Lanelet2: A high-definition map framework for the future of automated driving - Poggenhans, Fabian; Pauls, Jan-Hendrik; Janosovits, Johannes; Orf, Stefan; Naumann, Maximilian; Kuhnt, Florian; Mayr, Matthias (Proceedings (IEEE Conference on Intelligent Transportation Systems), 2018)
Although accurate and comprehensive maps are indispensable for highly automated driving, especially in complex urban scenarios, there are hardly any publications in which requirements for these maps are discussed. In our opinion, such maps must meet high demands in terms of accuracy, completeness, verifiability and extensibility, so that the resulting complexity can only be handled by an enclosing, carefully designed software framework. In this paper we therefore introduce the open-source map framework Lanelet2 implemented in C++ and explain the underlying concept. The goal of Lanelet2 is not only to be usable for typical, isolated applications such as localization or motion planning, but for various potential applications of maps for highly automated driving. On the basis of both abstract and real examples we show the concrete structure of Lanelet2 maps and its use for automated driving.