Abstract
Maintaining temporal stability is crucial in multi-agent trajectory prediction. Insufficient regularization to uphold this stability often results in fluctuations in kinematic states, leading to inconsistent predictions and the amplification of errors. In this study, we introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE). This framework assesses the interactive motion of agents by employing neural interaction energy, which captures the dynamics of interactions and illustrates their influence on the future trajectories of agents. To bolster temporal stability, we introduce two constraints: inter-agent interaction constraint and intra-agent motion constraint. These constraints work together to ensure temporal stability at both the system and agent levels, effectively mitigating prediction fluctuations inherent in multi-agent systems. Comparative evaluations against previous methods on four diverse datasets highlight the superior prediction accuracy and generalization capabilities of our model.
Abstract (translated)
保持时间稳定性对于多智能体轨迹预测至关重要。通常，保持这种稳定性需要足够的正则化来保持，否则会导致运动状态的波动，从而导致预测的不一致性和误差放大的问题。在这项研究中，我们引入了一个名为多智能体轨迹预测通过神经相互作用能量（MATE）的框架。这个框架通过使用神经相互作用能量来评估智能体的相互作用运动，捕捉了互动的动态，并突出了它们对智能体未来轨迹的影响。为了加强时间稳定性，我们引入了两个约束：智能体间交互约束和智能体间运动约束。这些约束共同作用，确保了系统和智能体层面的时间稳定性，有效地减轻了多智能体系统中的预测波动。与之前的方法相比，在四个不同的数据集上的比较评估结果表明，我们模型的预测准确性和泛化能力都具有优势。
URL
https://arxiv.org/abs/2404.16579
