Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
Abstract
In this paper, we review the state of the art in Out-of-Distribution (OoD) segmentation, with a focus on road obstacle detection in automated driving as a real-world application. We analyse the performance of existing methods on two widely used benchmarks, SegmentMeIfYouCan Obstacle Track and LostAndFound-NoKnown, highlighting their strengths, limitations, and real-world applicability. Additionally, we discuss key challenges and outline potential research directions to advance the field. Our goal is to provide researchers and practitioners with a comprehensive perspective on the current landscape of OoD segmentation and to foster further advancements toward safer and more reliable autonomous driving systems.
Cite
Text
Shoeb et al. "Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.Markdown
[Shoeb et al. "Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.](https://mlanthology.org/cvprw/2025/shoeb2025cvprw-outofdistribution/)BibTeX
@inproceedings{shoeb2025cvprw-outofdistribution,
title = {{Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art}},
author = {Shoeb, Youssef and Nowzad, Azarm and Gottschalk, Hanno},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2025},
pages = {4310-4320},
url = {https://mlanthology.org/cvprw/2025/shoeb2025cvprw-outofdistribution/}
}