Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data
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AbstractConnected cars have a massive impact on the automotive sector, and whilst this catalyst and disruptor technology introduce threats, it brings opportunities to address existing vehicle-related crimes such as carjacking. Connected cars are fitted with sensors, and capable of sophisticated computational processing which can be used to model and differentiate drivers as means of layered security. We generate a dataset collecting 14 hours of driving in the city of London. The route was 8.1 miles long and included various road conditions such as roundabouts, traffic lights, and several speed zones. We identify and rank the features from the driving segments, classify our sample using Random Forest, and optimise the learning-based model with 98.84% accuracy (95% confidence) given a small 10 seconds driving window size. Differences in driving patterns were uncovered to distinguish between female and male drivers especially through variations in longitudinal acceleration, driving speed, torque and revolutions per minute.
CitationAhmadi-Assalemi, G., al-Khateeb, H.M., Maple, C., Epiphaniou, G., Hammoudeh, M., Jahankhani, H. and Pillai, P. (2021) Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data. Computers and Electrical Engineering, 91, 107047. https://doi.org/10.1016/j.compeleceng.2021.107047
JournalComputers and Electrical Engineering
DescriptionThis is an accepted manuscript of an article published by Elsevier in Computers & Electrical Engineering, available online at: https://doi.org/10.1016/j.compeleceng.2021.107047 The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/