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

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

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
• Additional Information: The preprint of this article is available from Preprints: https://doi.org/10.20944/preprints202102.0048.v1