Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

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; Carlos Francisco Moreno-García

doi:10.20944/preprints202102.0048.v1

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.

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.

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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.

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Abstract

Keywords

Subject

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