Summary of Mart: Multiscale Relational Transformer Networks For Multi-agent Trajectory Prediction, by Seongju Lee et al.
MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction
by Seongju Lee, Junseok Lee, Yeonguk Yu, Taeri Kim, Kyoobin Lee
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a novel architecture, called MultiscAle Relational Transformer (MART), for multi-agent trajectory prediction in autonomous driving and understanding the surrounding environment. MART is a hypergraph transformer that considers individual and group behaviors, building upon graph neural networks, graph transformers, and hypergraph neural networks. The core module of MART consists of an encoder with two components: Pair-wise Relational Transformer (PRT) and Hyper Relational Transformer (HRT). HRT integrates hyperedge features into the transformer mechanism, allowing attention weights to focus on group-wise relations. Additionally, the paper proposes Adaptive Group Estimator (AGE), designed to infer complex group relations in real-world environments. Experimental results on three real-world datasets demonstrate that MART achieves state-of-the-art performance, enhancing ADE/FDE by 3.9%/11.8% on the NBA dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new model called MART that helps predict the movements of multiple agents, like cars or people, in situations like traffic jams or sports games. This is important for self-driving cars and understanding how groups work together. The model uses special parts called transformers to understand both individual and group behaviors. It also has another part that helps it focus on relationships between different groups. To test the model, they used real-world data from three sources: NBA games, street scenes in Switzerland, and urban areas in Europe. The results showed that MART performed better than other models in predicting movements. |
Keywords
* Artificial intelligence * Attention * Encoder * Transformer
