Summary of Mstf: Multiscale Transformer For Incomplete Trajectory Prediction, by Zhanwen Liu et al.
MSTF: Multiscale Transformer for Incomplete Trajectory Prediction
by Zhanwen Liu, Chao Li, Nan Yang, Yang Wang, Jiaqi Ma, Guangliang Cheng, Xiangmo Zhao
First submitted to arxiv on: 8 Jul 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 Multiscale Transformer (MSTF) framework is designed to address the challenge of predicting trajectories with incomplete data, a crucial component in autonomous driving systems. The model integrates a Multiscale Attention Head and an Information Increment-based Pattern Adaptive module to capture global dependencies in motion across different scales and extract continuity representation of motion. This approach enables MSTF to generate predictions consistent with motion continuity, outperforming state-of-the-art models on two large-scale real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new framework for predicting vehicle trajectories that can handle incomplete data. The Multiscale Transformer model uses attention mechanisms and pattern adaptation to learn from missing values in the data. This helps the model make more accurate predictions, which is important for autonomous driving systems that need to predict where other cars might go. |
Keywords
» Artificial intelligence » Attention » Transformer
