Summary of Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture, by Juanwu Lu et al.
Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture
by Juanwu Lu, Can Cui, Yunsheng Ma, Aniket Bera, Ziran Wang
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The paper proposes a generative model called Sequential Neural Variational Agent (SeNeVA) that predicts future trajectories for moving objects and quantifies uncertainty. The goal is to improve the safety and robustness of autonomous vehicles by distinguishing Out-of-Distribution data and achieving competitive performance on datasets like Argoverse 2 and INTERACTION. SeNeVA’s performance is evaluated using metrics such as Final Displacement Error, Average Displacement Error, and Miss Rate. The paper also provides qualitative and quantitative analysis to assess the proposed model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper suggests a new way for self-driving cars to predict where other cars or objects might go next and figure out how certain they are about their predictions. This is important because it helps make the cars safer and more reliable. The researchers propose a system called SeNeVA that can do this task well, even when it’s unsure or dealing with unexpected situations. They test SeNeVA on two big datasets and show that it performs almost as well as other top methods. |
Keywords
» Artificial intelligence » Generative model
