Autonomous vehicle (AV) is progressing rapidly, but there are still many shortcomings when interacting with humans. To address this problem, it is necessary to study the human behaviors in human-AV interactions, and build a predictive model of human decision-making in the interaction. In turn, modelling human behavior in human-AV interaction can help us better understand human perception of AVs and human driving strategies. In this work, we first train multi-level AV agents using reinforcement learning (RL) models to imitate three mentalizing levels (i.e., level-0, level-1, and level-2), and then design a human-AV driving task that subjects interact with each level of AV agents in a two-lane merging scenario. Both human and AV driving behaviors are recorded. We found that conservative subjects obtain more rewards because of the randomness of the RL agents. Our results indicate that (i) human driving strategies are flexible and changeable, which allows to quickly adjust the strategy to maximize the reward when gaming against AV; (ii) human driving strategies are related to mentalizing ability, and subjects with higher mentalizing scores drive more conservatively. Our study shed lights on the relationship between human driving policy and mentalizing in human-AV interactions, and it can inspire the next-generation AV.