Semi-automatic Generation of Lanelet2 Maps for Autonomous Driving
Name
Valentina Sagris
Abstract
High-definition (HD) maps are essential in autonomous driving (AD) by providing data about the static driving environment for vehicle localisation, route planning, perception and manoeuvre decision-making. To conduct research in autonomous driving, the Autonomous Driving Lab (ADL) of the University of Tartu needs HD maps. This thesis presents the database structure and the effective workflow for the semi-automatic generation of HD map elements. In particular, it tests the capability of the PostGIS spatial data-base in helping to minimise manual work. The main goals of this thesis can be summarised into the follow-ing topics. The ADL's previous mapping efforts were overviewed, and the unified database design for storing spatial data required in various HD map formats was proposed. Then previously collected data was used to develop an algorithm for the semi-automatic generation of missing elements – spatial features needed to construct proper lanelets, the fundamental primitives of maps in Lanelet2 format. Further on, the Lanelet2 requirements for the shared bounds and automated finding of semantic relationships between primitives that constitute a lanelet were addressed. The critical step here was producing spatial elements, which we call 'relations' that establish the spatial relationships between a centreline and its bounds in the database. Final-ly, the data converter for HD maps in Lanelet2 format was proposed. It transforms spatial primitives of PostGIS into primitives of Lanelet2, which are nodes, ways and relations. Due to PostgreSQL's capability to store the XML datatype, elements for each primitive were created in the database. Further on, the database was accessed from the python script, where XML root was made, and the following elements were loaded from the database into the root to create a proper Lanelet2 file.
The workflow was tested on two sites: Lai-Jakobi-Kroonuaia and Narva-Roosi-Puiestee. It helped to assess the algorithm's robustness in various road network configurations and improve its performance. It turned out that the algorithm performance is very good with standard road structures where plain street stretches meet in T-shape or X-shape intersections. Also, the algorithm is capable of performing well in areas of com-plex street intersections, but some human assistance is needed. The statistics obtained for comparison of the proposed solution with a fully manual process of map elements digitisation demonstrated a considerable reduction in time and human efforts in HD map creation. Due to the PostGIS functionality, the data geo-processing was impressively fast. Depending on road structure complexity, the amount of effort needed per one kilometre of lanes can drop by 45% to 65%.
The workflow was tested on two sites: Lai-Jakobi-Kroonuaia and Narva-Roosi-Puiestee. It helped to assess the algorithm's robustness in various road network configurations and improve its performance. It turned out that the algorithm performance is very good with standard road structures where plain street stretches meet in T-shape or X-shape intersections. Also, the algorithm is capable of performing well in areas of com-plex street intersections, but some human assistance is needed. The statistics obtained for comparison of the proposed solution with a fully manual process of map elements digitisation demonstrated a considerable reduction in time and human efforts in HD map creation. Due to the PostGIS functionality, the data geo-processing was impressively fast. Depending on road structure complexity, the amount of effort needed per one kilometre of lanes can drop by 45% to 65%.
Graduation Thesis language
English
Graduation Thesis type
Master - Data Science
Supervisor(s)
Edgar Sepp, Tambet Matiisen
Defence year
2023