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The functional elements of decision making system are fuzzy, adaptive and self-learning for intelligent ground vehicles. As is well-known, operating environment of unmanned ground vehicles (UGVs) is complex, unknown and time-changing. And on the other hand, exact dynamic model of the vehicle is relatively difficult to gain. However, the changing of special dynamic parameters and the man-made driving laws of velocities and running direction are easily available. Therefore, this paper attempts to provide an approach based on fuzzy Q-learning algorithm for studying autonomous navigation and control system’s design, which aims to make unmanned vehicles adaptive and robust under complex and time-changing environment. The presented approach utilizes the drivers’ empirical knowledge for.