用粗略俯视图场景流提升自动驾驶4D占位预测精度
FSF-Net: Enhance 4D Occupancy Forecasting with Coarse BEV Scene Flow for Autonomous Driving
- 用易生成的粗略俯视场景流近似三维场景流
- 在Occ3D数据集上指标比顶尖方法高10.87%
- 适合关注自动驾驶感知安全的工程师
4D占位预测是自动驾驶中的关键技术，可应对复杂交通场景中的潜在风险。场景流是描述4D占位图演变趋势的关键要素，但在真实场景中准确预测场景流极具挑战。本文发现，在多数交通场景中，俯视图（BEV）场景流可近似表示三维场景流，且粗略的BEV场景流易于生成。基于此，提出基于粗略BEV场景流的4D占位预测方法FSF-Net。首先构建以粗略BEV场景流为基础的通用占位预测架构；其次，设计基于向量量化Mamba（VQ-Mamba）网络，挖掘时空结构化场景特征以增强表征能力；随后，设计基于U-Net的质量融合（UQF）网络，有效融合由场景流预测的粗略占位图与潜在特征，生成细粒度预测结果。在公开的Occ3D数据集上进行了大量实验，FSF-Net相较于当前最优方法，交并比（IoU）和平均交并比（mIoU）分别提升9.56%和10.87%。因此，我们认为所提出的FSF-Net有助于提升自动驾驶安全性。
原文摘要 · Abstract (English)
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.
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