## 1 Introduction  
Maritime transport plays a critical role in the global economy and in the movement of goods. It is the backbone of international trade, enabling the efficient and cost-effective transportation of vast quantities of products across the world's oceans [1]. The United Nations Conference on Trade and Development expects maritime trade volume to grow by more than 3% during the 2024-2028 period [2]. However, maritime transport faces several significant challenges, including busy ports, long journeys, major accidents, and greenhouse gas emissions. Additionally, current regional conflicts and escalating international tensions have intensified international shipping demands, thereby exacerbating the issues faced by maritime transport. For example, oil shipments reached record distances in 2022, driven by the devastation of the war in Ukraine [3]. Similarly, grain shipments traveled farther in 2023 than in any previous year, as grain importers were forced to seek alternative exporters, such as the United States and Brazil, necessitating long-distance transportation. The widespread application of intelligent technologies in the shipping industry, particularly the development of Maritime Autonomous Surface Ships (MASSs), offers promising solutions to these challenges. MASSs can enhance the efficiency and safety of maritime transport by optimizing port operations, reducing travel times, minimizing human error, and lowering emissions [4]. In 2021, the global autonomous ships market had a revenue share of over 89 million USD, and it is projected to grow at a compound annual growth rate of 6.81% through 2031 [5]. As the shipping industry continues to evolve, the integration of autonomous technology stands to address the urgent and complex problems of maritime transport, paving the way for a more resilient and sustainable future [6]. MASSs integrate a variety of advanced technologies, to achieve autonomous navigation and operation. Their core technologies include navigation systems (such as GPS, inertial navigation systems, and electronic chart display and information systems), sensing and recognition technologies (such as radar, LiDAR, and computer vision), communication systems (such as satellite communication and radio communication), data processing and artificial intelligence (such as edge-computing and machine-learning algorithms), and autonomous control systems (such as rudder and propulsion system control and automatic docking systems) [7; 8]. These technologies work in concert, to enable MASSs to autonomously navigate under various sea conditions, perform complex navigation tasks, and enhance efficiency, safety, and environmental performance. The extensive application of MASSs in the shipping industry is poised to reduce the involvement of human operators, thereby significantly mitigating the likelihood of human-related maritime accidents [9]. Autonomous navigation technology is the critical core that determines whether MASSs can safely navigate without human intervention. This technology involves the use of sensors, artificial-intelligence algorithms, and automatic control systems to enable a ship to autonomously perceive its environment, plan routes, and execute navigation tasks. Currently, the predominance of deep learning-based autonomous-navigation algorithms is observable [8]. For example, [PERSON] et al. [10] explored the use of deep learning to integrate multiple sensor modalities into autonomous-navigation algorithms for ships, allowing for decision making without human supervision. [PERSON] et al. [11] developed deep-learning algorithms for multiple target detection and tracking using sensor fusion to enhance autonomous navigation and collision avoidance for the Unmanned Surface Vehicle (USV) Aragon. However, complex traffic and water conditions, as well as various extreme situations and corner cases, pose significant challenges to deep learning-based autonomous navigation technology. This challenge is known in the deep learning field as the \"long tail\".