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Continual Assurance of Learning-Enabled, Cyber-Physical Systems (LE-CPS)

Case Study: Safety Verification of an Unmanned Underwater Vehicle


This manuscript evaluates the safety of a neural network controller that seeks to ensure that an Unmanned Underwater Vehicle (UUV) does not collide with a static object in its path. To achieve this, we utilize methods that can determine the exact output reachable set of all the UUV's components through the use of star-sets. The star-set is a computationally efficient set representation adept at characterizing large input spaces. It supports cheap and efficient computation of affine mapping operations and intersections with half-spaces. The system under consideration in this work represents a more complex system than Neural Network Control Systems (NNCS) previously considered in other works, and consists of a total of four components. Our experimental evaluation uses four different scenarios to show that our star-set based methods are scalable and can be efficiently used to analyze the safety of real-world cyber-physical systems (CPS).


Year of Publication
Conference Name
2020 IEEE Security and Privacy Workshops (SPW)