Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation
<|reference_start|>Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation: 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.<|reference_end|>
@article{shen2024parking,
    title={Parking of Connected Automated Vehicles: Vehicle Control, Parking
  Assignment, and Multi-agent Simulation},
    author={Xu Shen, Yongkeun Choi, Alex Wong, Francesco Borrelli, Scott Moura,
  Soomin Woo},
    journal={arXiv preprint arXiv:2402.14183},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14183},
    primaryClass={eess.SY cs.SY}
}