Collaborative Localization with Truth Discovery for Heterogeneous and Dynamic Vehicular Networks
Paper i proceeding, 2020

Collaborative localization over vehicular networks is challenging if quality varies among the collected multiple sources of information. These information sources are either from vehicle on-board sensors or remote sensing using vehicular communications. The variation in the quality of remote sensor information may cause estimation performance deterioration, even threatening the system security. In this paper, we propose a distributed localization framework with truth discovery for heterogeneous and dynamic vehicular networks. Firstly, it allows vehicles to learn which neighboring vehicles they should cooperate with. Secondly, it is resilient against the quality variation of the shared information between the connected vehicles.

5G mobile communication

wireless sensor networks

Remote sensing

quality of information

Kalman filters

distributed localization

truth discovery

Sensors

vehicular ad hoc networks

Vehicle-to-everything

vehicular networks

Signal to noise ratio

sensor placement

Vehicle dynamics

Författare

Fuxi Wen

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Tommy Svensson

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

IEEE Vehicular Technology Conference

15502252 (ISSN)

Vol. 2020-May 9128766

91st IEEE Vehicular Technology Conference, VTC Spring 2020
Antwerp, Belgium,

Automotive 5G Integrated Security and Communications

Chalmers, 2018-01-01 -- 2019-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Telekommunikation

Kommunikationssystem

Datavetenskap (datalogi)

DOI

10.1109/VTC2020-Spring48590.2020.9128766

Mer information

Senast uppdaterat

2021-01-13