Summary of Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information, by Rei Tamaru et al.
Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information
by Rei Tamaru, Pei Li, Bin Ran
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 aim to improve pedestrian trajectory prediction for applications like traffic management and autonomous driving by incorporating trip information as a new modality into existing models. They propose RNTransformer, a generic model that captures global social interactions, and demonstrate its performance enhancements in various local pedestrian trajectory prediction models on multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting where people will walk to improve traffic safety and efficiency. Scientists want to understand how individual behaviors, social interactions, and road conditions affect this. They have developed different models to predict pedestrian trajectories, but these are limited by not considering all the factors that influence a person’s path. To solve this, they propose using trip information as a new way to make more accurate predictions. |
