A Fused Data Based Real-Time Collision Warning System for Ferries in the Yangtze River

Abstract

The risks for ferries in the Yangtze River are relatively high, as they frequently cross the main traffic flows, leading to more intersections with other upwards and downwards ships. Although some studies have developed many models to assess collision risks in the Yangtze River, collision warning studies on ferries are scant. Meanwhile, most of the current collision studies evaluate risk based on AIS data, which are incapable of providing real-time ship information as they are discrete-time series data. In this work, fused data combining radar and AIS data are applied in a real-time ship collision warning model to assess the dynamic risk for ferries in the Yangtze River. Firstly, data fusion technology is proposed to acquire refined ship trajectories from AIS and radar data. Then, a widely used geometric collision model is enhanced to assess the real-time collision risk for ferries. And lastly, to illustrate the model, a real case of a ferry crossing through the Yangtze River is studied. The real-time risk values of the ferries are calculated based on fused data inputs, and the output results indicate that the use of fused data provides more accurate and continuous real-time ship risks. Thus, the proposed approach is evidenced to support the development of smart maritime surveillance.