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Onboard LiDAR-Camera Deployment Optimization for Pavement Marking Distress Fusion Detection.

Ciyun Lin1,2, Wenjian Sun1, Ganghao Sun1

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This study introduces an optimized onboard sensor deployment for detecting pavement marking distress. The method enhances traffic safety by improving detection accuracy in challenging conditions like shadows and varying light.

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Area of Science:

  • Road safety engineering
  • Sensor fusion technology
  • Artificial intelligence in transportation

Background:

  • Pavement markings are essential for traffic guidance and safety but degrade over time.
  • Periodic inspection of retroreflectivity and diffuse illumination is crucial for maintaining safety standards.
  • Existing detection methods struggle with complex traffic conditions like shadows and changing light.

Purpose of the Study:

  • To propose an optimized onboard sensor deployment method for pavement marking distress detection.
  • To enhance the accuracy and robustness of pavement marking inspection systems.
  • To adapt detection capabilities to dynamic environmental and traffic conditions.

Main Methods:

  • Assessed the detection capabilities of Light Detection and Ranging (LiDAR) and camera sensors.
  • Mathematically formulated the LiDAR-camera deployment optimization problem for distress fusion detection.
  • Developed an improved Red Fox Optimization (RFO) algorithm with enhanced mechanisms to solve the deployment problem.

Main Results:

  • Achieved a density of 5217 LiDAR points per data frame on pavement markings (0.58 m).
  • Demonstrated a relative error of less than 7% between calculated and field test measurements.
  • Validated the method's robustness in real-world scenarios, mitigating environmental interference.

Conclusions:

  • The proposed sensor deployment optimization method effectively detects pavement marking distress.
  • The system demonstrates high accuracy and robustness, even under challenging environmental conditions.
  • This approach contributes to improved road safety through reliable pavement marking inspection.