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Because of the high accuracy and low cost, learning-based methods have been widely used to model driver behaviors in various scenarios. However, the performance of learning-based methods depend heavily on the quantity and coverage of the driving data. When the new driver with insufficient data is considered, the accuracy of these methods cannot be guaranteed any more. To solve this problem, the balanced distribution adaptation (BDA) is used to build the new driver's decision making model in the lane change (LC) scenario. Meanwhile, a transfer learning (TL) based regression model, modified BDA (MBDA) is proposed to predict the driver's steering behavior during the LC maneuver. Cross validation (CV) based model selection (MS) method is developed to obtain the optimal parameters in model training process. A series of experiments are carried out based on the simulated and naturalistic driving data …