METHOD OF DECISION SUPPORT FOR NAVIGATORS FOR AUTOMATED CONTROL OF VESSEL TRAFFIC SAFETY BASED ON ECDIS DATA
https://doi.org/10.33815/2313-4763.2024.1.28.022-040
Abstract
Objective of the research is to develop a method for integrating automated decision support tools for navigator on the bridge of a sea vessel, considering the factors of uncertainty in the completeness of ECDIS data.
The primary problem of the research addressed is the need for accurate and efficient Decision Support Systems (DSS) that account for uncertainties in electronic navigational data.
Research Methodology involves the development of automated modules: an OCR processing module for ECDIS images using the Tesseract library, a module for comparing textual and geolocation data from ECDIS screenshots using text comparison algorithms and geolocation calculations (Haversine formula), a geographic data visualization module on an interactive map using the Folium library, and a decision support module for navigators that includes analyzing of navigational data, determining their similarity, and providing recommendations.
Research results demonstrate that the developed DSS significantly enhances navigation safety, reduces travel time by 7% to 18%, and saves fuel, lubricants, and electricity. It increases the accuracy and efficiency of navigation by automating OCR processing (capturing ECDIS screenshots in real-time, preprocessing images to enhance OCR accuracy, extracting text, and saving it to files), text and geolocation data comparison (analyzing information and geographic data to determine their similarity, loading data from files, and calculating similarity), and data visualization on an interactive map (creating maps with markers and routes based on geographic data).
Practical significance of the research lies in improving navigational decision-making processes, reducing navigator workload, enhancing situational awareness, and minimizing collision risks in maritime navigation. The DSS automates critical aspects of navigation operations, reducing the likelihood of human errors.
Prospects for further research include improving data integration methods to enhance the accuracy and reliability of the DSS. Future work will benefit from the use of artificial neural networks to obtain better approximations. An important aspect of DSS development is identifying navigator qualification parameters to ensure logical conclusions regarding their actions and prevent undesirable consequences. Further research is necessary to expand and verify the effectiveness of the DSS in real maritime navigation conditions, which will improve algorithms for analyzing large volumes of data and integrating artificial intelligence to provide more adaptive and autonomous solutions.
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