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Bayesian Inference for Time Delay Systems with Application to Connected Automated Vehicles

Ji, Xunbi A. and Molnár, Tamás G. and Gorodetsky, Alex A. and Orosz, Gábor (2021) Bayesian Inference for Time Delay Systems with Application to Connected Automated Vehicles. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE , Piscataway, NJ, pp. 3259-3264. ISBN 978-1-7281-9142-3. https://resolver.caltech.edu/CaltechAUTHORS:20220105-422089400

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Abstract

In this paper, a Bayesian inference problem is set up to infer the time delay and the resistance models from the dynamics of a connected automated vehicle. The delayed rejection adaptive Metropolis Markov chain Monte Carlo method is applied to obtain the posterior distributions of time delay and resistance parameters simultaneously using experimental data. The estimations of the time delay are shown to be consistent among multiple datasets and different resistance models. The distribution of the posterior indicates that there exist multiple modes in time delay, corresponding to different behaviors in acceleration and deceleration.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/itsc48978.2021.9564457DOIArticle
ORCID:
AuthorORCID
Molnár, Tamás G.0000-0002-9379-7121
Orosz, Gábor0000-0002-9000-3736
Additional Information:© 2021 IEEE.
DOI:10.1109/itsc48978.2021.9564457
Record Number:CaltechAUTHORS:20220105-424251800
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220105-422089400
Official Citation:X. A. Ji, T. G. Molnár, A. A. Gorodetsky and G. Orosz, "Bayesian Inference for Time Delay Systems with Application to Connected Automated Vehicles," 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 3259-3264, doi: 10.1109/ITSC48978.2021.9564457
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112710
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:09 Jan 2022 21:38
Last Modified:09 Jan 2022 21:38

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