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LEAS: An AI-based Demonstrator as Decision Support Tool for Traffic Monitoring at VTS Centers

Stach Thomas, Koch Paul

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2025

Vol. 19 No. 1

301--308

Maritime traffic is prevalent worldwide, with particularly high density in coastal waters. To ensure safety and efficiency, Vessel Traffic Service (VTS) centers monitor and coordinate maritime traffic. For this purpose, VTS centers utilize various sensor and communication technologies such as radar, Automatic Identification System (AIS), electro-optical systems or radio communication. Additionally, any Vessel Traffic Service Operator (VTSO) is motivated to utilize a Decision Support Tool (DST). The LEAS project addresses emerging challenges at VTS centers. One key challenge results from the continuous evolution of maritime traffic, in particular, its ever increasing automation and autonomization. Another key challenge is the growing shortage of skilled workers. Consequently, it is crucial to process increasing volume of maritime traffic data while maintaining or improving safety and efficiency. DSTs at VTS centers must be adapted to these emerging challenges, accordingly. In the LEAS project, we develop and evaluate a demonstrator which represents a DST. This demonstrator is being developed in close collaboration with VTSOs to address these challenges. Most notably, it has a situation detection which makes use of Artificial Intelligence (AI) methods and displays relevant information in an intuitive Human-Machine Interface (HMI). The demonstrator is evaluated using simulated traffic scenarios in the German Bight and Baltic Sea, with VTSOs as test subjects. This paper provides an overview of the project and demonstrator. First, we introduce the key requirements for the demonstrator and discuss their impact on the system architecture. Next, we present its AI-based situation detection. We explain the underlying formalism of the situation detection and resolution as well as its implementation in the demonstrator. Finally, we evaluate the capabilities and limitations. The paper concludes with an outlook to future work with focus on potential deployment at DST at VTS centers.

VerifAI: framework for functional verification of AI-based systems in the maritime domain

Stach Thomas, Koch Paul, Constapel Manfred, Portier Martin, Schmid Helmut

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2024

Vol. 18 No. 3

585--591

With the continuous emergence and steady development of new technologies the way for Maritime Autonomous Surface Ship (MASS) is being paved. However, this manifold of available and imminent technologies challenges regulatory bodies and auditing authorities. Technologies which make use of Artificial Intelligence (AI), in particular Machine Learning (ML), play a special role. On one hand, they are not covered by current regulations or audit processes and, on the other hand, they may represent black boxes whose behaviours are not readily explainable and thus impede audit processes even further. In an upcoming study titled VerifAI the authors focus on this gap within European and German regulatory bodies and auditing authorities. The technological scope lies on MASS-related products which rely on partially or fully AI-based systems. In the present article the original authors summarize the outlined study. The authors review the current regulatory status concerning audit processes and the market situation concerning available and imminent (partially) AI-based systems of MASS-related products. To close the gap a conceptual, integrated framework consisting of a Safety Guideline for the manufacturers and a Verification Guideline for the auditing authorities is presented. The framework aims to give regulatory bodies and auditing authorities an overview of necessary steps for robust verification of safe products without hindering innovation or requiring in-depth knowledge about the (black box-like) systems. The results are condensed into recommendations for actions, listing the most important results, and proposing entry points as well as future research in the field of verifying (partially) AI-based MASS-related products.