The goal of the joint study is to develop an advanced navigation support system that will lead to autonomous collision avoidance, using rule-based artificial intelligence and deep reinforcement learning algorithm. This will enable systems to estimate several Obstacle Zones by Target (OZT) among different ships and propose a route that minimises the risk of a collision.

The company is teaming up on the study with MOL Marine, National Maritime Research Institute, Port and Aviation Technology, Tokyo University of Marine Science and Technology, MOL Techno-Trade, and YDK Technologies.

Demonstration testing with Tokyo University of Marine Science and Technology's Shioji Maru has been conducted in congested sea areas such as Tokyo Bay as a part of continuous efforts to develop computational algorithms using OZT and avoidance route computational algorithms. The test confirmed the ability to estimate OZT targeting several target ships in actual operation and develop and suggest avoidance routes in real time onboard and verified the system's effectiveness in supporting collision avoidance. The test also aimed to develop a collision avoidance system that realises medium-to-long-term strategies for avoidance navigation well before target ships pose a risk in congested sea lanes, and takes into consideration the experience of maritime officer and other personnel in terms of safety and security.