Lane graph as path: Continuity-preserving path-wise modeling for online lane graph construction
Online lane graph construction is a promising but challenging task in autonomous driving.
Previous methods usually model the lane graph at the pixel or piece level, and recover the …
Previous methods usually model the lane graph at the pixel or piece level, and recover the …
Learning and aggregating lane graphs for urban automated driving
Lane graph estimation is an essential and highly challenging task in automated driving and
HD map learning. Existing methods using either onboard or aerial imagery struggle with …
HD map learning. Existing methods using either onboard or aerial imagery struggle with …
EarthNets: Empowering artificial intelligence for Earth observation
Earth observation (EO), aiming at monitoring the state of planet Earth using remote sensing
data, is critical for improving our daily lives and living environment. With a growing number …
data, is critical for improving our daily lives and living environment. With a growing number …
Rngdet: Road network graph detection by transformer in aerial images
Road network graphs provide critical information for autonomous-vehicle applications, such
as drivable areas that can be used for motion planning algorithms. To find road network …
as drivable areas that can be used for motion planning algorithms. To find road network …
Collaborative dynamic 3d scene graphs for automated driving
Maps have played an indispensable role in enabling safe and automated driving. Although
there have been many advances on different fronts ranging from SLAM to semantics …
there have been many advances on different fronts ranging from SLAM to semantics …
Insightmapper: A closer look at inner-instance information for vectorized high-definition mapping
Vectorized high-definition (HD) maps contain detailed information about surrounding road
elements, which are crucial for various downstream tasks in modern autonomous driving …
elements, which are crucial for various downstream tasks in modern autonomous driving …
Sledge: Synthesizing driving environments with generative models and rule-based traffic
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world
driving logs. Its core component is a learned model that is able to generate agent bounding …
driving logs. Its core component is a learned model that is able to generate agent bounding …
InsMapper: Exploring inner-instance information for vectorized HD mapping
Vectorized high-definition (HD) maps contain detailed information about surrounding road
elements, which are crucial for various downstream tasks in modern autonomous vehicles …
elements, which are crucial for various downstream tasks in modern autonomous vehicles …
Topomlp: An simple yet strong pipeline for driving topology reasoning
Topology reasoning aims to comprehensively understand road scenes and present drivable
routes in autonomous driving. It requires detecting road centerlines (lane) and traffic …
routes in autonomous driving. It requires detecting road centerlines (lane) and traffic …
Graph-based topology reasoning for driving scenes
Understanding the road genome is essential to realize autonomous driving. This highly
intelligent problem contains two aspects-the connection relationship of lanes, and the …
intelligent problem contains two aspects-the connection relationship of lanes, and the …