  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 on aging
but still functional fleets, is described as being in a very early and emerging
phase. This introduces very large 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 paper
surveys the existing work regarding the implementation of DL methods in
ASV-related fields. 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 the current challenges and
future research directions.
