Summary of Data-efficient Trajectory Prediction Via Coreset Selection, by Ruining Yang and Lili Su
Data-efficient Trajectory Prediction via Coreset Selection
by Ruining Yang, Lili Su
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel data-efficient training method for trajectory prediction models, which is crucial for understanding complex driving environments. The current methods struggle with processing large-scale raw data and lack diversity in training datasets, as easy-medium scenarios dominate. To address this, the authors introduce coreset selection to strategically select a representative subset of data while balancing scenario difficulties. This approach achieves a state-of-the-art compression ratio, allowing models to be trained using only 50% of the dataset with little performance decline. The selected coreset also demonstrates excellent generalization ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to train computers to predict where cars will go next on the road. They use lots of data from cameras and sensors in cars to teach these computers. But there’s a problem: most of the data is easy driving scenarios, like stop signs and traffic lights. The hard ones, like dense traffic, are really rare. To solve this, they came up with a new method that picks out just the right amount of data from all the information. This helps the computer learn faster and better. Even if you only use half the data, it still works great! |
Keywords
» Artificial intelligence » Generalization
