A hierarchical control system for smart parking lots with automated vehicles: Improve efficiency by leveraging prediction of human drivers

In this work, we introduce a hierarchical architecture for management of multiple automated vehicles in a parking lot provided the existence of human-driven vehicles. The proposed architecture consists of three layers: behavior prediction, vehicle coordination and maneuver control, with the first two sitting in the infrastructure and the third one equipped on individual vehicles. We assume all three layers share a consistent view of the environment by considering it as a grid world. The grid occupancy is modeled by the prediction layer via collecting information from automated vehicles and predicting human-driven vehicles. The coordination layer assigns parking spots and grants permissions for vehicles to move. The vehicle control embraces the distributed model predictive control (MPC) technique to resolve local conflicts occurred due to the simplified vehicle models used in the design of the prediction and coordination layers. Numerical evaluation shows the effectiveness of the proposed control system.