The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Ship collision avoidance methods: State-of-the-art
Open accessAbstract
Collision prevention is critical for navigation safety at sea. At early ages, researchers aimed at developing navigational assistance systems for enhancing situational awareness of human operators as human is at the core of collision avoidance. Recently, autonomous vehicles have gained a remarkable amount of attention with a focus on solving collision problems by machines. This results in two groups of studies, both working on preventing collisions but with different focuses: one aims at conflict detection, and the other focuses on conflict resolution. This paper offers a comprehensive overview of collision prevention techniques based on the three basic processes of determining evasive solutions, namely, motion prediction, conflict detection, and conflict resolution. The strengths and weaknesses of different methods for these three fundamental processes are discussed. Limitations and new challenges are highlighted. Moreover, this review points out the differences between the research for manned and unmanned ships and how the research in the two domains can learn from each other. A potential roadmap for the transition from existing manned ships to fully unmanned ships is provided in the end.
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Cites
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Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles
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Real-time obstacle avoidance for fast mobile robots
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Cited by
An Overview of Recent Advances in Coordinated Control of Multiple Autonomous Surface Vehicles
Peng, Z., Wang, J., Wang, D., & Han, Q.-L. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3004343 · Cited by 757
Survey of deep learning for autonomous surface vehicles in marine environments
Qiao, Y., Yin, J., Wang, W., Duarte, F., Yang, J., & Ratti, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235911 · Cited by 192
Emerging approaches applied to maritime transport research: past and future
Yan, R., Wang, S., Zhen, L., & Laporte, G. (2021) · Communications in Transportation Research · doi:10.1016/j.commtr.2021.100011 · Cited by 111
Metadata
- Authors
- Huang, Y., Chen, L., Chen, P., Negenborn, R. R., & van Gelder, P. H. A. J. M.
- Venue
- Safety Science
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 121
- Pages
- 451-473
- ISSN
- 0925-7535 (ISSN-L)
- OpenAlex
- W2976348340
- Cited by
- 431
- References
- 67
- FWCI
- 31.58
- Type
- journal article
- Language
- en
- Open access
- green
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- Huang, Y., Chen, L., Chen, P., Negenborn, R. R., & van Gelder, P. H. A. J. M. (2020). Ship collision avoidance methods: State-of-the-art. Safety Science. https://doi.org/10.1016/j.ssci.2019.09.018
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
