Reliable detection of various objects and road users in the surrounding environment is crucial for the safe operation of automated driving systems (ADS). Despite recent progresses in developing highly accurate object detectors based on Deep Neural Networks (DNNs), they still remain prone to detection errors, which can lead to fatal consequences in safety-critical applications such as ADS. An effective remedy to this problem is to equip the system with run-time monitoring, named as introspection in the context of autonomous systems. Motivated by this, we introduce a novel introspection solution, which operates at the frame level for DNN-based 2D object detection and leverages neural network activation patterns. The proposed approach pre-processes the neural activation patterns of the object detector's backbone using several different modes. To provide extensive comparative analysis and fair comparison, we also adapt and implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. We compare the performance of the proposed solution in terms of error detection, adaptability to dataset shift, and, computational and memory resource requirements. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.

翻译：可靠检测周围环境中的各类物体和道路使用者对于自动驾驶系统的安全运行至关重要。尽管基于深度神经网络的高精度目标检测器近年来取得了显著进展，但其仍然容易产生检测错误，在使用于自动驾驶这类安全关键应用时可能引发致命后果。针对该问题的有效解决方案是为系统配备运行时监控机制——在自主系统领域称为自省方法。受此启发，我们提出了一种新颖的自省方案，该方案在帧级别对基于深度神经网络的2D目标检测进行监控，并利用神经网络激活模式。所提方法通过多种模式对目标检测器骨干网络的神经激活模式进行预处理。为提供广泛的对比分析与公平比较，我们还基于KITTI和BDD数据集，采用单阶段和两阶段目标检测器，适配并实现了多种现有最优自省机制用于2D目标检测误差检测。我们从误差检测性能、数据集偏移适应性以及计算与内存资源需求等方面比较了所提方案的性能。性能评估表明，所提自省方案优于现有最优方法，在BDD数据集上漏检率绝对降低幅度达9%至17%。