Summary of Intention-aware Denoising Diffusion Model For Trajectory Prediction, by Chen Liu et al.
Intention-aware Denoising Diffusion Model for Trajectory Prediction
by Chen Liu, Shibo He, Haoyu Liu, Jiming Chen
First submitted to arxiv on: 14 Mar 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 proposed Intention-aware denoising Diffusion Model (IDM) improves trajectory prediction for autonomous driving by decoupling uncertainty into intention and action components. This paper tackles two challenges: modeling true distribution under diverse intentions and reducing inference time. IDM achieves state-of-the-art results on the Stanford Drone Dataset (SDD) and ETH/UCY dataset, with an FDE of 13.83 pixels and 0.36 meters, respectively. Compared to the original diffusion model, IDM reduces inference time by two-thirds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving requires predicting future trajectories to avoid collisions. To do this, researchers use generative models that produce multiple possible paths. However, these models often struggle with representing uncertainty and can be slow to compute. This paper proposes a new approach called the Intention-aware denoising Diffusion Model (IDM). IDM breaks down uncertainty into two parts: intention and action. It then uses this understanding to create a more efficient model that can predict future paths quickly. |
Keywords
» Artificial intelligence » Diffusion model » Inference
