000011143 001__ 11143
000011143 005__ 20250109145511.0
000011143 0247_ $$2doi$$a10.24868/11143
000011143 037__ $$aGENERAL
000011143 245__ $$aA structured metric approach to compare marine collision avoidance algorithms
000011143 269__ $$a2024-11-05
000011143 336__ $$aConference Proceedings
000011143 520__ $$aThe rapid advancement of technologies enabling autonomous ship capabilities has outpaced the development of corresponding legislative and regulatory frameworks, creating a bottleneck in the global application of autonomous ships as defined by the four MASS degrees of autonomy. 
A significant issue is the absence of a well-defined process for certifying new algorithms and systems to be installed on board. Recently, a reliable, open-access structured set of scenarios, including several challenging COLREG encounter situations, has been published, intended for testing the numerous path-planning algorithms developed over the years.
When connected to the testing framework, the modules must accomplish two primary tasks: determining the applicable COLREG rules (COLREG classification) and computing an evasive route. 
This paper focuses on defining an approach to evaluate the performance of collision avoidance algorithms through dedicated metrics. 
These metrics are formulated to quantitatively compare escape manoeuvres according to relevant performance indexes, helping a human-based compliance evaluation assess COLREG adherence.
The operation of the comparison metrics is demonstrated by testing an existing collision avoidance algorithm developed by the authors. 
This demonstration underscores the effectiveness of the analyzed algorithms in efficiently managing the challenging scenarios proposed in the literature. 
Finally, the paper provides valuable suggestions for modifying and improving the testing scenarios to enhance the robustness of the comparison metrics.
000011143 7001_ $$aZaccone, R$$uUniversity of Genoa
000011143 7001_ $$aDonnarumma, S$$uUniversity of Genoa
000011143 7001_ $$aMartelli, M$$uUniversity of Genoa
000011143 773__ $$tConference Proceedings of iSCSS
000011143 773__ $$jiSCSS 2024
000011143 8564_ $$uhttps://library.imarest.org/record/11143/files/iSCSS_24_paper_13.pdf$$9e49ad767-7b57-470f-b259-78236c637890$$s1880517