用共振振动建模行人轨迹，解耦社交行为与随机性。
Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations
- 将轨迹变化分解为独立振动分量，模拟不同原因的反应。
- 在多个数据集上实现更精准的轨迹预测，提升可解释性。
- 适合关注行人行为建模与可解释预测的研究者。
近期，智能体轨迹预测受到广泛关注。然而，准确建模智能体意图与社交行为仍具挑战，尤其难以以可解释且解耦的方式模拟各成分中的独特随机性。受振动系统及其共振特性的启发，我们提出Resonance（Re）模型，将行人轨迹编码与预测为“共振动”形式。该模型将轨迹变化与随机性分解为多个振动分量，分别模拟智能体对各个刺激的响应，并将最终轨迹表示为这些独立振动的叠加。此外，借助振动及其频谱特性，社会交互表征可通过模拟共振现象学习，进一步增强模型可解释性。在多个数据集上的实验验证了其在定量和定性层面的有效性。
原文摘要 · Abstract (English)
Learning to forecast trajectories of intelligent agents has caught much more attention recently. However, it remains a challenge to accurately account for agents' intentions and social behaviors when forecasting, and in particular, to simulate the unique randomness within each of those components in an explainable and decoupled way. Inspired by vibration systems and their resonance properties, we propose the Resonance (short for Re) model to encode and forecast pedestrian trajectories in the form of ``co-vibrations''. It decomposes trajectory modifications and randomnesses into multiple vibration portions to simulate agents' reactions to each single cause, and forecasts trajectories as the superposition of these independent vibrations separately. Also, benefiting from such vibrations and their spectral properties, representations of social interactions can be learned by emulating the resonance phenomena, further enhancing its explainability. Experiments on multiple datasets have verified its usefulness both quantitatively and qualitatively.
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