Texting or browsing the web on a smartphone while driving, called distracted driving, significantly increases the risk of car accidents. There have been a number of proposals for the prevention of distracted driving, but none of them has addressed its important challenges completely and effectively. To remedy this deficiency, we present an event-driven solution, called Automatic Identification of Driver's Smartphone (AIDS), which identifies a driver's smartphone by analyzing and fusing the phone's sensory information related to common vehicle-riding activities, such as walking toward the vehicle, standing near the vehicle while opening a vehicle door, entering the vehicle, closing the door, and starting the engine. AIDS extracts features useful for identification of the driver's phone from diverse sensors available in commodity smartphones. It identifies the driver's phone before the vehicle leaves its parked spot, and differentiates seated (front or rear) rows in a vehicle by analyzing the subtle electromagnetic field spikes caused by the starting of the engine. To evaluate the feasibility and adaptability of AIDS, we have conducted extensive experiments: a prototype of AIDS was distributed to 12 participants, both males and females in their 20 and 30s, who have driven seven different vehicles for three days in real-world environments. Our evaluation results show that AIDS identified the driver's phone with an 83.3-93.3 percent true positive rate while achieving a 90.1-91.2 percent true negative rate at a marginal increase of the phone's energy consumption.