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

• URL: http://arxiv.org/abs/2503.08695v2
• Date: Sat, 05 Apr 2025 02:11:26 GMT
• Title: Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
• Authors: Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk,
• Abstract summary: 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.<n>We analyse the performance of existing methods on two widely used benchmarks, SegmentMeIfYouCan Obstacle Track and LostAndFound-NoKnown.
• Score: 1.3654846342364308
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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