SKETCH: 長期的な船舶軌道予測のための意味的キーポイント条件付け
SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
将来の軌道を高レベルの次キーポイント（NKP）で条件付けし、長期予測を大域的意図決定と局所運動モデリングに分解することで、船舶軌道の長期的な予測精度を向上させた研究。
詳しい要約
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著者: Linyong Gan, Zimo Li, Wenxin Xu, Xingjian Li, Jianhua Z. Huang, Enmei Tu, Shuhang Chen
分類: cs.RO, cs.AI
原文アブストラクト
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 navigational intent. This formulation decomposes long-horizon prediction into global semantic decision-making and local motion modeling, effectively restricting the support of future trajectories to semantically feasible subsets. To efficiently estimate the NKP prior from historical observations, we adopt a pretrain-finetune strategy. Extensive experiments on real-world AIS data demonstrate that the proposed method consistently outperforms state-of-the-art approaches, particularly for long travel durations, directional accuracy, and fine-grained trajectory prediction.
