標題: CAPatternMiner: Mining Ship Collision Avoidance Behavior from AIS Trajectory Data
作者: Lei, Po-Ruey
Xiao, Li-Pin
Wen, Yu-Ting
Peng, Wen-Chih
交大名義發表
National Chiao Tung University
關鍵字: AIS trajectory data;ship collision avoidance;conflict detection;collision avoidance pattern mining
公開日期: 1-Jan-2018
摘要: The improvement of collision avoidance for ship navigation in encounter situation is an important topic in maritime traffic safety. Most research on maritime collision avoidance has focused on planning a safe path for a ship to keep away from the approaching ship under the requirements of the International Regulations for Preventing Collision at Sea (COLREGs). However, the specific anticollision actions are actually carried out by the navigators' own experience according to the local encounter situation. In this paper, different from the existing works, we discover the collision avoidance behavior from real ships' movement, i.e., AIS trajectory data. However, the uncertainty of maritime trajectory data brings the challenge of collision avoidance behavior mining. To achieve our goal, we propose CAPatternMiner to provide a framework to discover the ships' anti-collision behavior, which is effective in the encounter situation, and generate the discovered behavior in form of collision avoidance pattern. Furthermore, a prototype of CAPatternMiner is built for pattern analysis and visualization and also benefits a deeper understanding of collision avoidance behavior on maritime traffic. The proposed framework will be applied to the developing of pattern-aware collision avoidance system to improve the maritime traffic safety.
URI: http://dx.doi.org/10.1145/3269206.3269221
http://hdl.handle.net/11536/150984
DOI: 10.1145/3269206.3269221
期刊: CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
起始頁: 1875
結束頁: 1878
Appears in Collections:Conferences Paper