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

arXiv: https://arxiv.org/abs/2210.08487 · HTML: https://mlsanity.com/paper/2210.08487/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 242 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks](https://mlsanity.com/paper/1806.04331/) (2018)
- [Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network](https://mlsanity.com/paper/1806.04828/) (2018)
- [Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles](https://mlsanity.com/paper/2008.07240/) (2020)
- [Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning](https://mlsanity.com/paper/1912.08578/) (2019)
- [A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box](https://mlsanity.com/paper/2004.07124/) (2020)
- [Ship classification from overhead imagery using synthetic data and domain adaptation](https://mlsanity.com/paper/1905.03894/) (2019)
- [Roboat II: A Novel Autonomous Surface Vessel for Urban Environments](https://mlsanity.com/paper/2007.10220/) (2020)

## Built on by (most cited first, 11 of 11)

- [NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar](https://mlsanity.com/paper/2408.17207/) (2024)
- [A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation](https://mlsanity.com/paper/2407.08263/) (2024)
- [UnderwaterVLA: Dual-brain Vision-Language-Action architecture for Autonomous Underwater Navigation](https://mlsanity.com/paper/2509.22441/) (2025)
- [SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles](https://mlsanity.com/paper/2311.11580/) (2023)
- [Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests](https://mlsanity.com/paper/2505.10033/) (2025)
- [Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping](https://mlsanity.com/paper/2411.04915/) (2024)
- [Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring](https://mlsanity.com/paper/2410.05892/) (2024)
- [ARBoids: Adaptive Residual Reinforcement Learning With Boids Model for Cooperative Multi-USV Target Defense](https://mlsanity.com/paper/2502.18549/) (2025)
- [Predictive Modeling of Maritime Radar Data Using Transformer Architecture](https://mlsanity.com/paper/2512.17098/) (2025)
- [Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture](https://mlsanity.com/paper/2605.02529/) (2026)
- [PAANI : On Device Visual Evidence Fusion and Explainable Guidance for River Robot Simulation](https://mlsanity.com/paper/2609.22353/) (2026)

## Abstract (arXiv)

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.