自动驾驶中基于查询引导多边形和CLIP的道路感知异常分割
Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving
AI总结：
研究自动驾驶中传统语义分割模型应对未知物体能力不足问题，提出轻量级无需再训练等的道路感知异常分割框架，利用查询级掩码置信等并引入CLIP模块，提升异常预测性能。
摘要：
传统语义分割模型在封闭集假设下运行，难以识别自动驾驶中的未知或意外物体。我们提出了一个轻量级、后处理、道路感知异常分割框架，无需重新训练、OOD数据和辅助监督。通过利用查询级掩码置信度并推导多边形道路先验来检测可能对应异常的间隙区域。引入基于CLIP的零射击语义过滤模块以抑制误报。在三个公共基准上的评估显示出强大性能。
英文摘要：
Traditional semantic segmentation models operate under a closed-set assumption and struggle to recognize unknown or unexpected objects-an essential capability for autonomous driving. As a result, such models often misclassify or overlook out-of-distribution (OOD) road anomalies, posing safety risks in open-world environments. We present a lightweight, postprocessing, road-aware anomaly segmentation framework that requires no retraining, no OOD data, and no auxiliary supervision. Our approach builds on a mask transformer-based segmentation network by exploiting query-level mask confidence and deriving a polygonal road prior to detect gap regions that may correspond to anomalies. To further suppress false positives, we introduce a CLIP-based zero-shot semantic filtering module using in-distribution prompts, with optional generalized OOD prompts. By jointly leveraging spatial priors and semantic verification, our framework produces robust and interpretable anomaly predictions. Evaluation on three public benchmarks-Fishyscapes, SMIYC, and RoadAnomaly-shows consistently strong performance. In particular, our method outperforms the training-free baseline Maskomaly on most metrics and achieves the highest AP on Fishyscapes LostAndFound. These results demonstrate the practicality and deployability of our approach for real-world autonomous driving systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
