弹道
标杆管理
领域(数学)
计算机科学
领域(数学分析)
计算智能
电流(流体)
工程类
运筹学
运输工程
人工智能
纯数学
天文
营销
业务
数学分析
物理
电气工程
数学
作者
Xiaocai Zhang,Xiuju Fu,Zhe Xiao,Haiyan Xu,Zheng Qin
标识
DOI:10.1109/tits.2022.3192574
摘要
The growing availability of maritime IoT traffic data and continuous expansion of the maritime traffic volume, serving as the driving fuel, propel the latest Artificial Intelligence (AI) studies in the maritime domain. Among the most recent advancements, vessel trajectory prediction is one of the most essential topics for assuring maritime transportation safety, intelligence, and efficiency. This paper presents an up-to-date review of existing approaches, including state-of-the-art deep learning, for vessel trajectory prediction. We provide a detailed explanation of data sources and methodologies used in the vessel trajectory prediction studies, highlight a discussion regarding the auxiliary techniques, complexity analysis, benchmarking, performance evaluation, and performance improvement for vessel trajectory prediction research, and finally summarize the current challenges and future research directions in this field.
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