Summary of Towards Generalizable and Interpretable Motion Prediction: a Deep Variational Bayes Approach, by Juanwu Lu et al.
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
by Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Goal-based Neural Variational Agent (GNeVA) is an interpretable generative model for predicting the behavior of surrounding vehicles in mixed traffic flows. Unlike traditional end-to-end models, GNeVA estimates the spatial distribution of long-term destinations using a variational mixture of Gaussians, enabling target-driven motion prediction with robust generalizability to out-of-distribution cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNeVA is designed for autonomous vehicles to safely navigate through mixed traffic flows by predicting the behavior of human-driven vehicles. The model uses a variational mixture of Gaussians to estimate the spatial distribution of long-term destinations, making it more interpretable and accurate than traditional end-to-end models. |
Keywords
» Artificial intelligence » Generative model
