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.

翻译：监控自动驾驶系统感知模块中目标检测的完整性对于确保安全性至关重要。尽管基于深度神经网络的目标检测器取得了最新进展，但其容易产生检测错误的问题，特别是在研究较少的3D目标检测领域，仍然是一个重大隐患。现有的2D目标检测完整性监控（也称为内省）机制主要利用基于深度神经网络检测器骨干网络最后一层的激活模式。然而，这种方法可能无法充分应对3D目标检测中数据的复杂性和稀疏性。为此，本文对从骨干网络不同层提取的激活模式在3D目标检测器内省操作中的影响进行了深入研究。通过使用Kitti和NuScenes数据集并结合PointPillars和CenterPoint检测器进行比较分析，我们证明使用较早层的激活模式能够提升完整性监控系统的错误检测性能，但同时也增加了计算复杂度。为满足自动驾驶系统的实时运行要求，我们还提出了一种新颖的内省方法，该方法结合了检测器骨干网络多层激活模式，并报告了其性能表现。