Automated valet parking is one of the future visions of fully automated autonomous vehicles . An autonomous vehicle automatically drives in a parking lot and parks in a specified empty parking space. Passengers can request their autonomous vehicles from any place. All works done by conventional valet parking employees are done automatically.
An automated valet parking system is detected each parking space condition from sensors installed in a parking lot. Accurate detection of parking space condition is important to appropriately provide a parking space position for autonomous vehicles. There are many technologies to detect parking space conditions. The number of vehicles that have passed through the gates installed at the parking lot’s entrance and exit is used to estimate the parking space condition in a gated parking lot . It is possible to determine the availability of parking spaces, but it is impossible to determine the condition of each parking space. Magnetic sensors [3, 4] and inductive-loop vehicle detectors  are buried in the ground and detect the presence or absence of a vehicle above the sensors by changing the magnetic force. Ultrasonic-based vehicle detectors  are installed on the top and sides of parking spaces and detect the presence or absence of a vehicle based on the reflection of the ultrasonic wave emitted from the sensor. These sensors only scan a portion of the parking space and detect the presence or absence of a vehicle. Parking lots where autonomous vehicles and non-autonomous vehicles coexist are conceivable in the early stages of the spread of autonomous vehicles. Non-autonomous vehicles may park beyond a parking space, and parking spaces adjacent to the vehicle may be inaccessible. Sensors that scan only a portion of the parking space are ineffective in this situation. To change the position of the parking spaces in the parking lot, it is necessary to dig up or remove the sensor. Vision-based vehicle detectors [7,8,9] are installed in places where the vast area can monitor the parking space conditions using image processing technologies and deep learning. Since visual-based vehicle detectors can three-dimensionally detect parking spaces, it can handle the protrusion of the parking space of the parked vehicle. Even if the parking space position is changed in the parking lot, it is not necessary to change the sensor position. However, in places where the light source is insufficient, such as parking lots without lights at night, nothing is reflected in the camera image, so the vehicle cannot be detected.
Three-dimensional light detection and ranging (3D-LiDAR) are also sensors that can detect spaces in three-dimensions. A 3D-LiDAR measures range by irradiating lasers and measuring the time for the reflected light that hits objects to return to the receiver. A 3D-LiDAR outputs point cloud data that summarizes the reflection points. Since a 3D-LiDAR uses lasers, it can accurately detect space in three dimensions, even in places with no light source. There are several methods to detect parking space conditions using a 3D-LiDAR. Lee et al.  proposed a method for detecting the condition of parking spaces using a 3D-LiDAR mounted on the top of the autonomous vehicle. In this method, an autonomous vehicle equipped with a 3D-LiDAR approaches a parking space, and the condition of the parking space is determined based on the presence or absence of point cloud data from the road surface to the height of the 3D-LiDAR. Because this method assumes that the road surface is horizontal with respect to the vehicle’s posture, it cannot be used in areas where the road surface is uneven. Thornton et al.  also proposed a method for detecting the condition of a parallel parking space on a public road using a 3D-LiDAR mounted on the top of the autonomous vehicle. Based on the curbs, this method detects the parking space condition.Parking spaces with detectable curbs are determined to be vacant, while parking spaces with undetectable curbs are determined to be occupied.This method cannot be used in the parking spaces without curbs. In addition, there is no literature reporting the detection of parking spaces only using 3D-LiDARs installed at fixed points in a parking lot.
This paper proposes estimation method of parking space conditions using multiple 3D-LiDARs installed at various fixed points in a parking lot. The contributions of this paper are two points.
The first point is classification method of point cloud data using a high-precision map. A high-precision map is one that has lane-level detail and is used by autonomous vehicles for self-position estimation and route selection. A high-precision map reflects the actual size of parking spaces and road surface height. As a result, using the high-precision map’s parking space road surface information, the point cloud data in the parking space can be extracted and classified as observation points of objects and road surfaces.
The second point is an estimation method of parking space condition by coordinating multiple 3D-LiDARs. The presence or absence of point cloud data in a parking space is related to the parking space’s condition. After identifying the point cloud data in each 3D-LiDAR, the number of objects and road surfaces point cloud data in each parking space are calculated. When the same parking space is detected in a 3D-LiDAR with different installation locations, the number of object and road surface observation points is summed. Finally, the parking space condition is estimated based on the number of object and road surface observation points.
The remainder of this paper is organized as follows. Frist, Section 2 describes the assumptions of this study. Next, Section 3 introduces our proposed method Subsequently, Section 4 evaluates the method in an actual environment, and Section 5 presents the results. Finally, Section 6 discusses our proposed system, and Section 7 concludes the paper.