Summary of Traj-llm: a New Exploration For Empowering Trajectory Prediction with Pre-trained Large Language Models, by Zhengxing Lan et al.
Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models
by Zhengxing Lan, Hongbo Li, Lingshan Liu, Bo Fan, Yisheng Lv, Yilong Ren, Zhiyong Cui
First submitted to arxiv on: 8 May 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 This paper proposes Traj-LLM, a novel approach that leverages Large Language Models (LLMs) to predict future trajectories of dynamic traffic actors. By dissecting agent and scene features into a form that LLMs understand, Traj-LLM utilizes the powerful comprehension abilities of LLMs to capture high-level scene knowledge and interactive information. The model is enhanced by lane-aware probabilistic learning, powered by the Mamba module, and a multi-modal Laplace decoder. Experimental results show that Traj-LLM outperforms state-of-the-art methods across evaluation metrics, even when using only 50% of the dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to predict where cars will go in the future. They’re trying to make their predictions more accurate by using a new kind of computer program called a Large Language Model. This program is good at understanding language and can help the prediction model know what’s important about traffic scenes. The researchers also added some special features to help the program focus on lanes and understand how different things interact with each other. They tested their approach and found that it works really well, even when they only used half of the data they had. |
Keywords
» Artificial intelligence » Decoder » Large language model » Multi modal
