To address the complexity of ship trajectory prediction, this study explored the efficacy of the Mamba model, a relatively new deep-learning framework. In order to evaluate the performance of the Mamba model relative to traditional models, which often struggle to cope with the dynamic and nonlinear nature of maritime navigation data, we analyzed a dataset consisting of intricate ship trajectory data. The prediction accuracy and inference speed of the model were evaluated using metrics such as the mean absolute error (MAE) and root mean square error (RMSE). The Mamba model not only excelled in terms of the computational efficiency, with inference times of 0.1759 s per batch—approximately 7.84 times faster than the widely used Transformer model—it also processed 3.9052 samples per second, which is higher than the Transformer model’s 0.7246 samples per second. Additionally, it demonstrated high prediction accuracy and the lowest loss among the evaluated models. The Mamba model provides a new tool for ship trajectory prediction, which represents an advancement in addressing the challenges of maritime trajectory analysis when compared to existing deep-learning methods.

With the advancements of large language models in text and visual tasks, researchers are increasingly exploring their applications in medical scenarios. While existing studies have successfully applied these models to medical dialogue systems, challenges remain in accurately calculating medication dosages and handling multi-turn reasoning due to the low tolerance for error in medical contexts. To address this, we propose Math Multi-step Reasoning in Medical Dialogue Generation (MMR), which enhances reasoning by iteratively breaking down complex problems into simpler questions using a “Least to Most Prompting” (LMP)strategy. MMR integrates Chain of Thought, React mechanisms, and Retrieval-Augmented Generation (RAG) with a domain-specific knowledge base to improve reasoning accuracy. Supported by the MedDGQA dataset, MMR outperforms state-of-the-art methods in both objective and subjective evaluations.

The attribution of anomalous vessel behaviors is crucial for maritime traffic supervision and safety decision-making. However, existing approaches often lack the capability to systematically interpret multidimensional anomaly features and reveal their underlying behavioral mechanisms, limiting the transparency and practical value of anomaly attribution. To address these challenges, we propose a multidimensional anomalous vessel behavior attribution and decision-support framework based on large language models (LLMs). Specifically, the framework first employs Behavioral Anomaly Semantic Mapping (BASM) to transform multidimensional anomaly features into structured semantic units governed by logical constraints; it then leverages knowledge-guided reasoning (KGRP-PCoT) with LLMs to perform multi-step inference, enabling systematic attribution from low-level anomaly observations to high-level behavioral mechanisms. Experiments conducted on AIS data from the Wusongkou waters demonstrate that the proposed framework significantly outperforms traditional methods. Quantitative evaluations show that the framework achieves a BLEU-4 score of 0.91 and a BERTScore of 0.98 when integrated with advanced LLMs like DeepSeek. Furthermore, the ablation study confirms that the proposed BASM and KGRP-PCoT mechanisms improve the BERTScore from approximately 0.86 to 0.98. It not only reveals the causal mechanisms underlying anomalous behaviors more accurately but also improves logical consistency and regulatory compliance, confirming its practical utility and decision-support value.

Ship path planning is a central challenge in autonomous navigation for unmanned surface vehicles and maritime autonomous surface ships. It is not simply a shortest-path problem, but a constrained sequential decision process that must reconcile collision risk, route efficiency, COLREGs compliance, vessel dynamics, and environmental uncertainty. Here we review the field through a unified framework based on planning scope, decision basis, and deployment requirements. We examine search- and sampling-based, geometric and rule-based, optimization-based, learning-driven, and hybrid methods, with particular emphasis on deep reinforcement learning for discrete decisions, continuous manoeuvring, multi-vessel interaction, and safety-oriented control. Representative studies are compared across objective and reward design, state representation, exploration and policy optimization, rule integration, disturbance modelling, simulation platforms, and operational validation. The synthesis identifies persistent barriers, including ambiguous rule formalization, partial observability, strategic coupling among vessels, inconsistent benchmarks, limited cross-scenario generalization, and insufficient full-scale validation. We further discuss priority directions in explicit safety constraints, digital twins, transfer and meta-learning, world models, scalable multi-agent coordination, and large-model-assisted mission reasoning. We argue that progress will depend less on further algorithmic proliferation than on integrated, verifiable architectures that combine data-driven adaptation with model-based structure, standardized evaluation, and staged real-world assurance.

