Summary of Interpretable Interaction Modeling For Trajectory Prediction Via Agent Selection and Physical Coefficient, by Shiji Huang et al.
Interpretable Interaction Modeling for Trajectory Prediction via Agent Selection and Physical Coefficient
by Shiji Huang, Lei Ye, Min Chen, Wenhai Luo, Dihong Wang, Chenqi Xu, Deyuan Liang
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes ASPILin, a novel approach to improve trajectory prediction by manually selecting interacting agents in the target agent’s environment. The model replaces traditional attention scores with physical correlation coefficients, enhancing interpretability and reducing computational costs. Surprisingly, these simple modifications lead to improved prediction performance. The authors deliberately simplified other aspects of the model, such as map encoding, to demonstrate efficiency and outperform state-of-the-art methods on INTERACTION, highD, and CitySim datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how agents interact with each other in a environment. Right now, most models just learn patterns between agents without thinking about why those patterns are important. The new approach, ASPILin, manually picks the agents that matter and uses physical reasons to predict what will happen next. It’s surprisingly good at predicting trajectories and also uses less computer power than other methods. The scientists tested it on different maps and it did even better than others. |
Keywords
» Artificial intelligence » Attention
