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Robust Lane-Detection Method for Low-Speed Environments.

Qingquan Li1,2, Jian Zhou3, Bijun Li4

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China. liqq@szu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a robust lane detection method for autonomous vehicles in low-speed environments. The system efficiently identifies lane markings using advanced edge extraction and line detection, ensuring real-time performance.

Keywords:
Bresenham line voting space (BLVS)Kalman filterlane detectionsymmetrical local threshold (SLT)

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous vehicles require reliable lane detection for safe navigation.
  • Existing vision-based methods often struggle in low-speed scenarios and varying road conditions.

Purpose of the Study:

  • To develop a robust and efficient vision-based lane detection method for low-speed autonomous driving.
  • To enhance lane detection accuracy and real-time performance in diverse road environments.

Main Methods:

  • Dynamic construction of detection regions and improved symmetrical local threshold edge extraction.
  • Novel Bresenham line voting space for efficient line segment detection.
  • Geometric fitting for adapting to straight, polyline, and curved road shapes, coupled with Kalman filtering for tracking.

Main Results:

  • The proposed method demonstrates robustness and efficiency in lane detection.
  • Successful adaptation to various road geometries, including curves and polylines.
  • Real-time performance suitable for autonomous vehicle applications.

Conclusions:

  • The developed lane detection system effectively addresses challenges in low-speed environments.
  • The method offers a reliable and efficient solution for autonomous vehicle navigation.
  • Experimental validation confirms the system's practical applicability and performance.