DocumentCode :
2516614
Title :
Using statistical models to characterize eco-driving style with an aggregated indicator
Author :
Andrieu, Cindie ; Pierre, Guillaume Saint
Author_Institution :
Vehicle-Infrastruct.-Driver Interactions Res. Unit, IFSTTAR, Versailles-Satory, France
fYear :
2012
fDate :
3-7 June 2012
Firstpage :
63
Lastpage :
68
Abstract :
This paper presents the construction of an aggregated indicator of a fuel-efficient driving style, in order to construct an efficient Ecological Driving Assistance System (EDAS). Such an eco-index can be used to detect eco-driving behaviour, but also to give to the driver useful advices to help him improving his driving efficiency without deteriorating safety. The logistic regression is used to model our experimental dataset of twenty subjects driving twice the same route: normally or following the golden rules of eco-driving. Depending on some driving indicators, the estimated probability of being an eco-driver is used as an eco-index to characterize that driving pattern. This work show how such a simple aggregated indicator, related to driving dynamics rather than fuel consumption, can be useful for driver monitoring and information. Two models, from the simplest to the most complicated, are compared, and their performances analysed.
Keywords :
driver information systems; ecology; estimation theory; fuel economy; probability; regression analysis; aggregated indicator construction; driver monitoring; driving dynamics; driving efficiency; eco-driving behaviour detection; eco-driving style; ecological driving assistance system; fuel-efficient driving style; logistic regression; probability estimation; statistical model; Biological system modeling; Data models; Engines; Fuels; Gears; Logistics; Vehicles; Driving behaviour; EDAS; Eco-driving; Logistic regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2012 IEEE
Conference_Location :
Alcala de Henares
ISSN :
1931-0587
Print_ISBN :
978-1-4673-2119-8
Type :
conf
DOI :
10.1109/IVS.2012.6232197
Filename :
6232197
Link To Document :
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