This research aims to develop a controller that has the ability to control high speed vehicle in avoiding obstacles while following prescribed path as accurately as possible. Every instant, the vehicle decides by itself how to modify its direction according to its environment. Each obstacle is represented independent of the others as a bell-shaped hump by the Gaussian functions which serve as an obstacle recognition system. Multi-objective cost function is formed for the planning strategy to generate, evaluate and select plans so that the vehicle can select which direction to move. Neural network and optimal preview controllers are utilized to control a full linear steering model of a vehicle to increase path following accuracy. To conduct the specified works, MATLAB software was extensively used. Simulated obstacle avoidance has confirmed the capability of a vehicle to precisely avoid collision with obstacles while traveling on high speed along its predetermined path.