The authors developed an optimal map-aided estimation process for motor vehicles. Each position measurement is translated onto each road in the vicinity of the measurement. The map-aided estimates are used to update a series of Kalman filters, each of which represents one of the possible spatial trajectories that the vehicle could be following. The best spatial trajectory and the current position estimate is determined by maximizing a probability measure that combines each filter's goodness of fit with the likelihood of measurements that contributed to that filter. The purpose of this paper is to describe a means for characterizing empirical source of information, such as road rules, traffic data, and road types, so that they can be integrated with the map-aided estimator to increase the likelihood of identifying the correct trajectory. A number of empirical information sources are described including but not limited to: road type, road capacity, traffic flows, road rules, traffic control system data, and past drive behavior. These information sources are relatively easy to obtain but their disparate nature makes them difficult to combine. This paper describes how the above sources and sources like them can be characterized as probability distributions and subsequently combined using Bayesian probability techniques and then integrated with the map-aided estimator that also has its basis in probability theory. The effectiveness of the characterization is demonstrated by comparing simulations of the map-aided estimator extended with and without the empirical information. The primary aim of the information characterization is the improvement of the map-aided estimator. The now characterized information, however, can be used in other motor vehicle positioning systems. In systems relying on map matching to correct sensor drift in dead-reckoning for example, empirical information can be used to improve the likelihood of matching to the correct roads. At the end of this paper the reader should have an appreciation for the application and modeling of sources of empirical information that can be used to aid in the position estimation of a motor vehicle.

  • Supplemental Notes:
    • Five volumes of papers and one volume of abstracts comprise the published set of conference materials.
  • Corporate Authors:


  • Authors:
    • Scott, C A
    • Drane, C R
  • Conference:
  • Publication Date: 1995-11


  • English

Media Info

  • Pagination: p. 573

Subject/Index Terms

Filing Info

  • Accession Number: 00721142
  • Record Type: Publication
  • Report/Paper Numbers: Volume 2
  • Files: TRIS
  • Created Date: May 27 1996 12:00AM