Abstract
Maritime Search and Rescue (MSAR) operations face significant challenges due to high uncertainty,
dynamic conditions, and resource constraints. Additionally, rigid organizational structures and hierarchical
human-centered communication frameworks, fail to adapt to the challenging conditions of maritime
environments. This paper provides a comprehensive review of the integration of Artificial Intelligence (AI)
into MSAR operations, highlighting how AI can transform these systems through enhanced decision-
making, real-time adaptability, decentralized autonomy, and resource optimization. Through analysis and
synthesis, we identified and categorized key challenges in traditional SAR frameworks, such as inherent
environmental and structural challenges. We discussed AI-driven solutions that offer efficient, autonomous,
resilient, and decentralized coordination. Our thematic and statistical analysis of existing literature reveals
significant research gaps, particularly regarding the holistic integration of AI across all SAR stages toward a
decentralized fully autonomous paradigm shift. The paper also considers the technological challenges for the
integration and adaptation of AI in SAR. By envisioning fully autonomous, AI-driven MSAR operations,
this study sets the stage for future research and practical innovations, aiming to improve effectiveness and
efficiency in maritime rescue efforts.
dynamic conditions, and resource constraints. Additionally, rigid organizational structures and hierarchical
human-centered communication frameworks, fail to adapt to the challenging conditions of maritime
environments. This paper provides a comprehensive review of the integration of Artificial Intelligence (AI)
into MSAR operations, highlighting how AI can transform these systems through enhanced decision-
making, real-time adaptability, decentralized autonomy, and resource optimization. Through analysis and
synthesis, we identified and categorized key challenges in traditional SAR frameworks, such as inherent
environmental and structural challenges. We discussed AI-driven solutions that offer efficient, autonomous,
resilient, and decentralized coordination. Our thematic and statistical analysis of existing literature reveals
significant research gaps, particularly regarding the holistic integration of AI across all SAR stages toward a
decentralized fully autonomous paradigm shift. The paper also considers the technological challenges for the
integration and adaptation of AI in SAR. By envisioning fully autonomous, AI-driven MSAR operations,
this study sets the stage for future research and practical innovations, aiming to improve effectiveness and
efficiency in maritime rescue efforts.
| Original language | English |
|---|---|
| Article number | 106692 |
| Journal | Marine Policy |
| Volume | 178 |
| Number of pages | 23 |
| ISSN | 0308-597X |
| DOIs | |
| Publication status | Accepted/In press - 19 Mar 2025 |
Keywords
- AI-driven decision making system
- Artificial intelligence
- Dynamic uncertain environment
- Marine governance
- Maritime search rescue
- System architecture
