通过显式建模行人交互模式，提升轨迹预测的准确性与可信度。
Trustworthy Pedestrian Trajectory Prediction via Pattern-Aware Interaction Modeling
- 基于Transformer显式捕捉同步、冲突等交互模式
- 在ETH/UCY数据集上误差降低6.58%，高密度场景表现更优
- 提供可解释的交互分析，适合需要可靠预测的应用
准确可靠的行人轨迹预测对智能应用至关重要，但复杂的人群交互使可信预测仍具挑战。以往方法多采用黑箱建模交互，虽性能强，却难以保证实际部署中的可靠性。为此，我们提出InSyn（交互同步网络），一种基于Transformer的新模型，能显式捕捉多样化的交互模式（如同步行走或冲突）并有效建模方向敏感的社会行为。同时，我们设计了序列起始训练策略（SSOS），以缓解时序预测中常见的初始步发散问题。在ETH和UCY数据集上的实验表明，该模型不仅在预测精度上优于近期黑箱基线，尤其在高密度场景下表现突出，还实现了透明的交互建模，案例研究验证了其可解释性。此外，SSOS策略显著提升序列预测性能，将初始步预测误差降低约6.58%。代码已开源。
原文摘要 · Abstract (English)
Accurate and reliable pedestrian trajectory prediction is critical for the application of intelligent applications, yet achieving trustworthy prediction remains highly challenging due to the complexity of interactions among pedestrians. Previous methods often adopt black-box modeling of pedestrian interactions. Despite their strong performance, such opaque modeling limits the reliability of predictions in real-world deployments. To address this issue, we propose InSyn (Interaction-Synchronization Network), a novel Transformer-based model that explicitly captures diverse interaction patterns (e.g., walking in sync or conflicting) while effectively modeling direction-sensitive social behaviors. Additionally, we introduce a training strategy, termed Seq-Start of Seq (SSOS), designed to alleviate the common issue of initial-step divergence in numerical time-series prediction. Experiments on the ETH and UCY datasets demonstrate that our model not only outperforms recent black-box baselines in prediction accuracy, especially under high-density scenarios, but also provides transparent interaction modeling, as shown in the case study. Furthermore, the SSOS strategy proves to be effective in improving sequential prediction performance, reducing the initial-step prediction error by approximately 6.58%. Code is avaliable at https://github.com/rickzky1001/InSyn
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