## 6 Discussion  
The development and implementation of the S-MTGS marks a significant leap forward in the verification of key technologies for MASS. The synthetic nature of the maritime traffic scenarios generated by the S-MTGS allows for comprehensive evaluation of MASS technologies in various conditions, including challenging and extreme situations. In this discussion, we delve into the implications of the S-MTGS and explore its advantages and limitations. The integration of a maritime traffic GAP and GRP within the S-MTGS is a noteworthy feature. This dual-generator approach enables the creation of scenarios that mimic real-world interactions between virtual ships, enhancing the authenticity of the verification process. The utilization of historical maritime data further contributes to the realism of the generated scenarios. This integration ensures that MASS technologies undergo thorough testing under diverse conditions, addressing the need for reliability in both standard and extreme maritime situations. One of the strengths of the S-MTGS lies in its ability to facilitate the generation of diverse scenarios effortlessly. Traditional methods often require individual setup for each specific scenario, such as head-on encounters. With the S-MTGS, scenarios can be generated randomly or based on a normal distribution, streamlining the process and allowing for a more comprehensive assessment of MASS technologies. This efficiency is crucial in accelerating the development and deployment of autonomous maritime navigation systems. Furthermore, the dynamic adaptation of scenarios to the ship's position is a key advantage offered by the S-MTGS. In complex situations involving waypoints, the system ensures that secondary scenario ships can be generated even if a MASS deviates from its planned route due to collision avoidance maneuvers. This dynamic feature enables effective testing and evaluation of MASS performance under realistic conditions, surpassing the limitations of traditional methods restricted to scenarios crafted around the ship's planned route. However, it is crucial to acknowledge the limitations of the current study, which include three key aspects. First, the uniformity in collision avoidance patterns exhibited by all virtual ships due to using a single collision avoidance algorithm represents a significant constraint. This uniform approach does not fully capture the diverse range of collision risks and behavioral patterns of noncompliant virtual ships found in real-world maritime scenarios. Therefore, future studies should focus on diversifying collision avoidance patterns in order to enhance the reliability of performance verification. Second, as the system proposed in this paper is aimed at evaluating other systems, it poses a unique challenge  
Figure 10: Multi-ship scenario: head-on situation generated by S-MTGS. when assessing its own reliability. The complexity of evaluating a system designed to assess others is a critical task that has not been extensively analyzed in our current research. Recognizing this as a limitation, we intend to undertake a thorough reliability assessment in our future research endeavors. This analysis will be vital to ensure the effectiveness and accuracy of our system in evaluating other systems, thereby significantly enhancing the robustness and applicability of our findings. Third, the proposed system aims to evaluate other systems by creating virtual ships using stored historical maritime traffic data or encounter situations with the own ship. In principle, providing a real-life traffic situation simulation environment is not considered. To provide a real-life traffic situation simulation environment, the geographical size of the maritime traffic environment to be reproduced and the number of ships within it must be considered, and computing power requirements must be determined accordingly. To implement this, there are techniques that can preferentially process ships within the field of view, techniques to process only moving ship data, etc.