Digital Twin for Pedestrian Safety Warning

This repository contains the source code and supporting files for the research paper:

Digital Twin for Pedestrian Safety Warning at a Single Urban Traffic Intersection
Yongjie Fu, Mehmet K. Turkcan, Vikram Anantha, Zoran Kostic, Gil Zussman, Xuan Di
IEEE Intelligent Vehicles Symposium (IV), 2024
DOI: 10.1109/IV55156.2024.10588544

📌 Overview

This project develops a digital twin-based system that enhances pedestrian safety at urban traffic intersections. The system integrates smart city infrastructure, machine learning-based trajectory forecasting, and real-time communication between infrastructure and mobile users.

🏗 System Components

• Object Detection & Tracking:
  Real-time detection and tracking using YOLOv8 and ByteTrack on video feeds from high-mounted COSMOS cameras.

• Trajectory Prediction:
  Transformer-based models predict future positions of vehicles and pedestrians.

• Time-to-Collision (TTC) Estimation:
  Collision risks are calculated using predicted trajectories and TTC thresholds.

• MQTT Communication:
  Vehicle and pedestrian data are transmitted using the MQTT protocol to client devices.

• Mobile Warning App:
  A smartphone app receives data and alerts pedestrians of potential collisions via UI and vibration/audio cues.

• CARLA Simulation (optional):
  Digital twin implementation in CARLA for virtual testing and hardware-in-the-loop validation.

📚 Citation

If you use this codebase or refer to our work in your research, please cite the following paper:

BibTeX

@inproceedings{fu2024digital,
  title     = {Digital Twin for Pedestrian Safety Warning at a Single Urban Traffic Intersection},
  author    = {Fu, Yongjie and Turkcan, Mehmet K. and Anantha, Vikram and Kostic, Zoran and Zussman, Gil and Di, Xuan},
  booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)},
  pages     = {2640--2645},
  year      = {2024},
  organization = {IEEE},
  doi       = {10.1109/IV55156.2024.10588544}
}