LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning
Accurate open-world obstacle detection is critical for autonomous driving. Current anomaly segmentation methods suffer from a fundamental blind spot: they over-rely on texture novelty to identify out-of-distribution (OoD) objects while ignoring contextual spatial logic. Furthermore, mitigating the resulting false positives often requires cascading massive vision models, introducing unacceptable inference latency. To address these issues, we propose Layout-Aware Road Anomaly Detection (LARAD), shifting the paradigm from appearance matching to spatial-logic reasoning. First, we introduce the Spatial-Logic Violation Synthesis (SLVS) pipeline, which generates training samples that are texture...
| Authors | Shiyi Mu, Xujie Chen, Shugong Xu |
|---|---|
| Primary category | cs.CV |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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