Enhancing Situation Awareness in Highly Automated Shipping using High-Resolution Electronic Navigational Charts

Kurzfassung

Highly automated vessel operations depend on accurate and reliable information about the vessel's surroundings. This paper addresses the challenges of ensuring navigational safety, even when the integrity of Electronic Navigational Charts (ENC) is compromised. In maritime navigation, it is crucial to assess where the vessel is positioned in relation to objects of the harbor infrastructure. We propose High-Resolution Electronic Navigational Charts (HR-ENCs) as a tool to capture sensor data, combining multiple remote-sensing technologies using a loosely-coupled sensor fusion framework. By accurately identifying and georeferencing maritime objects, we aim to enhance Situation Awareness (SA) and ensure safe navigation of both manned and unmanned vessels. Integrating data from camera, LiDAR and GNSS, as well as existing ENCs, we create HR-ENCs to reflect current conditions within harbors. By focusing on the detection of discrepancies between observed conditions and charted objects, HR-ENCs pose actionable harbor charts containing the relevant navigational data. Further, we outline the infrastructure to distribute HR-ENCs among maritime systems, showcasing the potential for harbor-wide, consistently updated HR-ENCs that are available to all stakeholders. In addition, we explore applications and challenges in using HR-ENCs beyond ENC verification, highlighting their potential for the development of Advanced Driver Assistance Systems (ADAS) for collision avoidance and berthing. HR-ENCs promise to improve navigational safety, particularly in complex harbor environments, and support the advancement of highly automated maritime systems, thereby contributing to the broader adoption of autonomous maritime operations.