Demestichas, K.Masikos, M.Adamopoulou, E.Dreher, S.Diaz De Arkaya, A.2024-07-242024-07-242012Demestichas , K , Masikos , M , Adamopoulou , E , Dreher , S & Diaz De Arkaya , A 2012 , ' Machine-Learning methodology for energy efficient routing ' , Paper presented at 19th Intelligent Transport Systems World Congress, ITS 2012 , Vienna , Austria , 22/10/12 - 26/10/12 pp. EU-00226 .conferencehttps://hdl.handle.net/11556/4722Eco-driving assistance systems encourage economical driving behaviours and support the driver in optimizing his driving style to achieve fuel economy and consequently emission reduction. Energy efficient routing is one of the especially pertinent issues related to the autonomy of Fully Electric Vehicles (FEVs). This paper introduces a novel methodology for energy efficient routing, based on the realization of dependable energy consumption predictions for the various road segments constituting an actual or potential vehicle route, and it is mainly performed by means of machine-learning functionality, through the use of the so-called Machine-Learning Engines. The proposed methodology, the functional architecture implementing it, as well as first experimental results are presented in detail.enginfo:eu-repo/semantics/restrictedAccessconference outputConsumption predictionEnergy efficiencyMachine-learningRoutingComputer Networks and CommunicationsComputer Science ApplicationsControl and Systems EngineeringElectrical and Electronic EngineeringMechanical EngineeringTransportationSDG 7 - Affordable and Clean Energyhttp://www.scopus.com/inward/record.url?scp=84896970180&partnerID=8YFLogxK