Benchmarking the Robustness of Panoptic Segmentation for Automated Driving

arXiv:2402.15469v1 Announce Type: new
Abstract: Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique to identify and categorise objects, impending hazards, and driveable space at a pixel level. While segmentation quality is generally associated with the quality of the camera data, a comprehensive understanding and modelling of this relationship are paramount for AAD system designers. Motivated by such a need, this work proposes a unifying pipeline …

abstract arxiv automated benchmarking cs.cv decision driving functions hazards identify making objects panoptic segmentation perception pixel quality robustness segmentation situational awareness space type