Model Predictive Control for Lane Merging Automation With Recursive Feasibility Guarantees and Its Experimental Validation

Abstract

To improve on road safety when autonomous vehicles (AVs) are introduced for highway or urban driving, in this article, we design an automated merging algorithm for an AV into a mixed-traffic flow scenario (i.e., traffic including autonomous and manually driven vehicles). In particular, we propose a novel model predictive control (MPC)-based solution to perform a merging procedure from a double lane into a single lane and continue with (adaptive) cruise control [(A)CC] functionality after the merge in one integrated algorithm. The proposed MPC balances fast progress along the path with comfort, while obeying a state-dependent safety distance and velocity bounds. Recursive feasibility, leading to safety and proper behavior (i.e., rigorously satisfying constraints), is guaranteed by the design of proper terminal sets, extending existing terminal sets in the literature. The resulting MPC problem is a mixed-integer quadratic program (MIQP) problem, which can be solved for global optimality. Through numerical simulations and experimental validation of the algorithm with multibrand cars, we demonstrate desirable behavior and verify the effectiveness of the proposed MPC merging scheme.