Proximally Optimal Predictive Control Algorithm for Path Tracking of Self Driving Cars
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 *Both authors contributed equally to this research. dynamic simulation experiments pertaining to autonomous path tracking, employing an adaptive control law for longitudinal motion control of the vehicle. Wemeasured 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.
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