提出4D场景持续性新基准与模型，提升自动驾驶对动态环境的预测能力。
OccSTeP: Benchmarking 4D Occupancy Spatio-Temporal Persistence
- 无分词器世界模型，通过体素状态持续融合时空信息。
- 在缺失或噪声数据下仍保持性能，语义mIoU达23.70%（+6.56%）。
- 适合自动驾驶场景建模与长期预测研究者使用。
自动驾驶需要对3D场景具备鲁棒的持续理解能力，以应对时间扰动并预判未来行为。本文提出4D占用空间-时间持续性（OccSTeP）概念，涵盖两种任务：反应式预测（‘接下来会发生什么’）和主动式预测（‘若采取特定行动会如何’）。首次构建包含复杂挑战场景（如错误语义标签、丢帧）的OccSTeP基准。为此提出OccSTeP-WM模型，一种无需分词器的世界模型，以密集体素表示场景状态，并随时间增量融合时空上下文。该模型采用线性复杂度注意力主干与循环状态空间模块，捕获长程空间依赖，同时通过自车运动补偿持续更新场景记忆。该设计支持在线推理，在历史传感器输入缺失或噪声环境下仍表现稳健。大量实验验证了该方法的有效性，实现平均语义mIoU 23.70%（+6.56%提升）和占用IoU 35.89%（+9.26%提升）。数据与代码将开源于https://github.com/FaterYU/OccSTeP。
原文摘要 · Abstract (English)
Autonomous driving requires a persistent understanding of 3D scenes that is robust to temporal disturbances and accounts for potential future actions. We introduce a new concept of 4D Occupancy Spatio-Temporal Persistence (OccSTeP), which aims to address two tasks: (1) reactive forecasting: ''what will happen next'' and (2) proactive forecasting: "what would happen given a specific future action". For the first time, we create a new OccSTeP benchmark with challenging scenarios (e.g., erroneous semantic labels and dropped frames). To address this task, we propose OccSTeP-WM, a tokenizer-free world model that maintains a dense voxel-based scene state and incrementally fuses spatio-temporal context over time. OccSTeP-WM leverages a linear-complexity attention backbone and a recurrent state-space module to capture long-range spatial dependencies while continually updating the scene memory with ego-motion compensation. This design enables online inference and robust performance even when historical sensor input is missing or noisy. Extensive experiments prove the effectiveness of the OccSTeP concept and our OccSTeP-WM, yielding an average semantic mIoU of 23.70% (+6.56% gain) and occupancy IoU of 35.89% (+9.26% gain). The data and code will be open source at https://github.com/FaterYU/OccSTeP.
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