Design and Simulation of the Adaptive Cruise Control System of an Autonomous Vehicle Moving in a Heavily Trafficked Street

This study offers a broader comparative investigation for finding the best control algorithm, considering some factors for the Adaptive Cruise Control (ACC) system of a vehicle in a highly concentrated traffic condition. In this study, only longitudinal vehicle movement was taken into account. Here, three mostly used control algorithms were adopted for comparison: Proportional-Integral-Derivative (PID), Model Predictive Control (MPC), and Fuzzy Logic. Each algorithm was designed and simulated using MATLAB and Simulink. The parameters of PID were determined using Particle Swarm Optimization. The parameters of MPC and fuzzy logic were validated for several values with several simulations, and thus the optimum parameter values were found. The criteria for validating the results were two: passenger comfort and safety. The velocity transition and acceleration were taken as the measure of comfort, and the relative distance was taken as the measure of safety. To evaluate how the system performed for the controllers, the curves plotted in the Simulink models were examined. PID outperformed the other two algorithms in every criterion. It maintained the headway with the highest accuracy and had the smoothest acceleration and velocity transition.