research-article

Dead reckoning using time series regression models

Authors:

João B. Pinto Neto,

Nathalie Mitton,

Miguel Elias M. Campista,

Luís Henrique M. K. CostaAuthors Info & Claims

SMARTOBJECTS '18: Proceedings of the 4th ACM MobiHoc Workshop on Experiences with the Design and Implementation of Smart Objects

Article No.: 6, Pages 1 - 6

https://doi.org/10.1145/3213299.3213305

Published: 25 June 2018 Publication History

Abstract

Connected car technology promises to drastically reduce the number of accidents involving vehicles. Nevertheless, this technology requires the vehicle precise location to work. The adoption of Global Positioning System (GPS) as a navigation device imposes limitations to geolocation information under non-line-of-sight conditions. This work introduces the Time Series Dead Reckoning System (TedriS) as a solution for dead reckoning navigation when the GPS fails. TedriS uses Time Series Regression Models (TSRM) and the data from the rear wheel speed sensor of the vehicle to estimate the absolute position. The process to estimate the position is carried out in two phases: training and predicting. In the training phase, a novel technique applies TSRM and stores the relationship between the GPS and the rear wheel speed data; then in the predicting phase, this relationship is used. We analyze TedriS using traces collected at the campus of Federal University of Rio de Janeiro (UFRJ), Brazil, and with indoor experiments with a robot. Results show an accuracy compatible with dead-reckoning navigation state-of-art systems.

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Cited By

Zheng XPeng XZhao JWang X(2022)Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory DataApplied Sciences10.3390/app12231190512:23(11905)Online publication date: 22-Nov-2022
https://doi.org/10.3390/app122311905
Chen YCai WLiu EDiallo STolk AGiabbanelli P(2021)Hot Area Targeting Dead Reckoning for Distributed Virtual EnvironmentsProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459260(129-137)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3437959.3459260
Gomes LCosta L(2020)Vehicular Dead Reckoning Based on Machine Learning and Map Matching2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)10.1109/VTC2020-Fall49728.2020.9348804(1-5)Online publication date: Nov-2020
https://doi.org/10.1109/VTC2020-Fall49728.2020.9348804

Index Terms

Dead reckoning using time series regression models

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Information & Contributors

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Published In

cover image ACM Conferences

SMARTOBJECTS '18: Proceedings of the 4th ACM MobiHoc Workshop on Experiences with the Design and Implementation of Smart Objects

June 2018

69 pages

ISBN:9781450358576

DOI:10.1145/3213299

General Chairs:
Pietro Manzoni
Universitat Politècnica de València, Spain
,
Claudio E. Palazzi
Università degli Studi di Padova, Italy
,
Program Chairs:
Valeria Loscrí
Inria Lille-Nord Europe / FUN, France
,
Anna Maria Vegni
Roma Tre University, Rome, Italy

Copyright © 2018 ACM.

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2018

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Mobihoc '18

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SIGMOBILE

Mobihoc '18: The Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

June 25, 2018

California, Los Angeles

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Overall Acceptance Rate 15 of 41 submissions, 37%

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Cited By

Zheng XPeng XZhao JWang X(2022)Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory DataApplied Sciences10.3390/app12231190512:23(11905)Online publication date: 22-Nov-2022
https://doi.org/10.3390/app122311905
Chen YCai WLiu EDiallo STolk AGiabbanelli P(2021)Hot Area Targeting Dead Reckoning for Distributed Virtual EnvironmentsProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459260(129-137)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3437959.3459260
Gomes LCosta L(2020)Vehicular Dead Reckoning Based on Machine Learning and Map Matching2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)10.1109/VTC2020-Fall49728.2020.9348804(1-5)Online publication date: Nov-2020
https://doi.org/10.1109/VTC2020-Fall49728.2020.9348804

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