May 14, 2024, 4:46 a.m. | Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos, Roger Woodman

cs.CV updates on

arXiv:2405.07600v1 Announce Type: new
Abstract: 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 …

3d object 3d object detection abstract ads aim arxiv assessment automated deep neural network detection dnn driving errors filtering integrity monitoring network neural network object patterns raw safety spatial systems type

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