Neural Interaction Energy for Multi-Agent Trajectory Prediction
arxiv(2024)
摘要
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.
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