Operational Data-Driven Feedback for Safety Evaluation of Agent-Based Cyber-Physical Systems

Safety regulation of safety-critical agent-based cyber-physical systems (CPS) which are manufactured in large scale such as next-gen aircrafts, autonomous driving vehicles, and medical devices is a multifaceted problem. CPS deployments can be presented with new safety-critical scenarios and novel inputs. Hence, operational characteristics of the CPS can be quite different from its safety approved design. This article considers a safety assurance solution where operational data from the sensors and actuators in the field of deployment is fed back to the manufacturing process through the Internet of Things infrastructure to assure and improve operational safety. It considers two cases: 1) model-aware, where the safety assured CPS design is fully specified; 2) model-agnostic, where limited specifications exist. For both the cases, it presents a data science based approach, N-HyMn, that learns a hybrid automaton model of the operational characteristics of the CPS from the input/output (I/O) traces of the observable parameters. For the model-aware case, it investigates the presence of inconsistencies between the learned model and the specifications model provided by the manufacturer, thus facilitating the detection of safety problems that may have been overlooked. For the model-agnostic case, it can detect potential safety failures. We show the usage of N-HyMn on the Medtronic Minimed 670 G system. N-HyMn correctly infers the hybrid automaton specifications of the Minimed 670 G and was able to detect a self-adaptation mechanism that is not declared explicitly in the certification documents of the U Food and Drug Administration. N-HyMn has a computational complexity of O(kn^2), where k is the number of samples in the I/O trace, and n is the number of continuous variables.