Mecanismo Eficiente de Localização Cooperativa para Veículos Autônomos Conectados

• Wellington Lobato Junior Unicamp
• Allan M. de Souza Unicamp
• Eduardo Cerqueira UFPA
• Denis Rosário UFPA
• Leandro Villas Unicamp

DOI: https://doi.org/10.5753/sbrc.2021.16731

Abstract

The robust and precise location is fundamental to guarantee the functioning of Connected Autonomous Vehicles (CAVs) applications. However, vehicular navigation mechanisms (GNSS) are affected by several urban areas, mainly due to the refraction or reflection of satellite signals in construction. Thus, to use the GNSS location and satisfy the requirements of the CAV applications, it is necessary to consider data fusion techniques to obtain a more efficient positioning through the exchange of data between the CAVs. In this scenario, this article presents a data fusion mechanism for cooperative vehicular localization, called DUELAR. The mechanism considers the Kalman No Smell Filter combined with a track level map matching algorithm to correct the location errors. The simulation results showed that the DUELAR reduces the location error by at least 70% compared to other approaches in the literature.

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