Global and local frameworks for vehicle state estimation using temporally previewed mapped lane features

Alexander A. Brown, Sean N. Brennan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes a method for using a forward-looking monocular camera along with previewed road geometry from a high-fidelity, low-dimensional map to estimate lateral planar vehicle states by measuring the vehicle's temporally anticipated reference trajectory. Theoretical estimator performance from a steady-state Kalman Filter implementation of the estimation framework is calculated for various look-ahead distances and vehicle speeds. Application of this filter structure to real driving data is also briefly discussed. The use of temporally previewed measurements of a vehicle's reference path is shown to greatly improve the accuracy of vehicle planar state estimates, and shows promise for use in closed-loop lane keeping and driver assist applications.

Original languageEnglish (US)
Title of host publication2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013
Pages127-133
Number of pages7
DOIs
StatePublished - Nov 13 2013
Event2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013 - Gold Coast, QLD, Australia
Duration: Jun 23 2013Jun 23 2013

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Other

Other2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013
CountryAustralia
CityGold Coast, QLD
Period6/23/136/23/13

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Automotive Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Global and local frameworks for vehicle state estimation using temporally previewed mapped lane features'. Together they form a unique fingerprint.

  • Cite this

    Brown, A. A., & Brennan, S. N. (2013). Global and local frameworks for vehicle state estimation using temporally previewed mapped lane features. In 2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013 (pp. 127-133). [6615238] (IEEE Intelligent Vehicles Symposium, Proceedings). https://doi.org/10.1109/IVWorkshops.2013.6615238