A Remote Operator's Ship Collision Avoidance Performance Evaluation Model: Comparison Between Human and AI Decisions in the Remote Operation Simulation Training.
Saved in:
| Title: | A Remote Operator's Ship Collision Avoidance Performance Evaluation Model: Comparison Between Human and AI Decisions in the Remote Operation Simulation Training. |
|---|---|
| Authors: | Hwang, Taemin (AUTHOR), Youn, Ik-Hyun (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Oct2025, Vol. 41 Issue 19, p12218-12228. 11p. |
| Subjects: | Maritime safety, System safety, Synthetic training devices, Authentic assessment, Statistical decision making |
| Abstract: | This research develops a performance evaluation model of humans in avoiding ship collision situations compared to the AI ship collision avoidance system (CAS). A human, the remote operator (RO) of Maritime Autonomous Surface Ships (MASS), ought to make compact collision avoidance (CA) decisions to secure safety and efficiency during remote operations. Hence, evaluating RO's CA performance is important; however, research on developing evaluation methods concentrates merely on evaluating trainees based on instructor's guidelines, while artificial intelligence (AI) makes CA decisions through parameter-based calculation. Therefore, this research proposes a CA performance evaluation model for RO of MASS based on CAS intervening of RO trainee's simulation training in ship collision avoidance. In each time interval, the RO's decisions showed divergent behaviors under given situational conditions compared to the CAS's behaviors in equivalent situations. Findings denote the benefits of CAS intervention methods and RO performance evaluation model based on the proposed performance features. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | This research develops a performance evaluation model of humans in avoiding ship collision situations compared to the AI ship collision avoidance system (CAS). A human, the remote operator (RO) of Maritime Autonomous Surface Ships (MASS), ought to make compact collision avoidance (CA) decisions to secure safety and efficiency during remote operations. Hence, evaluating RO's CA performance is important; however, research on developing evaluation methods concentrates merely on evaluating trainees based on instructor's guidelines, while artificial intelligence (AI) makes CA decisions through parameter-based calculation. Therefore, this research proposes a CA performance evaluation model for RO of MASS based on CAS intervening of RO trainee's simulation training in ship collision avoidance. In each time interval, the RO's decisions showed divergent behaviors under given situational conditions compared to the CAS's behaviors in equivalent situations. Findings denote the benefits of CAS intervention methods and RO performance evaluation model based on the proposed performance features. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2025.2453610 |
