decision-making Approach Based on Bayesian Belief Network for Ship Crossing in the Inland Waterway

Ship-crossing is a common behavior within inland waterways, which will influence the navigation safety for both own crossing-ship and ships using channels. This paper proposes a human-like decision-making approach based on Bayesian belief network (BBN) for ship-crossing by considering ship behaviors and maneuverability. The input variables consider the motion information of ships and the predicted information in future moment, while the evaluation variables are used for determining the intention of crossing-ship. The proposed model is validated in the real crossing scenario in the midstream of Yangtze River. From the results of scenario of ship-crossing, the proposed approach can be used for navigation assistance of ship crossing in busy waterways.