Data-Driven Methods for Classification of Driving Styles in Buses Petrozavodsk State University

Format:

Conference/Event

Author/Creator:

Karginova, Karginova, author.

Contributor:

Byttner, Stefan

Svensson, Magnus

Conference Name:

SAE 2012 World Congress & Exhibition (2012-04-24 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2012

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2012-01-0744

Access Restriction:

Restricted for use by site license