Parking area extraction device, parking area extraction method, and parking area extraction program

Abstract

The present invention provides a parking area extraction device that extracts a parking area, that is, an area in which to park a vehicle within a site, the parking area extraction device comprising: an acquisition unit that acquires map information including the site; an extraction unit that uses the map information to divide the site and extract parking area candidates; and an estimation unit that estimates a parking area by deriving a probability for each of the candidates.

Description

[Technical Field]

[0001] The disclosed technology relates to a parking area extraction device, a parking area extraction method, and a parking area extraction program. [Background technology]

[0002] In recent years, with the aim of realizing autonomous driving of vehicles, there has been active development of technology to generate map information (dynamic maps) that includes highly accurate three-dimensional geospatial information that can determine the vehicle's own position at the lane level, and road conditions such as congestion and accidents. Map information is sometimes created using the results of vehicles traveling on various roads and measuring the surroundings of the roads using cameras and sensors.

[0003] Incidentally, map information covers not only roads but also parking lots where vehicles are parked. It is important for map information for parking lots to grasp the area and shape of the parking lot as map information in order to alert users when they are driving their vehicles and when they are walking in the parking lot. When creating map information for parking lots, static map information and cameras and sensors installed in the parking lot are used to create the map information for the parking lot. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Keiichi Yamada, Morimichi Mizuno, Arata Yamamoto, Katsuaki Murano, Shuichi Sunahara, "Parking Lot Status Monitoring System Using Images," Transactions of the Institute of Electronics Engineers of Japan, Vol. 120, No. 6, 2000, pp. 784-790<URL:https: / / www.jstage.jst.go.jp / article / ieejeiss1987 / 120 / 6 / 120_6_784 / _pdf>

[0005] Non-Patent Document 1 discloses a technology for detecting the presence or absence of a vehicle in each parking stall in a parking lot by using an image taken by a camera installed at a high place in the parking lot. Summary of the Invention [Problem to be solved by the invention]

[0006] However, if cameras and sensors are not installed in the parking lot, it is not always possible to create map information for the parking lot.

[0007] The present disclosure has been made in consideration of the above circumstances, and aims to propose a parking area extraction device, a parking area extraction method, and a parking area extraction program that can create map information for a parking lot even when no cameras or sensors are installed in the parking lot. [Means for solving the problem]

[0008] A first aspect of the present disclosure is a parking area extraction device that extracts a parking area, which is an area for parking a vehicle within a site, and includes an acquisition unit that acquires map information including the site, an extraction unit that uses the map information to divide the site and extract candidate parking areas, and an estimation unit that derives the accuracy of each candidate and estimates the parking area.

[0009] A second aspect of the present disclosure is a parking area extraction method for extracting a parking area, which is an area within a site for parking a vehicle, by acquiring map information including the site, dividing the site using the map information to extract candidate parking areas, and deriving the accuracy of each candidate to estimate the parking area.

[0010] A third aspect of the present disclosure is a parking area extraction program for causing a computer to function as the parking area extraction device according to the first aspect. [Effects of the Invention]

[0011] According to the disclosed technology, map information for a parking lot can be created even if no cameras or sensors are installed in the parking lot. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a block diagram showing a hardware configuration of a parking area extraction device according to an embodiment of the present invention. FIG. [Figure 2] 1 is a block diagram showing an example of a functional configuration of a parking area extraction device according to an embodiment of the present invention; [Figure 3] FIG. 10 is a data flow diagram showing an example of a data flow of an extraction process for explaining extraction of a parking area according to the present embodiment. [Figure 4] FIG. 2 is a schematic diagram showing an example of map information used to explain a target range according to the present embodiment. [Figure 5] 4 is a schematic diagram showing an example of map information for explaining extraction of parking area candidates according to the present embodiment; FIG. [Figure 6] 3 is a schematic diagram showing an example of map information used to explain extraction of a parking area according to the present embodiment; FIG. [Figure 7] 10 is a flowchart illustrating an example of a parking area extraction process according to the present embodiment. [Figure 8] 10 is a flowchart illustrating an example of a boundary line setting process according to the present embodiment. [Figure 9] 10 is a flowchart illustrating an example of a candidate extraction process according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0014] First, the hardware configuration of the parking area extraction device 10 according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the hardware configuration of the parking area extraction device 10 according to this embodiment.

