Research on the Classification of Ship Encounter Scenarios Based on CAE-LSTM

To tackle the challenge of recognizing similar ship encounter scenarios under multi-ship interference coupling and dynamic evolution, this paper proposes a classification method that combines a Convolutional Auto-Encoder (CAE) and a Long Short-Term Memory (LSTM) recurrent neural network model. To extract many genuine ship encounter scenarios from historical AIS data for further categorization, first, a method for extracting ship encounter scenarios taking spatiotemporal proximity restrictions is devised. Then, by setting a time window and rasterizing the scenarios, a CAE-based model is constructed to characterize the spatial interference of ships in the scenarios. Further, an LSTM network is used to learn temporal evolution features, achieving a low-dimensional spatiotemporal vector representation of ship encounter scenarios. Finally, hierarchical clustering is applied to classify different ship encounter scenarios based on these low-dimensional spatiotemporal vectors. The proposed method is validated through extensive experiments using data from Ningbo-Zhoushan Port, and the results show that this method can effectively extract real ship encounter scenarios and accurately identify similar scenarios. This research provides robust support for a deep understanding of ship encounter scenarios and the mining of similar ship behavior patterns.