Bow Crossing Range correlation of small vessels – AIS data analysis with prospective application to autonomous ships

• Autorzy: Stolzmann Łukasz

Identyfikatory

DOI

10.26408/121.04

• Języki publikacji: EN

Abstrakty

EN

The development of technology has reduced the crews of ships. This trend leads to at least partial elimination of human crews in favour of autonomous ships. As more and more of them will be introduced, a safety problem arises when manoeuvring the ships in relation to each other. Therefore, there is a need to identify the factors that have an impact on determining how to maintain safe distances between ships in order to find relationships that will be useful for the development of autonomous ships. This can currently only be analysed on samples of manned vessels. Therefore, this paper aims to analyse the correlation of the Bow Crossing Range (BCR) with other ship-related data provided by AIS on ships up to 100 m long. The results of this study may be found interesting by academia, maritime industry, and autonomous ship developers.

Słowa kluczowe

EN

Bow Crossing Range; BCR; AIS data; ship collision avoidance; maritime risk and safety; maritime traffic analysis; Maritime Autonomous Surface Ships; MASS

• Wydawca: Publishing House of Gdynia Maritime University
• Czasopismo: Scientific Journal of Gdynia Maritime University
• Rocznik: 2022
• Tom: nr 121
• Strony: 41--52
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Stolzmann Łukasz

• Gdynia Maritime University, 81-87 Morska Str., 81-225 Gdynia, Poland

Bibliografia

• 1. Bandara, D., Woodward, M., Chin, C., Jiang, D., 2020, Augmented Reality Lights for Compromised Visibility Navigation, Journal of Marine Science and Engineering, vol. 8, no. 12.
• 2. Chen, X., Jeong, J.C., 2007, Enhanced Recursive Feature Elimination, Sixth International Conference on Machine Learning and Applications (ICMLA 2007), pp. 429–435.
• 3. Gil, M., Kozioł, P., Wróbel, K., Montewka, J., 2022, Know Your Safety Indicator – A Determination of Merchant Vessels Bow Crossing Range Based on Big Data Analytics, Reliability Engineering & System Safety, vol. 220.
• 4. Gil, M., Wróbel, K., Montewka, J., 2019, Toward a Method Evaluating Control Actions in STPA-Based Model of Ship-Ship Collision Avoidance Process, Journal of Offshore Mechanics and Arctic Engineering, vol. 141.
• 5. Hansen, M.G., Jensen, T.K., Lehn-Schiøler, T., Melchild, K., Rasmussen, F.M., Ennemark, F., 2013. Empirical Ship Domain based on AIS Data, Journal of Navigation, vol. 66, no. 10.
• 6. IMO, 2003, COLREG: Convention on the International Regulations for Preventing Collisions at Sea, 1972, International Maritime Organization.
• 7. Kooij, C., Hekkenberg, R., 2021, Identification of a Task-Based Implementation Path for Unmanned Autonomous Ships, Maritime Policy & Management, pp. 1–17.
• 8. Li, M., Mou, J., Chen, L., He, Y., Huang, Y., 2021, A Rule-Aware Time-Varying Conflict Risk Measure for MASS Considering Maritime Practice, Reliability Engineering & System Safety, vol. 215(C).
• 9. Ożoga, B., Montewka, J., 2018, Towards a Decision Support System for Maritime Navigation on Heavily Trafficked Basins, Ocean Engineering, vol. 159, pp. 88–97.
• 10. Sang, L., Yan, X., Wall, A., Wang, J., Mao, Z., 2016, CPA Calculation Method Based on AIS Position Prediction, Journal of Navigation, vol. 69, pp. 1409–1426.
• 11. Statheros, T., Howells, G., Maier, K.M., 2008, Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques, Journal of Navigation, vol. 61, pp. 129–142.
• 12. Wawruch, R., Stupak, T., 2010, The Possibility of Use of the AIS Data Transmissions for Safety and Security Monitoring in the Polish Maritime Areas, IFAC Proceedings, vol. 43, pp. 58–63.
• 13. Wróbel, K., Gil, M., Krata, P., Olszewski, K., Montewka, J., 2021, On the Use of Leading Safety Indicators in Maritime and Their Feasibility for Maritime Autonomous Surface Ships, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk Reliability.
• 14. Yang, D., Wu, L., Wang, S., 2021, Can We Trust the AIS Destination Port Information for Bulk Ships? Implications for Shipping Policy and Practice, Transportation Research Part E: Logistics and Transportation Review, vol. 149.
• 15. Zaidan N. (ed.), 2017, Global Perspectives in MET: Towards Sustainable, Green and Integrated Maritime Transport, 18th Annual General Assembly of the International Association of Maritime Universities, IAMU 2017, Nikola Vaptsarov Naval Academy, Warna, Bulgaria, October 11–13, vol. I.
• 16. Zhang, W., 2015, A Method for Detecting Possible Near Miss Ship Collisions from AIS Data, Ocean Engineering, vol. 10.
• 17. Zhao, L., Shi, G., Yang, J., 2018, Ship Trajectories Pre-processing Based on AIS Data, Journal of Navigation, vol. 71, pp. 1210–1230.
• 18. Danish Maritime Authority, n.d. Danish Maritime Authority – Safety at Sea and Growth in the Maritime Industries in Denmark, https://www.dma.dk/Sider/default.aspx (10.09.2021).
• 19. Kongsberg Gruppen, 2017, n.d. Autonomous Ship Project, Key Facts about YARA Birkeland – Kongsberg Maritime, https://www.kongsberg.com/maritime/support/themes/autonomous-ship-projec... (10.09.2021).

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).