Abstract: In order to improve the adaptability of intelligent vehicles to different driving scenarios and their decision-making and control performance in complex scenarios, a scenario transferable decision-making and control method was proposed based on human-like behavior representation. Collecting human driving data, a decision-making and control model was constructed with the reinforcement learning method to carry out human-like behavior representation and decision primitive extraction, making the decision primitives chosen and scenario transfer functions suitable for complex scenarios. Furthermore, the transferable decision-making and control model was constructed with two dimensions, decision primitive transfer and decision primitive combination strategy transfer, and was verified in simulation. The simulation results show that the proposed scenario transferable decision-making and control method for intelligent vehicles can improve traffic efficiency up to 21.9% in similar scenarios, and the driving task completion rate can reach up to 97.5% in transferring between heterogeneous scenarios.