Collaborative Localization with Truth Discovery for Heterogeneous and Dynamic Vehicular Networks
Paper in proceedings, 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.

Vehicle dynamics


Signal to noise ratio

truth discovery

quality of information

sensor placement

Kalman filters

vehicular ad hoc networks

wireless sensor networks

Remote sensing

5G mobile communication

vehicular networks

distributed localization



Fuxi Wen

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Tommy Svensson

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

IEEE Vehicular Technology Conference

15502252 (ISSN)

Vol. 2020-May 9128766

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

Areas of Advance

Information and Communication Technology


Subject Categories


Communication Systems

Computer Science



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