Change Detection in HD-Maps Using Camera Images for Autonomous Driving
Name
Navid Bamdad Roshan
Abstract
Self-driving vehicles have been an exciting field of research for both industry and academia in the last decade. The map is one of the challenging aspects of this research. It has been centuries that maps are used in transportation for routing. Accordingly, autonomous vehicles can use maps for routing as well. Some maps that can be used for autonomous driving are called high-definition (HD) maps. HD maps are more accurate than ordinary maps. Details of the HD maps are in centimeter-level accuracy. They also have more details compared to regular maps. For instance, HD-maps have information about the surrounding environment of the autonomous vehicle like details about streets, lanes, traffic rules, traffic signs, traffic lights, etc. This additional information assists autonomous vehicles in perceiving the environment better to move safer and more efficiently. Thus, autonomous vehicles need up-to-date details in HD maps all the time. So, in case of any changes in the environment, the changes must be detected, and HD maps must be updated accordingly. Therefore, it is essential to design an automatic solution for detecting the changes in the environment. This work proposes an automatic streets’ drivable area change detection pipeline. The proposed pipeline detects any changes that alter the drivable path of the streets. The detected changes can be used to update the HD maps later.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Dmytro Fishman, Naveed Muhammad
Defence year
2021