Ship Autonomy

01openalex2026-09-24Yucheng Zhang, Dezhi Wang, Xiaori Gao, Jiayi Che +2 moreDOI ↗

A Decoupled Global-Local Planning Framework for COLREGs-Aware Maritime Autonomous Surface Ships in Coastal Waters (opens in a new tab)

This paper introduces a decoupled framework for COLREGs-compliant autonomous ship navigation in coastal waters, separating global path planning from local collision avoidance. The global layer uses Adaptive Angular-Domain Potential Field (AAPF) for path generation and Farsighted Sweep Trajectory Optimization (FSTO) for refinement, while the local layer employs Environment-Adaptive Dynamic Window Approach (EA-DWA) for real-time risk assessment and avoidance. Simulations demonstrate reduced computational burden and improved path usability, though performance depends on environment-specific parameter adjustments. The approach ensures compliance with maritime collision regulations while balancing efficiency and adaptability.

02openalex2026-09-21Ahui Xu, Yuanqiao Wen, Wenjun Tang, Yamin Huang +3 moreDOI ↗

MBSE-based development and verification of function-oriented ODDs for autonomous ships (opens in a new tab)

This study introduces an MBSE-based method to define, quantify, and verify operational design domains (ODDs) for autonomous ship functions. It converts regulatory, mission, and risk requirements into measurable ODD conditions, ensuring traceability across system architecture, verification scenarios, and runtime logic. The approach is demonstrated through a collision-avoidance case study, where ODD conditions are formalized and tested via model checking, simulations, and risk analysis. Results confirm consistency in model-level verification but do not validate real-world operational safety or threshold effectiveness.

03openalex2026-09-22Vladislav Maras, Cyril Alias, Aleksandar Radonjić, Danijela Pjevčević +2 moreDOI ↗

Rethinking Port Charges in the Age of Autonomous Shipping (opens in a new tab)

The paper examines how autonomous motor cargo vessels (aMCVs) will transform inland waterway transport, necessitating updates to outdated port charging models. Current pricing systems may unfairly disadvantage aMCVs despite their efficiency, as they rely on assumptions misaligned with autonomous operations. The study analyzes technological, economic, and regulatory factors, highlighting the need for resource-based pricing models to incentivize efficiency, sustainability, and safety. Digital platforms and actual infrastructure use are identified as key elements for future port charge calculations.

04openalex2026-09-23Jakob Lass-Ovens, Håvard Nordahl, Patrick Specht, Lars Andreas Lien WennersbergDOI ↗

Economics of autonomous ships: from systematic review to guidelines for economic assessments (opens in a new tab)

This paper reviews 23 studies on the economics of autonomous cargo ships, finding significant variation in methods, assumptions, and results. Many studies lack empirical cost data, relying instead on secondary sources without critical evaluation, which risks spreading biases. Transparency and documentation are often insufficient, making comparisons and reproducibility difficult. The authors propose guidelines to improve clarity and consistency in future economic assessments.

05openalex2026-09-25Junjie Gao, Liang Hong, Sibo ZhangDOI ↗

SeaTrack-DeepSORT: A Vision-Based Ship Detection and Multi-Object Tracking Framework for Complex Maritime Environments (opens in a new tab)

SeaTrack-DeepSORT is a vision-based framework for ship detection and multi-object tracking in complex maritime environments, addressing challenges like scale variations, occlusions, and camera motion. It combines SeaDet-YOLO, an enhanced YOLOv11 detector with ADown downsampling and a P2 detection head, with a modified DeepSORT tracker that uses confidence-dependent updates and motion-compensated association. The system improves detection and tracking accuracy while ensuring stable trajectories. Experiments on maritime datasets demonstrate its effectiveness for autonomous perception in unmanned surface vehicles.

06openalex2026-09-23DOI ↗

ADOPTION OF AUTONOMOUS MARITIME TOURISM IN EMERGING MARKETS: A DISCRIMINANT ANALYSIS OF BEHAVIOURAL INTENTION AMONG INDIAN TOURISTS (opens in a new tab)

This study explores factors influencing Indian tourists' acceptance of Autonomous Maritime Tourism (AMT), an AI-driven innovation in smart ports and navigation. Using the Technology Acceptance Model and Diffusion of Innovation theory, it surveyed 237 Indian tourists and analyzed data via discriminant function analysis. Social influence emerged as the strongest predictor of behavioral intention, followed by relative advantage, perceived complexity, and facilitating conditions. The findings highlight that social validation and perceived benefits outweigh risk factors in shaping AMT adoption in India.

07openalex2026-09-21Vallabh Deogaonkar, Abhilash S. SomayajulaDOI ↗

Importance of data richness in identification of ship maneuvering hydrodynamic models (opens in a new tab)

This study highlights the need for rich datasets to accurately model ship maneuvering hydrodynamics for autonomous surface vessels (ASVs). Current methods using standard maneuvers like turning circles or zig-zag tests fail to capture nonlinear interactions and higher-order effects. The authors propose a structured approach to measure data richness and introduce a new maneuver to improve the estimation of hydrodynamic coefficients. The findings aim to enhance the reliability of ASV guidance and control systems in complex scenarios.

