Clustering Environmental Conditions of Historical Accident Data to Efficiently Generate Testing Sceneries for Maritime Systems

Wuellner, Tim and Feuerstack, Sebastian and Hahn, Axel
Model-Based Safety and Assessment
Vessels are getting more and more equipped with highly-automated assistant systems that benefit from the use of machine learning. Such trained safety-critical systems demand for new means of Verification and Validation (V+V). Their complex decision making process is hidden and traditional system analysis and functional testing is no longer possible as the testing space becomes too large to test. Scenario-based V+V performed in a simulation environment is a promising approach to tackle these challenges, triggering potential system malfunctions and covering as much as possible of the problem space.
Springer International Publishing