Information processing device

Abstract

To estimate a degree of congestion of a parking lot based on information obtained by traveling of a vehicle.SOLUTION: An information processing device acquires position information including a parking position from a moving object, and estimates parking lot information relating to a parking lot existing in an area outside a road based on the position information of the plurality of moving objects entering the area outside a road. An estimating unit estimates a degree of congestion of the entire parking lot based on the parking position.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to a technique for collecting information about parking lots. [Background technology]

[0002] When distributing parking information, information on whether a parking lot is full or empty may be distributed. In conventional methods, when generating vacancy information, a management device installed in the parking lot that manages entrance and exit generates the information based on information on the number of cars that can be parked, the number of cars entering, and the number of cars leaving. However, in parking lots that do not have such a management device, it is not possible to generate vacancy information for the parking lot, which creates a problem in that it is not possible to distribute information on the congestion status of the parking lot, such as vacancy information.

[0003] Patent document 1 describes a method for determining that a parking lot is full when a vehicle passing around the parking lot slows down by more than a predetermined threshold and passes through the entrance to the parking lot. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-184820 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 has issues with accuracy because it is determined solely by the judgment of users passing by the parking lot. Furthermore, the technology described above can only determine whether the parking lot is full or empty, and cannot estimate the degree of congestion in the parking lot other than whether it is full or empty. Furthermore, the technology does not mention the degree of congestion depending on the parking location within the parking lot.

[0006] The above is one example of a problem to be solved by the present invention. An object of the present invention is to estimate the congestion level of a parking lot based on information obtained by driving a vehicle. [Means for solving the problem]

[0007] The invention described in claim 1 is an information processing device comprising an acquisition unit that acquires location information including parking locations from a mobile body, and an estimation unit that estimates parking lot information regarding parking lots located in an off-road area based on location information of multiple mobile bodies that have entered the off-road area, and the estimation unit estimates the overall congestion level of the parking lot based on the parking locations. [Brief explanation of the drawings]

[0008] [Figure 1] 1 shows a configuration of an information processing system according to an embodiment. [Figure 2] An example of probe data is shown. [Figure 3] 10 shows an example of parking lot data. [Figure 4] 10 is a flowchart of a parking lot area estimation process. [Figure 5] 10 is a flowchart of a parking lot identification process and an estimation process. [Figure 6] FIG. 10 is a diagram illustrating off-road driving. [Figure 7] FIG. 10 is a diagram illustrating a method for estimating a parking lot area. [Figure 8] An example of estimating a parking lot area from multiple driving planes is shown. [Figure 9] 10 is a flowchart of a congestion level estimation process. DETAILED DESCRIPTION OF THE INVENTION

[0009] In one preferred embodiment of the present invention, an information processing device includes an acquisition unit that acquires location information including parking positions from a mobile body, and an estimation unit that estimates parking lot information regarding parking lots located in an off-road area based on location information of multiple mobile bodies that have entered the off-road area, and the estimation unit estimates the overall congestion level of the parking lot based on the parking positions.

[0010] The information processing device acquires location information, including parking locations, from mobile objects and estimates parking lot information for parking lots in the off-road area based on the location information of multiple mobile objects that have entered the off-road area. Here, the estimation unit estimates the congestion level of the entire parking lot based on the parking locations. This information processing device can estimate the congestion level of the parking lot by analyzing the parking locations of mobile objects in the off-road area.

[0011] In one aspect of the information processing device, the estimation unit estimates the congestion degree based on the concentration degree of the distribution of the parking positions. In this aspect, the congestion degree is estimated based on the concentration degree of the distribution of the parking positions. Specifically, if the concentration degree is high, the congestion degree is estimated to be low, and if the concentration degree is low, the congestion degree is estimated to be high.

[0012] In another aspect of the information processing device, the estimation unit calculates the concentration level based on the area of a standard deviation ellipse of the parking location. In a preferred example, the estimation unit divides the parking location into a plurality of areas and estimates the congestion level for each area by calculating the area of the standard deviation ellipse for each area. In another preferred example, the estimation unit estimates the congestion level for each of a plurality of areas based on parking locations throughout the parking lot.

[0013] In another aspect of the information processing device, the acquisition unit acquires time information corresponding to the location information from the mobile object, and the estimation unit estimates the congestion level for each time period. In this aspect, the congestion level of the parking lot can be estimated for each time period.

[0014] In another preferred embodiment of the present invention, an information processing method executed by an information processing device includes an acquisition step of acquiring location information including parking locations from mobile objects, and an estimation step of estimating parking lot information regarding parking lots located in the off-road area based on location information of multiple mobile objects that have entered the off-road area, wherein the estimation step estimates an overall congestion level of the parking lot based on the parking locations. According to this information processing method, the congestion level of the parking lot can be estimated by analyzing the parking locations of mobile objects in the off-road area.

