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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3522-3531

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.