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Unsupervised driving performance assessment using free-positioned smartphones in vehicles

In-vehicle smartphones contain rich information of driving and therefore can be utilized to establish driver behavior profile. The GPS data indicates the occurrence of driving events like gas-hit, brake-hit, turn-right, turn-left, and forward-driving. The inertial measurement unit (IMU) data, provides good estimations of vehicle lateral, longitudinal, and vertical accelerations and rotations. One of the challenge using the smartphone as the estimator is the variety of device poses. This study proposes a coordinate transformation approach to address this task, which allows smartphones to be free-positioned in vehicles. With the converted vehicular 3-axis accelerations, this study also employs unsupervised clustering techniques to grade driving events by iteratively detect outliers. Discussion on risky events is also conducted.