arXiv Machine Learning August 4, 2026 By Sijin Sun, Liangbin Zhao, Xiuju Fu

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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NaviAIS: A Scenario-Level Vessel Trajectory Prediction Dataset withVectorized Lane Priors and the NaviLane Forecasting Framework

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EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting

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EnvShip-Bench: An Environment-Enhanced Benchmark for Short-Term Vessel Trajectory Prediction

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SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

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