Objectives This paper comprehensively reviews the research status of inland intelligent vessels, systematically analyzes the development of autonomous navigation for inland ships, and identifies key challenges and technical bottlenecks in critical inland waterways

Methods Grounded in the multi-space architecture of digital-intelligent mechanics, a new paradigm for digital-intelligent navigation of inland autonomous vessels is proposed, which integrates theoretical modeling, physical experiments, simulation computing, and big data with artificial intelligence.

Results We establish a digital-intelligent navigation theoretical framework for autonomous ships in key inland waterways, clarifying the intrinsic logic of core propositions such as cross-space dynamic modeling, nonlinear intelligent computing, and cross-domain transfer learning. Furthermore, we formulate a "ship-shore-cloud" collaborative cloud-control testing platform architecture, theoretically trying to overcome the limitations of traditional single-physical-space modeling in complex inland environments.

Conclusion We outlines the development directions of navigation technologies and theoretical frameworks for autonomous inland vessels in the era of intelligent navigation, providing theoretical foundations and technical support for the continued advancement and engineering application of next-generation autonomous navigation systems.