Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data

Connected 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 h 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 s 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.

Automated summary

Connected cars with sensors and computational processing can model and differentiate drivers for enhanced security. Research data demonstrates that driving patterns can be used to distinguish between drivers of different genders.