Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting
Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that g...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/987 |
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| author | Deuk-Jin Park Hong-Tae Kim Sang-A Park Tae-Yeon Kim Jeong-Bin Yim |
| author_facet | Deuk-Jin Park Hong-Tae Kim Sang-A Park Tae-Yeon Kim Jeong-Bin Yim |
| author_sort | Deuk-Jin Park |
| collection | DOAJ |
| description | Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (<i>μ</i>) and standard deviation (<i>σ</i>) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. |
| format | Article |
| id | doaj-art-e92ccb9dd7ad436080d21db5022ec80d |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e92ccb9dd7ad436080d21db5022ec80d2025-08-20T03:14:39ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113598710.3390/jmse13050987Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve FittingDeuk-Jin Park0Hong-Tae Kim1Sang-A Park2Tae-Yeon Kim3Jeong-Bin Yim4Division of Marine Production System Management, Pukyong National University, 45 Yongso-Ro, Busan 48513, Republic of KoreaKorea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of KoreaDivision of Marine Production System Management, Pukyong National University, 45 Yongso-Ro, Busan 48513, Republic of KoreaDivision of Marine Production System Management, Pukyong National University, 45 Yongso-Ro, Busan 48513, Republic of KoreaDepartment of Maritime AI and Cyber Security, Korea Maritime and Ocean University, 727 Taejong-Ro, Busan 49112, Republic of KoreaDespite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (<i>μ</i>) and standard deviation (<i>σ</i>) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement.https://www.mdpi.com/2077-1312/13/5/987navigator awarenessCOLREGs interpretationprobabilistic modelingGaussian fittingMaritime Autonomous Surface Ships (MASS) |
| spellingShingle | Deuk-Jin Park Hong-Tae Kim Sang-A Park Tae-Yeon Kim Jeong-Bin Yim Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting Journal of Marine Science and Engineering navigator awareness COLREGs interpretation probabilistic modeling Gaussian fitting Maritime Autonomous Surface Ships (MASS) |
| title | Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting |
| title_full | Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting |
| title_fullStr | Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting |
| title_full_unstemmed | Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting |
| title_short | Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting |
| title_sort | modeling navigator awareness of colregs interpretation using probabilistic curve fitting |
| topic | navigator awareness COLREGs interpretation probabilistic modeling Gaussian fitting Maritime Autonomous Surface Ships (MASS) |
| url | https://www.mdpi.com/2077-1312/13/5/987 |
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