Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location

Cesar Barrios; Henry Himberg; Yuichi Motai; Adel Sadek

doi:10.1109/itsc.2006.1707361

Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location

Cesar Barrios
, Henry Himberg
, Yuichi Motai
, Adel Sadek

Department of Civil, Structural & Environmental Engineering

University of Vermont

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

30 Scopus citations

Abstract

A multiple-model framework of Adaptive Extended Kalman Filters (EKF) for predicting vehicle position with the aid of Global Positioning System (GPS) data is proposed to improve existing collision avoidance systems. A better prediction model for vehicle positions provides more accurate collision warnings in situations that current systems can not handle correctly. The Multiple Model Adaptive Estimation System (MMAE) algorithm is applied to the integration of GPS measurements to improve the efficiency and performance. This paper evaluates the multiple-model system in different scenarios and compares it to other systems before discussing possible improvements by combining it with other systems for predicting vehicle location.

Original language	English
Title of host publication	Proceedings of ITSC 2006
Subtitle of host publication	2006 IEEE Intelligent Transportation Systems Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1053-1059
Number of pages	7
ISBN (Print)	1424400945, 9781424400942
DOIs	https://doi.org/10.1109/itsc.2006.1707361
State	Published - 2006
Event	2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006 - Toronto, ON, Canada Duration: Sep 17 2006 → Sep 20 2006

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)	2153-0009
ISSN (Electronic)	2153-0017

Conference

Conference	2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006
Country/Territory	Canada
City	Toronto, ON
Period	09/17/06 → 09/20/06

Access to Document

10.1109/itsc.2006.1707361

Cite this

Barrios, C., Himberg, H., Motai, Y., & Sadek, A. (2006). Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location. In Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference (pp. 1053-1059). Article 1707361 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/itsc.2006.1707361

Barrios, Cesar ; Himberg, Henry ; Motai, Yuichi et al. / Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location. Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. Institute of Electrical and Electronics Engineers Inc., 2006. pp. 1053-1059 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

@inproceedings{b9ffbce0d01540e1bcb1d3fe6182a9fa,

title = "Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location",

abstract = "A multiple-model framework of Adaptive Extended Kalman Filters (EKF) for predicting vehicle position with the aid of Global Positioning System (GPS) data is proposed to improve existing collision avoidance systems. A better prediction model for vehicle positions provides more accurate collision warnings in situations that current systems can not handle correctly. The Multiple Model Adaptive Estimation System (MMAE) algorithm is applied to the integration of GPS measurements to improve the efficiency and performance. This paper evaluates the multiple-model system in different scenarios and compares it to other systems before discussing possible improvements by combining it with other systems for predicting vehicle location.",

author = "Cesar Barrios and Henry Himberg and Yuichi Motai and Adel Sadek",

year = "2006",

doi = "10.1109/itsc.2006.1707361",

language = "English",

isbn = "1424400945",

series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1053--1059",

booktitle = "Proceedings of ITSC 2006",

address = "United States",

note = "2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006 ; Conference date: 17-09-2006 Through 20-09-2006",

}

Barrios, C, Himberg, H, Motai, Y & Sadek, A 2006, Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location. in Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference., 1707361, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Institute of Electrical and Electronics Engineers Inc., pp. 1053-1059, 2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006, Toronto, ON, Canada, 09/17/06. https://doi.org/10.1109/itsc.2006.1707361

Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location. / Barrios, Cesar; Himberg, Henry; Motai, Yuichi et al.
Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. Institute of Electrical and Electronics Engineers Inc., 2006. p. 1053-1059 1707361 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

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

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N2 - A multiple-model framework of Adaptive Extended Kalman Filters (EKF) for predicting vehicle position with the aid of Global Positioning System (GPS) data is proposed to improve existing collision avoidance systems. A better prediction model for vehicle positions provides more accurate collision warnings in situations that current systems can not handle correctly. The Multiple Model Adaptive Estimation System (MMAE) algorithm is applied to the integration of GPS measurements to improve the efficiency and performance. This paper evaluates the multiple-model system in different scenarios and compares it to other systems before discussing possible improvements by combining it with other systems for predicting vehicle location.

AB - A multiple-model framework of Adaptive Extended Kalman Filters (EKF) for predicting vehicle position with the aid of Global Positioning System (GPS) data is proposed to improve existing collision avoidance systems. A better prediction model for vehicle positions provides more accurate collision warnings in situations that current systems can not handle correctly. The Multiple Model Adaptive Estimation System (MMAE) algorithm is applied to the integration of GPS measurements to improve the efficiency and performance. This paper evaluates the multiple-model system in different scenarios and compares it to other systems before discussing possible improvements by combining it with other systems for predicting vehicle location.

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M3 - Conference contribution

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SN - 9781424400942

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

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Barrios C, Himberg H, Motai Y, Sadek A. Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location. In Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. Institute of Electrical and Electronics Engineers Inc. 2006. p. 1053-1059. 1707361. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/itsc.2006.1707361

Multiple model framework of adaptive extended Kalman filtering for predicting vehicle location

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