A Review of Research on Autonomous Collision Avoidance Performance Testing and an Evaluation of Intelligent Vessels

As intelligent vessel technology moves from the proof-of-concept stage to engineering applications, the performance testing and evaluation of autonomous collision avoidance algorithms have become core issues for safeguarding maritime traffic safety. The International Maritime Organization (IMO)’s Maritime Safety Committee (MSC), at its 109th session, agreed to a revised road map for the development of the Maritime Autonomous Surface Ships (MASS) Code; the field has experienced the development stages of single-vessel collision avoidance validation based on COLREGs, multimodal algorithm collaborative testing, and the current construction of a progressive validation system for the integration of a mix of virtual reality and actual reality. In recent years, relevant studies have achieved research achievements, especially in the compatibility of COLREGs and in accurate collision avoidance in complex situations, and other algorithm tests and evaluations have made great breakthroughs. However, a systematic literature review is still lacking. In this paper, we systematically review the research progress of autonomous collision avoidance performance testing and the evaluation of intelligent vessels; summarize the advantages and disadvantages of virtual testing, model testing, and full-scale vessel testing; and analyze the applicability and limitations of mainstream algorithms such as the velocity obstacle algorithm, the artificial potential field algorithm, and reinforcement learning. It focuses on the key technologies such as diverse scene generation, local scene slicing, and the construction of an evaluation index system. Finally, this paper summarizes the challenges faced by autonomous collision avoidance performance testing and the assessment of intelligent vessels and proposes potential technical solutions and future development directions in terms of virtual–real fusion testing, dynamic evaluation index optimization, and multimodal algorithm co-validation, aiming to provide a reference for the further development of this field.