AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting

• URL: http://arxiv.org/abs/2508.07668v1
• Date: Mon, 11 Aug 2025 06:39:45 GMT
• Title: AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting
• Authors: Hyobin Park, Jinwook Jung, Minseok Seo, Hyunsoo Choi, Deukjae Cho, Sekil Park, Dong-Geol Choi,
• Abstract summary: We propose a novel framework, AIS-LLM, which integrates time-series AIS data with a large language model (LLM)<n>This architecture enables the simultaneous execution of three key tasks: trajectory prediction, anomaly detection, and risk assessment of vessel collisions within a single end-to-end system.<n>By integratively analyzing task outputs to generate situation summaries and briefings, AIS-LLM presents the potential for more intelligent and efficient maritime traffic management.
• Score: 7.615963953174766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increase in maritime traffic and the mandatory implementation of the Automatic Identification System (AIS), the importance and diversity of maritime traffic analysis tasks based on AIS data, such as vessel trajectory prediction, anomaly detection, and collision risk assessment, is rapidly growing. However, existing approaches tend to address these tasks individually, making it difficult to holistically consider complex maritime situations. To address this limitation, we propose a novel framework, AIS-LLM, which integrates time-series AIS data with a large language model (LLM). AIS-LLM consists of a Time-Series Encoder for processing AIS sequences, an LLM-based Prompt Encoder, a Cross-Modality Alignment Module for semantic alignment between time-series data and textual prompts, and an LLM-based Multi-Task Decoder. This architecture enables the simultaneous execution of three key tasks: trajectory prediction, anomaly detection, and risk assessment of vessel collisions within a single end-to-end system. Experimental results demonstrate that AIS-LLM outperforms existing methods across individual tasks, validating its effectiveness. Furthermore, by integratively analyzing task outputs to generate situation summaries and briefings, AIS-LLM presents the potential for more intelligent and efficient maritime traffic management.

Related papers

• Zero-shot HOI Detection with MLLM-based Detector-agnostic Interaction Recognition [71.5328300638085]
  Zero-shot Human-object interaction (HOI) detection aims to locate humans and objects in images and recognize their interactions.<n>Existing methods, including two-stage methods, tightly couple interaction recognition with a specific detector.<n>We propose a decoupled framework that separates object detection from IR and leverages multi-modal large language models (MLLMs) for zero-shot IR.
  arXiv  Detail & Related papers  (2026-02-16T19:01:31Z)
• SMc2f: Robust Scenario Mining for Robotic Autonomy from Coarse to Fine [8.662817298688147]
  RefAV is an end-to-end framework that uses large language models (LLMs) to spatially and temporally localize scenarios.<n>SMc2f is a pipeline that employs vision-language models (VLMs) for coarse image-text filtering.<n> Experiments on public datasets demonstrate substantial gains in both retrieval quality and efficiency.
  arXiv  Detail & Related papers  (2026-01-17T11:25:55Z)
• AR-MOT: Autoregressive Multi-object Tracking [56.09738000988466]
  We propose a novel autoregressive paradigm that formulates MOT as a sequence generation task within a large language model (LLM) framework.<n>This design enables the model to output structured results through flexible sequence construction, without requiring any task-specific heads.<n>To enhance region-level visual perception, we introduce an Object Tokenizer based on a pretrained detector.
  arXiv  Detail & Related papers  (2026-01-05T09:17:28Z)
• DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models [60.713908578319256]
  We propose Direct Discrepancy Learning (DDL) to optimize the detector with task-oriented knowledge.<n>Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance.<n>MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs.
  arXiv  Detail & Related papers  (2025-09-15T10:59:57Z)
• Semantic-Aware Ship Detection with Vision-Language Integration [9.49989812166076]
  Ship detection in remote sensing imagery is a critical task with wide-ranging applications, such as maritime activity monitoring, shipping logistics, and environmental studies.<n>We propose a novel detection framework that combines Vision-Language Models (VLMs) with a multi-scale adaptive sliding window strategy.<n>We evaluate our framework through three well-defined tasks, providing a comprehensive analysis of its performance and demonstrating its effectiveness in advancing SASD from multiple perspectives.
  arXiv  Detail & Related papers  (2025-08-21T19:24:52Z)
• Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction [20.1863553357121]
  Current deep learning architectures for remote sensing are fundamentally rigid.<n>We introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling.<n> STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands.<n>It unifies various dense prediction tasks and diverse semantic class predictions.
  arXiv  Detail & Related papers  (2025-05-18T07:39:17Z)
• Graph Learning-Driven Multi-Vessel Association: Fusing Multimodal Data for Maritime Intelligence [6.674254442133529]
  We propose a graph learning-driven multi-vessel association (GMvA) method tailored for maritime multimodal data fusion.<n>By integrating AIS and CCTV data, GMvA leverages time series learning and graph neural networks to capture the features of vessel trajectories effectively.<n>Experiments on real-world maritime datasets confirm that GMvA delivers superior accuracy and robustness in multi-target association.
  arXiv  Detail & Related papers  (2025-04-12T12:45:55Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Towards Unified Token Learning for Vision-Language Tracking [65.96561538356315]
  We present a vision-language (VL) tracking pipeline, termed textbfMMTrack, which casts VL tracking as a token generation task. Our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target.
  arXiv  Detail & Related papers  (2023-08-27T13:17:34Z)
• Multi model LSTM architecture for Track Association based on Automatic Identification System Data [2.094022863940315]
  We propose a Long Short-Term Memory (LSTM) based multi-model framework for track association. We evaluate the performance of our approach using standard performance metrics, such as precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2023-04-04T03:11:49Z)
• A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data [2.094022863940315]
  This study introduces a 1D CNN-LSTM architecture-based framework for track association. The proposed framework takes the marine vessel's location and motion data collected through the Automatic Identification System (AIS) as input and returns the most likely vessel track as output in real-time.
  arXiv  Detail & Related papers  (2023-03-24T15:26:49Z)
• Traj-MAE: Masked Autoencoders for Trajectory Prediction [69.7885837428344]
  Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. We propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment. Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-12T16:23:27Z)
• Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences [49.91741677556553]
  We propose TempO, a temporal ordering pretext task for pre-training region-level feature representations for perception tasks. We embed each frame by an unordered set of proposal feature vectors, a representation that is natural for object detection or tracking systems. Extensive evaluations on the BDD100K, nuImages, and MOT17 datasets show that our TempO pre-training approach outperforms single-frame self-supervised learning methods.
  arXiv  Detail & Related papers  (2023-02-17T18:18:27Z)
• A Spatio-temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data [5.453186558530502]
  Tracking objects moving in real-time in a dynamic threat environment is important in national security and surveillance system. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm. We develop atemporal approach for tracking maritime vessels as the vessel's location and motion posing observations are collected by an Automatic Identification System.
  arXiv  Detail & Related papers  (2020-10-29T20:11:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.