Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Neural Interaction Energy for Multi-Agent Trajectory Prediction

Kaixin Shen, Ruijie Quan, Linchao Zhu, Jun Xiao, Yi Yang

TL;DR

The paper addresses temporal instability in multi-agent trajectory prediction by introducing MATE, a framework that uses neural interaction energy to model agent interactions and their impact on future paths. It enforces system-level and agent-level stability through two constraints—inter-agent interaction and intra-agent motion—implemented within a multi-edge GNN-based encoder–decoder with a Neural Interaction Energy Module. Empirical results across PHASE, Socialnav, Charged, and NBA datasets show superior predictive accuracy, high Graph Accuracy, and robust zero-shot generalization, with ablations confirming the additive benefits of both constraints. The approach advances multi-agent prediction by coupling interaction energy dynamics with PDE-inspired constraints, offering enhanced robustness and interpretability in diverse environments.

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.

Neural Interaction Energy for Multi-Agent Trajectory Prediction

TL;DR

The paper addresses temporal instability in multi-agent trajectory prediction by introducing MATE, a framework that uses neural interaction energy to model agent interactions and their impact on future paths. It enforces system-level and agent-level stability through two constraints—inter-agent interaction and intra-agent motion—implemented within a multi-edge GNN-based encoder–decoder with a Neural Interaction Energy Module. Empirical results across PHASE, Socialnav, Charged, and NBA datasets show superior predictive accuracy, high Graph Accuracy, and robust zero-shot generalization, with ablations confirming the additive benefits of both constraints. The approach advances multi-agent prediction by coupling interaction energy dynamics with PDE-inspired constraints, offering enhanced robustness and interpretability in diverse environments.

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.
Paper Structure (16 sections, 10 equations, 6 figures, 6 tables)

This paper contains 16 sections, 10 equations, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Formulation.
  5. Inter-agent Interaction Modeling
  6. Intra-agent Motion Modeling
  7. Model Architecture
  8. Loss for Training
  9. Experiments
  10. Datasets
  11. Setup
  12. Results for Trajectory Prediction
  13. Ablation Study
  14. Generalization to Unseen Scenarios
  15. Results of Different Dataset Sizes
  16. ...and 1 more sections

Figures (6)

  • Figure 1: The MATE model consists of (i) a MATE-Encoder responsible for extracting interaction latent between agents, and (ii) a MATE-Decoder integrated with a Neural Interaction Energy Module (NIEM). The NIEM incorporates an Energy Module that extracts the neural interaction energy features $\bm{E}$ of agents. These features are used for the subsequent networks. They are also leveraged to calculate the inter-agent interaction and intra-agent motion constraints through a Constraint Module. Refer to the Appendix for more details about the NIEM.
  • Figure 2: Qualitative analysis of our model on Socialnav dataset. Agent relations mean that the agent to the left of the arrow moves towards the agent to the right of the arrow.
  • Figure 3: Qualitative analysis of models on the Charged dataset. Different colored lines denote the trajectories of different charges.
  • Figure 4: Visualization of generalization on the Charged dataset. Different colored lines denote the trajectories of different charges.
  • Figure 5: Qualitative analysis of our model on NBA dataset. The red agent is the basketball, and the other colored agents represent players.
  • ...and 1 more figures