关键词: 奇异值分解, 特征金字塔网络, 递归轨迹生成器, 特征提取, 多模态, 轨迹预测

Abstract: Capturing high-dimensional social interactions and trend features is essential for accurately predicting the feasible future behaviors of agents. To address this complexity, previous research has reduced the dimensionality of input variables through parametric curve fitting to capture more useful information, while other studies have inferred future trajectories using recursive or synchronous methods. However, these methods have limitations: a single smooth curve struggles to effectively fit social dynamics, recursive strategies can lead to cumulative errors, and synchronous strategies overlook constraints between future steps, rendering kinematic predictions infeasible. To overcome these challenges, a method combining Singular Value Decomposition and Feature Pyramid Networks is proposed to reduce dimensionality and extract trend features, eliminating redundant information. Our method replaces the traditional Euclidean space with a feature space based on singular value decomposition to better model multimodal predictions across different models. Results from various depths of trend feature predictions are progressively fused from the bottom layer to the top layer, generating final predictions through a global-to-local recursive trajectory prediction method. This recursive method employs interpolation techniques of varying granularity, integrating global information with the boundary information from each iteration's region, continuously generating intermediate positional information for each area. Extensive experiments demonstrate that the proposed universal trajectory prediction framework significantly enhances the prediction accuracy and reliability of existing trajectory models on public benchmarks.

Key words: Singular Value Decomposition, Feature Pyramid Network, Recursive Trajectory Generator, Feature Extraction, Multimodal, Trajectory Prediction