Summary of Knowledge-aware Graph Transformer For Pedestrian Trajectory Prediction, by Yu Liu et al.
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction
by Yu Liu, Yuexin Zhang, Kunming Li, Yongliang Qiao, Stewart Worrall, You-Fu Li, He Kong
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. To improve prediction performance across various scenarios, a graph transformer structure is proposed to capture differences between training datasets. The structure incorporates a self-attention mechanism and domain adaptation module to enhance generalization ability. Additionally, an innovative metric considering cross-dataset sequences is introduced for training and evaluation purposes. Experimental results demonstrate the improved performance of this scheme compared to existing methods using popular public datasets like ETH and UCY. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict where pedestrians will go, which is important for self-driving cars. The problem is that predicting crowd trajectories can be tricky because people behave differently in different places. To fix this, a new way to use deep learning models is proposed. This approach uses a special kind of attention mechanism and adapts the model to fit different scenarios. It also introduces a new way to measure how well the model performs on different datasets. The results show that this approach does better than previous methods. |
Keywords
* Artificial intelligence * Attention * Deep learning * Domain adaptation * Generalization * Self attention * Transformer
