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

Alexander A. Brown; Sean N. Brennan

doi:10.1109/IVWorkshops.2013.6615238

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 proceeding › Conference 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 language	English (US)
Title of host publication	2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013
Pages	127-133
Number of pages	7
DOIs	https://doi.org/10.1109/IVWorkshops.2013.6615238
State	Published - Nov 13 2013
Event	2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013 - Gold Coast, QLD, Australia Duration: Jun 23 2013 → Jun 23 2013

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings

Other

Other	2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013
Country/Territory	Australia
City	Gold Coast, QLD
Period	6/23/13 → 6/23/13

All Science Journal Classification (ASJC) codes

Modeling and Simulation
Automotive Engineering
Computer Science Applications

Access to Document

10.1109/IVWorkshops.2013.6615238

Cite this

@inproceedings{41516f886bcd4cddbda34fc96b70f63d,

title = "Global and local frameworks for vehicle state estimation using temporally previewed mapped lane features",

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.",

author = "Brown, {Alexander A.} and Brennan, {Sean N.}",

year = "2013",

month = nov,

day = "13",

doi = "10.1109/IVWorkshops.2013.6615238",

language = "English (US)",

isbn = "9781479907953",

series = "IEEE Intelligent Vehicles Symposium, Proceedings",

pages = "127--133",

booktitle = "2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013",

note = "2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013 ; Conference date: 23-06-2013 Through 23-06-2013",

}

Brown, AA & Brennan, SN 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., 6615238, IEEE Intelligent Vehicles Symposium, Proceedings, pp. 127-133, 2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013, Gold Coast, QLD, Australia, 6/23/13. https://doi.org/10.1109/IVWorkshops.2013.6615238

Global and local frameworks for vehicle state estimation using temporally previewed mapped lane features. / Brown, Alexander A.; Brennan, Sean N.
2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013. 2013. p. 127-133 6615238 (IEEE Intelligent Vehicles Symposium, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Brown, Alexander A.

AU - Brennan, Sean N.

PY - 2013/11/13

Y1 - 2013/11/13

N2 - 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.

AB - 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.

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M3 - Conference contribution

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SN - 9781479907953

T3 - IEEE Intelligent Vehicles Symposium, Proceedings

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BT - 2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013

T2 - 2013 IEEE Intelligent Vehicles Symposium Workshops, IV Workshops 2013

Y2 - 23 June 2013 through 23 June 2013

ER -

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

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