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