The perception of the surrounding environment is a key requirement for autonomous driving
systems, yet the computation of an accurate semantic representation of the scene starting …

Semantic map construction under bird's-eye view (BEV) plays an essential role in
autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D …

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 …

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 …

Digital maps with lane-level details are the foundation of many applications. However,
creating and maintaining digital maps especially maps with lane-level details, are labor …

A lane-level intersection map is a cornerstone in high-definition (HD) traffic network maps for
autonomous driving and high-precision intelligent transportation systems applications such …

High-definition (HD) maps provide a complementary source of information for Advanced
Driver Assistance Systems (ADAS), allowing them to better understand the vehicle's …

Statistical learning techniques and increased computational power have facilitated the
development of self-driving car technology. However, a limiting factor has been the high …

Nowadays, autonomous vehicle technology is becoming more and more mature. Critical to
progress and safety, high-definition (HD) maps, a type of centimeter-level map collected …

The lane graph is a key component for building high-definition (HD) maps and crucial for
downstream tasks such as autonomous driving or navigation planning. Previously, He et …