Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
This work addresses the problem of detecting road obstacles for safer autonomous driving systems, but it is incremental as it reviews existing methods without introducing new techniques.
The paper reviews the state of the art in Out-of-Distribution segmentation for autonomous driving, analyzing existing methods on benchmarks like SegmentMeIfYouCan Obstacle Track and LostAndFound-NoKnown to highlight their performance and limitations.
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.