000007696 001__ 7696
000007696 005__ 20240531164756.0
000007696 02470 $$2doi$$a10.24868/issn.2515-818X.2020.055
000007696 035__ $$a4498300
000007696 037__ $$aGENERAL
000007696 245__ $$aScenario Identification for Safety Assessment of Autonomous Shipping using AIS Data
000007696 269__ $$a2020-10-05
000007696 336__ $$aConference Proceedings
000007696 520__ $$aWhile autonomy offers a solution to many issues facing the maritime and naval industry, assessing the safety and reliability of autonomous shipping is one of the key challenges. Established methods and standards for conventional ships are still relevant but do not account for the challenges that come with the application of Artificial Intelligence (AI) and cyber-physical systems for Maritime Autonomous Surface Ships (MASS). Such systems require specialised safety assessment techniques. Scenario-based safety assessment for autonomous systems is one of the potential approaches and represents the state of the art. In this research, we conduct a feasibility study on identification and classification of seafaring scenarios, as described in COLREG, from Automatic Identification System (AIS) data. Furthermore, the statistical distributions for the parameters of these scenarios are empirically determined. We illustrate the utility of our methods using real-life AIS data from the Strait of Dover. Results indicate that AIS data can be a rich data source for identifying real-life scenarios that can be used for safety assessment of MASS.
000007696 542__ $$fCC-BY-4.0
000007696 6531_ $$aAutonomous Shipping
000007696 6531_ $$aAutomatic Identification System
000007696 6531_ $$aScenario Identification and Classification
000007696 6531_ $$aScenario Based Safety Assessment
000007696 7001_ $$aSnijders, R$$uTNO, Monitoring and Control Services, Groningen, The Netherlands;
000007696 7001_ $$aElrofai, H$$uTNO, Integrated Vehicle Safety, Helmond, The Netherlands
000007696 773__ $$tConference Proceedings of INEC
000007696 773__ $$jINEC 2020
000007696 789__ $$whttps://zenodo.org/record/4498300$$2URL$$eIsIdenticalTo
000007696 85641 $$uhttps://www.imarest.org/events/inec-2020$$yConference website
000007696 8564_ $$9fc2ca138-2ba3-41fb-9fb6-3bd23cee2a30$$s4619639$$uhttps://library.imarest.org/record/7696/files/INEC_2020_Paper_90.pdf