Abstract
The wide spread of the Automatic Identification System (AIS) and related tools has motivated several maritime analytics operations. One of the most critical operations for the purpose of maritime safety is the so-called Vessel Collision Risk Assessment and Forecasting (VCRA/F), with the difference between the two lying in the time horizon when the collision risk is calculated: either at current time by assessing the current collision risk (i.e., VCRA) or in the (near) future by forecasting the anticipated locations and corresponding collision risk (i.e., VCRF). Accurate VCRA/F is a difficult task, since maritime traffic can become quite volatile due to various factors, including weather conditions, vessel manoeuvres, etc. Addressing this problem by using complex models introduces a trade-off between accuracy (in terms of quality of assessment / forecasting) and responsiveness. In this paper, we propose a deep learning-based framework that discovers encountering vessels and assesses/predicts their corresponding collision risk probability, in the latter case via state-of-the-art vessel route forecasting methods. Our experimental study on a real-world AIS dataset demonstrates that the proposed framework balances the aforementioned trade-off while presenting up to 70% improvement in R2 score, with an overall accuracy of around 96% for VCRA and 77% for VCRF.