提升船舶避碰预测精度，融合航行意图与真实航线信息
Design and Validation of an Intention-Aware Probabilistic Framework for Trajectory Prediction: Integrating COLREGS, Grounding Hazards, and Planned Routes
- 用动态贝叶斯网络融合船位、航向及航线规划信息
- 在真实AIS数据上验证，预测准确率显著优于基线模型
- 适合自动驾驶船舶路径规划与海事安全研究者
自主船舶导航系统中的碰撞规避能力至关重要，精准预测动态障碍物轨迹是关键。传统方法泛化能力差，且难以捕捉其他船舶的航行意图。尽管近期研究已尝试引入意图建模，但多基于本船视角理解情境。当前最先进的方法是动态贝叶斯网络（DBN），可综合多种潜在原因并支持不同船只对情境的不同解读。然而自提出以来，该模型结构未有实质性改进。本文通过引入搁浅风险因素与船舶航路点信息，增强原有DBN模型。所提方法利用历史自动识别系统（AIS）数据中提取的真实船船相遇案例进行验证。
原文摘要 · Abstract (English)
Collision avoidance capability is an essential component in an autonomous vessel navigation system. To this end, an accurate prediction of dynamic obstacle trajectories is vital. Traditional approaches to trajectory prediction face limitations in generalizability and often fail to account for the intentions of other vessels. While recent research has considered incorporating the intentions of dynamic obstacles, these efforts are typically based on the own-ship's interpretation of the situation. The current state-of-the-art in this area is a Dynamic Bayesian Network (DBN) model, which infers target vessel intentions by considering multiple underlying causes and allowing for different interpretations of the situation by different vessels. However, since its inception, there have not been any significant structural improvements to this model. In this paper, we propose enhancing the DBN model by incorporating considerations for grounding hazards and vessel waypoint information. The proposed model is validated using real vessel encounters extracted from historical Automatic Identification System (AIS) data.
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