A Remote Operator's Ship Collision Avoidance Performance Evaluation Model: Comparison Between Human and AI Decisions in the Remote Operation Simulation Training

• Published in: International journal of human-computer interaction Vol. 41; no. 19; pp. 12218 - 12228
• Main Authors: Hwang, Taemin, Youn, Ik-Hyun
• Format: Journal Article
• Language: English
• Published: Norwood Taylor & Francis 02.10.2025; Lawrence Erlbaum Associates, Inc
• Subjects: Artificial intelligence; Collision avoidance; Decisions; Maritime Autonomous Surface ships; Performance evaluation; Remote operator; simulation training
• ISSN: 1044-7318; 1532-7590; 1044-7318
• DOI: 10.1080/10447318.2025.2453610

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.