Can Priors be Trusted? Learning to Anticipate Roadworks

This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation during autonomous driving by assessing the probability that roadworks are being traversed. The authors compare two formulations of a roadwork prior: one based on Gaussian Process (GP) classification and the other on a more conventional Hidden Markov Model (HMM) in order to model correlations between nearby parts of a vehicle trajectory. Importantly, the authors formulation allows this prior to be updated efficiently and repeatedly to gain an ever more accurate model of the environment over time. In the absence of, or in addition to, any in-situ observations, information from dedicated web resources can readily be incorporated into the framework. The authors evaluate the model using real data from an autonomous car and show that although the GP and HMM are roughly commensurate in terms of mapping roadworks, the GP provides a more powerful representation and lower prediction error.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 927-932
  • Monograph Title: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012)

Subject/Index Terms

Filing Info

  • Accession Number: 01565040
  • Record Type: Publication
  • ISBN: 9781467330640
  • Files: TRIS
  • Created Date: May 15 2015 12:11PM