  Learning-enabled components (LECs) are widely used in cyber-physical systems
(CPS) since they can handle the uncertainty and variability of the environment
and increase the level of autonomy. However, it has been shown that LECs such
as deep neural networks (DNN) are not robust and adversarial examples can cause
the model to make a false prediction. The paper considers the problem of
efficiently detecting adversarial examples in LECs used for regression in CPS.
The proposed approach is based on inductive conformal prediction and uses a
regression model based on variational autoencoder. The architecture allows to
take into consideration both the input and the neural network prediction for
detecting adversarial, and more generally, out-of-distribution examples. We
demonstrate the method using an advanced emergency braking system implemented
in an open source simulator for self-driving cars where a DNN is used to
estimate the distance to an obstacle. The simulation results show that the
method can effectively detect adversarial examples with a short detection
delay.
