Driving Style Classification using Long-Term Accelerometer Information

Driving style can be characteristically divided into normal and aggressive. Related researches show that useful information about driving style can be extracted using vehicle's inertial measurement signals with the help of GPS. However, for public transportation the GPS sensor isn't necessary because of repetition of the route. This assumption helps to create low-cost intelligent public transport monitoring system that is capable to classify aggressive and normal driver. In this paper, we propose pattern recognition approach to classify driving style into aggressive or normal automatically without expert evaluation and knowledge using accelerometer data when driving the same route in different driving styles. 3-axis accelerometer signal statistical features were used as classifier inputs. The results show that aggressive and normal driving style classification of 100% precision is achieved using collected data when driving the same route.