Reference : Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/19792
Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring
English
Castignani, German mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Derrmann, Thierry mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Frank, Raphaël mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
19-Jan-2015
Intelligent Transportation Systems Magazine, IEEE
IEEE
7
1
91-102
Yes
International
1939-1390
[en] fuzzy logic ; driver profiling ; smartphone
[en] Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver.
http://hdl.handle.net/10993/19792
10.1109/MITS.2014.2328673

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