[0015] 1, the parking area extraction device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and a communication interface (I / F) 17. Each component is connected to each other so as to be able to communicate with each other via a bus 18. Note that the above-described configuration using the CPU and memory is merely an example, and the device may be implemented as a device that specializes in object detection and is equipped with a dedicated arithmetic circuit, for example.

[0016] The CPU 11 is a central processing unit that executes various programs and controls each part. That is, the CPU 11 reads programs from the ROM 12 or the storage 14 and executes the programs using the RAM 13 as a work area. The CPU 11 controls the above components and performs various arithmetic processing in accordance with the programs stored in the ROM 12 or the storage 14. In this embodiment, the ROM 12 or the storage 14 stores a parking area extraction program for extracting an area for parking a vehicle (hereinafter referred to as a "parking area").

[0017] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured with a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including the operating system and various data.

[0018] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs.

[0019] The display unit 16 is, for example, a liquid crystal display, and displays various information. The display unit 16 may also function as the input unit 15 by adopting a touch panel system.

[0020] The communication interface 17 is an interface for communicating with other devices such as a display device. For this communication, for example, a wired communication standard such as Ethernet (registered trademark) or FDDI, or a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark) is used. The communication interface 17 obtains input data from an external memory and transmits output data to the external memory.

[0021] Next, the functional configuration of the parking area extraction device 10 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the functional configuration of the parking area extraction device 10 according to this embodiment.

[0022] 2, the parking area extraction device 10 includes, as functional components, an acquisition unit 21, a target range extraction unit 22, a boundary line setting unit 23, a parking area candidate extraction unit 24, a vehicle detection unit 25, a memory unit 26, an accident extraction unit 27, and an estimation unit 28. When the CPU 11 executes a parking area extraction program, the CPU 11 functions as the acquisition unit 21, the target range extraction unit 22, the boundary line setting unit 23, the parking area candidate extraction unit 24, the vehicle detection unit 25, the memory unit 26, the accident extraction unit 27, and the estimation unit 28.

[0023] As shown in FIG. 3 as an example, the acquisition unit 21 acquires map information 30, aerial images 31, and report information 32. The map information 30 is information representing road structures (land features) including railway tracks, rivers, roads, sidewalks, boundary lines of premises, etc., and structures such as buildings. The map information according to this embodiment will be described as polygon information representing road structures (land features) as a point cloud indicating latitude and longitude. The map information will be described as representing each of the railway tracks, rivers, roads, sidewalks, structures, etc., separately by type. The aerial images 31 are, for example, images of the earth's surface photographed from the sky by an aircraft, an artificial satellite, or a drone. The report information 32 is information indicating the location of a vehicle accident that has been reported.

[0024] The target range extraction unit 22 extracts a target range for determining whether or not a parking area exists. For example, the target range extraction unit 22 extracts the destination or a site located around the vehicle as the target range. As an example, as shown in FIG. 4 , the target range extraction unit 22 uses map information to extract the site as a target range 40. As a result of extracting the target range 40, the target range extraction unit 22 identifies and extracts site boundaries 41, buildings 42, and sidewalks 43. Here, the site boundaries 41 are lines that separate roads, sidewalks, railroad tracks, etc., and the site refers to the area enclosed by these boundaries.

[0025] In the present embodiment, the destination or a site located around the vehicle is extracted as the target range 40. However, the present invention is not limited to this. For example, the target range 40 may be a site designated by the user.

[0026] The boundary setting unit 23 sets a boundary line that divides the target range 40 in order to extract a parking area within the target range 40. Specifically, the boundary line setting unit 23 derives and sets a boundary line that connects each of the site boundary line 41, the building 42, and the sidewalk 43.

