This study aims to exploit driving data collected from sensors embedded in our own smartphones. In this way, the smartphone can be made to act as our copilot and make us aware of risky decisions that we make behind the driving wheel. By using smartphones rather than, for example, a) Aftermarket systems, b) On Board Diagnostics (OBD) systems and, c) Vehicle-mounted video cameras, we achieve broad applicability of this study by allowing practically any driver in any car to be supported by this technology. As we know, smartphones come integrated with a variety of sensors, some of which (accelerometer, gyroscope, magnetometer) can be used to measure accelerations and forces that the smartphone itself experiences. We exploit this sensor data by means of a deep architecture composed of Inception layers and gated units that together learns the optimal amount of source to improve classification performance on driving maneuvers.