Unsignalized intersection driving is challenging for automated vehicles. For\nsafe and efficient performances, the diverse and dynamic behaviors of\ninteracting vehicles should be considered. Based on a game-theoretic framework,\na human-like payoff design methodology is proposed for the automated decision\nat unsignalized intersections. Prospect Theory is introduced to map the\nobjective collision risk to the subjective driver payoffs, and the driving\nstyle can be quantified as a tradeoff between safety and speed. To account for\nthe dynamics of interaction, a probabilistic model is further introduced to\ndescribe the acceleration tendency of drivers. Simulation results show that the\nproposed decision algorithm can describe the dynamic process of two-vehicle\ninteraction in limit cases. Statistics of uniformly-sampled cases simulation\nindicate that the success rate of safe interaction reaches 98%, while the speed\nefficiency can also be guaranteed. The proposed approach is further applied and\nvalidated in four-vehicle interaction scenarios at a four-arm intersection.\n