Deep learning–based framework for regional risk assessment in a multi–ship encounter situation based on the transformer network

A method based on the predictable Transformer network associated with a clustering method is introduced to build a framework for the regional collision risk assessment, which is an alternative to the traditional methods that have two problems: 1) The indicators are calculated based on the current navigation status of ships, not considering the dynamic characteristics and the variability of the ship's trajectory, which makes the calculated indicators inaccurate not allowing an accurate risk assessment. 2) Many deep learning–based algorithms used in ship trajectory prediction are not easy to be trained, as the model structure makes the features unable to be processed in parallel. First, the ships with potential collision risk are clustered by the Density–Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to divide the hotspots. Then, the possible locations of ships in the near future are calculated by a multi–step prediction model, i.e., the designed Transformer network. Finally, the ship pairs' collision risk and the regional collision risk are evaluated based on the predicted results. Based on the AIS data from the Yangtze River, the effectiveness of the proposed framework is verified through regional risk assessment for 41 moments and specific interpretation for three moments.