3D semantic occupancy prediction is central to autonomous driving, yet current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs, often overconfidently assigning anomalies to rare classes. We present ProOOD, a lightweight, plug-and-play method that couples prototype-guided refinement with training-free OOD scoring. ProOOD comprises (i) prototype-guided semantic imputation that fills occluded regions with class-consistent features, (ii) prototype-guided tail mining that strengthens rare-class representations to curb OOD absorption, and (iii) EchoOOD, which fuses local logit coherence with local and global prototype matching to produce reliable voxel-level OOD scores. Extensive experiments on five datasets demonstrate that ProOOD achieves state-of-the-art performance on both in-distribution 3D occupancy prediction and OOD detection. On SemanticKITTI, it surpasses baselines by +3.57% mIoU overall and +24.80% tail-class mIoU; on VAA-KITTI, it improves AuPRCr by +19.34 points, with consistent gains across benchmarks. These improvements yield more calibrated occupancy estimates and more reliable OOD detection in safety-critical urban driving. The source code is publicly available at https://github.com/7uHeng/ProOOD.

翻译：三维语义占据预测是自动驾驶的核心任务，但现有方法易受长尾类别偏差和分布外输入影响，常过度自信地将异常点错误归类至稀有类别。我们提出ProOOD——一种轻量级即插即用方法，将原型引导优化与免训练分布外评分相结合。ProOOD包含：(i) 原型引导语义填补模块，用类别一致特征填充被遮挡区域；(ii) 原型引导尾部类别挖掘模块，增强稀有类别表征以抑制分布外吸收效应；以及(iii) EchoOOD模块，融合局部逻辑一致性、局部与全局原型匹配，生成可靠的体素级分布外评分。在五个数据集上的大量实验表明，ProOOD在分布内三维占据预测与分布外检测任务上均实现最优性能。在SemanticKITTI数据集上，其整体mIoU提升+3.57%，尾部类别mIoU提升+24.80%；在VAA-KITTI数据集上，AuPRCr指标提升+19.34个百分点，且在各基准测试中表现一致。这些改进在安全关键的城市场景驾驶中产生了更校准的占据估计和更可靠的分布外检测。源代码已公开于https://github.com/7uHeng/ProOOD。