Summary of Smartpretrain: Model-agnostic and Dataset-agnostic Representation Learning For Motion Prediction, by Yang Zhou et al.
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
by Yang Zhou, Hao Shao, Letian Wang, Steven L. Waslander, Hongsheng Li, Yu Liu
First submitted to arxiv on: 11 Oct 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 Predicting the future motion of surrounding agents is crucial for autonomous vehicles (AVs) to operate safely in dynamic environments. However, the scarcity of large-scale driving datasets has hindered the development of robust and generalizable motion prediction models. To address this challenge, we propose SmartPretrain, a self-supervised learning framework that integrates contrastive and reconstructive SSL methods. Our approach is model-agnostic and dataset-agnostic, leveraging the strengths of both generative and discriminative paradigms to effectively represent spatiotemporal evolution and interactions. Additionally, SmartPretrain employs a scenario sampling strategy that integrates multiple datasets, enhancing data volume, diversity, and robustness. Extensive experiments on multiple datasets demonstrate that SmartPretrain consistently improves the performance of state-of-the-art prediction models across datasets, data splits, and main metrics. For instance, SmartPretrain significantly reduces the MissRate of Forecast-MAE by 10.6%. These results highlight SmartPretrain’s effectiveness as a unified, scalable solution for motion prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine driving in a busy city with self-driving cars around you. How do they know where everyone is going to go? To help them make good decisions, researchers need to develop better ways to predict where people and cars will move next. They’ve been limited by not having enough data from many different places. A new approach called SmartPretrain helps solve this problem by using a special kind of learning that doesn’t require human labeling. It works by looking at lots of scenarios and predicting what might happen in each one. This method has been tested on several datasets and shows great promise for improving the accuracy of self-driving cars. |
Keywords
» Artificial intelligence » Mae » Self supervised » Spatiotemporal
