  This paper introduces a novel approach to optimize the parking efficiency for
fleets of Connected and Automated Vehicles (CAVs). We present a novel
multi-vehicle parking simulator, equipped with hierarchical path planning and
collision avoidance capabilities for individual CAVs. The simulator is designed
to capture the key decision-making processes in parking, from low-level vehicle
control to high-level parking assignment, and it enables the effective
assessment of parking strategies for large fleets of ground vehicles. We
formulate and compare different strategic parking spot assignments to minimize
a collective cost. While the proposed framework is designed to optimize various
objective functions, we choose the total parking time for the experiment, as it
is closely related to the reduction of vehicles' energy consumption and
greenhouse gas emissions. We validate the effectiveness of the proposed
strategies through empirical evaluation against a dataset of real-world parking
lot dynamics, realizing a substantial reduction in parking time by up to 43.8%.
This improvement is attributed to the synergistic benefits of driving
automation, the utilization of shared infrastructure state data, the exclusion
of pedestrian traffic, and the real-time computation of optimal parking spot
allocation.