[0027] 5, the boundary setting unit 23 first derives a boundary line 44 connecting each vertex of a building 42 to the vertex of another building 42 located closest to it. Here, the boundary line setting unit 23 excludes any boundary line 44 that exceeds a predetermined length (e.g., 3 m) that allows vehicles to pass through. In other words, if the derived boundary line 44 is equal to or shorter than the predetermined length, it is set as the boundary line 44.

[0028] Next, the boundary line setting unit 23 derives and sets the shortest perpendicular line 45 from the vertex of the building 42 for which the boundary line 44 has not been set to the boundary line 41 of the site, the building 42, or the sidewalk 43. Here, if the derived perpendicular line 45 exceeds a predetermined length (for example, 3 m), the perpendicular line 45 is excluded. In other words, if the derived perpendicular line 45 is equal to or shorter than a predetermined length (for example, 3 m), it is set as the perpendicular line 45.

[0029] Furthermore, the boundary line setting unit 23 derives and sets the shortest perpendicular line 45 from the vertex of the building 42 for which the boundary line 44 is set to the boundary line 41 of the site, the building 42, or the sidewalk 43. Here, if the derived perpendicular line 45 exceeds a predetermined length (for example, 1.5 m), the perpendicular line 45 is excluded. In other words, if the derived perpendicular line 45 is equal to or shorter than the predetermined length, it is set as the perpendicular line 45.

[0030] As an example, as shown in FIG. 5 , the parking area candidate extraction unit 24 extracts parking area candidates (hereinafter referred to as "parking area candidates") 46. Specifically, if an area surrounded by a site boundary line 41, a building 42, a sidewalk 43, a boundary line 44, and a perpendicular line 45 does not face the site boundary line 41 (road) or the sidewalk 43, the parking area candidate extraction unit 24 excludes the area from the parking area candidate 46. Furthermore, if the area surrounded by the site boundary line 41, the building 42, the sidewalk 43, the boundary line 44, and the perpendicular line 45 is equal to or smaller than a predetermined area, the parking area candidate extraction unit 24 excludes the area from the parking area candidate 46. In other words, if the area surrounded by the site boundary line 41, the building 42, the sidewalk 43, the boundary line 44, and the perpendicular line 45 faces the site boundary line 41 (road) or the sidewalk 43 and exceeds a predetermined area, the parking area candidate extraction unit 24 extracts the area as the parking area candidate 46. In the present embodiment, the parking area candidate 46 is extracted based on the position and area of ​​the target area. However, this is not limiting. The parking area candidate 46 may also be extracted based on the circumscribing rectangle of the target area. For example, if the aspect ratio of the circumscribing rectangle of the target area falls within a predetermined range, the parking area candidate 46 may be extracted.

[0031] The vehicle detection unit 25 uses the aerial image 31 to detect vehicles contained within the target range 40, and outputs the positions of the detected vehicles as the detection results.

[0032] The storage unit 26 stores the detection results obtained by the vehicle detection unit 25.

[0033] The accident extraction unit 27 uses the notification information to extract accidents that have occurred within the target range 40, and outputs the location where the accident occurred as the extraction result. In the present embodiment, the form in which the location where the accident occurred is extracted as the extraction result has been described. However, this is not limiting. The extraction result may also include the amount of change in the occurrence of accidents within the target range 40 and the type of accident.

[0034] The estimation unit 28 extracts a parking area from the candidate parking area 46 using the detection results detected by the vehicle detection unit 25 and the extraction results extracted by the accident extraction unit 27. Specifically, as shown in FIG. 6 as an example, the estimation unit 28 associates the position of each detected vehicle 47 with each candidate parking area 46, derives the number of vehicles 47 relative to the area of ​​the candidate parking area 46 (hereinafter referred to as "vehicle density"), and derives an accuracy according to the vehicle density. For example, the estimation unit 28 increases the accuracy of the candidate parking area 46 as the vehicle density increases.

