In the early stages of the spread of autonomous vehicles, it is conceivable to operate an automated valet parking system in parking lots where autonomous vehicles and non-autonomous vehicles coexist. Since non-autonomous vehicles may park beyond the parking space, it is necessary to estimate parking space conditions three-dimensionally. This paper proposes a method to estimate the parking space conditions using multiple 3D-LiDARs that can detect the space three-dimensionally. In the evaluation experiment, multiple 3D-LiDARs were installed in the parking lot of a public facility, and the estimation accuracy of the proposed method was evaluated in various situations.

Vehicle-to-Everything (V2X) communications provide opportunities for information sharing among vehicles, edge servers, and cloud services. By the collection and extraction of sensing information from vehicles, such as communication quality or free space size, the edge server in V2X communications can improve its sensing and perception coverage. However, the collection of sensing data from vehicles consumes a large amount of wireless resources and computing resources at the edge server. The objective of this study is to extract object sensing information from vehicles, including the minimum or maximum of the sensing values, with low resource consumption and with high scalability. We propose a method that transforms the extraction of sensing information into a two-level procedure that includes (1) the local sharing and extraction of sensing information among vehicles and (2) the efficient extraction of sensing information at the edge server. Moreover, hybrid communication methods are employed at vehicles, with a short range of communication between vehicles to reduce the consumption of wireless resources for the local sharing of sensing data. The evaluation results show that the proposed method highly reduces the number of reports from the vehicles to the edge server, with a small amount of network resource consumption and scalability.

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