Ferries are usually used for transporting passengers and vehicles among docks, and any deviation of the course can lead to serious consequences. Therefore, transportation ferries must be watched closely by local maritime administrators, which involves much manpower. With the use of historical data, this article proposes an intelligent method of integrating artificial potential field with Bayesian Network to trigger deviation warnings for a ferry based on its trajectory, speed and course. More specifically, a repulsive potential field-based model is first established to capture a customary waterway of ferries. Subsequently, a method based on non-linear optimisation is introduced to train the coefficients of the proposed repulsive potential field. The deviation of a ferry from the customary route can then be quantified by the potential field. Bayesian Network is further introduced to trigger deviation warnings in accordance with the distribution of deviation values, speeds and courses. Finally, the proposed approach is validated by the historical data of a chosen ferry on a specific route. The testing results show that the approach is capable of providing deviation warnings for ferries accurately and can offer a practical solution for maritime supervision.

中文翻译：

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基于人工势场和历史数据的渡轮偏差预警

渡轮通常用于在码头之间运送乘客和车辆，任何偏离航线都会导致严重后果。因此，运输渡轮必须受到当地海事管理人员的密切关注，涉及大量人力。本文利用历史数据，提出了一种将人工势场与贝叶斯网络相结合的智能方法，根据航迹、速度和航向触发轮渡偏差预警。更具体地说，首先建立了一个基于排斥势场的模型来捕获渡轮的习惯水道。随后，引入了一种基于非线性优化的方法来训练所提出的排斥势场的系数。然后可以通过势场量化渡轮与惯用路线的偏差。进一步引入贝叶斯网络，根据偏差值、速度和航向的分布触发偏差警告。最后，所提出的方法通过特定航线上所选渡轮的历史数据进行验证。测试结果表明，该方法能够准确地为渡轮提供偏航预警，为海事监管提供切实可行的解决方案。