Model-Based Derivation of Perception Accuracy Requirements for Vehicle Localization in Urban Environments

In this contribution, the authors address the model-based derivation of perception requirements based on upper bounds on vehicle localization uncertainty for urban driver assistance (UDA) and urban automated driving (UAD). The authors show that a probabilistic model for the estimation of map-relative localization accuracy can be obtained and utilized for proper parametrization of a perception system. Therefore, the paper at hand entails two main contributions: i) Proposal of a probabilistic model for localization accuracy in closed form under the assumption of a generic measurement model with Gaussian noise and a stochastic landmark distribution, ii) Presentation of a framework for model-based derivation of perception requirements which permit desired localization performance. To exemplify the application of their method, sensor parameters for a stereo vision system (e.g. stereo base-width) are determined and verified via comprehensive simulation experiments. This is conducted in the context of an urban automated lane keeping system under explicit consideration of non-existent or occluded lane markings and curb stones.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 712-718
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01601919
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:24PM