  The deep neural network (DNN) models are widely used for object detection in
automated driving systems (ADS). Yet, such models are prone to errors which can
have serious safety implications. Introspection and self-assessment models that
aim to detect such errors are therefore of paramount importance for the safe
deployment of ADS. Current research on this topic has focused on techniques to
monitor the integrity of the perception mechanism in ADS. Existing
introspection models in the literature, however, largely concentrate on
detecting perception errors by assigning equal importance to all parts of the
input data frame to the perception module. This generic approach overlooks the
varying safety significance of different objects within a scene, which obscures
the recognition of safety-critical errors, posing challenges in assessing the
reliability of perception in specific, crucial instances. Motivated by this
shortcoming of state of the art, this paper proposes a novel method integrating
raw activation patterns of the underlying DNNs, employed by the perception
module, analysis with spatial filtering techniques. This novel approach
enhances the accuracy of runtime introspection of the DNN-based 3D object
detections by selectively focusing on an area of interest in the data, thereby
contributing to the safety and efficacy of ADS perception self-assessment
processes.
