用傅里叶状态空间建模轨迹长程依赖与多模态不确定性
FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integration
- 分频域与时域双分支，傅里叶变换分解轨迹特征
- 计算量降22.5%，参数减少超40%，精度达最新水平
- 适合需要高效高精度轨迹预测的自动驾驶系统
精准轨迹预测对自动驾驶安全至关重要，但现有方法难以兼顾建模能力与计算效率。基于注意力的架构随智能体增多呈现二次复杂度，而循环模型又难以捕捉长程依赖与细微局部动态。为此，我们提出FoSS，一种双分支框架，融合频域推理与线性时间序列建模。频域分支通过离散傅里叶变换将轨迹分解为表征全局意图的幅值分量和刻画局部变化的相位分量，并经渐进螺旋重排模块保持谱序；两个选择性状态空间子模块（Coarse2Fine-SSM与SpecEvolve-SSM）以O(N)复杂度优化谱特征。时域分支则通过动态选择性状态空间模型在低复杂度下重构自注意力行为，保留长时序上下文。交叉注意力层融合时序与谱表示，可学习查询生成多候选轨迹，加权融合头表达运动不确定性。在Argoverse 1与Argoverse 2基准测试中，FoSS实现最先进精度，同时计算量降低22.5%，参数减少超40%。全面消融实验验证各组件必要性。
原文摘要 · Abstract (English)
Accurate trajectory prediction is vital for safe autonomous driving, yet existing approaches struggle to balance modeling power and computational efficiency. Attention-based architectures incur quadratic complexity with increasing agents, while recurrent models struggle to capture long-range dependencies and fine-grained local dynamics. Building upon this, we present FoSS, a dual-branch framework that unifies frequency-domain reasoning with linear-time sequence modeling. The frequency-domain branch performs a discrete Fourier transform to decompose trajectories into amplitude components encoding global intent and phase components capturing local variations, followed by a progressive helix reordering module that preserves spectral order; two selective state-space submodules, Coarse2Fine-SSM and SpecEvolve-SSM, refine spectral features with O(N) complexity. In parallel, a time-domain dynamic selective SSM reconstructs self-attention behavior in linear time to retain long-range temporal context. A cross-attention layer fuses temporal and spectral representations, while learnable queries generate multiple candidate trajectories, and a weighted fusion head expresses motion uncertainty. Experiments on Argoverse 1 and Argoverse 2 benchmarks demonstrate that FoSS achieves state-of-the-art accuracy while reducing computation by 22.5% and parameters by over 40%. Comprehensive ablations confirm the necessity of each component.
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