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Long-term driving behaviour modelling for driver identification

Abstract:

Driver identification constitutes an important enabling technology in intelligent transportation systems, allowing the development and the use of in-car personalised functionalities and thwarting unauthorised usage. In this work, we leverage the literature in authentication tasks (e.g. speaker recognition) and present a framework for driver identification which employs Support Vector Machine (SVM) and Universal Background Model schemes. Our framework operates on accelerator and break pedal si...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1109/ITSC.2018.8569610

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Pembroke College
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2018-12-10
Acceptance date:
2018-07-02
DOI:
Pubs id:
pubs:942839
URN:
uri:bc2c0a9c-df60-4d0a-93df-2cecbebab5c8
UUID:
uuid:bc2c0a9c-df60-4d0a-93df-2cecbebab5c8
Local pid:
pubs:942839

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