  Deep Neural Networks (DNNs) are popularly used for implementing autonomy
related tasks in automotive Cyber-Physical Systems (CPSs). However, these
networks have been shown to make erroneous predictions to anomalous inputs,
which manifests either due to Out-of-Distribution (OOD) data or adversarial
attacks. To detect these anomalies, a separate DNN called assurance monitor is
often trained and used in parallel to the controller DNN, increasing the
resource burden and latency. We hypothesize that a single network that can
perform controller predictions and anomaly detection is necessary to reduce the
resource requirements. Deep-Radial Basis Function (RBF) networks provide a
rejection class alongside the class predictions, which can be utilized for
detecting anomalies at runtime. However, the use of RBF activation functions
limits the applicability of these networks to only classification tasks. In
this paper, we show how the deep-RBF network can be used for detecting
anomalies in CPS regression tasks such as continuous steering predictions.
Further, we design deep-RBF networks using popular DNNs such as NVIDIA DAVE-II,
and ResNet20, and then use the resulting rejection class for detecting
adversarial attacks such as a physical attack and data poison attack. Finally,
we evaluate these attacks and the trained deep-RBF networks using a hardware
CPS testbed called DeepNNCar and a real-world German Traffic Sign Benchmark
(GTSB) dataset. Our results show that the deep-RBF networks can robustly detect
these attacks in a short time without additional resource requirements.
