Summary of Granp: a Graph Recurrent Attentive Neural Process Model For Vehicle Trajectory Prediction, by Yuhao Luo et al.
GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction
by Yuhao Luo, Kehua Chen, Meixin Zhu
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed Graph Recurrent Attentive Neural Process (GRANP) model is a novel approach for vehicle trajectory prediction that efficiently quantifies prediction uncertainty. Building on advanced deep-learning methods, GRANP combines an encoder with deterministic and latent paths, and a decoder to learn a latent distribution and quantify uncertainty. The encoder uses stacked Graph Attention Networks, LSTM, and 1D convolutional layers to extract spatial-temporal relationships. The model is evaluated on the highD dataset, achieving state-of-the-art results and efficient uncertainty quantification. Additionally, an intuitive case study demonstrates the interpretability of GRANP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GRANP is a new way to predict where vehicles will go. It helps prevent accidents by making sure cars know what other cars might do. The model uses special computer code to understand how cars move and interact with each other. This means it can make more accurate predictions about the future, which is important for self-driving cars. |
Keywords
» Artificial intelligence » Attention » Decoder » Deep learning » Encoder » Lstm
