To objectively and systematically understand the current status of reliability testing of maritime autonomous surface ships (MASS),

the current research status from three aspects: testing methods, testing technologies, and evaluation systems, and discusses the future development trends are analyzed. Specifically, it includes: conducting a visual analysis of 134 related papers using CiteSpace and VOSviewer to systematically sort out the research directions and development trends in the field of ship collision avoidance capability testing; sorting out the uses, advantages and disadvantages, and research status of the three major testing platforms: real ship testing, model testing, and virtual simulation testing; in-depth discussion on the development trends, feature comparisons, and challenges faced by the three mainstream testing scenario generation technologies based on expert knowledge, random sampling, and artificial intelligence; summarizing the evaluation indicators from four dimensions: data authenticity, scene complexity, risk, and generation efficiency; and on this basis, looking forward to future research directions.

The results show that virtual simulation testing has the advantages of low cost and high coverage and has become the main testing method. The ship collision avoidance capability testing method based on artificial intelligence has development potential in high-risk edge scenarios and ship interaction games, but the current research still faces challenges such as idealized motion models, lack of multi-ship dynamic game mechanisms, single evaluation indicators, and difficulties in virtual-to-real migration.

The research on testing scenario generation and deduction based on artificial intelligence has important research value and significance for promoting the testing of MASS.