Publications:Data-driven methods for classification of driving styles in buses

From ISLAB/CAISR
Revision as of 13:50, 13 March 2014 by SlawekBot (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Do not edit this section

Keep all hand-made modifications below

Title Data-driven methods for classification of driving styles in buses
Author Nadezda Karginova and Stefan Byttner and Magnus Svensson
Year 2012
PublicationType Conference Paper
Journal
HostPublication
Conference SAE 2012 World Congress & Exhibition, Cobo Center, Detroit, Michigan, USA, April 24-26, 2012
DOI http://dx.doi.org/10.4271/2012-01-0744
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:550683
Abstract Fuel consumption and vehicle breakdown depend upon the driving style of the driver, for example, hard driving style leads to more wear and consequently more failures of vehicle components. Because of this, it is important to identify and classify the driver’s driving style in order to give the driver feedback through a driver assistance system. The driver would then be able to detect and learn to avoid a driving style that is not appropriate. The input data is provided by different sensors installed in the vehicle, where different drivers and driving routes have been measured. The data is subjectively classified into two different driving styles: normal and hard. Hard driving style can be characterized, for example, by rapid acceleration and braking. Since it is not trivial to build a model which is able to distinguish hard driving from normal, a data mining approach has been employed. In the paper, several classifiers are compared (including e.g. neural networks and decision trees) and a discussion is made on the advantages and disadvantages of the different methods.