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Within the next several years, there will be a high level of autonomoustechnology that will be available for widespread use, which will reduce laborcosts, increase safety, save energy, enable difficult unmanned tasks in harshenvironments, and eliminate human error. Compared to software development forother autonomous vehicles, maritime software development, especially on agingbut still functional fleets, is described as being in a very early and emergingphase. This introduces very large challenges and opportunities for researchersand engineers to develop maritime autonomous systems. Recent progress in sensorand communication technology has introduced the use of autonomous surfacevehicles (ASVs) in applications such as coastline surveillance, oceanographicobservation, 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, hasbrought the concept of full autonomy one step closer to reality. This papersurveys the existing work regarding the implementation of DL methods inASV-related fields. First, the scope of this work is described after reviewingsurveys on ASV developments and technologies, which draws attention to theresearch 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 andfuture research directions.

Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment