## 9 Future Directions and Research Opportunities
### Addressing Unresolved Challenges  
Despite these advances, several critical areas remain unresolved. One of the most pressing concerns is cybersecurity. As autonomous vessels become more connected, they face growing exposure to cyber threats. Attacks on navigation, communication, or control systems could have serious consequences. Research is needed to develop advanced encryption, intrusion detection, and secure communication protocols tailored to maritime conditions [37; 162; 163]. Another challenge is achieving full interoperability between autonomous systems and the existing maritime infrastructure. The absence of universal standards for data exchange, communication protocols, and control interfaces continues to limit seamless integration. Addressing this requires concerted efforts by organizations such as the IMO and EU to develop standardized frameworks that support compatibility across the global fleet [164]. Ethical issues also persist. Questions around responsibility, transparency, and decision-making in emergencies demand well-defined governance structures. Explainable AI and human-in-the-loop mechanisms must be further developed to ensure accountable, fair, and ethically aligned operations. At the same time, resilience remains a key concern. Autonomous systems must be capable of managing extreme weather events, mechanical failures, and other unforeseen situations with minimal human intervention. Figure 8: Integration of autonomous ships with smart port systems (own elaboration). Environmental concerns, while partially addressed by fuel optimization algorithms, also require holistic consideration. The long-term ecological impact of autonomous technologies--such as sensor deployment, battery usage, and waste management--must be assessed in full lifecycle terms. Meanwhile, human-machine interaction continues to be a limiting factor. Ensuring that operators can intervene effectively in critical situations, understand AI logic, and provide oversight when needed will be central to hybrid operation models of the near future. In advancing research and development in autonomous maritime AI systems, access to high-quality datasets and open-source platforms is essential. Several standardized and publicly available resources are now enabling reproducible experimentation and benchmarking. For instance, the LARS Dataset [165] provides maritime panoptic obstacle detection data collected in diverse environments. The WaterScenes Dataset [166] offers multi-modal data for USV perception, including RGB images, radar point clouds, GPS, and IMU data. AISHub delivers real-time and historical AIS data streams widely used in maritime traffic analytics and collision prediction models. For vision-based AI models, the SeaDronesSee Dataset provides [167] annotated video data of maritime scenes, supporting object detection and classification tasks. Furthermore, the ROS Maritime AI Stack [168] is an emerging open-source framework that extends the Robot Operating System (ROS) for use in autonomous marine robotics, offering components for navigation, sensor integration, and control systems. These resources represent foundational tools that can accelerate the development and validation of robust AI agents in the maritime domain. The future of autonomous maritime systems lies at the intersection of AI innovation, sustainable operations, and resilient infrastructure. Continued advancements in reinforcement learning, synthetic data generation, and federated learning will make AI agents more adaptive and intelligent. Closer integration with smart ports will boost efficiency across the shipping chain, while modular and scalable designs will ensure flexibility across fleets. At the same time, addressing key challenges in cybersecurity, ethics, interoperability, and resilience will require sustained interdisciplinary research. Balancing full autonomy with human collaboration, as well as innovation with regulation, is essential to unlocking the full potential of maritime AI.