Summary of Lg-traj: Llm Guided Pedestrian Trajectory Prediction, by Pranav Singh Chib et al.
LG-Traj: LLM Guided Pedestrian Trajectory Prediction
by Pranav Singh Chib, Pravendra Singh
First submitted to arxiv on: 12 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 paper investigates using Large Language Models (LLMs) to improve pedestrian trajectory prediction tasks by inducing motion cues. The authors introduce LG-Traj, a novel approach that incorporates LLMs to generate motion cues from past/observed and future trajectories. The method uses singular value decomposition to augment observed trajectories and employs a transformer-based architecture with a motion encoder and social decoder. The authors demonstrate the effectiveness of their approach on popular pedestrian trajectory prediction benchmarks, including ETH-UCY and SDD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores using Large Language Models (LLMs) to predict pedestrian movement patterns in dynamic environments. The researchers develop a new method called LG-Traj that uses LLMs to generate motion cues from past and future trajectories. This helps the model better understand how pedestrians move together. The approach is tested on popular datasets and shows promising results. |
Keywords
» Artificial intelligence » Decoder » Encoder » Transformer
