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Found 6 papers, 0 papers with code
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.
Deep Multi-Sensor Lane Detection
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving.
DeepRoadMapper: Extracting Road Topology From Aerial Images
In contrast, in this paper we propose an approach that directly estimates road topology from aerial images.
TorontoCity: Seeing the World with a Million Eyes
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.
HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images
In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes.
Enhancing Road Maps by Parsing Aerial Images Around the World
In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks.
