Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
This study constructs a support vector machine model based on supervised learning to model the results of situation awareness for ship collision avoidance. To explain the model, collision risk situations were defined, and human situation recognition results were collected in the specified cases. Mor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/13/7521 |
| _version_ | 1797592135456784384 |
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| author | Jaeyoung Song Ruri Shoji Hitoi Tamaru Jun Kayano |
| author_facet | Jaeyoung Song Ruri Shoji Hitoi Tamaru Jun Kayano |
| author_sort | Jaeyoung Song |
| collection | DOAJ |
| description | This study constructs a support vector machine model based on supervised learning to model the results of situation awareness for ship collision avoidance. To explain the model, collision risk situations were defined, and human situation recognition results were collected in the specified cases. Moreover, it was used to build predictors and outcome variables. Finally, the constructed variable was applied to the classification model. This model provides insight into the results of the navigator’s encounter situation awareness when collision avoidance is required. The results indicate that the proposed model can be used to predict human situation awareness outcomes in given cases. |
| first_indexed | 2024-03-11T01:47:10Z |
| format | Article |
| id | doaj.art-8116f4faf6984c02b3bfbcd54c008973 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:10Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8116f4faf6984c02b3bfbcd54c0089732023-11-18T16:07:37ZengMDPI AGApplied Sciences2076-34172023-06-011313752110.3390/app13137521Modeling Human Encounter Situation Awareness Results Using Support Vector Machine ModelsJaeyoung Song0Ruri Shoji1Hitoi Tamaru2Jun Kayano3Department of Applied Environmental Systems, Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology, 2-1-6 Etchujima, Koto-Ku, Tokyo 135-8533, JapanPresident, National Institute of Maritime, Port and Aviation Technology, 6-38-1 Shinkawa, Mitaka-shi, Tokyo 181-0004, JapanDepartment of Maritime Systems Engineering, Tokyo University of Marine Science and Technology, 2-1-6 Etchujima, Koto-Ku, Tokyo 135-8533, JapanDepartment of Maritime Systems Engineering, Tokyo University of Marine Science and Technology, 2-1-6 Etchujima, Koto-Ku, Tokyo 135-8533, JapanThis study constructs a support vector machine model based on supervised learning to model the results of situation awareness for ship collision avoidance. To explain the model, collision risk situations were defined, and human situation recognition results were collected in the specified cases. Moreover, it was used to build predictors and outcome variables. Finally, the constructed variable was applied to the classification model. This model provides insight into the results of the navigator’s encounter situation awareness when collision avoidance is required. The results indicate that the proposed model can be used to predict human situation awareness outcomes in given cases.https://www.mdpi.com/2076-3417/13/13/7521collision avoidanceencounter situationclassification model |
| spellingShingle | Jaeyoung Song Ruri Shoji Hitoi Tamaru Jun Kayano Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models Applied Sciences collision avoidance encounter situation classification model |
| title | Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models |
| title_full | Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models |
| title_fullStr | Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models |
| title_full_unstemmed | Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models |
| title_short | Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models |
| title_sort | modeling human encounter situation awareness results using support vector machine models |
| topic | collision avoidance encounter situation classification model |
| url | https://www.mdpi.com/2076-3417/13/13/7521 |
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