Summary of Neural Interaction Energy For Multi-agent Trajectory Prediction, by Kaixin Shen et al.
Neural Interaction Energy for Multi-Agent Trajectory Prediction
by Kaixin Shen, Ruijie Quan, Linchao Zhu, Jun Xiao, Yi Yang
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This framework, called MATE (Multi-Agent Trajectory prediction via neural interaction Energy), addresses the issue of temporal stability in multi-agent trajectory prediction by employing neural interaction energy to capture the dynamics of interactions between agents. The MATE framework introduces two constraints: inter-agent interaction constraint and intra-agent motion constraint, which work together to ensure temporal stability at both system and agent levels, mitigating prediction fluctuations inherent in multi-agent systems. Comparative evaluations on four diverse datasets show that our model outperforms previous methods in terms of prediction accuracy and generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MATE is a new way to predict the movements of multiple agents. It’s like trying to guess where your friends will be at the next stoplight based on how they move around each other. The problem with current methods is that they can get stuck in patterns, predicting the same things over and over again. MATE fixes this by considering the interactions between agents and how they affect each other’s movements. This makes predictions more accurate and reliable. Tests show that MATE outperforms other methods on different types of data. |
Keywords
* Artificial intelligence * Generalization
