## 1 Introduction
This integrated approach allows autonomous ships to navigate predefined paths while efficiently avoiding static obstacles. [PERSON] et al. [24] presented a waypoint-following algorithm for a marine surface ship model, incorporating a PID heading autopilot controller based on [PERSON]'s second model. The controller parameters are identified using a least squares support vector machine and a smooth curved path is generated through piecewise cubic Hermite interpolation to minimize errors. [PERSON] et al. [25] proposed a methodology in which two autonomous surface vehicles (ASVs) demonstrate advanced cooperative navigation in a complex marine environment, utilizing the angle-guidance fast marching square method for motion planning and vector-field path-following for enhanced path accuracy. Equipped with state-of-the-art sensors and a PID control system, these ASVs effectively navigate complex environments, showcasing the efficacy of their technical approach in controlled experimental conditions. [PERSON] et al. [26] developed collision avoidance algorithms with partial adherence to COLREG as an ongoing process over several years. Similarly, [PERSON] [27] made significant contributions by emphasizing the importance of COLREG compliance, incorporating human-like behavior, and addressing scenarios involving non-compliant actors. [PERSON] et al. [28] utilized mathematical interpretations of COLREG rules to frame the challenge of devising a protocol-compliant and collision-free trajectory within the framework of Model Predictive Control (MPC). [PERSON] et al. [29] and [PERSON] et al. [30] subsequently demonstrated field applications of MPC-based collision avoidance algorithms. However, a notable limitation in most proposed algorithms is their assumption of access to nearly perfect information. Moreover, the utilization of information obtained through communication channels beyond the Automatic Identification System (AIS) is seldom integrated into these algorithms. Technology verification is the process of verifying the effective operation of new technologies or products in an actual environment. In general, it involves proof of concept, prototype development, pilot testing, performance evaluation, compliance with regulations and certification, market readiness assessment, and a pre-launch review. The verification of MASS technologies is necessary to ensure their safety and regulatory compliance. During verification, the accuracy and reliability of sensors, AI, and navigation automation systems are examined and their reliability under unexpected conditions is verified [31]. The process is helpful in building reliable technologies and optimizing performance, and can promote technological development by considering user and environmental requirements for sustainable maritime transport. Currently, the key technologies used in MASS are verified by installing them in full-scale ships. In addition, simulation environments are used to evaluate performance in dangerous or extreme situations. Performance evaluation in simulation environments has clear limitations; hence, methods to verify performance in the actual environment are necessary [32]. Consequently, in this study, we aimed to develop a system that evaluates the performance of the key technologies used in MASS by generating virtual traffic environment similar to the actual maritime traffic environment, making it possible to evaluate performance in dangerous or extreme situations. It was determined that the developed synthetic maritime traffic generation system can be used to safely and effectively evaluate the performance of the key technologies used in MASS in dangerous or severe situations as well as in general situations, potentially improving the reliability of technologies and optimizing their performance.