Proximally Optimal Predictive Control Algorithm for Path Tracking of Self-Driving Cars
This work addresses the challenge of balancing tracking efficiency and computational cost for autonomous vehicle steering, representing an incremental improvement over established control methods.
The paper tackled the problem of real-time path tracking for self-driving cars by proposing a proximally optimal predictive control algorithm, which reduced crosstrack and heading errors compared to existing controllers like PID, Pure-Pursuit, and Stanley, though specific numerical improvements were not detailed in the abstract.
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.