Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations

arXiv:2403.01172v1 Announce Type: new
Abstract: 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 …

abstract ads arxiv automated cs.cv detection driving environment networks neural networks objects systems type