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Design and Implementation of HD Mapping, Vehicle Control, and V2I Communication for Robo-Taxi Services.

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Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments.

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Area of Science:

  • Robotics and Intelligent Systems
  • Geographic Information Systems
  • Computer Vision

Background:

  • High-definition (HD) maps are essential for advanced automated driving (AD) systems, particularly for levels 4 and higher.
  • HD maps provide crucial environmental data beyond the limitations of conventional vehicle sensors.
  • These maps typically consist of pointcloud data for localization and vector data for path planning.

Purpose of the Study:

  • To introduce and detail two distinct, sequential workflows for generating HD maps.
  • To highlight the critical roles of registration and mapping processes in creating pointcloud and vector maps, respectively.
  • To validate the generated HD maps using established AD software stacks.

Main Methods:

  • Utilized open-source software (OSS) tools, specifically CloudCompare and ASSURE, for HD map generation.
  • Developed and documented two successive HD map generation workflows, focusing on registration and mapping stages.
  • Integrated and validated the generated HD maps within the Autoware OSS stack for AD systems.

Main Results:

  • Successfully generated HD maps using specified OSS tools and workflows.
  • Validated the efficacy of the generated HD maps through integration with localization and path-planning modules in Autoware.
  • Demonstrated the capability of HD maps to support environmental-monitoring vehicles operating at level 4 autonomy.

Conclusions:

  • The presented HD map generation workflows are effective and viable for creating detailed maps for automated driving.
  • The use of OSS tools like CloudCompare, ASSURE, and Autoware facilitates the development and validation of HD mapping technologies.
  • Generated HD maps are proven to be functional for enabling level 4 automated driving capabilities.