A comprehensive review of 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) highlights key challenges and solutions. The research synthesizes findings on situation awareness, human factors, interface design, and regulation, identifying opaque decision-making and human unsafe control actions as significant barriers. The review proposes an adaptive transparency framework that integrates operator state estimation with explainable decision support to enhance safety and timeliness in handover and emergency situations. AI
IMPACT Enhances understanding of AI's role in maritime safety and human-automation interaction.
RANK_REASON The cluster is based on a review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
- artificial intelligence
- human–computer interaction
- human unsafe control actions
- regulation
- situation awareness
- Zhuoyue Zhang
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