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首页> 外文期刊>International Journal of Performability Engineering >Collision Avoidance Situation Matching with Vessel Maneuvering Actions Identification from Vessel Trajectories
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Collision Avoidance Situation Matching with Vessel Maneuvering Actions Identification from Vessel Trajectories

机译：碰撞避免情况与船舶轨迹的船舶机动行动识别

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摘要

Vessel trajectories implied in AIS data are crucial to obtain a good understanding of the maritime traffic situation for shipping safety. Starting from raw AIS data, a trajectory database is created for vessels within surveillance area after parsing, noise reduction, and DBSCAN clustering. With mmsi as the key index, the trajectory for each vessel is extracted ordering by timestamp. To remove the time interval difference between points in trajectories, interpolation and cleaning are carried out on each vessel trajectory to get trajectories with equal time intervals. Through implied motion pattern computation between adjacent points in each trajectory, maneuvering actions can be identified. Then, sailing segments with continuous same maneuvering actions are merged. With sailing segments partition results, critical points are extracted for already known different collision avoidance situations. Trajectory similarity computation for different vessels are computed with our new multi-scale and multi-resolution trajectory matching method. Experiments for the recognition of collision avoidance situations show that the adoption of the matching algorithm with multi-scale and multi-resolution trajectories for different vessel pairs to complete collision avoidance situations analysis is effective and achieves good performance.
机译：AIS数据中暗示的船舶轨迹至关重要，以便对海上交通情况进行良好的运输安全性。从RAW AIS数据开始，解析后监视区域内的船只为船舶创建轨迹数据库，降噪和DBSCAN聚类。使用MMSI作为关键索引，通过时间戳提取每个船只的轨迹。为了消除轨迹点之间的点之间的时间间隔差，在每个血管轨迹上执行插值和清洁，以获得具有相等时间间隔的轨迹。通过在每个轨迹中的相邻点之间的隐含运动模式计算，可以识别机动动作。然后，合并具有持续相同的机动操作的帆船段。利用帆船段分区结果，提取关键点以用于已知的不同碰撞避免情况。通过我们的新多尺度和多分辨率轨迹匹配方法计算不同船只的轨迹相似性计算。识别碰撞避免情况的实验表明，采用不同血管对多尺度和多分辨率轨迹的匹配算法，以完成碰撞避免情况分析是有效的，实现了良好的性能。

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