Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art

Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025, pp. 4319-4329

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.

Related Material

[pdf] [arXiv]
[bibtex]
@InProceedings{Shoeb_2025_CVPR, author = {Shoeb, Youssef and Nowzad, Azarm and Gottschalk, Hanno}, title = {Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2025}, pages = {4319-4329} }