Review of Explainable Decision Support and Adaptive Human-Machine Interfaces for Automation Transparency in Maritime Autonomous Surface Ships

[2509.15959] Review of Explainable Decision Support and Adaptive Human-Machine Interfaces for Automation Transparency in Maritime Autonomous Surface Ships Skip to main content Search Submit Donate Log in Computer Science > Human-Computer Interaction arXiv:2509.15959 (cs) [Submitted on 19 Sep 2025 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: Review of Explainable Decision Support and Adaptive Human-Machine Interfaces for Automation Transparency in Maritime Autonomous Surface Ships Authors: Zhuoyue Zhang , Haitong Xu , Carlos Guedes Soares View PDF Abstract: Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-based operational modes, namely remote supervision (RSM) and remote control (RCM), and identify where human unsafe control actions (Human-UCAs) concentrate in handover and emergency loops; (ii) summarize evidence that transparency features (decision rationales, alternatives, confidence/uncertainty, and rule-compliance indicators) improve understanding and support trust calibration, though reliability and predictability often dominate trust; (iii) distill design strategies for transparency at three layers: sensor/SA acquisition and fusion, HMI/eHMI presentation (textual/graphical overlays, color coding, conversational and immersive UIs), and engineer-facing processes (resilient interaction design, validation, and standardization). We integrate methods for Human-UCA identification (STPA-Cog + IDAC), quantitative trust/SA assessment, and operator workload monitoring, and outline regulatory and rule-based implications including COLREGs formalization and route exchange. We conclude with an adaptive transparency framework that couples operator state estimation with explainable decision support to reduce cognitive overload and improve takeover timeliness. The review highlights actionable figure-of-merit displays (e.g., CPA/TCPA risk bars, robustness heatmaps), transparent model outputs (rule traceability, confidence), and training pipelines (HIL/MIL, simulation) as near-term levers for safer MASS operations. Subjects: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2509.15959 [cs.HC] (or arXiv:2509.15959v2 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2509.15959 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhuoyue Zhang [ view email ] [v1] Fri, 19 Sep 2025 13:18:54 UTC (3,386 KB) [v2] Tue, 25 Aug 2026 08:08:27 UTC (3,170 KB) Full-text links: Access Paper: View PDF view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2025-09 Change to browse by: cs cs.AI cs.CY References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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