In this paper, we present a bilevel, model predictive controller for coordination of automated vehicles at intersections. The bilevel controller consists of a coordination level, where intersection occupancy timeslots are allocated, and a vehicle level, where the control commands for the vehicles are computed. We establish persistent feasibility and stability of the bilevel controller under some mild assumptions and derive conditions under which closed-loop collision avoidance can be ensured with bounded position uncertainty. We thereafter detail an implementation of the coordination controller on a three-vehicle test bed, where the intersection-level optimization problem is solved using a distributed Sequential Quadratic Programing method. We present and discuss results from an extensive experimental campaign, where the proposed controller was validated. The experimental results indicate the practical applicability of the bilevel controller and show that safety can be ensured for large positioning uncertainties.

While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and specific conditions, and later on all public roads at all times, a phase transition will occur. Once sufficiently many autonomous vehicles are deployed, the opportunity for explicit coordination appears. This paper treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades.

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Abstract-In this paper, we address the problem of optimal and safe coordination of autonomous vehicles through a traffic intersection. We state the problem as a finite time, constrained optimal control problem, a combinatorial optimization problem that is difficult to solve in real-time. A low complexity computational scheme is proposed, based on a hierarchical decomposition of the original optimal control formulation, where a central coordination problem is solved together with a number of local optimal control problems for each vehicle. We show how the proposed decomposition allows a reduction of the complexity of the central problem, provided that approximated parametric solutions of the local problems are available beforehand. We derive conditions for the construction of the parametric approximations and demonstrate the method with a numerical example.

Abstract-In this paper we address the problem of coordinating automated vehicles at intersections, which we state as a constrained finite horizon optimal control problem. We present and study the properties of a primal decomposition of the optimal control problem. More specifically, the decomposition consists of an upper problem that allocates occupancy timeslots in the intersection, and lower-level problems delivering control policies for each vehicle. We investigate the continuity class of the upper problem, and show that it can be efficiently tackled using a standard sequential quadratic programming and that most computations can be distributed and performed by the participating vehicles. The paper is concluded with an illustrative numerical example.