Summary of Cross-domain Transfer Learning Using Attention Latent Features For Multi-agent Trajectory Prediction, by Jia Quan Loh et al.
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
by Jia Quan Loh, Xuewen Luo, Fan Ding, Hwa Hui Tew, Junn Yong Loo, Ze Yang Ding, Susilawati Susilawati, Chee Pin Tan
First submitted to arxiv on: 9 Nov 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 paper presents a novel spatial-temporal trajectory prediction framework for intelligent transportation systems, addressing the issue of deep learning models struggling to generalize across different traffic networks. The proposed framework combines a Transformer-based model with attention representation and graph convolutional network (GCN) for constructing dynamic feature embeddings that capture complex interactions between vehicles across multiple domains. Through two case studies in cross-city and cross-period settings, the paper demonstrates superior performance in trajectory prediction and domain adaptation compared to state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big deal about predicting where cars will go on roads, using special computer programs called deep learning models. The problem is that these models don’t work well when they’re used in different places or times. So, the researchers came up with a new way to make these models better by combining two types of computer programming: Transformers and Graph Convolutional Networks. They tested their idea on two real-world scenarios and showed it worked much better than other methods. |
Keywords
» Artificial intelligence » Attention » Convolutional network » Deep learning » Domain adaptation » Gcn » Transformer
