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An Embedded Multi-Sensor Data Fusion Design for Vehicle Perception Tasks

Mokhtar Bouain1,2, Karim M. A. Ali1, Denis Berdjag1, Nizar Fakhfakh2, and Rabie Ben Atitallah2
1. Univ. Valenciennes, CNRS, UMR 8201- LAMIH, F-59313 Valenciennes, France
2. Navya Company, Paris, France

Abstract—Nowadays, multi-sensor architectures are popular to provide a better understanding of environment perception for intelligent vehicles. Using multiple sensors to deal with perception tasks in a rich environment is a natural solution. Most of the research works have focused on PC-based implementations for perception tasks and very few concerns have been addressed for customized embedded designs. In this paper, we propose a Multi-Sensor Data Fusion (MSDF) embedded design for vehicle perception tasks using stereo camera and Light Detection and Ranging (LIDAR) sensors. A modular and scalable architecture based on Zynq-7000 SoC was designed.

Index Terms—Sensor Fusion, Embedded Systems, FPGA, Intelligent Vehicles.

Cite: Mokhtar Bouain, Karim M. A. Ali, Denis Berdjag, Nizar Fakhfakh, and Rabie Ben Atitallah, "An Embedded Multi-Sensor Data Fusion Design for Vehicle Perception Tasks," Journal of Communications, vol. 13, no. 1, pp. 8-14, 2018. Doi: 10.12720/jcm.13.1.8-14.