Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Platoon Merging Distance Prediction using a Neural Network Vehicle Speed Model
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 3720-3725Article in journal (Refereed) Published
Abstract [en]

Heavy-duty vehicle platooning has been an important research topic in recent years. By driving closely together, the vehicles save fuel by reducing total air drag and utilize the road more efciently Often the heavy-duty vehicles will catch-up in order to platoon while driving on the common stretch of road, and in this case, a good prediction of when the platoon merging will take place is required in order to make predictions on overall fuel savings and to automatically control the velocity prior to the merge. The vehicle speed prior to platoon merging is mostly infuenced by the road grade and by the local trafc condition. In this paper, we examine the infuence of road grade and propose a method for predicting platoon merge distance using vehicle speed prediction based on road grade. The proposed method is evaluated using experimental data from platoon merging test runs done on a highway with varying level of trafc. It is shown that under reasonable conditions, the error in the merge distance prediction is smaller than 8%.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 3720-3725
Keyword [en]
Intelligent Transport Systems, Neural Networks, Platoon Merging Distance, Platooning, Road Grade, Road Transportation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223061DOI: 10.1016/j.ifacol.2017.08.569ISI: 000423964800117Scopus ID: 2-s2.0-85031818738OAI: oai:DiVA.org:kth-223061DiVA, id: diva2:1182503
Funder
VINNOVA, 2014-06200Swedish Foundation for Strategic Research Swedish Research CouncilKnut and Alice Wallenberg Foundation
Note

QC 20180504

Available from: 2018-02-13 Created: 2018-02-13 Last updated: 2018-05-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Cicic, MladenJohansson, Karl Henrik
By organisation
Automatic Control
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 10 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf