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The project/research was financed in the framework of the project Lublin University of Technology - Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).
Transport is an area that is developing at a tremendous pace. This development applies
not only to electric and hybrid cars appearing more and more often on the road but also to those of
an autonomous or semi-autonomous nature. This applies to both passenger cars and vans. In many
different publications, you can find a description of a number of benefits of using automated guided
vehicles (AGV) for logistics and technical tasks, e.g., in the workplace. An important aspect is the use
of knowledge management and machine learning, i.e., artificial intelligence (AI), to design these types
of processes. An important issue in the construction of autonomous vehicles is the IT connection of
sensors receiving signals from the environment. These signals are data for deep learning algorithms.
The data after IT processing enable the decision-making by AI systems, while the used machine
learning algorithms and neural networks are also needed for video image analysis in order to identify
and classify registered objects. The purpose of this article is to present and verify a mathematical
model used to respond to vehicles’ demand for a transport service under set conditions. The optimal
conditions of the system to perform the transport task were simulated, and the efficiency of this
system and benefits of this choice were determined.