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

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, trac...

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Bibliographic Details
Published in:Applied sciences 2021-04, Vol.11 (7), p.2925
Main Authors: Cortés Gallardo Medina, Edgar, Velazquez Espitia, Victor Miguel, Chípuli Silva, Daniela, Fernández Ruiz de las Cuevas, Sebastián, Palacios Hirata, Marco, Zhu Chen, Alfredo, González González, José Ángel, Bustamante-Bello, Rogelio, Moreno-García, Carlos Francisco
Format: Article
Language:English
Subjects:
Algorithms
Automobiles
autonomous driving system
autonomous vehicle
Autonomous vehicles
Cameras
Cloud computing
Computer applications
Computer vision
Computing time
Deep learning
feature extraction
Long short-term memory
Machine learning
Machine vision
Neural networks
Parallel processing
Path planning
Recurrent neural networks
Roads & highways
segmentation
Teams
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11072925