The task of occupancy forecasting (OCF) involves utilizing past and present perception data to predict future occupancy states of autonomous vehicle surrounding environments, which is critical for downstream tasks such as obstacle avoidance and path planning. Existing 3D OCF approaches struggle to predict plausible spatial details for movable objects and suffer from slow inference speeds due to neglecting the bias and uneven distribution of changing occupancy states in both space and time. In this paper, we propose a novel spatiotemporal decoupling vision-based paradigm to explicitly tackle the bias and achieve both effective and efficient 3D OCF. To tackle spatial bias in empty areas, we introduce a novel spatial representation that decouples the conventional dense 3D format into 2D bird's-eye view (BEV) occupancy with corresponding height values, enabling 3D OCF derived only from 2D predictions thus enhancing efficiency. To reduce temporal bias on static voxels, we design temporal decoupling to improve end-to-end OCF by temporally associating instances via predicted flows. We develop an efficient multi-head network EfficientOCF to achieve 3D OCF with our devised spatiotemporally decoupled representation. A new metric, conditional IoU (C-IoU), is also introduced to provide a robust 3D OCF performance assessment, especially in datasets with missing or incomplete annotations. The experimental results demonstrate that EfficientOCF surpasses existing baseline methods on accuracy and efficiency, achieving state-of-the-art performance with a fast inference time of 82.33ms with a single GPU. Our code will be released as open source.

翻译：占据预测任务旨在利用过去和当前的感知数据预测自动驾驶车辆周围环境的未来占据状态，这对于障碍物避让和路径规划等下游任务至关重要。现有的三维占据预测方法难以对可移动物体生成合理的空间细节预测，且由于忽视了占据状态在时空维度上的偏差与不均匀分布，导致推理速度缓慢。本文提出一种新颖的时空解耦视觉范式，通过显式处理时空偏差实现高效且有效的三维占据预测。为消除空域空间偏差，我们提出一种新型空间表征方法，将传统稠密三维格式解耦为二维鸟瞰图占据及其对应高度值，从而仅需基于二维预测即可推导三维占据状态，显著提升效率。为降低静态体素的时间偏差，我们设计时序解耦机制，通过预测的流场实现实例级时序关联，以改进端到端占据预测性能。基于所设计的时空解耦表征，我们构建了高效多头网络EfficientOCF来实现三维占据预测。同时引入条件交并比新评估指标，为存在标注缺失或不完整的数据集提供更鲁棒的三维占据预测性能评估。实验结果表明，EfficientOCF在精度与效率上均超越现有基线方法，在单GPU上以82.33毫秒的快速推理时间达到最优性能。我们的代码将开源发布。