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

Masaya Mizutani , Manabu Tsukada (1) , Yuki Iida , Hiroshi Esaki

1 Department of Creative Informatics

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.

Mots clés: Roadside edge; PCD map; autonomous vehicles

Domaines: Robotique [cs.RO]; Réseaux et télécommunications [cs.NI]; Informatique mobile

Masaya Mizutani, Manabu Tsukada, Yuki Iida, Hiroshi Esaki. 3D maps distribution of self-driving vehicles using roadside edges. 13th International Workshop on Autonomous Self-Organizing Networks (ASON) held in conjunction with CANDAR 2020, Nov 2020, Okinawa, Japan. ⟨hal-02997520⟩