Decision Support System for autonomous vehicle maintenance management

Abstract

Autonomous vehicles are complex objects equipped with the tools for monitoring the technical condition and transmitting data for diagnostics and prediction. The design of new models of autonomous and robotic vehicles is inextricably linked with the development of a maintenance system. The efficient functioning of the maintenance system is ensured by the use of intelligent technologies and digital twins. The article proposes an approach to designing a decision support system for the maintenance and repair of a fleet of autonomous vehicles. The decision support system is focused on robotic agricultural vehicles. The proposed structure of the system is of a general nature and can be used in transport and logistics enterprises in various sectors of the economy. The general architecture of the maintenance system is presented, including the neuro-digital twin of the vehicle, the analytics unit, simulation models of operation processes, and service centers. The components of the neuro-digital twin are digital twins of car units, a knowledge base, domain ontology, artificial neural networks, and a team of experts. The proposed approach is based on a combination of intelligent technologies and simulation modeling in collaboration with a team of experts. A set of original simulation models for the operation of a fleet of autonomous vehicles has been developed. New models based on stochastic timed colored Petri nets are proposed to analyze the processes during the autonomous vehicle operation. The developed decision support system can be used both at the stage of virtual commissioning and in the real operation of autonomous vehicles.