Intelligent, In-Vehicle Autonomous Decision-Making Functionality for Driving Style Reconfigurations

Intelligent connected vehicles (ICVs) constitute a transformative technology attracting immense research effort and holding great promise in providing road safety, transport efficiency, driving comfort, eco-friendly mobility. As the environment becomes more “connected”, manner which an ICV is driven (driving style) can dynamically vary from time to time, due change several parameters associated with personal traits ICV’s surroundings. This necessitates fast effective decisions be made for priori identifying most appropriate style ICV. Accordingly, main goal of this study present novel, in-vehicle autonomous decision-making functionality, enables ICVs dynamically, transparently, securely utilize best available (DS). The proposed functionality takes as input related driver’s characteristics preferences, well changing environment. A Naive Bayes learning classifier applied cognitive nature presented functionality. Three scenarios, regards drivers different preferences scenes situations, are illustrated, showcasing effectiveness