# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 1 Introduction
### Literature Review  
The advancement of autonomous and unmanned ship technologies represents a critical frontier in maritime research, with breakthroughs in autonomous navigation systems significantly enhancing marine operational efficiency. As these technologies mature, numerous navigation frameworks and computational models [5] have been developed, enabling novel solutions to complex navigational challenges, particularly in collision risk mitigation and collision avoidance strategies. Research on ship collision avoidance decision-making began in the 1950s and has transitioned from qualitative to quantitative analysis. Quantifying collision risk is the first step toward autonomous collision avoidance. Current studies primarily utilize the Collision Risk Index (CRI) [6] to identify collision risk, which can be categorized into macro and micro perspectives [7]. Early quantitative research on microscopic ship collision risk focused on the Distance to the Closest Point of Approach (DCPA) and the Time to the Closest Point of Approach (TCPA) [8; 9; 10]. [PERSON] [11] later introduced a method to quantify collision risk using a weighted sum of the DCPA and TCPA. The curvature of inland waterways necessitates frequent vessel course adjustments, causing erratic DCPA fluctuations that deviate substantially from actual closest encounter distances. Conventional DCPA and TCPA calculations fail to reliably identify collision risks in such scenarios due to constrained waterways' dynamic navigation patterns [12]. Subsequently, artificial intelligence has been progressively integrated into collision risk calculations [13; 14; 15]. [PERSON] [16] developed an SVM-based algorithm integrating ship domain interactions for risk quantification, while [PERSON], [PERSON] [17] proposed evidence theory to overcome neural networks' accuracy limitations and computational inefficiencies in such assessments. For specific waterways, researchers utilize AIS data to calculate collision risks. For instance, [PERSON] [18] leveraged AIS data combined with environmental, vessel, and meteorological parameters to evaluate navigation safety during port entry/exit maneuvers. Ship encounter dynamics identification forms the basis for collision avoidance decisions following risk quantification. Defined by vessels' relative positions, velocities, and courses [19], encounter situations determine navigation right-of-way allocation [20]. Most researchers assess encounter situations using parameters such as inter-ship distance and relative angles [21; 22; 23], with [PERSON] et al. [24; 25] employing fuzzy statistical methods for classification through relative angle membership functions. [PERSON] [26] established an identification model via collision rule analysis; conventional spatial parameter analyses often neglect spatiotemporal evolutionary patterns. [PERSON] [27] resolved this limitation through spatiotemporal feature integration and SVM-based classification systems. However, current research predominantly assumes stable inter-vessel relative motion in open-water scenarios,thereby disregarding the constraining effects of navigational environments on the dynamic evolution of ship encounter situations. Autonomous collision avoidance technology is a core component of autonomous ship navigation, requiring consideration of navigation rules, encounter situations, ship maneuverability, and environmental constraints to generate collision-free maneuvering schemes [28; 29; 30; 31]. Geometric approaches derive collision parameters (e.g., DCPA/TCPA) from encounter geometry for navigational guidance. While [PERSON]'s [32] two-ship collision avoidance decision framework prioritizes evasive maneuvers over tracking, it risks rule violations in complex scenarios. The Velocity Obstacle (VO) method, introduced by [PERSON] [33] in 1998, is widely applied to mobile robot obstacle avoidance. [PERSON] [34] developed a dynamic Reciprocal Velocity Obstacle (RVO) method by incorporating COLREGs-based responsibility allocation. [PERSON] et al.