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Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart, and they barely reflect human\u2010like behaviors in real\u2010world driving scenarios. Therefore, a novel hierarchical game\u2010theoretical framework is presented to represent naturalistic multimodal interactions among road users during simulation and testing, which is subsequently validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is used to model the decision\u2010making process. Next, a probing behavior is generated by the proposed game\u2010theoretic model, and is further applied to control the vehicle via the Markov chain. The proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human\u2010likeness of the proposed algorithm. The experimental results show that the proposed Bayesian game\u2010theoretic framework can effectively generate representative scenes of human\u2010like decision\u2010making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. 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