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.

翻译：保持时间稳定性在多智能体轨迹预测中至关重要。缺乏维持这种稳定性的充分正则化往往会导致运动状态波动，进而产生不一致的预测并放大误差。在本研究中，我们提出了一种名为"基于神经交互能量的多智能体轨迹预测"（MATE）框架。该框架通过采用神经交互能量来评估智能体的交互运动，捕捉交互动态，并揭示其对智能体未来轨迹的影响。为增强时间稳定性，我们引入了两种约束：智能体间交互约束与智能体内运动约束。这些约束协同作用，确保系统层和智能体层的时间稳定性，有效缓解多智能体系统固有的预测波动。在四个不同数据集上相较于先前方法的对比评估表明，我们的模型具有更优越的预测精度与泛化能力。