arXiv AI June 12, 2026 By Yongmin Kim, ByeongHoon Jeon, Sungil Kim

Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection

arXiv:2606. 13311v1 Announce Type: cross Abstract: Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where rare regimes can be critical information.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv AI

Jun 30

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

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.

By Youngseok Hwang, Sungho Bae, Dohun Lee, Jaeeun Seo, Jeehong Kim, Wonhee Lee, Hyunwoo Park

llms benchmarks safety

arXiv AI

Jun 17

MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories

arXiv:2606. 17978v1 Announce Type: new Abstract: Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection.

By Ruixin Song, Md Mahbub Alam, Zahra Sadeghi, Amilcar Soares, Jos\'e F. Rodrigues-Jr, Gabriel Spadon

rag benchmarks

arXiv Machine Learning

Jul 31

Context-Informed Ship Trajectory Prediction via Conditional Attention

arXiv:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.

By Yuan Guan, Chandler Squires, Timothy Hu, Pradeep Ravikumar

llms agents multimodal safety

arXiv Machine Learning

Aug 12

STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data

arXiv:2608. 10249v1 Announce Type: new Abstract: We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives.

By Bertram Hage, Alexander Schi{\o}tz, Felix Thomsen, Christian Rand, Peder Heiselberg

arXiv Machine Learning

Aug 4

From Vessel Trajectories to Safety-Critical Encounter Scenarios: A Generative AI Framework for Autonomous Ship Digital Testing

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.

By Sijin Sun, Liangbin Zhao, Xiuju Fu

agents benchmarks safety

Hugging Face Trending Papers

Aug 10

STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data

We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives. Variable-length trajectories are encoded with a custom BERT-based model trained via masked token modeling and clustered using CURE hierarchical clustering, producing physically interpretable trajectory groups without requiring a predefined number of clusters.

Related stories

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories

Context-Informed Ship Trajectory Prediction via Conditional Attention

STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data

From Vessel Trajectories to Safety-Critical Encounter Scenarios: A Generative AI Framework for Autonomous Ship Digital Testing

STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data