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Automated vehicles are envisioned to be an integral part of the next generation of transportation systems. Whether it is striving for full autonomy or incorporating more advanced driver assistance systems, high-accuracy vehicle localization is essential for automated vehicles to navigate the transportation network safely. In this paper, we propose a reinforcement learning framework to increase GPS localization accuracy. The framework does not make rigid assumptions on the GPS device hardware parameters or motion models, nor does it require infrastructure-based reference locations. The proposed reinforcement learning model learns an optimal strategy to make “corrections” on raw GPS observations. The model uses an efficient confidence-based reward mechanism, which is independent of geolocation, thereby enabling the model to be generalized. We incorporate a map matching-based regularization term to …