分析自动驾驶中分布外障碍物分割的现状与挑战
Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
- 聚焦自动驾驶场景，评估现有方法在两个主流数据集的表现
- 指出当前模型在真实世界应用中的局限性与泛化缺陷
- 为研究者提供关键问题与未来方向参考
本文综述了分布外（OoD）分割的最新进展，重点关注自动驾驶中的道路障碍物检测这一实际应用场景。我们分析了现有方法在两个广泛使用的基准数据集——SegmentMeIfYouCan Obstacle Track 和 LostAndFound-NoKnown 上的表现，揭示其优势、局限性及真实世界的适用性。同时，讨论了该领域面临的关键挑战，并提出了潜在的研究方向。目标是为研究人员和从业者提供对当前 OoD 分割领域的全面视角，推动更安全、更可靠的自动驾驶系统发展。
原文摘要 · Abstract (English)
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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