Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering

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.