From their beginnings in the car as a tool for A-to-B navigation, digital maps are experiencing an evolution process that will see them at the forefront of new applications designed to improve active safety and manage fuel consumption. These maps will, in effect, become a new vehicle sensor, with a range exceeding that of camera and radar systems, and an ability to work in all weathers and at night. These new maps will need to be more accurate than those used for navigation, and be fused with a minimized set of map attributes to create new vehicle-interpreted precision maps. This chapter will look at the applications that would benefit from these new maps, which in terms of both safety and energy management applications, provide precise knowledge of the road ahead. This allows the vehicles and drivers to be informed of potentially dangerous situations, and take actions based on exact knowledge of future slopes and curves in the road. In energy management terms, the knowledge of road slope will allow the most fuel-efficient routes to be chosen, and can be used to determine the range of Electric and Hybrid Electric Vehicles (EV/HEV), as well as optimizing engine and transmission for fuel efficiency. We will consider how such maps can be created using a number of different technologies, and how this collection methodology impacts their characteristics. As maps evolve and become more “connected,” the possibilities to update them, and access further geographic data and services, will further increase their usefulness.


Navigation System Electric Vehicle Road Segment Road Model Road Geometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Boriboonsomsin K, Barth M (2009) Impacts of road grade on fuel consumption and carbon dioxide emissions evidenced by use of advanced navigation systems. Transport Res Rec 2139:21–30CrossRefGoogle Scholar
  2. Craig J There will be maps for cars, and maps for people. GPS Business News.
  3. Dobson MW (2009) Creating robust functionalities, ADAS and 3D-road map databases. GeoInformatics 28–33Google Scholar
  4. Huang W, Bevly DM, Li X, Schnick S (2007) 3D road geometry based optimal truck fuel economy. In: Proceedings of ASME international mechanical engineering congress and exposition, Seattle, Washington, 11–15 Nov 2007Google Scholar
  5. Li X, Tennant K (2009) Vehicle energy management optimization using look-ahead three-dimensional digital road geometry. In: ITC World Congress, Stockholm, The Netherlands, 21–25 Sept 2009Google Scholar
  6. Zhang C, Vahidi A, Li x, Essenmacher d (2009a) Role of trip information preview in fuel economy of plug-in hybrid vehicles. In: ASME 2009 dynamic systems and control conference (DSCC2009), Hollywood, California, 12–14 Oct 2009Google Scholar
  7. Zhang C, Vahidi A, Pisu P, Li X, Tennant K (2009b) Utilizing road grade preview for increasing fuel economy of hybrid vehicles. In: Proceedings of the 12th IFAC symposium on control in transportation systems, Redondo Beach, February 2009Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.JCCMunichGermany

Personalised recommendations