Fault Diagnosis in Vehicular Networks Using Do-Calculus

Real time fault diagnosis is an integral part of modern automobile industries. Vehicles are no longer a sum of their mechanical parts but are built up of a number of interconnected programmable components. ECUs (Electronic Control Units) drive these components and that means if one component fails, it may lead to a complete failure. Research has thus focused on effectively solving this problem in real time. In this paper, we aim to present a working model that using Bayesian networks and intervenal calculus that accurately diagnoses faults in vehicular systems.