Optimal scheduling and motion planning of automated vehicles at intersections

The ever-growing diffusion of automation in road transport and the spreading of communication technologies applied to road infrastructures toward so-called smart roads is leading to a need for coordination methods for automated vehicles, to fully exploit the potentialities of such technologies to make road transport more efficient, safer, and greener. This study focuses on these issues, particularly determining the optimal scheduling and speeds of automated vehicles to cross intersections safely, without stopping and without the need for a traffic signal. To accomplish this, the problem is formulated as a mixed-integer linear programming (MILP) optimization problem for a generic intersection characterized by an arbitrary number of road segments and lanes. In addition, a discussion of the properties of the problem solutions, an application of the proposed approach to a case study, a solution strategy that can be used to solve large problem instances in a reasonable time, and a sensitivity analysis of the primary model parameters are provided. The considered case study shows that the proposed model can effectively avoid vehicle conflicts and increase the intersection capacity up to double with respect to both first-come first-served control policy and signalized intersections. • The paper addresses the control of automated vehicles at unsignalized intersections. • Vehicle motion planning and crossing scheduling are jointly optimized. • Speed limits are determined as a function of the vehicle trajectory. • Collision avoidance, together with safe and smooth speed profiles, are guaranteed. • The problem computational time and the solution performances are discussed.