| Systems that support a driver in traffic situations and reduce the total driver workload,is a growing research topic.Several of these support systems aim towards full or partial automatic driver assistance,such as those for longitudinal control that are often called Adaptive Cruise Control(ACC)systems.ACC is a kind of advanced assisted driving system(ADAS)that requires appropriate sensor technology,actuators and control devices and its system design requires data acquisition,control system design and validation procedures.ACC distinguishes itself from cruise control(CC)in its use of sensors that measure the headway distance and a controller which adjusts the velocity and distance to the vehicle in front,it autonomously adjust the vehicle’s speed according to current driving conditions in order to accomplish easy driving,preventing traffic accidents and increasing the traffic flow throughput.ACC improve safety,and reduce the total driver workload.The goal of this graduation project is to develop a method for autonomous vehicles in driving scenarios.A vehicle dynamics model is used to simulate the vehicle longitudinal dynamics in Matlab/Simulink software which makes the result more accurate according to the driving conditions.This paper proposed a variable safe distance model based on headway and also adopts a following car as a leading vehicle to evaluate the performance of traditional PID and adaptive fuzzy PID under typical working conditions.The simulation results show that in terms of safety and following performance of ACC system,the traditional PID algorithm and the adaptive fuzzy PID algorithm has better performance both in terms of front vehicle sudden acceleration,front vehicle sudden deceleration and other conditions,but adaptive fuzzy PID control algorithm is better than that of traditional PID algorithm.But both the controllers have good adaptability,and can ensure safety distance in sudden change of speed and other working conditions. |