[0035] Furthermore, the estimation unit 28 corrects the accuracy using the extraction result extracted by the accident extraction unit 27. Specifically, the estimation unit 28 uses the extraction result to derive the number of reports for the area of ​​the parking area candidate 46 (hereinafter referred to as "report density"), and corrects the accuracy according to the report density. For example, the correction is made so that the accuracy of the parking area candidate 46 increases as the report density increases. Furthermore, the estimation unit 28 may correct the accuracy so that the accuracy increases as the amount of change in accident occurrence increases, or may correct the accuracy so that it increases according to the type of accident (for example, a vehicle-to-vehicle collision, a personal injury accident, a property damage accident, etc.).

[0036] Furthermore, the estimation unit 28 corrects the accuracy of the parking area candidate 46 using the current detection result and the past detection result. For example, the number of vehicles 47 may vary depending on the time (weekday, holiday, whether there is an event or not, long vacation, etc.) and the time period (dawn, daytime, night, etc.) when the aerial image 31 was captured. Therefore, the estimation unit 28 compares the estimation result based on the current detection result with the estimation result based on the past detection result in accordance with the time series of the aerial image 31, and corrects the accuracy of the parking area candidate 46.

[0037] For example, if vehicle 47 is no longer detected from a certain point in time, estimation unit 28 determines that the parking lot is closed and corrects the accuracy to be lower. Also, if vehicle 47 was not detected for a while but is detected from a certain point in time, estimation unit 28 determines that the parking lot is newly opened and corrects the accuracy to be higher. Also, if vehicle 47 was not detected at dawn but is detected during the day, estimation unit 28 estimates that the parking area is one in which parking is possible for a limited period of time, and corrects the accuracy according to the time of day.

[0038] The estimation unit 28 determines that the parking area candidate 46 whose accuracy exceeds a predetermined threshold (e.g., 80 percent) is a parking area (parking lot), and outputs coordinates indicating the location of the area as the coordinate group information 33. In the present embodiment, the coordinate group information is described as outputting coordinates indicating the location of the area. However, the present invention is not limited to this. The accuracy of each parking area may be output together with the coordinates.

[0039] Next, the operation of the parking area extraction device 10 according to this embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of a parking area extraction process according to this embodiment. The parking area extraction program shown in Fig. 7 is executed by the CPU 11 reading and executing a parking area extraction program from the ROM 12 or the storage 14. The parking area extraction program shown in Fig. 7 is executed, for example, when an instruction to execute a process to extract a parking area is input.

[0040] In step S101, the CPU 11 acquires the map information 30 and the notification information 32.

[0041] In step S102, the CPU 11 acquires the aerial image 31.

[0042] In step S103, the CPU 11 uses the map information 30 to extract the target range 40.

[0043] In step S104, the CPU 11 executes boundary setting processing to set a boundary line 44 and a perpendicular line 45 for the target range 40 in the map information 30. The boundary setting processing will be described in detail later with reference to FIG.

[0044] In step S105, the CPU 11 executes a candidate extraction process to extract a parking area candidate 46 from an area surrounded by the site boundary line 41, the building 42, the sidewalk 43, the boundary line 44, and the perpendicular line 45. The candidate extraction process will be described in detail later with reference to FIG.

[0045] In step S106, the CPU 11 uses the aerial image 31 to detect the position of the vehicle 47 included in the target range 40.

[0046] In step S107, the CPU 11 derives the vehicle density for each of the parking area candidates 46, and derives the accuracy of the parking area candidate 46 according to the vehicle density.

[0047] In step S108, the CPU 11 determines whether or not there is any notification information 32 among the notification information 32 that corresponds to the target range 40. If there is any corresponding notification information 32 (step S108: YES), the CPU 11 proceeds to step S109. On the other hand, if there is no corresponding notification information 32 (step S108: NO), the CPU 11 proceeds to step S111.

[0048] In step S109, the CPU 11 extracts the notification information 32 corresponding to the target range 40.

[0049] In step S110, the CPU 11 derives the report density for each of the parking area candidates 46, and corrects the accuracy of the parking area candidate 46 in accordance with the report density.

[0050] In step S111, the CPU 11 acquires the detection results of past detections.

