Sensors 2017, 17(11), 2599; https://doi.org/10.3390/s17112599
A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters
Luis Medina 1, Miguel Diez-Ochoa 2, Raul Correal 2, Sergio Cuenca-Asensi 1, Alejandro Serrano 1
, Jorge Godoy 3, Antonio Martínez-Álvarez 1 and Jorge Villagra 3,*
, Jorge Godoy 3, Antonio Martínez-Álvarez 1 and Jorge Villagra 3,*
1
University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain
2
Ixion Industry & Aerospace SL, Julian Camarilo 21B, 28037 Madrid, Spain
3
Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Spain
*
Author to whom correspondence should be addressed.
Received: 15 September 2017 / Revised: 3 November 2017 / Accepted: 6 November 2017 / Published: 11 November 2017
(This article belongs to the Section Physical Sensors)
Abstract
Grid-based perception techniques in the automotive sector based on fusing information from different sensors and their robust perceptions of the environment are proliferating in the industry. However, one of the main drawbacks of these techniques is the traditionally prohibitive, high computing performance that is required for embedded automotive systems. In this work, the capabilities of new computing architectures that embed these algorithms are assessed in a real car. The paper compares two ad hoc optimized designs of the Bayesian Occupancy Filter; one for General Purpose Graphics Processing Unit (GPGPU) and the other for Field-Programmable Gate Array (FPGA). The resulting implementations are compared in terms of development effort, accuracy and performance, using datasets from a realistic simulator and from a real automated vehicle. View Full-TextKeywords:
Bayesian occupancy filter; FPGA; GPGPU; embedded system; ADAS
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Medina, L.; Diez-Ochoa, M.; Correal, R.; Cuenca-Asensi, S.; Serrano, A.; Godoy, J.; Martínez-Álvarez, A.; Villagra, J. A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters. Sensors 2017, 17, 2599.
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