In this paper, a roadside equipment (RSE)-assisted positioning method, i.e., global positioning system (GPS)-received signal strength (RSS) hybrid, is developed for lane-level positioning, which is fundamental to many applications in intelligent transportation systems. By exploiting the potential of RSE in existing pilots all over the world, this key component in connected vehicle networks can be used to achieve greater positioning accuracy than GPS positioning. The proposed method utilizes RSS data, which is commonly available in all connected vehicle networks, to update the GPS position and improve its accuracy based on a Bayesian approach. A method for lane positioning at a specific point is presented first, and then an extension to enable real-time lane positioning is proposed. Two typical types of RSE deployments for different traffic flow demands are considered, and the performance of the proposed method in each deployment is assessed. The proposed method features higher accuracy than existing GPS positioning methods and low complexity. To evaluate the proposed method, simulations are conducted, and the results demonstrate high accuracy and robustness. Moreover, field tests are also conducted, and the outcomes show that the proposed method can recognize the lane in which the target vehicle is traveling.