Summary of Proin: Learning to Predict Trajectory Based on Progressive Interactions For Autonomous Driving, by Yinke Dong et al.
ProIn: Learning to Predict Trajectory Based on Progressive Interactions for Autonomous Driving
by Yinke Dong, Haifeng Yuan, Hongkun Liu, Wei Jing, Fangzhen Li, Hongmin Liu, Bin Fan
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 A novel approach is proposed for accurate motion prediction of agents (pedestrians, cyclists, and vehicles) in autonomous driving scenarios. The method, known as progressive interaction network, enables the agent’s feature to progressively focus on relevant maps, capturing complex map constraints through graph convolutions at three stages: historical trajectory encoding, social interaction, and multi-modal differentiation. This allows for a more effective learning of agents’ feature representations. Additionally, a weight allocation mechanism is introduced for multi-modal training, ensuring each mode receives learning opportunities from single-mode ground truth data. Experimental results demonstrate the superiority of progressive interactions over existing one-stage approaches, showcasing promising performance in challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Accurate motion prediction is crucial for autonomous driving. Researchers have developed ways to capture map information to help agents like pedestrians and vehicles move safely. However, these methods require a lot of memory to store all the possible rules about how maps work. In this paper, scientists propose a new approach called progressive interaction network that allows agents to focus on relevant parts of the map as needed. This helps the agent learn better how to navigate its environment. The team also introduces a way to distribute learning tasks between different “modes” or ways of understanding the world. The results show that this new method is more effective than previous approaches in complex real-world scenarios. |
Keywords
* Artificial intelligence * Multi modal
