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Machine learning components such as deep neural networks are used extensivelyin Cyber-Physical Systems (CPS). However, such components may introduce newtypes of hazards that can have disastrous consequences and need to be addressedfor engineering trustworthy systems. Although deep neural networks offeradvanced capabilities, they must be complemented by engineering methods andpractices that allow effective integration in CPS. In this paper, we proposedan approach for assurance monitoring of learning-enabled CPS based on theconformal prediction framework. In order to allow real-time assurancemonitoring, the approach employs distance learning to transformhigh-dimensional inputs into lower size embedding representations. Byleveraging conformal prediction, the approach provides well-calibratedconfidence and ensures a bounded small error rate while limiting the number ofinputs for which an accurate prediction cannot be made. We demonstrate theapproach using three data sets of mobile robot following a wall, speakerrecognition, and traffic sign recognition. The experimental results demonstratethat the error rates are well-calibrated while the number of alarms is verysmall. Further, the method is computationally efficient and allows real-timeassurance monitoring of CPS.

Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning