With the embedded sensors – typically Inertial Measurement Units (IMU) and GPS, the smartphone could be leveraged as a low-cost sensing platform for estimating vehicle dynamics. However, the orientation and relative movement of the smartphone inside the vehicle yields the main challenge for platform deployment. This study proposes a solution of converting the smartphone-referenced IMU readings into vehicle-referenced accelerations, which allows free-positioned smartphone for the in-vehicle dynamics sensing. The approach is consisted of (i) geometry coordinate transformation techniques, (ii) neural networks regression of IMU from GPS, and (iii) adaptive filtering processes. Experiment is conducted in three driving environments which cover high occurrence of vehicle dynamic movements in lateral, longitudinal, and vertical directions. The processing effectiveness at five typical positions (three fixed and two flexible) are examined. Results are quantified as the normalized cross-correlation ratio, comparing a free-positioned device against a well-aligned device. The conversion of vertical acceleration is more successful, whereas the lateral and longitudinal accelerations processing outputs may vary. The coordinate transformation completes the most conversion process, and regression and filtering make additional adjustment. After discussion, a final implementation processing pipeline is suggested for the deployment of real-time system.