3D maps distribution of self-driving vehicles using roadside edges

Three-dimensional (3D) maps have become a shared digital infrastructure for autonomous vehicles, especially in urban areas. Point Cloud Data (PCD) maps are used for scan matching to enable self-localization. Autonomous vehicles need to maintain PCD maps along with the destination that is often decid...

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Veröffentlicht in:2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW) S. 40 - 45
Hauptverfasser: Mizutani, Masaya, Tsukada, Manabu, Iida, Yuki, Esaki, Hiroshi
Format: Tagungsbericht
Sprache:Englisch
Japanisch
Veröffentlicht: IEEE 01.11.2020
Schlagworte:
Autonomous vehicles
Bandwidth
Cloud computing
PCD map
Roadside edge
Servers
Three-dimensional displays
Time measurement
Urban areas
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Zusammenfassung:Three-dimensional (3D) maps have become a shared digital infrastructure for autonomous vehicles, especially in urban areas. Point Cloud Data (PCD) maps are used for scan matching to enable self-localization. Autonomous vehicles need to maintain PCD maps along with the destination that is often decided on demand and to keep the PCD map updated. In this paper, we propose a system that delivers PCD maps cached at roadside edges in real time. We implement the system in Autoware, an open-source software for autonomous driving. Subsequently, we evaluate whether the autonomous vehicle can simultaneously download the PCD map from its edge and enable self-localization. Our results show that autonomous vehicles can perform self-localization while downloading the PCD map from the edge server. Additionally, we measure the download time with variable bandwidth and examine the bandwidth in which the self-localization normally operates. In our results, the download time of the PCD map at 60 Mbps was 1.16 s at maximum, and it is indicated that 60 Mbps is the deadline for this system to work properly.
DOI:10.1109/CANDARW51189.2020.00021