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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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Train Distance Estimation in Turnout Area Based on Monocular Vision.

Yang Hao1,2, Tao Tang1, Chunhai Gao2

  • 1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary

This study introduces a monocular vision method for accurate train distance estimation in railway turnouts. The approach ensures safer autonomous urban transit by precisely locating trains and preventing collisions.

Keywords:
autonomous drivinginstance segmentationobject detectionurban railway transitvision

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Area of Science:

  • Computer Vision
  • Robotics
  • Transportation Engineering

Background:

  • Autonomous driving in urban rail transit requires precise train distance estimation, especially in turnout areas.
  • Traditional methods like Lidar and stereo vision struggle with accuracy due to large incident angles and distances in turnouts.

Purpose of the Study:

  • To propose a robust monocular vision-based method for estimating train distances in turnout areas.
  • To enhance the safety and efficiency of autonomous urban railway transit systems.

Main Methods:

  • Instance segmentation using YOLOv8 to detect train side windows.
  • Feature extraction to identify window vertical directions, upper, and lower edges.
  • Pinhole camera model application for precise distance calculation.

Main Results:

  • A dataset of 2477 images was created, achieving 92.43% MIoU and 97.47% MPA for instance segmentation.
  • The method demonstrated a mean RMSE of 0.9523 m for train distance estimation in four typical turnout scenarios.
  • The achieved accuracy is sufficient for determining crossover occupancy in turnout areas.

Conclusions:

  • The proposed monocular vision method offers a viable and accurate solution for train distance estimation in railway turnouts.
  • This technology significantly contributes to the safety of autonomous urban railway operations.
  • The method's performance validates its effectiveness in real-world metro environments.