A Two-Stage MIQP-Based Optimization Approach for Coordinating Automated Electric Vehicles in Confined Sites

In this paper, we present a high-level optimization-based control strategy for the coordination of electric automated vehicles (AVs) in confined sites. A centralized controller optimizes the state and input trajectories of all vehicles in the site such that collisions are avoided in cross-intersections, narrow roads, merge crossings, and charging stations, while also considering the charging process. Specifically, the controller consists of two optimization-based components. The first component is tasked with solving the combinatorial part of the problem, which corresponds to the order in which the vehicles pass the crossings, by solving a Mixed Integer Quadratic Problem (MIQP). The found combinatorial solution is then utilized for calculating the optimal state and input trajectories that are obtained by solving a Nonlinear Program (NLP). The control algorithm is compared with respect to alternative optimization-based approaches in simulation scenarios. For the presented scenario, our method achieves improved energy efficiency by up to 7.6% while slightly improving the average mission end time, and furthermore, it is capable of avoiding deadlocks.