Enabling autonomous navigation: adaptive multi-source risk quantification in maritime transportation
Enabling autonomous navigation: adaptive multi-source risk quantification in maritime transportation
Current studies on maritime navigation risks often overlook interactions between ships, dynamic surroundings, and static environmental factors, limiting insights into navigation safety in complex scenarios. This research presents an innovative methodology to quantify and integrate multi-source heterogeneous navigation risks, enabling a comprehensive assessment of overall risk levels. The framework comprises four components. First, a spatiotemporal risk monitoring domain model, developed using historical AIS data, incorporates risk monitoring and forbidden domains, enabling precise localisation and timing of risk evaluation. Second, heterogeneous navigation risk evaluation functions, addressing dynamic target and static environment risks, capture ships’ varying sensitivities to diverse risk sources. Third, risk quantification methods evaluate dynamic risks from temporal and spatial perspectives while categorising static risks into three types. Finally, an adaptive fusion method hierarchically aggregates multi-source risk data into a unified profile, reflecting navigators’ risk perception. Real-world AIS data validate the framework, constructing spatiotemporal risk models for three ship types and analysing navigation scenarios such as crossing, overtaking, and multi-ship encounters. Results demonstrate the framework's capability to enhance precision in navigation risk assessment, providing actionable insights and robust support for autonomous navigation and intelligent maritime systems. This methodology offers a promising tool for advancing safety in complex maritime environments.
AIS data, Adaptive risk fusion, Heterogeneous navigation risks, Intelligent transportation systems, Spatiotemporal risk modelling
Yang, Lichao
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Liu, Jingxian
0cd82a7d-41c8-4da2-9826-e01cb1685b1c
Zhou, Qin
22cc3c1b-50f4-41e0-9c3e-8cdf183a022e
Liu, Zhao
68f8f0b4-bd89-4c3b-8b40-97e708133f4f
Chen, Yang
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Wang, Yukuan
e53a38f1-42b6-46c1-b1cd-87a7304c1b9b
Liu, Yang
55c9ee73-e3cf-4c97-bbfa-ff4100dff171
14 April 2025
Yang, Lichao
a34ddc6c-6ce1-4f58-9551-0e66e2cb6aad
Liu, Jingxian
0cd82a7d-41c8-4da2-9826-e01cb1685b1c
Zhou, Qin
22cc3c1b-50f4-41e0-9c3e-8cdf183a022e
Liu, Zhao
68f8f0b4-bd89-4c3b-8b40-97e708133f4f
Chen, Yang
787f3e02-9c50-441c-9ec3-58a6922d5dcf
Wang, Yukuan
e53a38f1-42b6-46c1-b1cd-87a7304c1b9b
Liu, Yang
55c9ee73-e3cf-4c97-bbfa-ff4100dff171
Yang, Lichao, Liu, Jingxian, Zhou, Qin, Liu, Zhao, Chen, Yang, Wang, Yukuan and Liu, Yang
(2025)
Enabling autonomous navigation: adaptive multi-source risk quantification in maritime transportation.
Reliability Engineering and System Safety, 261, [111118].
(doi:10.1016/j.ress.2025.111118).
Abstract
Current studies on maritime navigation risks often overlook interactions between ships, dynamic surroundings, and static environmental factors, limiting insights into navigation safety in complex scenarios. This research presents an innovative methodology to quantify and integrate multi-source heterogeneous navigation risks, enabling a comprehensive assessment of overall risk levels. The framework comprises four components. First, a spatiotemporal risk monitoring domain model, developed using historical AIS data, incorporates risk monitoring and forbidden domains, enabling precise localisation and timing of risk evaluation. Second, heterogeneous navigation risk evaluation functions, addressing dynamic target and static environment risks, capture ships’ varying sensitivities to diverse risk sources. Third, risk quantification methods evaluate dynamic risks from temporal and spatial perspectives while categorising static risks into three types. Finally, an adaptive fusion method hierarchically aggregates multi-source risk data into a unified profile, reflecting navigators’ risk perception. Real-world AIS data validate the framework, constructing spatiotemporal risk models for three ship types and analysing navigation scenarios such as crossing, overtaking, and multi-ship encounters. Results demonstrate the framework's capability to enhance precision in navigation risk assessment, providing actionable insights and robust support for autonomous navigation and intelligent maritime systems. This methodology offers a promising tool for advancing safety in complex maritime environments.
Text
1-s2.0-S0951832025003199-main
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More information
Accepted/In Press date: 7 April 2025
e-pub ahead of print date: 8 April 2025
Published date: 14 April 2025
Keywords:
AIS data, Adaptive risk fusion, Heterogeneous navigation risks, Intelligent transportation systems, Spatiotemporal risk modelling
Identifiers
Local EPrints ID: 501740
URI: http://eprints.soton.ac.uk/id/eprint/501740
ISSN: 0951-8320
PURE UUID: a7ef835f-4b8f-4ff6-9b9b-c4f00348d66c
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Date deposited: 09 Jun 2025 17:41
Last modified: 22 Aug 2025 02:38
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Contributors
Author:
Lichao Yang
Author:
Jingxian Liu
Author:
Qin Zhou
Author:
Zhao Liu
Author:
Yang Chen
Author:
Yukuan Wang
Author:
Yang Liu
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