Summary of Improving Out-of-distribution Generalization Of Trajectory Prediction For Autonomous Driving Via Polynomial Representations, by Yue Yao et al.
Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations
by Yue Yao, Shengchao Yan, Daniel Goehring, Wolfram Burgard, Joerg Reichardt
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents an out-of-distribution (OoD) testing protocol for evaluating robustness in trajectory prediction models. The authors introduce a novel algorithm based on polynomial representations that achieves near state-of-the-art performance on in-distribution (ID) datasets while significantly improving OoD robustness. The proposed method reduces model size, training effort, and inference time compared to existing approaches. The paper also studies the effects of augmentation strategies on model generalization within the OoD testing protocol. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well a computer program can predict where things will move in the future. Right now, these programs are only tested with data they’ve seen before, but that’s not always what happens in real life. The authors came up with a new way to test these programs that shows how well they do when faced with new or unexpected situations. Their method is better and faster than others, and it can even make the programs more robust against surprises. |
Keywords
» Artificial intelligence » Generalization » Inference
