Summary of Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models, by Marion Neumeier et al.
Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
by Marion Neumeier, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
First submitted to arxiv on: 23 May 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 The conditioned Vehicle Motion Diffusion (cVMD) model is a novel network architecture for highway trajectory prediction using diffusion models. This architecture ensures drivability of predicted trajectories by integrating non-holonomic motion constraints and physical constraints into the generative prediction module. The cVMD model also performs uncertainty quantification, which is crucial in safety-critical applications. By integrating this uncertainty into the prediction process, the cVMD’s trajectory prediction performance improves significantly. The model was evaluated using the highD dataset, showing competitive accuracy compared to state-of-the-art models while providing guaranteed drivable trajectories and uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The cVMD model is a new way to predict where vehicles will move on highways. It makes sure that the predicted paths are safe and possible by adding special rules about vehicle motion and physical limits. This model also calculates how likely it is that the predicted path will happen, which is important for safety. By using this uncertainty in the prediction process, the cVMD model does better than other models at predicting where vehicles will go while still being safe. |
Keywords
» Artificial intelligence » Diffusion
