3D semantic occupancy prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, existing methods focus on in-distribution scenes, making them susceptible to Out-of-Distribution (OoD) objects and long-tail distributions, which increases the risk of undetected anomalies and misinterpretations, posing safety hazards. To address these challenges, we introduce Out-of-Distribution Semantic Occupancy Prediction, targeting OoD detection in 3D voxel space. To fill dataset gaps, we propose a Realistic Anomaly Augmentation that injects synthetic anomalies while preserving realistic spatial and occlusion patterns, enabling the creation of two datasets: VAA-KITTI and VAA-KITTI-360. Then, a novel framework that integrates OoD detection into 3D semantic occupancy prediction, OccOoD, is proposed, which uses Cross-Space Semantic Refinement (CSSR) to refine semantic predictions from complementary voxel and BEV representations, improving OoD detection. Experimental results demonstrate that OccOoD achieves state-of-the-art OoD detection with an AuROC of 65.50% and an AuPRCr of 31.83 within a 1.2m region, while maintaining competitive semantic occupancy prediction performance and generalization in real-world urban driving scenes. The established datasets and source code will be made publicly available at https://github.com/7uHeng/OccOoD.

翻译：三维语义占据预测对于自动驾驶至关重要，它提供了密集且语义丰富的环境表征。然而，现有方法主要关注分布内场景，使其易受分布外对象和长尾分布的影响，这增加了未检测到的异常和误判的风险，从而构成安全隐患。为应对这些挑战，我们提出了分布外语义占据预测，旨在三维体素空间中进行分布外检测。为填补数据集空白，我们提出了一种真实异常增强方法，该方法在注入合成异常的同时保持真实的空间和遮挡模式，从而能够创建两个数据集：VAA-KITTI 和 VAA-KITTI-360。随后，我们提出了一种将分布外检测集成到三维语义占据预测中的新颖框架 OccOoD，该框架利用跨空间语义精炼模块，通过互补的体素和鸟瞰图表示来精炼语义预测，从而提升分布外检测性能。实验结果表明，OccOoD 在 1.2 米区域内实现了最先进的分布外检测性能，其 AuROC 达到 65.50%，AuPRCr 达到 31.83%，同时在真实世界城市驾驶场景中保持了具有竞争力的语义占据预测性能和泛化能力。所建立的数据集和源代码将在 https://github.com/7uHeng/OccOoD 公开。