[2601.07855] RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.07855 (cs) [Submitted on 9 Jan 2026 (v1), last revised 27 Mar 2026 (this version, v2)] Title:RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution Authors:Subeen Lee, Siyeong Lee, Namil Kim, Jaesik Choi View a PDF of the paper titled RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution, by Subeen Lee and 2 other authors View PDF HTML (experimental) Abstract:For 3D perception systems to operate reliably in real-world environments, they must remain robust to evolving sensor characteristics and changes in object taxonomies. However, existing adaptive learning paradigms struggle in LiDAR settings where domain shifts and label-space evolution occur simultaneously. We introduce \textbf{Robust Autonomous Driving under Dataset shifts (RoAD)}, a benchmark for evaluating model robustness in LiDAR-based object classification under intertwined domain shifts and label evolution, including subclass refinement, unseen-class insertion, and label expansion. RoAD evaluates three learning scenarios with increasing adaptation, from fixed representations (zero-shot transfer and linear probing) to sequential updates (continual learning). Experiments span large-scale autonomous driving datasets, including Waymo, nuScenes, and Argoverse2. Our analysis identifies central failure modes: (i) \textit{limited transferability} under subclass refinement and unseen-class ...