基于变压器的多船轨迹预测与碰撞风险评估框架
Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment
- 采用并行流结构联合预测多船未来轨迹，融合运动与物理特征
- 在真实AIS数据上表现优于传统单船模型，支持多船协同评估
- 可量化碰撞风险，适合航海安全与智能决策系统应用
准确的船舶轨迹预测对提升态势感知和防止碰撞至关重要。现有数据驱动模型主要局限于单船预测，忽视船只间交互、航行规则及明确的碰撞风险评估。本文提出一种基于变压器的多船轨迹预测与碰撞风险分析框架。针对目标船，框架识别其周围船只，并通过并行流联合预测它们的未来轨迹：编码运动学与衍生物理特征，使用因果卷积捕捉时间局部性，空间变换实现位置编码，混合位置嵌入同时捕获局部运动模式与长程依赖。在大规模真实世界AIS数据上，以多船联合指标评估，模型表现出超越传统单船位移误差的预测能力。通过模拟预测轨迹间的交互，框架进一步量化潜在碰撞风险，为增强海上安全与决策支持提供可操作洞察。
原文摘要 · Abstract (English)
Accurate vessel trajectory prediction is essential for enhancing situational awareness and preventing collisions. Still, existing data-driven models are constrained mainly to single-vessel forecasting, overlooking vessel interactions, navigation rules, and explicit collision risk assessment. We present a transformer-based framework for multi-vessel trajectory prediction with integrated collision risk analysis. For a given target vessel, the framework identifies nearby vessels. It jointly predicts their future trajectories through parallel streams encoding kinematic and derived physical features, causal convolutions for temporal locality, spatial transformations for positional encoding, and hybrid positional embeddings that capture both local motion patterns and long-range dependencies. Evaluated on large-scale real-world AIS data using joint multi-vessel metrics, the model demonstrates superior forecasting capabilities beyond traditional single-vessel displacement errors. By simulating interactions among predicted trajectories, the framework further quantifies potential collision risks, offering actionable insights to strengthen maritime safety and decision support.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。