Abstract. Our team has developed a neural network for road recognition on our digital twin, aimed at enhancing transportation-related applications. The neural network is trained on large datasets of road images and utilizes various deep learning architectures and techniques to improve its accuracy and reliability. The embedded neural network can recognize different road features, such as lane markings, road signs, and obstacles, and can identify the location and direction of the road. The integration of this neural network in our digital twin can help optimize transportation-related operations, reduce accidents, and improve overall traffic flow. The developed neural network architecture and training methodology, as well as its performance evaluation on various datasets, are presented in this paper. Additionally, the paper discusses the future directions for research in this area and the potential of the developed neural network for other applications in the digital twin domain.

The history of the development of such a thing as the Internet of things in our country, as well as in the world, is gaining momentum. The communication of all devices that maintain communication and transmit data online are used constantly and around the clock. In order to accumulate data and combine it for the benefit of local residents, as well as advertising companies, in order to understand their needs and solve with the help of unobtrusive offers, all data obtained from gadgets and devices connected to the Internet are used.