08openalex2026-09-22Lulu Wang, ao xu, Mingxiao Sun, Tiantian LuanDOI ↗

Autonomous safety control of unmanned surface vessels formation driven by prescribed performance (opens in a new tab)

This paper presents a formation control framework for unmanned surface vessels (USVs) to improve cooperative tracking safety in complex marine environments. It uses a prescribed performance control method with adjustable boundaries to minimize tracking errors and reduce collision risks, while an integral disturbance observer addresses model uncertainty and external disturbances. A control barrier function (CBF) ensures safety by solving a quadratic programming problem, balancing formation tracking and security. Simulations show the method outperforms others in response speed, disturbance rejection, and input smoothness.

09crossref2026-09-22Dongqin Li, Qi Peng, Kun Zhang, Yan FengDOI ↗

SGR-YOLO: a lightweight ship detection model integrating visual modules and cross-scale fusion for complex maritime scenarios (opens in a new tab)

The SGR-YOLO model improves ship detection in complex maritime conditions by enhancing YOLOv8n with lightweight modifications. It integrates Sim-BiFPN for cross-scale feature fusion, GhostConv to reduce computational complexity, and RepViT to strengthen small-object detection. Tested on SeaShips and McShips datasets, it achieves 98.7% and 93.3% precision, respectively, while reducing parameters by up to 5.2M. The model maintains high performance in fog, occlusion, and long-distance scenarios, offering a real-time solution for resource-limited systems.

10openalex2026-06-11PADMAVATHY T V, Abdul QuadirDOI ↗

Quantum game-theoretic reinforcement learning for autonomous vehicle navigation and communication in vehicular networks (opens in a new tab)

This paper introduces a Quantum Game-Theoretic Reinforcement Learning (QGTRL) framework for autonomous vehicle (AV) navigation and communication in dynamic networks. The approach combines quantum-inspired policy encoding with game-theoretic coordination to improve decision-making, exploration efficiency, and scalability. Benchmarked against methods like DQN and MADDPG, QGTRL achieves 18.7% better path-planning accuracy, 15.3% lower communication latency, and 22.4% reduced energy consumption. The framework operates on classical hardware, making it practical for real-world deployment in cooperative AV systems.

11openalex2026-09-26J. Paul Liu, Uthpala Herath, Andrew PetersenDOI ↗

ASCEND: Personal AI Agents for Autonomous Scientific Computing Across HPC Clusters and GPU Workstations (opens in a new tab)

ASCEND is an AI-powered agent system that automates scientific computing tasks by translating researcher intent into job submissions and failure diagnostics across HPC clusters and GPU workstations. It operates via SSH, uses remote language models (Claude Code or Codex), and enforces site-specific policies without storing credentials. In tests, ASCEND autonomously fixed a tensor-device fault, replicated a weather-forecasting model’s results within 2.4% accuracy, parallelized a geophysical solver while preserving output integrity, and identified undefined behaviors in published code. A policy validator blocked most violations but also denied some legitimate requests.

12openalex2026-09-25Haoxiang Sun, Yuhang QiuDOI ↗

FuzzyCS-Proto: fuzzy client selection with class-wise prototype learning for federated ship underwater structural surface condition classification (opens in a new tab)

The paper introduces FuzzyCS-Proto, a federated learning framework for classifying underwater ship structural surface conditions, addressing challenges like non-IID data, image degradation, and class imbalance. It uses fuzzy client selection based on reliability metrics (validation performance, uncertainty, image quality, and class balance) and class-wise prototype learning to improve cross-client alignment. Tested on a dataset with five classes (corrosion, defect, marine growth, normal surface, and paint peel), FuzzyCS-Proto outperforms baselines like FedAvg and FedProx, achieving Macro-F1 scores of 0.6501, 0.6292, 0.6641, and 0.5074 across four federated settings. Ablation and sensitivity analyses confirm the method’s effectiveness and interpretability.

13crossref2026-09-24Julie Smeyers, Daniel A. MordesDOI ↗

Minding the gates: dual cell-autonomous and non-cell-autonomous mechanisms of TBK1 in ALS/FTD (opens in a new tab)

This paper reviews the role of TBK1 in ALS and FTD, diseases linked by TDP-43 pathology and shared genetic risks. TBK1 loss-of-function variants are a common genetic cause, with haploinsufficiency disrupting autophagy, endo-lysosomal, and immune pathways through both cell-intrinsic and external mechanisms. The authors emphasize TBK1’s role in regulating proteostasis and inflammation, particularly in microglia. The findings suggest targeting these pathways could inform ALS/FTD therapies.

14crossref2026-09-24Muyin Chen, Longyu JiangDOI ↗

Wiener-Guided Spectral Line Enhancement for Vessel Recognition (opens in a new tab)

The paper introduces Wiener-guided spectral line enhancement (WG-SLE), a trainable front end combining delayed-reference prediction with vessel recognition optimization. WG-SLE uses Wiener-initialized prototypes and stop-gradient blocks to enhance tonal cues while directing classification gradients. Tested on DeepShip and ShipsEar datasets, it improves accuracy by up to 6.77 percentage points over Audio Spectrogram Transformer baselines. Results demonstrate that delay-predictable spectral estimates provide complementary recognition information.

15crossref2026-09-24DOI ↗