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Platoon Merging Distance Prediction using a Neural Network Vehicle Speed Model
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-4472-6298
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. Scania CV AB.ORCID iD: 0000-0001-5107-2942
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2017 (English)In: IFAC-PapersOnLine, E-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 efficiently. 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 influenced by the road grade and by the local traffic condition. In this paper, we examine the influence 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 traffic. It is shown that under reasonable conditions, the error in the merge distance prediction is smaller than 8%.

Place, publisher, year, edition, pages
2017. Vol. 50, no 1, p. 3720-3725
Keywords [en]
Platooning, Intelligent Transport Systems, Neural Networks, Road Transportation, Platoon Merging Distance, Road Grade
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-245878DOI: 10.1016/j.ifacol.2017.08.569ISI: 000423964800117Scopus ID: 2-s2.0-85031818738OAI: oai:DiVA.org:kth-245878DiVA, id: diva2:1294803
Conference
20th IFAC World Congress, Toulouse, France (2017)
Funder
EU, Horizon 2020, 674875
Note

QC 20190308

Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-11-04Bibliographically approved

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Čičić, MladenLiang, Kuo-YunJohansson, Karl H.
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