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

Mokhtar Bouain , Karim Mohamed Abedallah Ali (1) , Denis Berdjag (1) , Nizar Fakhfakh , Rabie Ben Atitallah

1 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

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.

Mots clés: Sensor Fusion; Embedded Systems; FPGA; Intelligent Vehicles

Domaines: Automatique / Robotique; Informatique [cs]

Mokhtar Bouain, Karim Mohamed Abedallah Ali, Denis Berdjag, Nizar Fakhfakh, Rabie Ben Atitallah. An Embedded Multi-Sensor Data Fusion Design for Vehicle Perception Tasks. ICCSIT 2017 : 2017 10th International Conference on Computer Science and Information Technology (ICCSIT 2017), Oct 2017, Florence, Italy. ⟨hal-03412391⟩