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Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems.

Medina, Edgar Cortés Gallardo; Espitia, Victor Miguel Velazquez; Silva, Daniela Chípuli; de las Cuevas, Sebastián Fernández Ruiz; Hirata, Marco Palacios; Chen, Alfredo Zhu; González, José Ángel González; Bustamante-Bello, Rogelio; Moreno-García, Carlos Francisco

Authors

Edgar Cortés Gallardo Medina

Victor Miguel Velazquez Espitia

Daniela Chípuli Silva

Sebastián Fernández Ruiz de las Cuevas

Marco Palacios Hirata

Alfredo Zhu Chen

José Ángel González González

Rogelio Bustamante-Bello



Abstract

Autonomous vehicles are increasingly becoming a necessary trend towards building the 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, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

Citation

MEDINA, E.C.G., ESPITIA, V.M.V., SILVA, D.C., DE LAS CUEVAS, S.F.R., HIRATA, M.P., CHEN, A.Z., GONZÁLEZ, J.A.G., BUSTAMANTE-BELLO, R. and MORENO-GARCÍA, C.F. 2021. Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems. Applied sciences [online], 11(7), article 2925. Available from: https://doi.org/10.3390/app11072925

Journal Article Type Article
Acceptance Date Mar 18, 2021
Online Publication Date Mar 25, 2021
Publication Date Apr 1, 2021
Deposit Date Mar 19, 2021
Publicly Available Date Apr 22, 2021
Journal Applied sciences
Electronic ISSN 2076-3417
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 7
Article Number 2925
DOI https://doi.org/10.3390/app11072925
Keywords Autonomous vehicle; Autonomous driving system; Computer vision; Neural networks; Feature extraction; Segmentation; Assisted driving; Cloud computing; Parallelization
Public URL https://rgu-repository.worktribe.com/output/1168399

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