Abstract
An autonomous intelligent cruise control system was designed and simulated based on measured relative distance, speed, and acceleration. These constitute the fuzzy inputs. The results have shown that the system can be robust to noisy distance measurements and modular in structure to ease its implementation. A simple way of estimating the road condition was developed, implemented and tested in simulation. The results show that based on the measured relationship between the deceleration and brake pressure, the brake pressure output gain can be adjusted to prevent lock up of the tires and the consequent loss of stability. Two controller implementations were tested. The first one is based on heuristic knowledge of the system using Mamdani inference system. The second model was based on offline adaptive neuro-fuzzy controller model.