提出新模型统一处理多智能体轨迹预测，提升精度与效率。
PMM-Net: Single-stage Multi-agent Trajectory Prediction with Patching-based Embedding and Explicit Modal Modulation
- 用分块提取时序特征，图结构捕捉社交关系。
- 在多个公开数据集上优于现有最先进方法。
- 适合需要实时多智能体行为预测的场景。
分析和预测行人等智能体的轨迹对于具身智能应用至关重要。人类行为的内在不确定性以及复杂多样的智能体间社交互动使该任务比常见时间序列预测更具挑战性。本文探索了一种新型多智能体轨迹预测框架。具体而言，提出了基于分块的时序特征提取模块和基于图的社交特征提取模块，实现有效特征提取与跨场景泛化能力。此外，重新评估社交互动作用，提出一种基于显式模态调制的新方法，用于融合时序与社交特征，构建高效的单阶段推理流程。在多个公开基准数据集上的实验结果表明，所提模型性能显著优于现有最先进方法。代码已开源：github.com/TIB-K330/pmm-net。
原文摘要 · Abstract (English)
Analyzing and forecasting trajectories of agents like pedestrians plays a pivotal role for embodied intelligent applications. The inherent indeterminacy of human behavior and complex social interaction among a rich variety of agents make this task more challenging than common time-series forecasting. In this letter, we aim to explore a distinct formulation for multi-agent trajectory prediction framework. Specifically, we proposed a patching-based temporal feature extraction module and a graph-based social feature extraction module, enabling effective feature extraction and cross-scenario generalization. Moreover, we reassess the role of social interaction and present a novel method based on explicit modality modulation to integrate temporal and social features, thereby constructing an efficient single-stage inference pipeline. Results on public benchmark datasets demonstrate the superior performance of our model compared with the state-of-the-art methods. The code is available at: github.com/TIB-K330/pmm-net.
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