Proximally Optimal Predictive Control Algorithm for Path Tracking of Self-Driving Cars

TL;DR

This paper introduces a proximally optimal predictive control algorithm for autonomous vehicle path tracking that balances tracking accuracy and computational efficiency, outperforming traditional controllers in simulations.

Contribution

The paper presents a novel model-based lateral control algorithm that improves real-time path tracking by reducing computational cost while maintaining high accuracy.

Findings

Outperforms PID, Pure-Pursuit, and Stanley controllers in simulations

Reduces tracking errors compared to existing control laws

Ensures real-time applicability through low latency

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.