arXiv:2609.24358v1 Announce Type: new Abstract: Predicting when maritime systems require maintenance can be critical, avoiding hazards and costly consequences. To address this problem, this paper pro...
The study surveyed maritime professionals on their attitudes toward AI‑supported decision assistants in collision‑avoidance scenarios. Results show a generally positive disposition toward maritime technology, stable trust across scenarios, and nuanced, scenario‑sensitive ratings of explanation quality. Open‑ended feedback highlighted the importance of decision‑support, situational awareness, and confidence‑building, while raising concerns about AI reliability, over‑reliance, and loss of expertise.
arXiv:2609.24250v1 Announce Type: new Abstract: The shipping industry has a significant impact on the global economy, emphasizing the need for operational availability and safety through the use of e...
arXiv:2608. 08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different domains, most notable in the automotive sector.
arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.
The paper introduces a reliability‑aware short‑term roll prediction framework for unmanned surface vehicles (USVs) that combines a multi‑task learning architecture with an adaptive centralization strategy. The model uses a shared backbone to feed a regression head for precise roll prediction and a quantification head for confidence scoring, enabling accurate predictions alongside reliability estimates. Experiments on a real‑sea dataset show that the approach effectively quantifies prediction reliability and generalizes well across varying operational conditions.
arXiv:2606. 15240v1 Announce Type: new Abstract: Vessel trajectory prediction is important for intelligent shipping, maritime surveillance, and navigation safety.
arXiv:2609.13552v1 Announce Type: new Abstract: Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation...
This paper introduces a large AIS dataset from Finnish waters, covering the Baltic Sea and Finnish lakes, with 229 million data points. It curates and categorises existing AIS data, adds new recordings, and provides ship type listings and visualisations of traffic patterns. The resource is positioned to support research into maritime activities and vessel behaviour in this dynamic region.
arXiv:2509.15959v2 Announce Type: replace-cross Abstract: Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque de...
arXiv:2607. 21597v2 Announce Type: replace Abstract: Evaluating wildfire risk systems using standard machine-learning metrics such as F1-score or IoU is fundamentally flawed: these metrics assess event prediction accuracy, not the operational coherence of a continuous risk signal.
arXiv:2603. 28067v2 Announce Type: replace Abstract: Digital testing has emerged as a key paradigm for the development and verification of autonomous maritime navigation systems, yet the availability of realistic and diverse safety-critical encounter scenarios remains limited.