Machine Learning Assisted High-Definition Map Creation

In recent years, autonomous driving technologies have attracted broad and enormous interests from both academia and industry and are under rapid development. High-Definition (HD) Maps are widely used as an indispensable component of an autonomous vehicle system by researchers and practitioners. HD Maps are digital maps that contain highly precise, fresh and comprehensive geometric information as well as semantics of the road network and surrounding environment. They provide critical inputs to almost all other components of autonomous vehicle systems, including localization, perception, prediction, motion planning, vehicle control etc. Traditionally, it is very laborious and costly to build HD Maps, requiring a significant amount of manual annotation work. In this paper, we first introduce the characteristics and layers of HD Maps; then we provide a formal summary of the workflow of HD Map creation; and most importantly, we present the machine learning techniques being used by the industry to minimize the amount of manual work in the process of HD Map creation.