Summary of Cuqds: Conformal Uncertainty Quantification Under Distribution Shift For Trajectory Prediction, by Huiqun Huang et al.
CUQDS: Conformal Uncertainty Quantification under Distribution Shift for Trajectory Prediction
by Huiqun Huang, Sihong He, Fei Miao
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Conformal Uncertainty Quantification under Distribution Shift framework (CUQDS) addresses the limitation of existing trajectory prediction models by considering both reducing uncertainty and improving accuracy during training, while providing reliable uncertainty quantification in real-world scenarios. The CUQDS framework combines a learning-based Gaussian process regression module with an additional loss term to reduce estimated uncertainty, and a statistical-based Conformal P control module for online calibration under potential distribution shift. This approach enables accurate prediction of trajectories and their associated uncertainties for autonomous vehicle motion, ensuring safe and robust navigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CUQDS is a new way to predict where cars will go and how sure we should be about those predictions. The goal is to make self-driving cars better at planning their routes and avoiding accidents. Right now, most systems just guess where the car might go next without really knowing how certain they are. CUQDS changes that by trying to reduce uncertainty while still making good predictions. This helps keep our self-driving cars safe and on track. |
Keywords
» Artificial intelligence » Regression
