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.

自动驾驶系统中可靠的物体检测、审视和错误检测的应用，通过引入新的检视方案并利用神经网络激活模式，实现在基于深度神经网络的 2D 物体检测中的错误减少。