The lack of situational awareness is a major cause of ship collisions. Thus, enhancing the situational awareness of the stand-on ship is a key for navigational safety, where the intention estimation of the give-way ship is crucial. According to COLREGs, the stand-on ship is not allowed to take evasive actions until the give-way ship does not take proper actions timely. The stage that needs the stand-on ship to take actions plays as the second protective layer for the ship, which is named as ‘Stand-on Ship as Second Line of Defense’ (SLoD). A method to estimate the intention of the give-way ship and to trigger SLoD is proposed in this article. Four modules of the proposed method include: “data pre-processing” collects all traffic information and determines the ships' obligations; “action identification” pinpoints the turning points; “action uncertainty” generates a bounded reachable velocity considering the give-way ship's maneuverability; “conflict assessment” judges potential collision by using non-linear velocity obstacle algorithm. Several typical encounter scenarios are simulated to demonstrate the feasibility of the proposed method. The results show that intention estimation of the give-way ship improves the situational awareness of the stand-on ship, which can support the stand-on ship to make collision avoidance decisions.

The activity-based methodology is becoming an increasing way to calculate exhaust emissions from ships in a port. Existing studies make great effort to build and analyze ship emission inventory in a variety of ports by applying this method to historical ship trajectory data. This kind of static emission inventory however, cannot meet the needs of real-time ship emission monitoring. This article proposes a method of dynamic calculation of ship exhaust emissions based on real-time ship trajectory data. Firstly, real-time ship AIS messages are partitioned into continuous data blocks and go through a series of pre-processing operations, including trajectory extraction, association and interpolation. Ship activity parameters are then determined by database querying and regression analysis based on ship attributes. Subsequently, an improved activity-based methodology is employed to estimate exhaust emissions from ships in a distributed way. Based on the grid model, regional ship exhaust emissions can be statistically and dynamically calculated by the spatial allocation of all ship emissions. In a case study, a real-time monitoring platform for ship exhaust emissions in Shenzhen port is developed to demonstrate the effectiveness of the proposed method.