Lane-level navigation has received a lot of attention in recent years. It has played a great role in assisting route planning, as well as navigating automated vehicles. Aside from sticking to the planned route, abnormal traffic situations which result in blocking lanes could impact lane switching decisions. Unfortunately, there is currently no navigation system that can sense and track a vehicle's lane position and to advise the driver of lane switching decisions. Google Maps stores a priori the number of lanes and their directions at each highway exit and provides this information to drivers when navigating. However, even with this information, some drivers may not be able to make an informed decision regarding when and where to make a correct lane switch. This motivated us to develop a mechanism for the detection and tracking of real-time lane changes. In this paper, we propose a GPS-aiding system that can sense and track a vehicle's lane position. The system leverages smart phones' computing capability, rear cameras, and inertial motion sensors. With little extra computational overhead, the system applies computer vision techniques to achieve lane-level positioning. We also design a machine learning-based algorithm to detect and track lane switching. We conduct a series of experiments, analyze our system in real-world environments, and achieve very promising results. We believe our system can be a great asset to current smart phone navigation systems.