In recent years, Advanced Driver Assistance Systems (ADAS) have contributed significantly towards improving traffic mobility and reducing road accidents. A fundamental requirement to facilitate many ADAS functions is to acquire the position of surrounding vehicles. Accurate position information can be obtained using either sensor-based systems or Global Navigation Satellite Systems (GNSSs). For these systems to work well for practical road and weather conditions, advanced techniques and algorithms are needed which make the system complex and expensive to implement. Previously, the authors proposed and demonstrated a method of estimating accurate relative trajectories of surrounding vehicles with lane level resolution using standard GPS receivers and Dedicated Short Range Communication (DSRC) based Vehicle to Vehicle (V2V) communication. Building upon the previous work, in this paper the authors propose a cost-effective methodology to estimate the relative lane and position of surrounding vehicles in real time, which could facilitate many critical ADAS functions. The proposed methodology was evaluated using field tests on a freeway section, containing sharp curved road segments designed for a maximum degree of curvature for a speed of 120 kmh. The field test results show that the relative lane and position of surrounding vehicles can be identified in real time with 100% accuracy when the distance between the two vehicles was < 50m.