Methodology of Spatial Data Acquisition and Development of High-Definition Map for Autonomous Vehicles – Case Study from Wrocław, Poland
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Department of Geodesy and Geoinformatics, Wrocław University of Science and Technology; Faculty of Geoengineering, Mining and Geology, Poland
Submission date: 2024-01-11
Final revision date: 2024-02-28
Acceptance date: 2024-03-07
Online publication date: 2024-03-08
Publication date: 2024-03-08
Corresponding author
Damian Kasza   

Department of Geodesy and Geoinformatics, Wrocław University of Science and Technology; Faculty of Geoengineering, Mining and Geology, Wyb. Wyspiańskiego 27, 50-370, Wrocław, Poland
Civil and Environmental Engineering Reports 2024;34(1):87-103
Autonomous drive systems are a dynamically developed sector of the automotive industry. The key problem in such technological solutions is to provide a reliable navigation system, which is typically based on high-definition (HD) maps supporting the identification of the position of a maneuvering vehicle. HD maps should include possibly up-to-date and detailed information on traffic lanes and on the traffic rules and regulations on such lanes. An effective development of an HD map should be based on the geodetic measurement methods, which ensure efficient and accurate acquisition of spatial data. This article presents the results of an experiment consisting in the manipulation of data obtained with the use of the mobile laser scanning method and further in employing this data in the development of an HD map in an open-source environment. The applied measurement technology and the processing method allowed data of high resolution (frequently above 1000 points per m2) and of high accuracy (3D accuracy down to less than 5 cm). The obtained data were processed in the Vector Map Builder environment (which is accessible from the level of an internet browser) and the final product - HD map was created in the Lanelet2 open-source environment. The above-described experiments allowed two main conclusions. Most importantly, they demonstrate the importance of planning and performing in-field mobile laser scanning measurements. They also point to the important role of the human analyst who needs to manually vectorize the key elements of road infrastructure and to define traffic rules.
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