Data Issues in High-Definition Maps Furniture – A Survey

Abstract

The rapid advancements in sensing techniques, networking, and artificial intelligence (AI) algorithms in recent years have brought autonomous driving vehicles closer to common use in vehicular transportation. One of the fundamental components to enable autonomous driving functionalities are High-Definition (HD) maps – a type of map that carries highly accurate and much richer information than conventional maps. The creation and use of HD maps rely on advances in multiple disciplines, such as computer vision/object perception, geographic information systems, sensing, simultaneous localization and mapping, machine learning, etc. To date, several survey papers have been published describing the literature related to HD maps and their use in specialized contexts. In this survey, we aim to provide (1) a comprehensive overview of the issues and solutions related to HD maps and their use without attachment to a particular context; (2) a detailed coverage of the important domain knowledge of HD map furniture, from acquisition techniques and extraction approaches, through HD map–related datasets, to furniture quality assessment metrics, for the purpose of providing a comprehensive understanding of the entire workflow of HD map furniture generation, as well as its use.