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Simultaneous Localization and Map Change Update for the High Definition Map-Based Autonomous Driving Car.

Kichun Jo1, Chansoo Kim2, Myoungho Sunwoo3

  • 1Department of Smart Vehicle Engineering, Konkuk University, Seoul 05029, Korea. kichun.jo@gmail.com.

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Summary

This study introduces a Simultaneous Localization and Map Change Update (SLAMCU) algorithm for autonomous vehicles. It efficiently detects and updates High Definition (HD) map changes in real-time, enhancing navigation safety.

Keywords:
autonomous carscloud maphigh definition (HD) maplocalizationmap change detection

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Area of Science:

  • Autonomous Driving Systems
  • Robotics and AI
  • Geospatial Information Systems

Background:

  • High Definition (HD) maps are crucial for autonomous driving, offering environmental context beyond real-time perception.
  • Maintaining up-to-date HD maps is essential for autonomous vehicle safety and efficiency.
  • Existing methods may struggle with dynamic environmental changes.

Purpose of the Study:

  • To develop a novel algorithm for detecting and updating changes in HD maps for autonomous driving.
  • To ensure HD maps reflect real-world conditions accurately and promptly.
  • To enhance the reliability of autonomous navigation systems.

Main Methods:

  • A Simultaneous Localization and Map Change Update (SLAMCU) algorithm is proposed.
  • Dempster-Shafer evidence theory is employed for inferring map changes based on feature existence.
  • A Rao-Blackwellized Particle Filter (RBPF) is utilized for concurrent vehicle localization and map state updates.

Main Results:

  • The SLAMCU algorithm successfully detects and updates changes in HD maps.
  • Experimental evaluation using traffic sign HD maps in real traffic conditions demonstrated effectiveness.
  • The system integrates detected changes into the HD map database for shared awareness.

Conclusions:

  • The SLAMCU algorithm provides a robust solution for dynamic HD map maintenance in autonomous driving.
  • Real-time map updates improve the situational awareness and safety of autonomous vehicles.
  • This approach facilitates collaborative map updates among autonomous vehicles.