Automatic Road Boundaries Extraction for High Definition maps
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Autonomous Vehicles (AV) research moves forward and promises to create a safer and
more efficient driving process than the vast majority of humans can achieve. However,
just as humans, Autonomous Vehicles still rely on the maps of their surroundings to
conduct most of their operational sub-tasks. These maps are enriched with a large
quantity of additional information for a more accurate representation of the natural
world, earning the common name of High Definition (HD) Map. The rapid increase
of the field’s popularity also brings a great deal of attention to the HD maps creation
and maintenance. Still, to this day, almost all HD maps are created using many human
hours of expert labor, raising their cost and creating barriers to broader adoption. In this
work, we research recent advancements of HD maps automatic creation and apply novel
methods to extract road information, namely road boundaries. We strive to create an
automatic system capable of extracting the necessary information from LIDAR data from
vehicles deployed in urban conditions, with a high degree of accuracy and tolerance to
externalities, such as weather conditions or road construction details. In order to evaluate
the system, we use the publicly available Nuscenes dataset and compare automatically
created road boundaries with provided manually drafted ground truth. The system
achieves a precision score of 0.62 and a recall score of 0.31 at the distance tolerance of
40 cm.
Description
Keywords
autonomous driving, high definition maps, computer vision, point cloud’s processing