# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 1 Introduction
### Motivation and Contribution  
Currently, significant progress has been made in autonomous navigation research. However, most studies have predominantly focused on open-sea navigation under favorable weather conditions, which--while nearing practical maturity--reveal a critical gap in addressing inland tributary waterways. To address this gap, this article systematically identifies core challenges in autonomous navigation decision-making for inland ships through three key research deficiencies:A methodology integrating RPRCIRCA compliance and good seamanship for collision risk identification in inland tributaries remains absent. The complexity and variability of inland navigation environments (e.g., channel dynamics, hydrographic conditions, and traffic patterns) render conventional indicator-based risk identification methods inadequate.
2. Adaptive optimization frameworks for autonomous navigation decisions are underdeveloped. In dynamic inland river environments, incomplete or inaccurate information may lead to prediction errors in ship maneuvering processes and target vessel trajectories. Furthermore, existing methods lack mechanisms to adaptively adjust decision-making based on real-time error feedback.
3. Current autonomous navigation decision-making methods insufficiently address the interplay between ship maneuvering dynamics and environmental constraints. Specifically, the nonlinear maneuvering characteristics of underactuated ships and time-varying traffic environments--critical factors influencing collision avoidance decisions--are not yet fully quantified.  
In response to the challenges posed by inland tributary waterways, an advanced autonomous navigation decision-making method is proposed. This method is designed to detect potential collision risks and provide ships with actionable guidance on when and whether to execute evasive maneuvers. Adhering to the requirements of the RPRCIRCA and good seamanship, a position projection method integrated with a ship domain model is developed to accurately assess collision risks in inland tributary waterways. By considering the avoidance intentions of the target ship, the collision avoidance principles and optimal timing of actions are determined for the current encounter scenario. Furthermore, based on the predicted behavior of the target ship, a collision avoidance decision-making model is proposed. This model combines a ship maneuvering motion model and course control method with an inland waterway route tracking technique to derive the optimal autonomous navigation decision-making strategy for the given situation. The proposed autonomous navigation decision-making scheme is dynamically adjusted in response to changes in ship position, demonstrating enhanced robustness in handling complex and variable inland tributary waterway encounters. Additionally, the method incorporates considerations of ship maneuverability, compliance with the RPRCIRCA, and adherence to good seamanship, making it well suited for autonomous navigation decision-making in complex inland tributary waterways.