This paper studies control strategies of intelligent vehicles at unsignalized intersections. By considering the interaction among multiple vehicles and communication disturbances, the problem is formulated as a robust differential game in which the controlled vehicle computes actions using disturbed information of locally neighboring vehicles. Both cooperative and non-cooperative differential game models are considered. The controlled vehicle optimizes its own cost in the non-cooperative game while coordinates its strategy with other vehicles to optimize a joint cost in the cooperative game. We show that the local optimal strategy of each intelligent vehicle defined by a single optimization problem converges to global robust Nash equilibrium in the non-cooperative game, and converges to the global robust Pareto-Nash equilibrium in the cooperative game. The effectiveness of the robust differential game method is verified by simulations under two scenarios. Results show that under the proposed differential game approach, vehicles pass through unsignalized intersections more quickly than methods using traditional discrete game approaches. In addition, vehicles can avoid colliding with each other and obstacles at the presence of communication disturbances.