Vehicle Trajectory Estimation based on Dynamic Bayesian Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we propose a method to estimate the most likely region a vehicle will transverse when the current position and a priori spatial-temporal trajectory data of multiple vehicles are known. The proposed solution is based on a hidden Markov chain that models the trajectory of each vehicle. The estimation of the vehicle trajectory relies on Viterbi algorithm, which identifies the most likely trajectory as a new vehicle's location is known. The proposed modeling and estimation methodology is evaluated using real mobility traces sampled from multiple taxis traveling in the city of Porto in Portugal. The estimation performance of the most likely region a vehicle will cross is between 32% and 89%, depending on several factors that include the probability of trajectory's occurrence and the number of previous locations considered in the estimation methodology (Markov order). Finally, we discuss the advantages and limitations of the proposed method.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
DOIs
Publication statusPublished - May 2020
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Vehicular Technology Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2020-May
ISSN (Print)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
CountryBelgium
CityAntwerp
Period25/05/2028/05/20

Keywords

  • Bayesian Networks
  • Estimation and Modeling.
  • Mobility Estimation

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