On the application of neural network technologies for control problems in cognitive transportation systems

Abstract

In the work, the functions of cognition are brought in line with the aspects of the functioning of cognitive transport systems and technologies. We consider the mathematical apparatus of cognitive technologies – neural network control systems, based on trained multi-layer neural networks of direct action in the class of adaptive control systems for dynamic objects. The possibility and variants of their inclusion in the control system of the transport system are clarified. On the basis of the considered approach, a country road recognition system based on a convolutional neural network has been developed, which will automatically segment rural roads using satellite images and build a road network diagram to analyze the spatial development of transport networks and to control unmanned vehicles. The development of a neural network based on U-net was carried out in Python 3x. The training set consists of 880 images prepared by hand mark up. The accuracy of the developed model when testing on prepared samples was 64%. According to the results of the study, conclusions were drawn and prospects for further functional development of the developed tools were determined.