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Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems
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Version 1
: Received: 30 January 2021 / Approved: 1 February 2021 / Online: 1 February 2021 (14:50:20 CET)
A peer-reviewed article of this Preprint also exists.
Cortés Gallardo Medina, E.; Velazquez Espitia, V.M.; Chípuli Silva, D.; Fernández Ruiz de las Cuevas, S.; Palacios Hirata, M.; Zhu Chen, A.; González González, J.Á.; Bustamante-Bello, R.; Moreno-García, C.F. Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems. Appl. Sci. 2021, 11, 2925. Cortés Gallardo Medina, E.; Velazquez Espitia, V.M.; Chípuli Silva, D.; Fernández Ruiz de las Cuevas, S.; Palacios Hirata, M.; Zhu Chen, A.; González González, J.Á.; Bustamante-Bello, R.; Moreno-García, C.F. Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems. Appl. Sci. 2021, 11, 2925.
Journal reference: Appl. Sci. 2021, 11, 2925
DOI: 10.3390/app11072925
Abstract
Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.
Keywords
autonomous driving systems; computer vision; neural networks; feature extraction; segmentation; assisted driving; cloud computing; parallelization
Subject
MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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