[0015] In another preferred embodiment of the present invention, a program executed by an information processing device including a computer causes the computer to function as an acquisition unit that acquires location information, including parking locations, from mobile objects, and an estimation unit that estimates parking lot information regarding parking lots located in the off-road area based on location information of multiple mobile objects that have entered the off-road area, and the estimation unit estimates the overall congestion level of the parking lot based on the parking locations. The above-mentioned information processing device can be realized by executing this program on a computer. The program can be stored and accessed on a storage medium. [Example]

[0016] Preferred embodiments of the present invention will now be described with reference to the drawings. [System Configuration] Fig. 1(A) shows the configuration of an information processing system to which the present invention is applied. The information processing system includes a server 10 and a navigation device 20 mounted on a vehicle 3. The server 10 and the navigation device 20 are configured to be capable of wireless communication. Note that for the sake of convenience, only one vehicle 3 is shown in Fig. 1, but in reality, a large number of vehicles 3 communicate with the server 10.

[0017] 1(B) shows the configuration of the server 10. The server 10 includes a communication unit 11, a control unit 12, a map database (hereinafter, "database" will be abbreviated as "DB") 13, and a probe DB .

[0018] The communication unit 11 receives probe data from the navigation device 20. The probe data is generated by the navigation device 20 as the vehicle 3 travels. The map DB 13 stores map data. The map data includes link data indicating links corresponding to roads and node data corresponding to intersections, as well as parking lot data relating to parking lots. The probe DB 14 stores probe data received from a large number of navigation devices 20.

[0019] The control unit 12 controls the entire server 10. The control unit 12 is configured with a computer such as a CPU, and performs predetermined processes by executing programs prepared in advance. Specifically, the control unit 12 performs processes such as receiving probe data from the navigation device 20 and estimating a parking lot area based on the probe data stored in the probe DB 14.

[0020] [Probe data] Next, the probe data will be described. Fig. 2 shows an example of the probe data. The probe data is generated by the navigation device 20 at predetermined time intervals (for example, every few seconds) as the vehicle 3 travels, and the example in Fig. 2 shows probe data at multiple locations generated at predetermined time intervals. As shown in the figure, the probe data includes "travel distance," "latitude," "longitude," "date and time," "speed," "direction of travel," "road type," and "back flag."

[0021] "Distance traveled" is the distance traveled by vehicle 3, and the cumulative distance traveled measured by the odometer is used. "Latitude" and "Longitude" indicate the latitude and longitude of the vehicle 3's location. "Date and time" indicates the date and time when each piece of probe data was generated. "Speed" indicates the speed of vehicle 3 at the time of measurement. "Direction of travel" indicates the direction of travel of vehicle 3 at the time of measurement. Note that the direction of travel is indicated by a direction with north being 0 degrees.

[0022] "Road type" is information indicating the type of road on which the vehicle 3 is traveling, with expressways being designated as "1," toll roads as "2," national highways as "3," prefectural roads as "4," and city roads as "5." In this embodiment, the navigation device 20 sets the road type to "-1" when the vehicle 3 is traveling in an area other than a road. Therefore, when the road type is "-1," the vehicle 3 is traveling off a road.

[0023] The "back flag" is a flag that indicates whether the vehicle 3 is backing up (moving backward). A back flag of "0" indicates forward movement, and a back flag of "1" indicates backing up (moving backward). The back flag is generated based on whether the gear of the vehicle 3 is in the reverse position.

[0024] [Parking lot data] Next, we will explain parking lot data. Figure 3 shows an example of parking lot data. Parking lot data is prepared for each entrance to the parking lot. Therefore, if one parking lot has multiple entrances, multiple parking lot data are prepared for one parking lot. As shown in the figure, parking lot data includes "ID," "latitude," "longitude," "direction," and "name."

[0025] "ID" is an identification number that uniquely identifies the entrance to the parking lot. "Latitude" and "Longitude" are the latitude and longitude of the entrance to the parking lot. "Orientation" indicates the direction in which vehicle 3 will travel when entering the entrance to the parking lot. For example, if the entrance to the parking lot faces north, vehicle 3 will enter by traveling south from the north side of the parking lot, so the orientation of the entrance will be south. Note that in this example, the orientation is indicated by an angle with north being 0 degrees.

[0026] "Name" is the name of the parking lot. In the example of FIG. 3, parking lot data is prepared for each entrance to the parking lot, but instead, parking lot data may be prepared for each parking lot. However, even in this case, the parking lot data includes the latitude, longitude, and direction of the parking lot entrance. In other words, it includes information indicating the position and direction of each entrance to the parking lot (if there are multiple entrances).

