10.5281/zenodo.4644085
https://zenodo.org/records/4644085
oai:zenodo.org:4644085
Nikos Piperigkos
Nikos Piperigkos
Computer Engineering & Informatics Department University of Patras,
Aris S. Lalos
Aris S. Lalos
Computer Engineering & Informatics Department University of Patras,
Kostas Berberidis
Kostas Berberidis
Computer Engineering & Informatics Department University of Patras,
Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization
Zenodo
2021
CAV, Cooperative Localization and Tracking, Multi-modal fusion, V2V
2021-03-29
eng
10.5281/zenodo.4644084
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
Extended Kalman Filters have been widely applied for tracking the location of moving semi-autonomous vehicles.
The latter are equipped with a multitude of sensors generating multi-modal data, while at the same time they are capable of
cooperating via Vehicle-to-Vehicle communication technologies. In this paper, we have formulated a cooperative tracking scheme
based on Extended Kalman Filter, in order to cope with erroneous GPS location information. It performs multi-modal fusion in a
centralized and distributed manner, assuming the existence of an overall fusion center or local interaction among neighbouring
and connected vehicles only. It features the property of encoding in a linear form the different measurement modalities, including
range and GPS measurements, exploiting the connectivity topology of cooperating vehicles, using the graph Laplacian
operator. The extended experimental evaluation using realistic vehicle trajectories extracted by CARLA autonomous driving
simulator, verify the significant reduction of GPS error under various realistic conditions. Moreover, both schemes outperform
existing cooperative localization methods. Finally, the distributed tracking approach exhibits similar performance and in specific
cases outperforms the centralized counterpart.
European Commission
10.13039/501100000780
871738
Cross-layer cognitive optimization tools & methods for the lifecycle support of dependable CPSoS