[0051] In step S112, the CPU 11 corrects the accuracy of the parking area candidate 46 using the current detection result and the past detection result.

[0052] In step S113, the CPU 11 outputs the parking area candidate 46 whose accuracy exceeds a predetermined threshold as coordinate group information.

[0053] In step S114, the CPU 11 determines whether or not to end the process. If the process is to be ended (step S114: YES), the CPU 11 ends the parking area extraction process. On the other hand, if the process is not to be ended (step S114: NO), the CPU 11 proceeds to step S102.

[0054] Next, the boundary line setting process according to this embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the boundary line setting process according to this embodiment. The boundary line setting program shown in Fig. 8 is executed by the CPU 11 reading and executing a boundary line setting program from the ROM 12 or the storage 14. The boundary line setting program shown in Fig. 8 is executed, for example, when an instruction to execute a process of setting a boundary line is input.

[0055] In step S201, the CPU 11 selects a vertex of the building 42.

[0056] In step S202, the CPU 11 derives a boundary line 44 that connects the vertices of the selected building 42 to the vertices of another building 42 that is located closest to the selected building 42.

[0057] In step S203, the CPU 11 determines whether the derived boundary line 44 is equal to or shorter than a predetermined length. If the derived boundary line 44 is equal to or shorter than the predetermined length (step S203: YES), the CPU 11 proceeds to step S204. On the other hand, if the derived boundary line 44 is not equal to or shorter than the predetermined length (exceeds the predetermined length) (step S203: NO), the CPU 11 proceeds to step S205.

[0058] In step S204, the CPU 11 sets a boundary line 44 at the vertices of the selected building 42.

[0059] In step S205, the CPU 11 derives the perpendicular line 45 that is the shortest from the vertex of the selected building 42 to the boundary line 41 of the site, the building 42, or the sidewalk 43.

[0060] In step S206, CPU 11 determines whether or not the derived perpendicular line 45 is equal to or shorter than a predetermined length. If the derived perpendicular line 45 is equal to or shorter than the predetermined length (step S206: YES), CPU 11 proceeds to step S207. On the other hand, if the derived perpendicular line 45 is not equal to or shorter than the predetermined length (exceeds the predetermined length) (step S206: NO), CPU 11 proceeds to step S208. Here, the predetermined length of perpendicular line 45 is, for example, 1.5 m when boundary line 44 is set, and is, for example, 3 m when boundary line 44 is not set.

[0061] In step S207, the CPU 11 sets a perpendicular line 45 to the vertex of the selected building 42.

[0062] In step S208, the CPU 11 determines whether or not the process of setting the boundary lines 44 and perpendicular lines 45 has been executed for all vertices of the building 42. If all vertices have been processed (step S208: YES), the CPU 11 ends the boundary line setting process. On the other hand, if not all vertices have been processed (if unprocessed vertices exist) (step S208: NO), the CPU 11 proceeds to step S201 and selects the next vertex of the building 42.

[0063] Next, the candidate extraction process according to this embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of the candidate extraction process according to this embodiment. The candidate extraction program shown in Fig. 9 is executed by the CPU 11 reading and executing a candidate extraction program from the ROM 12 or the storage 14. The complementary extraction program shown in Fig. 9 is executed, for example, when an instruction to execute a process of extracting a parking area candidate is input.

[0064] In step S301, the CPU 11 selects an area surrounded by the boundary line 41, the building 42, the sidewalk 43, the boundary line 44, and the perpendicular line 45 of the site.

[0065] In step S302, CPU 11 determines whether the selected region exceeds a predetermined area. If the selected region exceeds the predetermined area (step S302: YES), CPU 11 proceeds to step S303. On the other hand, if the selected region does not exceed the predetermined area (is equal to or smaller than the predetermined area) (step S302: NO), CPU 11 proceeds to step S305.

[0066] In step S303, the CPU 11 determines whether the selected area faces the boundary line 41 (road) of the site or the sidewalk 43. If the selected area faces the boundary line 41 or the sidewalk 43 of the site (step S303: YES), the CPU 11 proceeds to step S304. On the other hand, if the selected area does not face the boundary line 41 or the sidewalk 43 of the site (step S303: NO), the CPU 11 proceeds to step S305.

