Ghassan Al-Falouji, Tom Beyer, Shang Gao und Sven Tomforde haben einen Artikel mit dem Titel Steering Towards Maritime Safety with True Motion Predictions Ensemble auf dem Workshop “Workshop on self-improving system integration (SISSY), ACSOS” veröffentlicht. Dies ist das Thema des Artikels:

Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user- adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising Systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency.

Ghassan Al-Falouji, Tom Beyer, Shang Gao und Sven Tomforde haben einen Artikel mit dem Titel Steering Towards Maritime Safety with True Motion Predictions Ensemble auf dem Workshop “Workshop on self-improving system integration (SISSY), ACSOS” veröffentlicht. Dies ist das Thema des Artikels:

Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user- adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising Systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency.