Constructing HD semantic maps is a central component of autonomous driving. However,
traditional pipelines require a vast amount of human efforts and resources in annotating and …

High-definition (HD) semantic map generation of the environment is an essential component
of autonomous driving. Existing methods have achieved good performance in this task by …

Autonomous driving systems require High-Definition (HD) semantic maps to navigate
around urban roads. Existing solutions approach the semantic mapping problem by offline …

High-definition (HD) map provides abundant and precise environmental information of the
driving scene, serving as a fundamental and indispensable component for planning in …

High-definition (HD) map provides abundant and precise static environmental information of
the driving scene, serving as a fundamental and indispensable component for planning in …

High-definition (HD) semantic maps are a crucial component for autonomous driving on
urban streets. Traditional offline HD maps are created through labor-intensive manual …

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 …

Vectorized high-definition map online construction has garnered considerable attention in
the field of autonomous driving research. Most existing approaches model changeable map …

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited
due to the deployed expensive sensors and time-consuming computation. Camera-based …

Inferring traffic object such as lane information is of foremost importance for deployment of
autonomous driving. Previous approaches focus on offline construction of HD map inferred …