The Design of Vehicle Adaptive Cruise Control Based on Macroscopic Traffic Behavior Applying Model Predective Control Strategy

Nowadays, driver-assistance systems are important subjects in vehicle technologies. The Adaptive Cruise Control (ACC) system is one of the emerging vehicle technologies which was mainly designed to enhance driver comfort and passenger’s safety. The objective of this study is to propose an ACC algorithm based on the Model Predictive Control (MPC) framework and macroscopic traffic flow features such as density and speed.
In this paper, a novel adaptive cruise control system with a hierarchical control architecture is developed. An upper level controller calculates the vehicle desired longitudinal acceleration considering traffic flow parameters and a lower level controller determines the control signal of throttle and brake pedal to track the desired acceleration.
The upper level controller is synthesized under the framework of MPC theory in which the deterministic and stochastic models of traffic behavior has been taken into consideration. The desired acceleration and the desired future angle of throttle are calculated in ACC system using the model predictive control. Deterministic traffic behavior and stochastic behavior of front vehicle are considered as the inputs to the ACC system block. In vehicle dynamic model box, a complete longitudinal dynamic model of vehicle including engine, transmission system, tire and vehicle body dynamics are applied.
The complete longitudinal dynamic model of the vehicle, considering macroscopic traffic parameters and stochastic behavior of the front vehicle in ACC system are the novelties of this paper in comparison to other works conducted in this field.