Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-18T13:44:18.984Z Has data issue: false hasContentIssue false

Dynamic risk prewarning in ship encounter process considering domain violation

Published online by Cambridge University Press:  03 August 2021

Tingrong Qin*
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Guoliang Ma
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Dongyang Li
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Xinjie Zhou
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Xingjie He
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Weijiong Chen
Affiliation:
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
*
*Corresponding author. E-mail: [email protected]

Abstract

A ship's perception of risk is an important basis for collision avoidance. To improve such perception, several risk measurement parameters on the ship domain are determined, including the approach factor, the time to domain violation (TDV) and the possible collision domain. Then, a risk hierarchy prewarning (RHP) model based on the violation detection of a ship domain is proposed, in which a two-level alarm scheme is adopted accordingly. A low-intensity alarm will be activated by reaching the minimum approach factor and the TDV threshold, and a high-intensity alarm will be activated by the factor of the possible collision domain and the TDV threshold. Subsequently, a novel guard zone in ARPA radar utilising the RHP model has been developed to establish a ship's risk perception system for officers on watch at sea. The model proposed in this paper can not only enhance the veracity of risk assessment around our own ship, but also be used as a decision support system for collision avoidance.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chai, T., Weng, J. and Li, G. (2020). Estimation of vessel collision frequency in the Yangtze river estuary considering dynamic ship domains. Journal of Marine Science and Technology, 25, 964977.CrossRefGoogle Scholar
Chen, P., Huang, Y., Mou, J. and van Gelder, P. H. A. J. M. (2018). Ship collision candidate detection method: A velocity obstacle approach. Ocean Engineering, 170, 186198.CrossRefGoogle Scholar
Chin, H. and Debnath, A. (2009). Modeling perceived collision risk in port water navigation. Safety Science, 47, 14101416.CrossRefGoogle Scholar
Coldwell, T. (1983). Marine traffic behaviour in restricted waters. Journal of Navigation, 36, 430444.CrossRefGoogle Scholar
Davis, P., Dove, M. and Stockel, C. (1980). A computer simulation of marine traffic using domains and arenas. Journal of Navigation, 33, 215222.CrossRefGoogle Scholar
Davis, P., Dove, M. and Stockel, C. (1982). A computer simulation of multi-ship encounters. Journal of Navigation, 35, 347352.CrossRefGoogle Scholar
Dinh, G. and Im, N. (2016). The combination of analytical and statistical method to define polygonal ship domain and reflect human experiences in estimating dangerous area. International Journal of E-Navigation and Maritime Economy, 4, 97108.CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. Journal of Navigation, 24, 543552.CrossRefGoogle Scholar
Goodwin, E. (1973). A statistical study of ship domains. Journal of Navigation, 26, 130.CrossRefGoogle Scholar
Hansen, M., Jensen, T., Lehn-Schioler, T., Melchild, K., Rasmussen, F. and Ennemark, F. (2013). Empirical ship domain based on AIS data. Journal of Navigation, 66, 931940.CrossRefGoogle Scholar
Huang, Y., van Gelder, P. H. A. J. M. and Wen, Y. (2018). Velocity obstacle algorithms for collision prevention at sea. Ocean Engineering, 151, 308321.CrossRefGoogle Scholar
Im, K. and Luong, T. (2019). Potential risk ship domain as a danger criterion for real-time ship collision risk evaluation. Ocean Engineering, 194, 106610.CrossRefGoogle Scholar
Kearon, J. (1977). Computer Programs for Collision Avoidance and Track Keeping. Conference on Mathematical Aspects on Marine Traffic, 229242.Google Scholar
Li, B. and Pang, F. (2013). An approach of vessel collision risk assessment based on the D-S evidence theory. Ocean Engineering, 74, 1621.CrossRefGoogle Scholar
Liu, J., Zhou, F., Li, Z., Wang, M. and Liu, R. (2016). Dynamic ship domain models for capacity analysis of restricted water channels. Journal of Navigation, 69, 481503.CrossRefGoogle Scholar
