Lateral vehicle state and environment estimation using temporally previewed mapped lane features

Alexander A. Brown, Sean N. Brennan

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

This paper proposes a model-based method to estimate lateral planar vehicle states using a forward-looking monocular camera, a yaw rate gyroscope, and an a priori map of road superelevation and temporally previewed lane geometry. Theoretical estimator performance from a steady-state Kalman-filter implementation of the estimation framework is calculated for various look-ahead distances and vehicle speeds. The application of this filter structure to real driving data is also explored, along with error characteristics of the filter on straight and curved roads, with both superelevated and flat profiles. The effect of superelevation on estimator performance is found to be significant. Experimental and theoretical analysis both show that the benefits of state estimation using previewed lane geometry improve with increasing lane preview, but this improvement diminishes due to increased lane tracking errors at distances beyond 20 m ahead of the vehicle.

Original languageEnglish (US)
Article number6977925
Pages (from-to)1601-1608
Number of pages8
JournalIEEE Transactions on Intelligent Transportation Systems
Volume16
Issue number3
DOIs
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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