A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers' actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.
