The Planning Process of Transport Tasks for Autonomous Vans—Case Study

Abstract

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.