Abstract
With the continuous evolution of maritime technology, there is a growing need for analysing and modelling vessel behaviour in complex waterway systems. This paper presents an extension and utilisation of the Surface Vessel Nautical Behaviour Analysis (SV-NBA) framework for in-depth spatio-temporal analysis of maritime surface vessels’ behaviour in Kiel Fjord. Leveraging one year of collected Automatic Identification System (AIS) data, we extracted features from the recorded data. Three feature sets are generated and compared using expert-knowledge features, methods for feature selection, and Denoising Autoencoder latent space representation. Behaviour modelling and analysis utilises clustered data from the three feature sets, employing Gaussian Mixture Models (GMM). The trained GMM models serve as digital shadows, enabling storage-efficient representation of vessel behaviour and facilitate applications such as online situational awareness and marine-traffic management. These digital shadows serve as observer-layer in a dynamic autonomous system of system, offering insights into maritime activities and enhancing navigation safety in busy waterways. This research contributes to the advancement of autonomous navigation systems and supports efficient strategies for managing maritime traffic.
This research has been partly funded by the German Federal Ministry for Digital and Transport within the project “CAPTN Förde Areal II - Praxisnahe Erforschung der (teil)autonomen, emissionsfreien Schifffahrt im digitalen Testfeld” (45DTWV08D).
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Al-Falouji, G., Gao, S., Haschke, L., Nowotka, D., Tomforde, S. (2024). Enhancing Maritime Behaviour Analysis Through Novel Feature Engineering and Digital Shadow Modelling: A Case Study in the Kiel Fjord. In: Fey, D., Stabernack, B., Lankes, S., Pacher, M., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2024. Lecture Notes in Computer Science, vol 14842. Springer, Cham. https://doi.org/10.1007/978-3-031-66146-4_7
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