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Dempster-Shafer 이론 기반의 선박충돌위험성 평가에 관한 연구

Study on the Evaluation of Ship Collision Risk based on the Dempster-Shafer Theory

  • 박진완 (서해지방해양경찰청 목포광역VTS센터) ;
  • 정중식 (목포해양대학교 해상운송학부 )
  • Jinwan Park (Mokpo Regional Vessel Traffic Service Center, Korea Coast Guard Region-West) ;
  • Jung Sik Jeong (Department of Maritime Transportation Science, Mokpo National Maritime University)
  • 투고 : 2023.08.08
  • 심사 : 2023.08.29
  • 발행 : 2023.08.31

초록

본 논문은 선박이 조우하는 상황에서 충돌의 위험에 대한 판단을 지원하여 충돌사고를 예방하기 위하여 선박충돌위험성을 평가하는 방법을 제안하고자 한다. 선박의 항해는 불확실성이 다수 내포되어 있기 때문에 충돌의 위험을 평가할 때 선박충돌위험성이 가진 불확실성을 고려할 필요가 있다. 본 논문은 불확실성을 처리하고 각 상대 선박의 충돌의 위험을 실시간으로 평가하기 위하여 Dempster-Shafer 이론을 적용한다. 선박충돌위험의 평가 요인으로 DCPA(distance at closest point approach), TCPA(time to closest point approach), 상대 선박과의 거리, 상대방위, 속도비율 등이 사용되며, 각 평가 요인별 멤버쉽 함수로 계산된 기본확률배정함수(basic probability assignment)는 Dempster-Shafer 이론의 융합 규칙을 통하여 융합된다. 선박들이 실제로 조우하는 상황에서 수집된 선박자동식별장치 데이터를 사용하여 제안된 방법을 실험한 결과 평가의 적합성이 검증되었다. 선박간 조우 상황에서의 실시간으로 충돌위험성을 평가함으로써 인적오류로 인한 충돌사고를 예방할 수 있으며, 해상교통관제시스템과 자율운항선박의 충돌회피시스템에도 활용될 것으로 기대된다.

In this study, we propose a method for evaluating the risk of collision between ships to support determination on the risk of collision in a situation in which ships encounter each other and to prevent collision accidents. Because several uncertainties are involved in the navigation of a ship, must be considered when evaluating the risk of collision. We apply the Dempster-Shafer theory to manage this uncertainty and evaluate the collision risk of each target vessel in real time. The distance at the closest point approach (DCPA), time to the closest point approach (TCPA), distance from another vessel, relative bearing, and velocity ratio are used as evaluation factors for ship collision risk. The basic probability assignments (BPAs) calculated by membership functions for each evaluation factor are fused through the combination rule of the Dempster-Shafer theory. As a result of the experiment using automatic identification system (AIS) data collected in situations where ships actually encounter each other, the suitability of evaluation was verified. By evaluating the risk of collision in real time in encounter situations between ships, collision accidents caused by human errora can be prevented. This is expected to be used for vessel traffic service systems and collision avoidance systems for autonomous ships.

키워드

참고문헌

  1. Lenart, A. S.(2015), Analysis of Collision Threat Parameters and Criteria, J. Navig., Vol. 68, No. 5, pp. 887-896.  https://doi.org/10.1017/S0373463315000223
  2. Li, B. and F. Pang(2013), An approach of vessel collision risk assessment based on the D-S evidence theory, Ocean Engineering, Vol. 74, pp. 16-21.  https://doi.org/10.1016/j.oceaneng.2013.09.016
  3. Goodwin, E. M.(1978), Marine encounter rates, J. Navig., Vol. 31, No. 3, pp. 357-369.  https://doi.org/10.1017/S0373463300041904
  4. Voorbraak, F.(1991), On the justification of Dempster's rule of combination, Artificial Intelligence, Vol. 48, pp. 171-197.  https://doi.org/10.1016/0004-3702(91)90060-W
  5. Sharer, G.(1976), A Mathematical Theory of Evidence, Princeton University Press, Princeton, N J. 
  6. Chin, H. C. and A. K. Debnath(2009), Modeling perceived collision risk in port water navigation, Saf. Sci., Vol. 47, No. 10, pp. 1410-1416.  https://doi.org/10.1016/j.ssci.2009.04.004
  7. International Maritime Organization(1972), Convention on the International Regulations for Preventing Collisions at Sea, 1972(COLREGs), http://www.imo.org/en/pages/default.aspx. 
  8. Ahn, J. H., K. P. Rhee, and Y. J. You(2012), A study on the collision avoidance of a ship using neural networks and fuzzy logic, Appl. Ocean Res., Vol. 37, pp. 162-173.  https://doi.org/10.1016/j.apor.2012.05.008
  9. Kearon, J.(1977), Computer program for collision avoidance and track keeping, in Proceedings of the International Conference on Mathematics Aspects of Marine Traffic, pp. 229-242. 
  10. Park, J. and J. Jeong(2020), Assessment of Ship Collision Risk in Coastal Waters by Fuzzy Comprehensive Evaluation, J. Korean Inst. Intell. Syst., Vol. 30, No. 4, pp. 325-330.  https://doi.org/10.5391/JKIIS.2020.30.4.325
  11. Park, J. and J. S. Jeong(2021), An Estimation of Ship Collision Risk Based on Relevance Vector Machine, J. Mar. Sci. Eng., vol. 9, no. 5, p. 538. 
  12. Park, J., J. Jeong and Y. Park(2021), Ship trajectory prediction based on bi-LSTM using spectral-clustered AIS data, J. Mar. Sci. Eng., Vol. 9, No. 9, 1037. 
  13. Park, J. and J. S. Jeong(2022), Assessment of Collision Risk in Vessel Traffic Service Areas using Fuzzy Comprehensive Evaluation, in 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), pp. 1-4. 
  14. Zhou, J. and C. Wu(2004), Construction of the collision risk factor model, Journal of Ningbo University, Vol. 17, pp. 61-65. 
  15. Korean Maritime Safety Tribunal(2021), 2020 Annual Report of Marine Accident Statistics, https://www.kmst.go.kr. 
  16. Zadeh, L. A.(1984), Review of Shafer's mathematical theory of evidence, AI Magazine 5, pp. 81-83. 
  17. Gang, L., Y. Wang, Y. Sun, L. Zhou, and M. Zhang(2016), Estimation of vessel collision risk index based on support vector machine, Adv. Mech. Eng., Vol. 8, No. 11, pp. 1-10.  https://doi.org/10.1177/1687814016671250
  18. Florea, M. C., A. Jousselme, E. Bosse, and D. Grenier(2008), Robust combination rules for evidence theory, Information Fusion, Vol. 10, pp. 183-197.  https://doi.org/10.1016/j.inffus.2008.08.007
  19. Xu, Q., X. Meng, and N. Wang(2009), Intelligent evaluation system of ship management, Mar. Navig. Saf. Sea Transp., Vol. 4, No. 4, pp. 787-790. 
  20. Fujii, Y. and H. Yamanouchi(1973), The distribution of collisions in Japan and methods of estimating collision damage, J. Navig., Vol. 26, No. 1, pp. 108-113. https://doi.org/10.1017/S0373463300022931