[0067] In step S304, the CPU 11 extracts the selected area as a parking area candidate 46.

[0068] In step S305, the CPU 11 determines whether the process of extracting parking area candidates has been executed for all areas. If all areas have been processed (step S305: YES), the CPU 11 ends the candidate extraction process. On the other hand, if all areas have not been processed (if there are unprocessed areas) (step S305: NO), the CPU 11 proceeds to step S301 to select the next area.

[0069] As described above, according to this embodiment, map information for a parking lot can be created even if no cameras or sensors are installed in the parking lot.

[0070] In the above embodiment, the accuracy is estimated and corrected using the detection results of the vehicles 47 and the extraction results of the notification information. However, this is not limiting. The accuracy may be estimated and corrected taking into account the weighting of each. For example, if the number of detected vehicles 47 in the detection results of the vehicles 47 is less than a predetermined number, the accuracy may be corrected by adding a weight value to the correction based on the extraction results of the notification information. Furthermore, if the number of notification information in the extraction results is less than a predetermined number, the accuracy may be estimated by adding a weight value to the estimation based on the detection results of the vehicles 47.

[0071] In the above embodiment, coordinate group information indicating the coordinates of the parking area is output. However, this is not limiting. Changes in the parking area over time may also be output. For example, the parking area extraction device 10 may acquire multiple aerial images 31 as time-series data, detect vehicles 47 from each aerial image 31, derive a probability for each aerial image 31, and output the probability for each time series. This allows the possibility of use as a parking lot to be estimated for each time period. For example, if many vehicles 47 are detected but there is little change over time, the area may be estimated to be a vehicle 47 dealership or a vehicle 47 showroom. Furthermore, for example, since fixed asset tax is levied on land on January 1 each year, an area estimated as a parking area during the New Year holidays or the like has a high probability of actually being a parking area. That is, the probability may be derived and selected taking into account daily, weekly, monthly, or yearly changes, such as applying the probability for aerial images 31 taken during the New Year holidays or the like, from among the multiple probabilities derived for each time series.

[0072] In the above embodiment, an example has been described in which it is estimated whether the area within the target range 40, excluding the building 42, is a parking area. However, this is not limiting. For example, if a vehicle 47 parked on the roof of the building 42 is detected, it may be estimated that the building 42 is a multi-story parking lot.

[0073] In the above embodiment, the parking area candidate 46 is divided into areas surrounded by the boundary line 41, the building 42, and the sidewalk 43 of the site. However, the present invention is not limited to this. The parking area candidate 46 may be further divided. For example, when the positions of the extracted vehicles 47 correspond to the parking area candidate 46, the areas of the parking area candidate 46 may be classified into clusters according to the positions of the vehicles 47, and an area where the vehicles 47 are concentrated may be estimated as a parking area within the parking area candidate 46. Furthermore, the positions indicating the estimated parking areas may be output as coordinate group information.

[0074] In the above embodiment, the accuracy is estimated according to the vehicle density of the parking area candidate 46. However, this is not limiting. The accuracy may be estimated according to the parking situation of the vehicles 47. For example, if the vehicles 47 associated with the parking area candidate 46 are parked regularly, the accuracy may be estimated to be high. Here, "regularly" refers to, for example, a state in which the vehicles 47 are parked at equal intervals, the vehicles 47 are parked along the boundary line 41, the vehicles 47 are parked facing each other, the vehicles 47 are facing the same direction, and the vehicles 47 are parked on the periphery and in the center of the area.

[0075] In the above embodiment, the accuracy is corrected according to the vehicle density of the parking area candidate 46. However, this is not limiting. The accuracy may be estimated according to the distribution of the vehicles 47. For example, if the positions of the vehicles 47 associated with the parking area candidate 46 are concentrated near the building 42 and near the entrance and exit of the site, the accuracy of the parking area candidate 46 may be estimated to be high.

