  This work presents proximally optimal predictive control algorithm, which is
essentially a model-based lateral controller for steered autonomous vehicles
that selects an optimal steering command within the neighborhood of previous
steering angle based on the predicted vehicle location. The proposed algorithm
was formulated with an aim of overcoming the limitations associated with the
existing control laws for autonomous steering - namely PID, Pure-Pursuit and
Stanley controllers. Particularly, our approach was aimed at bridging the gap
between tracking efficiency and computational cost, thereby ensuring effective
path tracking in real-time. The effectiveness of our approach was investigated
through a series of dynamic simulation experiments pertaining to autonomous
path tracking, employing an adaptive control law for longitudinal motion
control of the vehicle. We measured the latency of the proposed algorithm in
order to comment on its real-time factor and validated our approach by
comparing it against the established control laws in terms of both crosstrack
and heading errors recorded throughout the respective path tracking
simulations.
