The development and application of Taiwan HD Maps for smart mobile mapping technology - C.-Y. Hsieh; K.-W. Chiang; M.-L. Tsai; C.-S. Yang; S. Lin; Y.-F. Chang; J.-C. Juang; C.-K. Wang; C.-F. Lin; H. Darweesh; P.-L. Li (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)
The rapid advancement of Intelligent Transport Systems (ITS) has significantly propelled the development of autonomous vehicle technologies in recent years. HD Maps are crucial for autonomous driving technology due to their high accuracy, rich road scene semantic data, real-time traffic conditions, and driving experience information. However, how to produce HD Maps efficiently and meet the accuracy for autonomous vehicles are important challenge. This study proposes related methods to meet the requirement. First one is to publish HD Maps guidelines and standards which are published since 2018, with updated versions scheduled for publication in 2024 and 2025. These documents outline the operating procedures, content format, verification details, and other information to ensure that the final HD Maps maintain consistent accuracy and quality. Second, a semi-automated HD Maps production tool to generate lane lines, stop lines, crosswalks, and other features essential for autonomous vehicle control systems is proposed. This study also defines the procedures and variant detection tools for certified third-party mapping technologies. These tools are designed to improve the efficiency of HD Maps production while reducing costs. Finally, this study creates seamless outdoor and indoor HD Maps in Taiwan which includes closed fields, real roads, and indoor parking lots, supports various application scenarios for autonomous vehicles. This integrated approach not only promotes the advancement of autonomous driving technologies but also drives improvements in mapping methodologies. Establishing a robust HD Maps infrastructure is expected to accelerate the development of fully autonomous vehicles, bringing their widespread deployment closer to reality.