Mou, J., Li, M., Hu, W., Zhang, X., Gong, S., Chen, P. and He, Y. (2020). Mechanism of dynamic automatic collision avoidance and the optimal route in multi-ship encounter situations. Journal of Marine Science and Technology, 26, 141158.Google Scholar
Pietrzykowski, Z. (2008). Ship's fuzzy domain– a criterion for navigational safety in narrow fairways. Journal of Navigation, 61, 499514.CrossRefGoogle Scholar
Pietrzykowski, Z. and Uriasz, J. (2009). The ship domain – a criterion of navigational safety assessment in an open sea area. Journal of Navigation, 62, 93108.CrossRefGoogle Scholar
Pietrzykowski, Z. and Wielgosz, M. (2021). Effective ship domain – impact of ship size and speed. Ocean Engineering, 219, 108423.CrossRefGoogle Scholar
Rawson, A. and Brito, M. (2021). A critique of the use of domain analysis for spatial collision risk assessment. Ocean Engineering, 219, 108259.CrossRefGoogle Scholar
Rawson, A., Rogers, E., Foster, D. and Philips, D. (2014). Practical application of domain analysis: Port of London case study. Journal of Navigation, 67, 193209.CrossRefGoogle Scholar
Ren, Y., Mou, J., Yan, Q. and Zhang, F. (2011). Study on Assessing Dynamic Risk of Ship Collision. First International Conference on Transportation Information and Safety (ICTIS), 27512757.CrossRefGoogle Scholar
Szlapczynski, R. (2006). A unified measure of collision risk derived from the concept of a ship domain. Journal of Navigation, 59, 477490.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2016). An analysis of domain-based ship collision risk parameters. Ocean Engineering, 126, 4756.CrossRefGoogle Scholar
Szlapczynski, R., Krata, P. and Szlapczynska, J. (2018). Ship domain applied to determining distances for collision avoidance manoeuvres in give-way situations. Ocean Engineering, 165, 4354.CrossRefGoogle Scholar
Tak, C. and Spaans, J. (1997). A model for calculating a maritime risk criterion number. Journal of Navigation, 30, 287295.Google Scholar
Wang, N. (2010). An intelligent spatial collision risk based on the quaternion ship domain. Journal of Navigation, 63, 733749.CrossRefGoogle Scholar
Wang, N. (2013). A novel analytical framework for dynamic quaternion ship domains. Journal of Navigation, 66, 265281.CrossRefGoogle Scholar
Wang, Y. and Chin, H. (2016). An empirically-calibrated ship domain as a safety criterion for navigation in confined waters. Journal of Navigation, 69, 257276.CrossRefGoogle Scholar
Weng, J., Liao, S. and Yang, D. (2020). Methodology for estimating waterway traffic capacity at Shanghai estuary of the Yangtze river. Journal of Navigation, 73, 7591.CrossRefGoogle Scholar
Yoo, Y. and Lee, J. (2019). Evaluation of ship collision risk assessments using environmental stress and collision risk models. Ocean Engineering, 191, 106527.CrossRefGoogle Scholar
Zhang, L., Wang, H. and Meng, Q. (2015). Big data-based estimation for ship safety distance distribution in port waters. Transportation Research Record: Journal of the Transportation Research Board, 2479, 1624.CrossRefGoogle Scholar
Zhang, X., Mou, J., Zhu, J. and Chen, P. (2017). Capacity analysis for bifurcated estuaries based on ship domain theory and its applications. Transportation Research Record: Journal of the Transportation Research Board, 2611, 5664.CrossRefGoogle Scholar
Zhang, M., Montewka, J., Manderbacka, T., Kujala, P. and Hirdaris, S. (2021). A Big data analytics method for the evaluation of ship - ship collision risk reflecting hydrometeorological conditions. Reliability Engineering & System Safety, 213, 107674.CrossRefGoogle Scholar
Zhen, R., Riveiro, M. and Jin, Y. (2017). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, 492501.CrossRefGoogle Scholar
Zheng, K., Chen, Y., Jiang, Y. and Qiao, S. (2020). A SVM based ship collision risk assessment algorithm. Ocean Engineering, 202, 107062.CrossRefGoogle Scholar
Zhou, D. and Zheng, Z. (2019). Dynamic fuzzy ship domain considering the factors of own ship and other ships. Journal of Navigation, 72, 467482.CrossRefGoogle Scholar
Zhu, X., Xu, H. and Lin, J. (2001). Domain and its model based on neural networks. Journal of Navigation, 54, 94103.CrossRefGoogle Scholar