Summary of Ms-net: a Multi-path Sparse Model For Motion Prediction in Multi-scenes, by Xiaqiang Tang et al.
MS-Net: A Multi-Path Sparse Model for Motion Prediction in Multi-Scenes
by Xiaqiang Tang, Weigao Sun, Siyuan Hu, Yiyang Sun, Yafeng Guo
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 Multi-Scenes Network (MS-Net) is a multi-path sparse model that tackles the challenging task of motion prediction in autonomous driving. The network is trained using an evolutionary process to selectively activate parameters during inference, producing scene-specific predictions. This approach abstracts the motion prediction task as a multi-task learning problem, encouraging the network to share common knowledge between scenes while optimizing for each individual scene. MS-Net outperforms existing state-of-the-art methods on well-established pedestrian motion prediction datasets like ETH and UCY, and ranks second place on the INTERACTION challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving is a complex task that requires predicting human behavior in different scenarios. This paper proposes a new approach called Multi-Scenes Network (MS-Net) to predict motion. MS-Net uses an evolutionary process to learn which parts of its model are most important for each scenario, like merging or roundabouts. By doing this, MS-Net can make better predictions for each scene without having to use the same model for all scenarios. The results show that MS-Net performs well on established datasets and is a step forward in autonomous driving technology. |
Keywords
» Artificial intelligence » Inference » Multi task
