This paper presents an automated driving control algorithm for the control of an autonomous vehicle. In order to develop a highly automated driving control algorithm, one of the research issues is to determine a safe driving envelope with the consideration of probable risks. While human drivers maneuver the vehicle, they determine appropriate steering angle and acceleration based on the predictable trajectories of the surrounding vehicles. Therefore, not only current states of surrounding vehicles but also predictable behaviors of that should be considered in determining a safe driving envelope. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, the safe driving envelope over a finite prediction horizon is defined in consideration of probabilistic prediction of future positions of surrounding vehicles. A model predictive control (MPC) approach is used to determine appropriate steering angle and acceleration to control the vehicle satisfying the safe driving envelope over a finite prediction horizon. If a dynamics model which integrates a longitudinal motion and a lateral motion is used to determine the control inputs, a nonlinear optimization problem should be solved at each time step. However, a computational burden to solve a nonlinear MPC problem is a critical barrier for its implementation. Therefore a parallel architecture which decides a desired steering angle and a desired longitudinal acceleration separately is designed to reduce a computational burden. The effectiveness of the proposed control algorithm is evaluated via computer simulations.
