Investigation of Route-Independent Aggressive and Safe Driving Features Obtained from Accelerometer Signals

System that can identify safe and aggressive driving behavior is useful in nowadays transportation service sector: both in private and public. In this paper, we propose a systematic approach (step-by-step) for driving style classification and investigate features that best describe differences between aggressive and safe driving styles when driving information is collected from 3-axis accelerometer (G-sensor) alone which is ubiquitous and inexpensive inertial measurement sensor. Data were collected from light vehicle when driving in aggressive and save driving style. Features investigated do not depend on same route assumption and can be used as universal. Using driving features a classification task is performed by applying Random Forest algorithm. Results show that high classification accuracy (95.5%) using experimental data can be obtained when applying proposed input feature set.