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Social Psychology and Society

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2221-1527

ISSN (online): 2311-7052

DOI: http://dx.doi.org/10.17759/sps

License: CC BY-NC 4.0

Started in 2010

Published quarterly

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Neural network model for recognition of driving strategies and interaction of drivers in traffic conditions 239

Efremov S.B., post graduate student of the Department of psychology of Management, Moscow State University of Psychology and Education, Moscow, Russia, 0971090@gmail.com
Abstract
The paper presents a neural network model for recognizing driving strategies based on the interaction of drivers in traffic flow conditions. The architecture of the model, based on self-organizing map (SOM), consisting of various neural networks based on RBF (Radial Basis Function). The purpose of this work is to describe the architecture and structure of the neural network model, which allows to recognize the strategic features of driving. Our neural network is able to identify the interaction strategies of cars (drivers) in traffic flow conditions, as well as to identify such behavioral patterns of movement that can be correlated with different types of dangerous driving. From the results of the study, it follows that neural networks of the SOM RBF type are able to recognize and classify the types of interactions in traffic conditions based on modeling the analysis of the trajectories of cars. This neural network showed a high percentage of recognition and clear clustering of similar driving strategies.

Keywords: road and traffic environment, road behavior, road user interaction strategies, driving strategies, neural network model, self-organizing maps

Column: Methodological Tools

DOI: http://dx.doi.org/10.17759/sps.2018090413

For Reference

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