Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910904052875264 |
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| author | Shoeb, Youssef Nowzad, Azarm Gottschalk, Hanno |
| author_facet | Shoeb, Youssef Nowzad, Azarm Gottschalk, Hanno |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art Shoeb, Youssef Nowzad, Azarm Gottschalk, Hanno Computer Vision and Pattern Recognition Robotics Image and Video Processing 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. |
| title | Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art |
| topic | Computer Vision and Pattern Recognition Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2503.08695 |