# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 5 Discussion  
### Result and Discussion  
The core autonomous navigation decision-making algorithm combines the MMG model-based ship collision avoidance motion process derivation and the exhaustive course-speed maneuver method, and successfully identifies the navigation intention of TSs and realizes autonomous collision avoidance and navigation tracking under the multi-ship encounter scenarios under the premise of conforming to the ships' motion characteristics  
Figure 19: Autonomous navigation trajectories of scenario 3.  
Figure 20: Multi-ship encounter data of scenario 3.  
and the navigation rules. The feasibility and effectiveness of the autonomous navigation decision-making algorithm in inland tributary waterway scenarios are proved through several simulation experiments. The results and discussions are as follows.  
The trajectory tracking methodology demonstrates consistent navigational performance in both linear and curvilinear channel segments, as evidenced by the simulation results shown in Figure 15. The ship's path-following errors relative to prescribed recommended routes remain constrained within acceptable operational thresholds throughout all navigation phases. In single-ship encounter scenario 1, the OS successfully anticipates the TS's navigational intent through real-time analysis of its maneuvering patterns, thereby optimizing the collision avoidance strategy. This adaptive approach enables the simultaneous execution of coordinated avoidance maneuvers and precise route tracking while maintaining operational efficiency. In multi-ship encounter scenario 2, the collision risk between the OS and TS\({}_{3}\) reaches maximum intensity. Collision avoidance can be achieved through portside maneuvers or speed reduction, though the methodology requires dual considerations: eliminating immediate collision threats while preventing new navigational conflicts with TS\({}_{1}\) and TS\({}_{7}\). Comparative analysis reveals that speed reduction minimizes heading alterations, proving optimal for narrow channel operations. Consequently, the OS maintains the original heading while decelerating to follow recommended routes, resuming speed to overtake TS\({}_{2}\). Scenario 3 necessitates combined maneuvers when TS\({}_{7}\) completes its turn, as speed reduction alone proves insufficient. The implemented strategy integrates continuous deceleration with turning 9\({}^{\circ}\) to starboard (276\({}^{\circ}\)\(\rightarrow\)285\({}^{\circ}\)), balancing safety margins and channel constraints. Navigational safety is confirmed through relative distance metrics in Figures 18 and 20, demonstrating sustained separation exceeding minimum safety thresholds between the OS and all TSs, while channel boundary compliance remains intact throughout the maneuvers.  
The proposed collision avoidance scheme employs an exhaustive search method with iterative computational validation. However, by pre-filtering solutions that violate environmental constraints, ship maneuverability limits, navigation rules, or good seamanship, we significantly narrow the search space before optimization. This strategy not only reduces computational complexity but also mitigates the risk of local optima, which is a common pitfall in metaheuristic approaches like genetic algorithms or particle swarm optimization [42]. In contrast, our method ensures globally optimal solutions through constrained brute-force verification. Coupled with an optimized algorithmic architecture, this framework achieves real-time performance (\(\sim\)0.1 s per decision cycle) while maintaining superior stability compared to stochastic intelligent algorithms, which often exhibit inconsistent outputs under similar multi-constraint scenarios.