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...
By Zhuoyue Zhang, Haitong Xu, Carlos Guedes Soares
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...
By Alexandre Barreto (George Mason University), Shou Matsumoto (George Mason University), Jorge Valverde-Rebaza (Tecnol\'ogico de Monterrey), Cleiton Ataide (DECEA: Department of Airspace Control), Paulo Costa (George Mason University)
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.
By Julius Wirbel, P. Nicholas Hansen, Line K. H. Clemmensen, Roberto Galeazzi
The article discusses the necessity of building trust in AI for railway applications, highlighting that AI is currently limited to non‑safety critical uses due to stringent industry standards. It proposes focusing on three key areas—robustness, Operational Design Domain (ODD), and explainability—to meet compliance and safety requirements. By integrating these domains within a safe MLOps environment, the authors argue that regulatory acceptance and public confidence can be achieved, enabling broader AI adoption in mission‑critical railway systems.
By Lefebvre Renard Cl\'ement, L\'eb\'e Vincent, Da Silva Ribeiro Pereira Ricardo, Sundell Johan, Jaoul Arnaud Saiah Kenza, Mijatov\'ic Nenad
arXiv:2607. 08285v1 Announce Type: new Abstract: Current AI evaluation frameworks focus primarily on technical performance, including accuracy, robustness, reasoning ability, and policy compliance.
By Marcos Economides, Paul M. Sacher, Samuel Salzer, Alexis Michelle Abellar, Fendi Tsim, Antoine Ferr\`ere
arXiv:2607. 20065v1 Announce Type: new Abstract: Enterprise strategic decision support requires AI systems that are not only accurate, but also uncertainty-aware, risk-calibrated, explainable, and governance-compliant.
By Tian Qiu, Li Yan, Mahabubur Rahman Miraj, Shanqin Yi, Md Intekhab Rahman Galib, Jahid Hasan