arXiv:2601.18537v3 Announce Type: replace-cross Abstract: Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors. Existing methods often struggle to maintain global directional consistency, leading to drifting or implausible trajectories when extrapolated over long time horizons. To address this issue, we propose a semantic-key-point-conditioned trajectory modeling framework, in which future trajectories are predicted by conditioning on a high-level Next Key Point (NKP) that captures navig
The paper was recently published, demonstrating an incremental advancement in AI trajectory prediction, a key component for autonomous navigation.
Improved long-horizon trajectory prediction directly impacts the reliability and safety of autonomous systems, especially in complex environments like maritime navigation.
The proposed method, SKETCH, offers a more robust approach to maintaining directional consistency in long-term vessel trajectory predictions, reducing the risk of implausible outcomes.
- · Autonomous shipping companies
- · Maritime logistics
- · AI navigation software developers
- · Defense contractors utilizing autonomous vessels
- · Legacy vessel navigation systems
- · Companies relying on less accurate prediction models
More reliable autonomous navigation for commercial and military vessels, leading to increased adoption.
Reduced human intervention in vessel operations, potentially impacting maritime labor markets and increasing efficiency.
Enhanced strategic capabilities for navies through more sophisticated autonomous fleets and improved mission planning.
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Read at arXiv cs.AI