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	NEURAL NETWORK APPLICATION FOR DETECTION OF ROAD ACCIDENTS</div>

Zikratova Tatyana Viktorovna, Igor A. Zikratov

2020 , VOLUME 20, NUMBER 2 ( march-april )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2020-20-2-301-305

NEURAL NETWORK APPLICATION FOR DETECTION OF ROAD ACCIDENTS

T. V. Zikratova, I. A. Zikratov

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Article in Russian

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Zikratova T.V., Zikratov I.A. Neural network application for detection of road accidents. Scientiﬁc and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 2, pp. 301–305 (in Russian). doi: 10.17586/2226-1494-2020-20-2-301-305

Abstract

Subject of Research. The paper considers the issues of neural network application for detection and prediction of road accidents. The overtaking process of cars with crossing into oncoming trafﬁc is analyzed. The potential possibility of road accident reduction while overtaking is shown owing to intellectual assessment of road situation dynamics development. Method. We proposed to use a two-class classiﬁer based on a neural network. Road situations while overtaking with crossing into oncoming trafﬁc were the objects of classiﬁcation. The data on them was transmitted to the neural network input in the form of the frame set, that is, a graphical representation of discrete states of the “group of vehicles — section of the road” system. Frame formation was expected to be carried out as a result of information exchange between detectors and vehicle-mounted sensors, and road infrastructure, which is developed within the framework of the “smart city” paradigm. Main Results. The road situation is classiﬁed as “Dangerous” in case of high vehicle collision probability while overtaking and “Safe”, otherwise. If the situation is considered as “Dangerous”, the vehicle central processor generates an appropriate effect on the vehicle control elements to prevent an accident. Results of situation simulation implemented on Tensor Flow open software library for machine learning are obtained. They showed high prediction accuracy (0.96) on artiﬁcial data set. Practical Relevance. The results of the work can be used in promising unmanned and manned vehicles having radio communication with road infrastructure elements within the “smart city” concept to prevent road accidents caused by dangerous overtaking.

Keywords: prediction, detection, simulation, Bayesian classiﬁer, neural networks, safety, unmanned vehicles

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