An Intelligent Management Functionality for In-Vehicle Driving Style Reconfigurations Leveraging on Bayesian Networking Principles

Abstract

The ceaseless evolution of Information and Communication Technologies (ICT) is reflected on their migration towards the Future Internet (FI) era, which is characterized, among others, by powerful and complex network infrastructures, and innovative applications, services and content. An area of applications that finds prosperous ground in the FI era lies in the world of road transportation and intelligent vehicles. In particular, recent and future ICT findings are envisaged to contribute to the enhancement of transportation efficiency at various levels, such as traffic, parking, safety and emergency management. In this context, the goal of this paper is to introduce an Intelligent Management Functionality (IMF) that enables vehicles to operate each time in the best available Driving Style (DS) by responding quickly to changing driving environment situations and driver’s preferences. Such a functionality aims to operate on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience and, finally, selecting the optimal DS. Knowledge is obtained through the exploitation of Bayesian networking principles and Naive-Bayes modeling. Indicative simulation results showcase the effectiveness of the proposed system.