Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving

Related tags

Deep LearningGSAN
Overview

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

  1. Install/Update python dependency library

    pip install -r requirements.txt
    
  2. Build the directory

    python buildfolder.py
    

Task1: Lane-changing classification

  1. Get the data

  2. Run all cells in highD_data_process.ipynb

Task2: Trajectory prediction

  1. Get the data

  2. Format the data to fit GSAN model

    python datapreprocessing.py
    
Owner
YE Luyao
PhD Candidate in Computer Science.
YE Luyao
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