Psychological Driving Style Estimation from GPS Sensor Data Alone

Abstract

Automobiles are essential to society, but accidents involving older drivers have risen. Driving assistance systems have been developed to prevent this problem. The next goal is to implement a system that provides adaptive assistance suitable for drivers with different characteristics. Thus, accurately estimating drivers' characteristics is crucial. Some studies use in-vehicle sensor data through a Controller Area Network (CAN) for this estimation but require additional equipment for data collection. This study focuses on developing a psychological driving style recognition model from Global Positioning System (GPS) data, which can be easily accessible. The experimental results show that the model with GPS data achieved F1-macro and AUC greater than the random-assignment baseline on seven and eight items of the Driving Style Questionnaire (DSQ), respectively. Furthermore, the results suggest that this model works well for DSQ estimation when comparing the model with CAN data. This GPS-based model contributes to developing personalized systems.