Energy-driven Routing and Navigation for Advanced Driver Assistance Systems

It is advocated that the penetration and commercial viability of Fully Electric Vehicles depends strongly on their range autonomy as well as on the ability to integrate charging procedures into everyday life activities in an unobtrusive way. Energy-efficient route planning strongly contributes in this direction by optimizing (reducing) the energy required to reach a certain decision. This paper introduces a novel machine learning approach for routing and navigation that renders the vehicle capable of predicting (thus avoiding) energy consuming routes. The main processes, application and services supporting the aforementioned functionality are described in detail.

• Supplemental Notes:
  • Abstract reprinted with permission from Intelligent Transportation Society of America.
• Corporate Authors:

  ITS America

  1100 17th Street, NW, 12th Floor
  Washington, DC  United States  20036
• Authors:
  • Adamopoulou, Evgenia
  • Masikos, Michael
  • Demestichas, Konstantinos
  • Gorini, M
• Conference:
  • 19th ITS World Congress
  • Location: Vienna , Austria
  • Date: 2012-10-22 to 2012-10-26
• Publication Date: 2012

Language

• English

Media Info

• Media Type: Digital/other
• Features: CD-ROM; Figures; References; Tables;
• Pagination: 10p
• Monograph Title: 19th ITS World Congress, Vienna, Austria, 22 to 26 October 2012

Subject/Index Terms

• TRT Terms: Driver support systems; Driving; Electric vehicle charging; Electric vehicles; Energy conservation; Range (Vehicles); Routing
• Subject Areas: Energy; Environment; Highways; Vehicles and Equipment; I15: Environment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01494024
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 24 2013 9:15AM