Deep learning-based ship collision risk assessment under limited data: a Variational Autoencoder with Multilayer Perceptron and convolutional neural network approach

Driven by significant concerns about the risk of ship collisions within the maritime industry, this paper aims to propose an enhanced and intelligent method for assessing collision risks utilizing deep learning techniques. A novel research framework is introduced for assessing potential collision risk of ships entering monitored waterways by leveraging the Automatic Identification System (AIS) data, deep learning methods, and expert knowledge. Specifically, this framework includes 1) the selection of multifaceted ship collision assessment indicators. 2) The development of an intelligent model for risk assessment under limited data. A deep learning model, based on variational autoencoder (VAE) and incorporating multilayer perceptron (MLP) and convolutional neural network (CNN), is proposed. The MLP is employed to upsample features and address issues related to small sample sizes and variable correlations, while the CNN serves as the feature extractor. To improve iterative effectiveness, a novel loss function is introduced. Comparative experiments have shown that the proposed model outperforms existing baselines in predictive performance. By applying the proposed method in the Yangtze Estuary Deepwater Channel, the study reveals several findings and provides recommendations. Results indicate that the most critical risk indicators are the degree of ship course deviation, ship size, and ship speed.