{"title":"Data Issues in High Definition Maps Furniture – A Survey","authors":"Andi Zang, Runsheng Xu, Goce Trajcevski, Fan Zhou","doi":"10.1145/3627160","DOIUrl":null,"url":null,"abstract":"The rapid advancements in sensing techniques, networking and AI algorithms in the recent years have brought the autonomous driving vehicles closer to common use in vehicular transportation. One of the fundamental components to enable the autonomous driving functionalities are the High Definition (HD) maps – a type of maps that carry 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 system, 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 maps 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.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"249 1","pages":"0"},"PeriodicalIF":1.2000,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Spatial Algorithms and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3627160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid advancements in sensing techniques, networking and AI algorithms in the recent years have brought the autonomous driving vehicles closer to common use in vehicular transportation. One of the fundamental components to enable the autonomous driving functionalities are the High Definition (HD) maps – a type of maps that carry 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 system, 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 maps 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.
期刊介绍:
ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.