The upraise of autonomous driving technologies asks for maps characterized bya broad range of features and quality parameters, in contrast to traditional navigation maps which in most cases are enriched graph-based models. This paper tackles several uncertainties within the domain of HD Maps. The authors give an overview about the current state in extracting road features from aerial imagery for creating HD maps, before shifting the focus of the paper towards remote sensing technology. Possible data sources and their relevant parameters are listed. A random forest classifier is used, showing how these data can deliver HD Maps on a country-scale, meeting specific quality parameters
The continuous flow of autonomous vehicle-based data could revolutionize current map updating proced...
High definition (HD) map data is a key feature to enable highly automated driving. With the advent o...
Infrastructure and traffic monitoring are two of the most innovative applications for automatically ...
The upraise of autonomous driving technologies asks for maps characterized bya broad range of featur...
The knowledge about the placement and appearance of lane markings is a prerequisite for the creation...
Remote Sensing can contribute in many ways to the topic of Transport. In this contribution we will f...
This contribution describes a new way of generating highly accurate high definition (HD) maps of the...
Technologies related to autonomous driving have been advancing rapidly for the past few years, and t...
Autonomous driving technology is now evolving at an unprecedented speed. HD maps, which are embedded...
Maps are constantly developing, also, the newly defined High Definition (HD) maps increase the map c...
With growing attention being devoted to autonomous vehicle (AV) safety, people have recently attache...
peer reviewedIn cooperative, connected, and automated mobility (CCAM), the more automated vehicles c...
Extracting information about roads is important for many applications, such as infrastructure monito...
In this paper we present an approach to enhance existing maps with fine grained segmentation categ...
High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Altho...
The continuous flow of autonomous vehicle-based data could revolutionize current map updating proced...
High definition (HD) map data is a key feature to enable highly automated driving. With the advent o...
Infrastructure and traffic monitoring are two of the most innovative applications for automatically ...
The upraise of autonomous driving technologies asks for maps characterized bya broad range of featur...
The knowledge about the placement and appearance of lane markings is a prerequisite for the creation...
Remote Sensing can contribute in many ways to the topic of Transport. In this contribution we will f...
This contribution describes a new way of generating highly accurate high definition (HD) maps of the...
Technologies related to autonomous driving have been advancing rapidly for the past few years, and t...
Autonomous driving technology is now evolving at an unprecedented speed. HD maps, which are embedded...
Maps are constantly developing, also, the newly defined High Definition (HD) maps increase the map c...
With growing attention being devoted to autonomous vehicle (AV) safety, people have recently attache...
peer reviewedIn cooperative, connected, and automated mobility (CCAM), the more automated vehicles c...
Extracting information about roads is important for many applications, such as infrastructure monito...
In this paper we present an approach to enhance existing maps with fine grained segmentation categ...
High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Altho...
The continuous flow of autonomous vehicle-based data could revolutionize current map updating proced...
High definition (HD) map data is a key feature to enable highly automated driving. With the advent o...
Infrastructure and traffic monitoring are two of the most innovative applications for automatically ...