Summary of Trajdiffuse: a Conditional Diffusion Model For Environment-aware Trajectory Prediction, by Qingze (tony) Liu et al.
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
by Qingze, Danrui Li, Samuel S. Sohn, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 TrajDiffuse method is a planning-based trajectory prediction approach that uses a guided conditional diffusion model to predict human or vehicle trajectories with good diversity, while also considering environmental constraints. This paper improves upon existing models by producing accurate and diverse predictions that avoid collisions with the surrounding environment. The authors form the trajectory prediction problem as a denoising impainting task and design a map-based guidance term for the diffusion process. They demonstrate the effectiveness of TrajDiffuse through experiments on the nuScenes and PFSD datasets, achieving state-of-the-art (SOTA) results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Trajectory prediction is important for many applications, like predicting where people will walk or cars will drive. But current models often focus too much on making accurate predictions or creating diverse possibilities, without considering things like avoiding collisions with other objects. This paper proposes a new approach called TrajDiffuse that tries to solve this problem by using a special type of model called a guided conditional diffusion model. This model is designed to produce predictions that are both accurate and diverse, while also respecting the rules of the environment. The authors tested their method on two different datasets and found that it works well, producing results that are as good or better than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
