Abstract
This study presents a deep learning framework to support regional ship behavior prediction using historical AIS data. The framework is meant to aid in proactive collision avoidance, in order to enhance the safety of maritime transportation systems. In this study, it is suggested to decompose the historical ship behavior in a given geographical region into clusters. Each cluster will contain trajectories with similar behavior characteristics. For each unique cluster, the method generates a local model to describe the local behavior in the cluster. In this manner, higher fidelity predictions can be facilitated compared to training a model on all available historical behavior. The study suggests to cluster historical trajectories using a variational recurrent autoencoder and the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm. The past behavior of a selected vessel is then classified to the most likely clusters of behavior based on the softmax distribution. Each local model consists of a sequence-to-sequence model with attention. When utilizing the deep learning framework, a user inputs the past trajectory of a selected vessel, and the framework outputs the most likely future trajectories. The model was evaluated using a geographical region as a test case, with successful results.
Highlights
Effective maritime traffic monitoring is essential for maintaining the integrity of maritime transportation systems
Limited work has been conducted on utilizing deep learning to enhance the safety of maritime transportation systems
One area of interest is in aiding maritime situation awareness via proactive collision avoidance
Summary
Effective maritime traffic monitoring is essential for maintaining the integrity of maritime transportation systems. The safety of human life, as well as that of material assets, and the ocean environment, depend on conducting safe maritime operations. Maritime situation awareness can be argued to be one of the most essential elements with regards to maintaining the safety of such systems. All navigators must have an adequate degree of situation awareness to effectively conduct operations at sea. In this context, the primary challenge relates to detecting obstacles and predicting close-range encounter situations. Effective collision avoidance can be viewed as a key component of safe maritime transportation systems
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.