[0076] In the above embodiment, the map information is polygon information. However, this is not limiting. For example, if the map information includes identification information for identifying features, the boundary line 41 of the site may be set using the identification information. For example, if the identification information includes information such as a "private road within the site," a "park," and a "railroad," the boundary line 41 of the site may be set taking the identification information into consideration. Furthermore, if the identification information includes information on a "private road within the site," the corresponding location may be treated as a road (to be excluded from parking area candidates) or may not be included in the setting of the boundary line 44 and the perpendicular line 45.

[0077] In the above embodiment, a form in which a vehicle 47 is detected from the aerial image 31 has been described. However, this is not limiting. White lines, car poles, chain gates, wheel chocks, and the like that indicate parking areas may also be detected from the aerial image 31. For example, white lines, car poles, chain gates, wheel chocks, and the like that indicate parking areas may be detected from the aerial image 31, and the detection results may be used to extract a parking area candidate 46 or estimate the accuracy of the parking area candidate 46. Here, the white lines, car poles, chain gates, wheel chocks, and the like are examples of "features."

[0078] In the above embodiment, the parking area candidate 46 is extracted using the map information 30, and the accuracy of the parking area candidate 46 is estimated using the aerial image 31. However, the present invention is not limited to this. The parking area candidate 46 may be extracted using the aerial image 31, and the accuracy of the parking area candidate 46 may be estimated using the map information 30.

[0079] In the above embodiment, the parking area candidate 46 is extracted using the map information 30, and the accuracy of the parking area candidate 46 is estimated using the aerial image 31. However, this is not limiting. The parking area candidate 46 may be extracted using only the map information and the accuracy of the parking area candidate 46 may be derived, or the parking area candidate 46 may be extracted using only the aerial image 31 and the accuracy of the parking area candidate 46 may be estimated. For example, the parking area candidate 46 may be extracted using the map information, the area of ​​the parking area candidate 46 may be derived, and the accuracy based on the area may be derived. Furthermore, the parking area candidate 46 may be extracted using the aerial image, vehicles 47 included in the parking area candidate 46 may be detected, and the vehicle density of the parking area candidate 46 may be derived to derive the accuracy.

[0080] In the above embodiment, a perpendicular line 45 is set from the vertex of the building 42 to the boundary line 41 of the site and the sidewalk 43. However, this is not limited to this. A boundary line may be set from the boundary line 41 of the site and the sidewalk 43 to the vertex of the building 42. Here, the boundary line to be set is the shortest boundary line among the boundaries set for the vertex of the building 42, and is set to a boundary line that is equal to or shorter than a predetermined length.

[0081] In the above embodiments, the parking area extraction process executed by the CPU by reading software (programs) may be executed by various processors other than the CPU. Examples of processors in this case include dedicated electrical circuits, such as programmable logic devices (PLDs) (such as field-programmable gate arrays (FPGAs)) whose circuit configuration can be changed after manufacture, and application-specific integrated circuits (ASICs) that are processors with circuit configurations specifically designed to execute specific processes. The parking area extraction process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor devices.

[0082] In addition, in each of the above embodiments, the parking area extraction processing program is described as being pre-stored (installed) in the storage 14, but the present invention is not limited to this. The program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0083] The following additional notes are provided regarding the above-described embodiments.

[0084] (Additional note 1) Memory and at least one processor coupled to said memory; Including, The processor: A parking area extraction device that extracts a parking area that is an area for parking a vehicle within a site, Obtaining map information including the site; Using the map information, divide the site and extract candidates for the parking area; deriving a probability of each of the candidates to estimate the parking area; The parking area extraction device is configured as follows.

