Shared Experiences In Intelligent Transportation Systems (bibtex)
by , , , ,
Abstract:
In this paper, a scheme is outlined within which the autonomous vehicles in an intelligent transportation system exchange information about their environment. Benefiting from each other�s experiences, the vehicles can locally adapt their behaviour in order to perform the desired flexible adjustments of the overall system behaviour. Based on the vehicles� shared knowledge, an exploration and planning strategy for selecting routes according to a set of given customer constraints is described. Meanwhile, sensor input from the environment is used to cooperatively learn the best routes and keep the knowledge about the world up-to-date.
Reference:
Shared Experiences In Intelligent Transportation Systems (Wilhelm Dangelmaier, Holger Giese, Florian Klein, Hendrik Renken, Peter Scheideler), In Proc. of the IAV 2004 - The 5th Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal (M. Ribeiro, J. Santos-Victor, eds.), Elsevier Science, 2004.
Bibtex Entry:
@InProceedings{DGKRS04_ag,
AUTHOR = {Dangelmaier, Wilhelm and Giese, Holger and Klein, Florian and Renken, Hendrik and Scheideler, Peter},
TITLE = {{Shared Experiences In Intelligent Transportation Systems}},
YEAR = {2004},
MONTH = {July},
BOOKTITLE = {Proc. of the IAV 2004 - The 5th Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal},
PAGES = {231--236},
EDITOR = {Ribeiro, M. and Santos-Victor, J.},
PUBLISHER = {Elsevier Science},
ABSTRACT = {In this paper, a scheme is outlined within which the autonomous vehicles in an intelligent transportation system exchange information about their environment. Benefiting from each other\^{a}��s experiences, the vehicles can locally adapt their behaviour in order to perform the desired flexible adjustments of the overall system behaviour. Based on the vehicles\^{a}�� shared knowledge, an exploration and planning strategy for selecting routes according to a set of given customer constraints is described. Meanwhile, sensor input from the environment is used to cooperatively learn the best routes and keep the knowledge about the world up-to-date.}
}
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