A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

TLDR

Artificial potential fields assign potentials to obstacles and road structures but ignore vehicle dynamics, while optimal controllers incorporate dynamics but treat obstacles as constraints; integrating both approaches is necessary for stable, optimal path planning. The paper introduces a model predictive path‑planning controller whose objective combines potential functions with vehicle dynamics. The controller is modeled and simulated on a CarSim vehicle model in complex test scenarios. Simulation results show that the controller distinctly handles various obstacles and road structures, successfully avoids collisions, obeys road regulations, and plans paths according to their relative importance while maintaining appropriate vehicle dynamics.

Abstract

Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.