Run-time Monitoring of 3D Object Detection in Automated Driving Systems
  Using Early Layer Neural Activation Patterns
Hakan Yekta Yatbaz Mehrdad Dianati Konstantinos Koufos Roger Woodman
Monitoring the integrity of object detection for errors within the perception
module of automated driving systems (ADS) is paramount for ensuring safety.
Despite recent advancements in deep neural network (DNN)-based object
detectors, their susceptibility to detection errors, particularly in the
less-explored realm of 3D object detection, remains a significant concern.
State-of-the-art integrity monitoring (also known as introspection) mechanisms
in 2D object detection mainly utilise the activation patterns in the final
layer of the DNN-based detector's backbone. However, that may not sufficiently
address the complexities and sparsity of data in 3D object detection. To this
end, we conduct, in this article, an extensive investigation into the effects
of activation patterns extracted from various layers of the backbone network
for introspecting the operation of 3D object detectors. Through a comparative
analysis using Kitti and NuScenes datasets with PointPillars and CenterPoint
detectors, we demonstrate that using earlier layers' activation patterns
enhances the error detection performance of the integrity monitoring system,
yet increases computational complexity. To address the real-time operation
requirements in ADS, we also introduce a novel introspection method that
combines activation patterns from multiple layers of the detector's backbone
and report its performance.