5083342
doi
10.1109/CAMAD50429.2020.9209312
oai:zenodo.org:5083342
user-eu
Aris S. Lalos
Industrial Systems Institute Athena Research Center Patras,
Kostas Berberidis
Computer Engineering & Informatics Department University of Patras
Graph based Cooperative Localization for Connected and Semi-Autonomous Vehicles
Nikos Piperigkos
Computer Engineering & Informatics Department University of Patras
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Cooperative Localization, 5G, CAVs, Multimodal fusion
<p>Cooperative Real-time Localization is expected to play a crucial role in various applications in the field of Connected and Semi-Autonomous vehicles (CAVs), such as collision avoidance/warning, cooperative adaptive cruise control, etc. Future 5G wireless systems are expected to enable costeffective Vehicle-to-Everything (V2X) systems, allowing CAVs to share the measured data with other entities of the network. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance to neighboring vehicles and relative angle or azimuth angle, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighboring vehicles. This method is based on the so called Laplacian Processing, a Graph Signal Processing tool, that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system, significantly outperforming current state of the art approaches, in terms of localization mean square</p>
Zenodo
2020-09-30
info:eu-repo/semantics/conferencePaper
5083341
user-eu
award_title=Cross-layer cognitive optimization tools & methods for the lifecycle support of dependable CPSoS; award_number=871738; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/871738; funder_id=00k4n6c32; funder_name=European Commission;
1625795307.750303
814142
md5:7b94b797157d55d6661030846db8db3b
https://zenodo.org/records/5083342/files/CAMAD2020_final.pdf
public