# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 6 Conclusions  
In this study, an autonomous navigation model is developed by combining a ship collision avoidance model and a ship trajectory tracking model based on ship motion dynamics. The principal contributions of this research are twofold. First, the proposed methodology enables effective collision risk identification and generates actionable avoidance recommendations tailored to the vessel's maneuverability characteristics. Second, it incorporates dynamic detection of TS course-speed variations, intention interpretation, and adaptive decision recalibration mechanisms. Furthermore, all course-speed adjustments are executed through a holistic framework that systematically integrates inland waterway environmental constraints, good seamanship, regulatory compliance, and vessel maneuvering performance limitations, ensuring reliable performance in multi-vessel collision avoidance scenarios within complex inland waterway environments.  
While simulation results demonstrate the effectiveness of the proposed method, several limitations warrant further investigation. Firstly, the current model oversimplifies target ship behavior by exclusively considering course alterations as collision avoidance maneuvers, overlooking the complexity of real-world navigation scenarios. Furthermore, the ship motion model incorporates only wind and current environmental influences, whereas practical navigation environments involve multiple factors--including bank effects, ship-to-ship interactions, and shallow-water effects--that significantly impact vessel maneuverability and induce nonlinear steering responses, which remain insufficiently captured in the current framework. Additionally, the model's treatment of target vessel dynamics remains constrained, with motion characteristics simplified into a uniform non-linear pattern, contrasting with the more sophisticated kinematic modeling applied to the OS. This discrepancy in motion representation may compromise the accuracy of collision risk identification and avoidance strategy formulation.  
To enhance the applicability of the proposed method in practical inland tributary navigation environments, future work will incorporate vessel motion models considering shallow-water effects, bank effects, and ship-to-ship interaction effects. Autonomous navigation simulations will be conducted using real-world AIS data. Additionally, addressing highly complex encounter scenarios through sequential course and speed adjustments to achieve autonomous collision avoidance remains a critical research direction. In addition, we will also consider a hybrid approach combining the methodological framework of this paper with machine learning to achieve faster iterative decision updating in more complex encounter scenarios.  
**Author Contributions:** Conceptualization, J.C.; methodology, J.C.; software, J.C.; validation, J.C.; formal analysis, J.C.; investigation, J.C.; resources, J.C.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C., L.H., L.X., H.L., G.H. and Y.H.; visualization, J.C.; supervision, L.H. and Y.H.; project administration, L.H. and Y.H.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partially supported by the Research Program of Hubei Key Laboratory of Inland Shipping Technology, grant number NHHY2023004, and Hubei Provincial Natural Science Foundation of China, grant number 2023 AFB148.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.