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SaferDrive: An NLG-based behaviour change support system for drivers

Published online by Cambridge University Press:  19 February 2018

DANIEL BRAUN Open the ORCID record for DANIEL BRAUN [Opens in a new window] ,
EHUD REITER  and
ADVAITH SIDDHARTHAN
DANIEL BRAUN
Affiliation:
Department of Informatics, Technical University of Munich, Munich, Germany e-mail: daniel.braun@tum.de
EHUD REITER
Affiliation:
Department of Computing Science, University of Aberdeen, Aberdeen, UK e-mail: e.reiter@abdn.ac.uk
ADVAITH SIDDHARTHAN
Affiliation:
Knowledge Media Institute, The Open University, Milton Keynes, UK e-mail: advaith.siddharthan@open.ac.uk
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Abstract

Despite the long history of Natural Language Generation (NLG) research, the potential for influencing real world behaviour through automatically generated texts has not received much attention. In this paper, we present SaferDrive, a behaviour change support system that uses NLG and telematic data in order to create weekly textual feedback for automobile drivers, which is delivered through a smartphone application. Usage-based car insurances use sensors to track driver behaviour. Although the data collected by such insurances could provide detailed feedback about the driving style, they are typically withheld from the driver and used only to calculate insurance premiums. SaferDrive instead provides detailed textual feedback about the driving style, with the intent to help drivers improve their driving habits. We evaluate the system with real drivers and report that the textual feedback generated by our system does have a positive influence on driving habits, especially with regard to speeding.

Type
Article
Information
Natural Language Engineering , Volume 24 , Issue 4 , July 2018 , pp. 551 - 588
Copyright
Copyright © Cambridge University Press 2018 

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