Autonomous ship collision avoidance requires rule-consistent, interpretable, and effective decisions across different encounter situations. This paper proposes the Logic-Guided Interpretable Deep Reinforcement Learning framework (LG-IDRL) that connects Drools-based COLREGs reasoning with continuous proximal policy optimization (PPO). The rule engine identifies encounter responsibilities and urgency, supplies dynamically activated reward terms, and defines an admissible set for projecting the proposed two-dimensional rudder–acceleration action into the intersection of rule and physical limits when this set is nonempty. In five-seed held-out evaluations, LG-IDRL achieved a task success rate of $92.9\pm 2.1\%$, a collision rate of $1.6\pm 0.4\%$, an automatically and independently assessed behaviour-level rule-compliance rate of $98.7\pm 0.4\%$, and a mean path length of $16.7\pm 0.3$ nm. An adapted recent GRU–PPO–COLREG baseline achieved $89.1\pm 1.7\%$, $3.9\pm 0.9\%$, $95.8\pm 1.2\%$, and $17.4\pm 0.3$ nm, respectively, under the same evaluation protocol. The results characterize performance within six specified two-ship-encounter families; they do not establish guaranteed collision avoidance or generalization to unrestricted maritime traffic.

Understanding complex vessel behaviors in port waters is important for maritime traffic supervision, navigation safety management, and intelligent maritime decision-making. However, existing AIS-based studies often focus on isolated behavior recognition or trajectory-level analysis, with limited integration of vessel attributes, navigation scenarios, motion states, and temporally organized behavioral processes. We develop a knowledge graph-driven framework for complex vessel behavior recognition and frequent behavior sequence pattern mining using AIS data. BehaviorEvents are constructed from continuous-navigation segments and integrated with water-area scenarios, motion states, vessel attributes, and temporal relationships to form a unified semantic representation. Based on this representation, interpretable semantic rules are used for event-level complex behavior recognition, while PrefixSpan is applied to Scene–SpeedState–TurningState token sequences to discover recurrent multi-event behavior patterns. Independent expert evaluation, semantic ablation, and sensitivity analyses are used to assess recognition credibility, contextual semantic constraints, and robustness, while a vessel-level Discovery–Validation strategy evaluates the reproducibility of frequent patterns. Experiments on AIS data from Xiamen Port waters involve 16,400 vessels, 239,877 continuous-navigation segments, and 2,457,965 BehaviorEvents, of which 624,561 match at least one predefined semantic rule or candidate condition. Independent expert evaluation of R1–R7 yields a macro-average confirmation rate of 92.11% and a Cohen’s κ of 0.746. Semantic ablation shows that Scene and VesselTypeClass provide important contextual constraints on broad motion-based rule activations, while sensitivity analyses indicate that the main recognition results remain stable under perturbations of motion-state, duration, and temporal-segmentation parameters. From 75,144 valid compressed behavior-token sequences, PrefixSpan identifies recurrent patterns involving medium-speed transit with course adjustments, low-speed–stop combinations, and maneuvering-related behaviors. The dominant Top-20 patterns showed substantial overlap and broadly consistent ranking across the vessel-level Discovery and Validation subsets, with a Jaccard overlap of 0.9048 and a Spearman rank correlation of 0.9654. Comparative evaluation with a normalized relational representation further shows equivalent analytical results, while the knowledge graph provides explicit organization of semantic relationships, temporal paths, and event-level traceability. These results indicate that the proposed framework provides a unified and interpretable semantic basis for connecting event-level complex vessel behavior recognition with sequence-level frequent behavior pattern mining in complex port environments.