Driver assistance systems modeling by model predictive control
Recently, an optimal control framework has been put forward to model human driver behavior and Advanced Driver Assistance Systems (ADAS). Although the models are shown to be face valid, applications are limited to the openloop optimal control problem and assume that a driver expects a certain dynamic speed profile of his predecessor. While human drivers are assumed to be good in anticipating traffic, ADAS are poor at predicting the dynamics of other vehicles, due to limited looking-ahead range and errors of sensors and actuators. This contribution furthers the optimal control framework to model automatic ADAS vehicles by relaxing the assumption that ADAS vehicles can predict the dynamics of other vehicles and solving the optimal control problem in a receding horizon way. This work assumes that within a prediction horizon, other vehicles are driving at stationary conditions (zero accelerations). An ADAS vehicle predicts system dynamics based on this assumption and makes control decisions to optimize its cost. The modeling framework is applied to design ACC systems and EcoACC systems, where multiple control objectives including safety, efficiency and sustainability are taken into account. The prediction horizon is tuned by face validating the behavior of a controlled vehicle. Simulation comparisons show that EcoACC systems result in higher fuel efficiency and a smoother vehicular behavior compared to ACC systems, although this depends on the exact formulation of the objectives.