GSAN: A Python repository from XIAOYEJIAYOU

GSAN

Introduction

Code for paper GSAN: Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving, which was published on IEEE Internet of Things Journal. And the link is https://ieeexplore.ieee.org/document/9474961.

To reference the code, please cite this publication:

  @article{ye2021gsan,
    title={GSAN: Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving},
    author={Ye, Luyao and Wang, Zezhong and Chen, Xinhong and Wang, Jianping and Wu, Kui and Lu, Kejie},
    journal={IEEE Internet of Things Journal},
    year={2021},
    publisher={IEEE}
  }

Datasets

For lane-changing prediction task, we choose the open-source High-way Drone (HighD) Dataset.
For trajectory prediction task, we choose NGSIM I-80 and US-101 Dataset.
Datasets(NGSIM us-101, i-80 and HighD) are not included in the repo, please download by yourself from the official website.

Quick Start

Install/Update python dependency library
```
pip install -r requirements.txt
```
Build the directory
```
python buildfolder.py
```

Task1: Lane-changing classification

Get the data
- HighD: https://www.highd-dataset.com/
Run all cells in highD_data_process.ipynb

Task2: Trajectory prediction