Automatic transportation systems nowadays play a key role in decreasing human errors and accelerating traffic flow. To implement controllers aiming at optimizing commute in terms of comfort and safety demands a rigorous modeling of the system. An accurate full-scale model will result in a more precise and reliable simulation. On the other hand, the growing number of vehicles and consequent rise in accidents associated with lack of driver attention highlights the need for driver assistant systems whereby more driver convenience, reducing accident, safety and comfort could be provided. In the present study, a complete nonlinear model of longitudinal vehicle dynamics has been chosen in order to make the model more compliant with reality and to minimize simulation and control uses errors. In the control section, a novel approach to developing an adaptive cruise control system is proposed in which the host vehicle acceleration is not only influenced by target car motion but also by the macroscopic motion of the traffic flow. The results indicate that the pile up resulted from sudden braking could be avoided by using a predictive control over vehicle acceleration which takes account of the motion of both front car and traffic jam. In the low level control section, a fuzzy control based on tracking error minimization is employed to maintain desired acceleration through calculating throttle angle and brake pedal. Such control command is then applied to the longitudinal model so as to appraise the select model performance in the driver assistant system.