4D occupancy forecasting is one of the important techniques for autonomous driving, which can avoid potential risk in the complex traffic scenes. Scene flow is a crucial element to describe 4D occupancy map tendency. However, an accurate scene flow is difficult to predict in the real scene. In this paper, we find that BEV scene flow can approximately represent 3D scene flow in most traffic scenes. And coarse BEV scene flow is easy to generate. Under this thought, we propose 4D occupancy forecasting method FSF-Net based on coarse BEV scene flow. At first, we develop a general occupancy forecasting architecture based on coarse BEV scene flow. Then, to further enhance 4D occupancy feature representation ability, we propose a vector quantized based Mamba (VQ-Mamba) network to mine spatial-temporal structural scene feature. After that, to effectively fuse coarse occupancy maps forecasted from BEV scene flow and latent features, we design a U-Net based quality fusion (UQF) network to generate the fine-grained forecasting result. Extensive experiments are conducted on public Occ3D dataset. FSF-Net has achieved IoU and mIoU 9.56% and 10.87% higher than state-of-the-art method. Hence, we believe that proposed FSF-Net benefits to the safety of autonomous driving.

翻译：4D占据预测是自动驾驶的重要技术之一，能够在复杂交通场景中规避潜在风险。场景流是描述4D占据图演变趋势的关键要素，然而在真实场景中难以预测精确的场景流。本文发现，在大多数交通场景中，鸟瞰图（BEV）场景流可近似表征3D场景流，且粗粒度BEV场景流易于生成。基于此思路，我们提出基于粗粒度BEV场景流的4D占据预测方法FSF-Net。首先，我们构建了基于粗粒度BEV场景流的通用占据预测架构；其次，为增强4D占据特征表征能力，提出基于矢量量化的Mamba（VQ-Mamba）网络以挖掘时空结构化场景特征；随后，为有效融合由BEV场景流预测的粗粒度占据图与潜在特征，设计了基于U-Net的质量融合（UQF）网络以生成细粒度预测结果。在公开数据集Occ3D上进行的大量实验表明，FSF-Net在IoU与mIoU指标上分别比现有最优方法提升9.56%与10.87%。因此，我们认为所提出的FSF-Net有助于提升自动驾驶的安全性。