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Road Curb Detection: A Historical Survey.

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This survey reviews two decades of road curb detection research for autonomous vehicles. It analyzes methods using sensors like LiDAR and vision, crucial for safe navigation in urban settings.

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Area of Science:

  • Robotics and Computer Vision
  • Autonomous Systems Engineering

Background:

  • Road curbs are essential for traffic management and road delineation.
  • Accurate curb detection is a fundamental requirement for autonomous vehicle navigation in urban environments.
  • Significant research has focused on curb detection using various sensors over the past two decades.

Purpose of the Study:

  • To provide a comprehensive survey of road curb detection methods.
  • To analyze the evolution and advancements in this research field.
  • To identify key tasks and applications of curb detection in autonomous driving.

Main Methods:

  • Review of sensor-based approaches, including vision and Light Detection and Ranging (LiDAR).
  • Identification and categorization of tasks within road curb detection algorithms.
  • Analysis of existing literature on curb detection techniques and their performance.

Main Results:

  • Detailed examination of diverse curb detection strategies developed over the last 20 years.
  • Classification of methods based on sensor types, algorithms, and application domains.
  • Identification of common challenges and emerging trends in curb detection.

Conclusions:

  • Road curb detection remains a critical and active research area for autonomous navigation.
  • Sensor fusion and advanced algorithms continue to improve detection accuracy and robustness.
  • Further research is needed to address complex urban scenarios and enhance system reliability.