Summary of Multi-scale Temporal Fusion Transformer For Incomplete Vehicle Trajectory Prediction, by Zhanwen Liu et al.
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction
by Zhanwen Liu, Chao Li, Yang Wang, Nan Yang, Xing Fan, Jiaqi Ma, Xiangmo Zhao
First submitted to arxiv on: 2 Sep 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 The proposed Multi-scale Temporal Fusion Transformer (MTFT) framework addresses the limitation of existing motion prediction methods by handling missing values caused by object occlusion and perception failures. The MTFT consists of the Multi-scale Attention Head (MAH) and Continuity Representation-guided Multi-scale Fusion (CRMF) module, which leverages multi-head attention to capture multi-scale motion representation from different temporal granularities and guides fusion with continuity representation of vehicle motion. This approach enables accurate decoding of future trajectory consistent with vehicle motion trend. The proposed model outperforms state-of-the-art models on four datasets derived from highway and urban traffic scenarios, demonstrating a comprehensive performance improvement of more than 39% on the HighD dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method for predicting car movements in real-world traffic situations. Right now, many methods are not good at dealing with missing information that can happen when cars or obstacles block our view. The new method uses something called multi-scale attention to look at car movements from different angles and time scales. It also uses continuity representation to make sure the predictions fit together smoothly over time. This helps make more accurate predictions about where cars will go next. The researchers tested their method on four different datasets and found that it did much better than other methods, especially on complex highway scenarios. |
Keywords
» Artificial intelligence » Attention » Multi head attention » Transformer
