Reliable spatial and motion perception is essential for safe autonomous navigation. Recently, class-agnostic motion prediction on bird's-eye view (BEV) cell grids derived from LiDAR point clouds has gained significant attention. However, existing frameworks typically perform cell classification and motion prediction on a per-pixel basis, neglecting important motion field priors such as rigidity constraints, temporal consistency, and future interactions between agents. These limitations lead to degraded performance, particularly in sparse and distant regions. To address these challenges, we introduce \textbf{PriorMotion}, an innovative generative framework designed for class-agnostic motion prediction that integrates essential motion priors by modeling them as distributions within a structured latent space. Specifically, our method captures structured motion priors using raster-vector representations and employs a variational autoencoder with distinct dynamic and static components to learn future motion distributions in the latent space. Experiments on the nuScenes dataset demonstrate that \textbf{PriorMotion} outperforms state-of-the-art methods across both traditional metrics and our newly proposed evaluation criteria. Notably, we achieve improvements of approximately 15.24\% in accuracy for fast-moving objects, an 3.59\% increase in generalization, a reduction of 0.0163 in motion stability, and a 31.52\% reduction in prediction errors in distant regions. Further validation on FMCW LiDAR sensors confirms the robustness of our approach.

翻译：可靠的空间与运动感知对于安全自主导航至关重要。近期，基于激光雷达点云生成的鸟瞰图（BEV）单元网格进行类别无关运动预测的方法受到广泛关注。然而，现有框架通常在像素级别执行单元分类与运动预测，忽略了重要的运动场先验，如刚性约束、时间一致性以及智能体间的未来交互作用。这些局限性导致性能下降，尤其在稀疏与远距离区域更为明显。为应对这些挑战，我们提出\textbf{PriorMotion}——一种创新的生成式框架，专为类别无关运动预测设计，通过将关键运动先验建模为结构化潜在空间中的分布来实现其整合。具体而言，我们的方法利用栅格-矢量表征捕捉结构化运动先验，并采用具有动态与静态分量的变分自编码器来学习潜在空间中的未来运动分布。在nuScenes数据集上的实验表明，\textbf{PriorMotion}在传统指标及我们新提出的评估标准上均优于现有最优方法。值得注意的是，我们在快速移动物体的预测准确率上实现了约15.24\%的提升，泛化能力提高3.59\%，运动稳定性降低0.0163，远距离区域的预测误差减少31.52\%。在FMCW激光雷达传感器上的进一步验证证实了我们方法的鲁棒性。