A general framework to support the navigation side of autonomous ships is discussed in this study. That consists of various maritime technologies to achieve the required level of ocean autonomy. Decision-making processes in autonomous vessels will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. Each onboard application in autonomous vessels may require localized decision-making modules, therefore that will introduce a distributed intelligence type strategy. Hence, future ships will be agent-based systems with distributed intelligence throughout vessels. The main core of this agent should consist of deep learning type technology that has presented promising results in other transportation systems, i.e. self-driving cars. Deep learning can capture helmsman behavior, therefore that type system intelligence can be used to navigate autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning type technology including situation awareness and collision avoidance. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e. regulatory failures and violations, under autonomous ships are also discussed with the possible solutions as the main contribution of this study. Furthermore, additional considerations, i.e. performance standards with the applicable limits of liability, terms, expectations and conditions, towards evaluating ship behavior as an agent-based system on collision avoidance situations are also illustrated in this study.
Proactive Collision Avoidance for Autonomous Ships: Leveraging Machine Learning to Emulate Situation Awareness
