Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments

作者：Yuanyuan Qiao, Jiaxin Yin, Wei Wang, Fábio Duarte, Jie Yang, Carlo Ratti · 发表于：IEEE Transactions on Intelligent Transportation Systems · 年份：2023 · DOI：10.1109/tits.2023.3235911 · 被引用次数：165 · 研究领域：Maritime Navigation and Safety、Underwater Vehicles and Communication Systems、Oil Spill Detection and Mitigation

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current ch...