Development of intelligent situational awareness system (iSAS) for maritime autonomous surface ships: preliminary field tests at sea

Abstract

This paper presents the development of an intelligent situational awareness system (iSAS) for the autonomous navigation of maritime autonomous surface ships (MASSs) maneuvering in a marine environment. A sensor fusion-based multimodal system including cameras, lidar, and radar was configured to seamlessly and reliably detect and identify various maritime objects at sea. In particular, Considering the unique characteristics of each sensor, we designed and implemented detection approaches to quickly detect objects. Subsequently, an extended Kalman filter (EKF)-based tracking filter technique was applied to estimate the detected objects’ position, speed, and course information. Furthermore, considering a time-varying uncertainty contained in the estimated information, the estimated information was employed to evaluate a quantitative risk indicator of the potential collision risk on the predictive course of the MASS. Preliminary field tests were carried out at sea to demonstrate the practical feasibility of the developed iSAS, and their results were described.