## 1 Introduction
### Motivation for Research  
In this paper, as shown in Figure 1, we define an intelligent parking infrastructure (which refers to either a parking lot or a parking structure) as one that is equipped with a central command station that receives information from all sensors and interacts with autonomous vehicles in real time. In an automated valet parking system, the central command station is capable of determining the number of available parking spaces through geomagnetic sensors and obtaining the locations of these parking spaces through a pre-stored layout of the parking infrastructure. After an AV enters the parking infrastructure, the central command station utilizes information from multiple cameras to provide a road map to the allocated parking space, enabling the self-parking to be efficient. However, as development and implementation of automated parking systems move forward, there will be a transition period to fully automated valet parking unless the city government phases out HVs and mandates purchase of AVs, which is very unlikely, or designates parking spaces only for AVs. Thus, in an intelligent parking infrastructure, there will generally be both self-driving and human-driving vehicles. Self-driving vehicles can interact with the parking infrastructure in real time based on V2X communication. Human-driving vehicles are limited by non-intelligent devices that disengage any interaction with the central command station. Figure 1: Flowchart of the entire process of parking space allocation and route selection for an autonomous vehicle. Consider the scenario when both a HV and an AV are looking for parking spaces. In order for the central command station not to allocate the same parking space for the AV as the driver would choose, the station must be able to predict with high probability the choice of parking space that the driver would make. [PERSON] et al. proposed four preference factors that could affect the choice of parking spaces for an individual driver [9]. Logit models, based on fuzzy logic theory, were established to describe driver preference selection, which were then used to improve parking experience and alleviate difficulties in parking [9; 10]. Although fuzzy evaluation methods have been widely employed with proven success to determine factors, which are otherwise difficult to quantify, and make optimal choices incorporating expert opinions, there are only few published studies related to parking choice preference. In this paper, by adopting the fuzzy evaluation method, a preference ranking method of parking spaces in an intelligent parking infrastructure is established based on four representative factors that would affect the parking space selection of drivers. In an automated valet parking system, route selection is also a key enabling technology [11; 12]. Since the AV might not know a priori the overall layout of the parking infrastructure, it is necessary for the central command station to guide the self-driving vehicle to the allocated parking space according to the current conditions of the intended route. In general, in searching for a parking space, the shorter the path distance, the better. There are several methods to find the shortest path, such as the Dijkstra and Floyd algorithms [13; 14]. [PERSON] et al. [15] studied the shortest path planning problem of mobile robots based on the Floyd algorithm, focusing on the node selection problem for mobile robot path planning and determination of the weighting factor of each passable road. Experiments have also illustrated that the Floyd algorithm has the advantage of providing the shortest path selection for mobile robots [15]. Based on a known layout of the environment, Dijkstra algorithm can also efficiently find the shortest path between two points. Although the Floyd algorithm is slightly more time-consuming than the Dijkstra algorithm, it is a dynamic programming algorithm aimed to solve the shortest path problem between multiple source points.