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IJMLC 2020 Vol.10(5): 612-617 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.5.981

Vehicle Velocity Control in Case of Two Vehicle Platoon Merging into One Vehicle Platoon

Eisuke Kita and Miichiro Yamada

Abstract—In vehicle platoon, vehicles travel in the row at very short vehicle head distance by the help of the mechanical and the electric systems. The platoon is very important technique for increasing the road traffic capacity safely. The previous studies focus on the safe and stable control of the vehicle platoon. The next aim in the vehicle platoon, however, is the vehicle control of the vehicle platoon in some actual traffic cases. Merging vehicle platoons at the intersection is considered as one of the actual traffic cases in this study. Then, the aim of this study is to discuss the velocity control of the vehicles in the platoon. The velocity control model is defined as the function of the head distance and the velocity difference. The model parameters are determined by stability analysis of the velocity control model. The validity of the model is discussed in the computer simulation and the experiment of LEGO MINDSTORM NXT.

Index Terms—Helly model, robot experiment, simulation, vehicle platoon, vehicle following model.

Eisuke Kita is with the Nagoya University, Graduate School of Informatics, Nagoya 464-8601, Japan (e-mail: kita@i.nagoya-u.ac.jp).
Miichiro Yamda is with the Nagoya University, Graduate School of Information Science, Nagoya 464-8601, Japan.

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Cite: Eisuke Kita and Miichiro Yamada, "Vehicle Velocity Control in Case of Two Vehicle Platoon Merging into One Vehicle Platoon," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 612-617, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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