[0085] (Additional note 2) A non-transitory storage medium storing a program that can be executed by a computer to execute a parking area extraction process to extract a parking area that is an area for parking a vehicle within a site, The parking area extraction process includes: Obtaining map information including the site; Using the map information, divide the site and extract candidates for the parking area; deriving a probability of each of the candidates to estimate the parking area; Non-transitory storage medium. [Explanation of symbols]

[0086] 10 Parking area extraction device 11 CPU 12 ROM 13 RAM 14. Storage 15 Input section 16 Display 17 Communication I / F 18 Bus 21 Acquisition Department 22 Target range extraction section 23 Boundary setting section 24 Parking area candidate extraction unit 25 Vehicle detection unit 26 Memory section 27 Accident Extraction Section 28 Estimation part 30 Map Information 31 Aerial Images 32 Report Information 33 Coordinate Group Information 40 Scope 41 Property boundaries 42 Buildings 43 Sidewalk 44 Borderline 45 Perpendicular 46 Parking Area Candidates 47 vehicles

Claims

1. A parking area extraction device that extracts a parking area, which is an area for parking a vehicle within a site, comprising: an acquisition unit that acquires map information including the site; an extraction unit that uses the map information to divide the site and extract candidates for the parking area; a notification information acquisition unit that acquires notification information indicating a vehicle accident that has been reported within the premises; an estimation unit that uses the notification information to derive a probability of each of the candidates and estimates the parking area; A parking area extraction device comprising:

2. A parking area extraction device that extracts a parking area, which is an area for parking a vehicle within a site, comprising: an acquisition unit that acquires map information including the site; an extraction unit that uses the map information to divide the site and extract candidates for the parking area; an aerial image acquisition unit that acquires aerial images of the site taken from above; a detection unit that detects vehicles within the premises using the aerial image; an estimation unit that uses a detection result of detecting the vehicle to derive a probability of each of the candidates and estimates the parking area, the aerial image acquisition unit acquires a plurality of the aerial images representing time-series data; The estimation unit derives at least one of the accuracy corresponding to a change in the time-series data and the accuracy for each time series using the time-series data. Parking area extraction device.

3. A parking area extraction device that extracts a parking area, which is an area for parking a vehicle within a premises, comprising: an acquisition unit that acquires map information including the site; an extraction unit that uses the map information to divide the site and extract candidates for the parking area; an aerial image acquisition unit that acquires aerial images of the site taken from above; a detection unit that detects vehicles within the premises using the aerial image; an estimation unit that uses the detection result of the vehicle to derive a degree of accuracy for each of the candidates according to a vehicle density that indicates the number of vehicles relative to the area of ​​the candidate, and estimates the parking area; A parking area extraction device comprising:

4. Instead of the map information, the parking area is extracted using features that indicate parking areas included within the site, which are extracted from an aerial image of the site taken from above. The parking area extraction device according to any one of claims 1 to 3.

5. A parking area extraction method for extracting a parking area that is an area for parking a vehicle within a site, the method comprising: acquiring map information including the site; Dividing the site into candidate parking areas using the map information; obtaining report information indicating a reported vehicle accident within the premises; and deriving a probability of each of the candidates using the notification information to estimate the parking area. Parking area extraction method.

6. A parking area extraction method for extracting a parking area, which is an area for parking a vehicle within a site, comprising: acquiring map information including the site; Dividing the site into candidate parking areas using the map information; Acquiring an aerial image of the site from above; Detecting vehicles within the premises using the aerial imagery; and deriving a probability of each of the candidates using a detection result of detecting the vehicle, thereby estimating the parking area; acquiring the aerial images includes acquiring a plurality of the aerial images representing time series data; The step of estimating the parking area includes deriving at least one of the accuracy according to a change in the time-series data and the accuracy for each time series using the time-series data. Parking area extraction method.

7. A parking area extraction method for extracting a parking area, which is an area for parking a vehicle within a site, comprising: acquiring map information including the site; Dividing the site into candidate parking areas using the map information; Acquiring an aerial image of the site from above; Detecting vehicles within the premises using the aerial imagery; and deriving a degree of accuracy corresponding to a vehicle density indicating the number of vehicles relative to an area of ​​the candidate using a detection result of detecting the vehicle, for each of the candidates, to estimate the parking area. Parking area extraction method.

8. A parking area extraction program for causing a computer to function as the parking area extraction device according to any one of claims 1 to 3.

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