Summary of Followgen: a Scaled Noise Conditional Diffusion Model For Car-following Trajectory Prediction, by Junwei You et al.
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
by Junwei You, Rui Gan, Weizhe Tang, Zilin Huang, Jiaxi Liu, Zhuoyu Jiang, Haotian Shi, Keshu Wu, Keke Long, Sicheng Fu, Sikai Chen, Bin Ran
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 In this paper, researchers develop a novel approach for predicting vehicle trajectories in autonomous driving and advanced driver assistance systems (ADAS). They introduce a scaled noise conditional diffusion model that integrates detailed inter-vehicle interactions and car-following dynamics into a generative framework. The model uses a cross-attention-based transformer architecture to capture intricate inter-vehicle dependencies, improving prediction accuracy. Experimental results on real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is important because it helps us better predict where vehicles will go in different situations. The researchers created a new way to look at how cars follow each other and how traffic works. They used this information to make a model that can accurately predict what will happen with vehicle trajectories. This is helpful for self-driving cars and systems that help people drive. |
Keywords
» Artificial intelligence » Cross attention » Diffusion model » Transformer
