AUV Intelligent Decision-Making System Empowered by Deep Learning: Evolution, Challenges and Future Prospects

The intelligent decision-making systems of Autonomous Underwater Vehicles (AUVs) are undergoing a significant transformation, shifting from traditional control theories to data-driven paradigms. Deep learning (DL) serves as the primary driving force behind this evolution; however, its application in complex and unstructured underwater environments continues to present unique challenges. To systematically analyze the development, current obstacles, and future directions of DL-enhanced AUV decision-making systems, this paper proposes an innovative ‘four-module’ decomposition framework consisting of information processing, understanding, judgment, and output. This framework enables a structured review of the progression of DL technologies across each stage of the AUV decision-making information flow. To further bridge the gap between theoretical advancements and practical implementation, we introduce a task complexity–environment uncertainty four-quadrant analytical matrix, offering strategic guidance for selecting appropriate DL architectures across diverse operational scenarios. Additionally, this work identifies key challenges in the field as well as anticipates future developments to solve these challenges. This paper aims to provide researchers and engineers with a comprehensive and strategic overview to support the design and optimization of next-generation AUV decision-making architectures.