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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 556 - 564 (9page)
- DOI
- 10.5302/J.ICROS.2021.21.0063
이용수
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초록· 키워드
오류제보하기
With recent advancements in artificial intelligence technologies, the academia and industry have heightened performance expectations when designing autonomous ships, and major ship building companies and governments have been conducting large-scale research projects around the world. This paper proposes a novel design concept, and presents the key features and preliminary results of a situational awareness system for autonomous ships, named the iSAS (Intelligent Situational Awareness System), and is being developed as part of the Korea Autonomous Surface Ships (KASS) project launched in April 2020. The iSAS comprises deep-learning algorithms for detecting marine objects using camera, radar, and LiDAR (Light Detection and Ranging), a probabilistic-based data association and tracking algorithm and a new collision risk evaluation method. Because the iSAS estimates motions of all and each objects along with their semantic information, it could not be said as a simple replacement of what the captain does. By sequentially installing the iSAS on a small test ship and a demonstration ship during the project, we will simultaneously perform algorithm development and field verification to achieve reliability in the real environment. The iSAS can be used not only for autonomous ships but also for manned ships to enhance safety and reduce costs in the near future.
목차
Abstract
I. 서론
II. 자율 운항 선박의 상황인식 시스템, iSAS
III. iSAS 알고리즘 구성
IV. 결론
REFERENCES
참고문헌 (32)
참고문헌 신청
S. Thombre, Z. Zhao, H. Ramm-Schmidt, J. M. V. García, T. Malkamäki, S. Nikolskiy, T. Hammarberg, H. Nuortie, M. Z. H. Bhuiyan, S. Särkkä, and V. V. Lehtola, “Sensors and AI techniques for situational awareness in autonomous ships: A Review,” IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3023957.
A. Komianos, “The autonomous shipping era. operational, regulatory, and quality challenges,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 12, no. 2, pp. 335-348, 2018, doi:10.12716/ 1001.12.02.15.
R. G. Wright, “Intelligent autonomous ship navigation using multi-sensor modalities,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 13, no. 3, pp. 503-510, 2019, doi:10.12716/1001.13.03.03.
X. Y. Zhou, Z. J. Liu, Z. L. W, and F. W. Wang, “Quantitative processing of situation awareness for autonomous ships navigation,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 13, no. 1, pp. 25-31, 2019, doi:10.12716/1001.13.01.01.
L. P. Perera and B. Murray, “Situation awareness of autonomous ship navigation in a mixed environment under advanced ship predictor,” Proceedings of the ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. Volume 7B: Ocean Engineering, Glasgow, Scotland, UK. Jun. 9-14, 2019, V07BT06A029. ASME. https://doi.org/10.1115/OMAE2019-95571
A. Komianos, “The autonomous shipping era. operational, regulatory, and quality challenges,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 12, no. 2, pp. 335-348, 2018, doi:10.12716/ 1001.12.02.15.
R. G. Wright, “Intelligent autonomous ship navigation using multi-sensor modalities,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 13, no. 3, pp. 503-510, 2019, doi:10.12716/1001.13.03.03.
X. Y. Zhou, Z. J. Liu, Z. L. W, and F. W. Wang, “Quantitative processing of situation awareness for autonomous ships navigation,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 13, no. 1, pp. 25-31, 2019, doi:10.12716/1001.13.01.01.
L. P. Perera and B. Murray, “Situation awareness of autonomous ship navigation in a mixed environment under advanced ship predictor,” Proceedings of the ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. Volume 7B: Ocean Engineering, Glasgow, Scotland, UK. Jun. 9-14, 2019, V07BT06A029. ASME. https://doi.org/10.1115/OMAE2019-95571
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