Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles
Denis Vladimirovich Endachev1, Pavel Alexandrovich Vasin2, Sergey Sergeyevich Shadrin3
1Denis Vladimirovich Endachev*, Federal State Unitary Enterprise Central Scientific Research Automobile and Automotive Institute “NAMI” (FSUE «NAMI»), Moscow, Russia.
2Pavel Alexandrovich Vasin, Federal State Unitary Enterprise Central Scientific Research Automobile and Automotive Institute “NAMI” (FSUE «NAMI»), Moscow, Russia.
3Sergey Sergeyevich Shadrin, Federal State Budgetare Education Institution of Higher Education Moscow Automobile and Road Constraction State University (MADI), Moscow, Russia.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 5295-5301 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9154088619/2019©BEIESP | DOI: 10.35940/ijitee.F91540981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This article considers modern rapid architectures of detecting neural networks, structural peculiarities of each selected neural network architectures are analyzed. Experiment is carried out on the basis of potentially dangerous situation upon autonomous vehicle movement; in the selected experimental environment a set of architectures for computer vision system of autonomous vehicle is analyzed, and traffic safety of autonomous vehicle is estimated under various weather conditions; computing time required for application of additional control and analysis algorithms is evaluated. Experimental results are analyzed aiming at development of reasonable selection of neural network architectures for object recognition required for variability of support of autonomous vehicle traffic. Conclusion about applicability of the considered neural network architectures is made for conditions of certain project.
Keywords: Automobile, Autonomous Wheeled Vehicle, Unmanned Vehicle, Neural Networks, Computer Vision, Machine Vision, Traffic Safety.