[0027] [Parking lot area acquisition process] Next, the parking lot area acquisition process will be described. The parking lot area acquisition process is executed by the server 10, and is a process for estimating the area of the parking lot based on probe data obtained from a large number of vehicles 3. This process is mainly executed by the control unit 12 of the server 10. In practice, this process is realized by a computer such as a CPU constituting the control unit 12 executing a program prepared in advance.

[0028] First, the server 10 receives probe data by wireless communication from a large number of vehicles 3 (step S10). The server 10 stores the received probe data in the probe DB 14.

[0029] Next, the server 10 performs parking lot identification (step S11). The parking lot identification process is a process for identifying parking lots from the probe data and parking lot data in the map data. The details of the parking lot identification process are shown in FIG. 5(A). First, the server 10 detects the start point of off-road traveling based on the probe data (step S21).

[0030] FIG. 6 is a diagram illustrating off-road driving. As shown in the figure, multiple roads intersect, with multiple plots (sites) formed by roads running north-south and roads running east-west. A vehicle mark X indicates the location of vehicle 3 indicated by probe data. A white vehicle mark X indicates a location on a prefectural road, and the probe data at this location includes the road type "4 (prefectural road)." A diagonally hatched vehicle mark X indicates a location on a city road, and the probe data at this location includes the road type "5 (city road)." A black vehicle mark X indicates a location off-road, and the probe data at this location includes the road type "-1 (off-road)."

[0031] Now, as shown in the figure, assume that vehicle 3 travels west on road R3 (prefectural road), turns left at the intersection of roads R2 and R3 onto road R2 (city road), turns left again to enter section 50 where a parking lot is located, travels through the parking lot, and finally parks vehicle 3 facing west (see dashed line 62). In this case, server 10 refers to the probe data showing the travel trajectory shown in Figure 6, and determines that the point where the road type first becomes "-1 (off-road)" (the point indicated by dashed line 61 in Figure 6) is the start point of off-road travel.

[0032] Next, the server 10 refers to the parking lot data in the map DB 13 and searches for parking lots having entrances within a predetermined threshold range from the coordinates of the start point of the off-road traveling obtained in step S21 (step S22). Then, from among the multiple parking lots obtained by the search, the server 10 identifies a parking lot whose entrance direction matches the traveling direction of the vehicle 3 at the start point of the off-road traveling and has an entrance that is closest to the start point of the off-road traveling (step S23). Then, the process returns to the main routine shown in FIG.

[0033] Next, the server 10 determines whether or not the parking lot has been identified in the parking lot identification process of step S11, i.e., whether or not a corresponding parking lot has been found (step S12). If a corresponding parking lot has not been found (step S12: No), the server 10 determines that the parking lot indicated by the currently obtained probe data has not yet been registered in the map DB 13, and registers the parking lot in the map DB 13 (step S13). Specifically, the server 10 generates new parking lot data and registers it in the map DB 13. At this time, a new ID is assigned to the new parking lot data, the coordinates of the start point of the off-road traveling are set as "latitude" and "longitude," the traveling direction of the vehicle 3 at the start point of the off-road traveling is set as "direction," and the name of the facility or the like closest to the start point of the off-road traveling is set as "name."

[0034] In this way, when a new parking lot is registered in step S13, or when it is determined that a parking lot has been identified in step S12, the server 10 extracts off-road driving data from the probe data and stores it in the probe DB 14 (step S14). In the example of Fig. 2, data on points where the road type is "-1" is stored as off-road driving data.

[0035] Next, the server 10 executes estimation processing using the off-road driving data (step S15). FIG. 5(B) is a flowchart of the estimation processing. First, the server 10 acquires off-road driving data obtained for the same parking lot from the probe DB 14 (step S31). Next, the server 10 generates a driving surface based on each off-road driving data (step S32). Here, a "driving surface" is a surface having a certain distance centered on coordinates included in the off-road driving data. Now, assuming that the off-road driving data has a trajectory 42 shown in FIG. 7(A), the server 10 generates a driving surface 44 that has a width approximately the width of a standard vehicle relative to the trajectory 42, as schematically shown in FIG. 7(B). The server 10 generates such a driving surface 44 for each piece of off-road driving data.

[0036] The server 10 then spatially combines the multiple travel surfaces 44 generated in step S32 to estimate a parking lot area (step S33). FIG. 8 shows an example of estimating a parking lot area from multiple travel surfaces 44, showing multiple travel surfaces 44 obtained in the same parking lot. In this example, it is possible to distinguish between an area where the vehicle 3 is moving in a relatively linear manner and an area where the vehicle 3 has turned around and parked. Therefore, the server 10 estimates the area where the vehicle 3 has turned around and parked as a parking area 51, and estimates the area where the vehicle 3 is moving in a relatively linear manner as a travel area 52b. In the example of FIG. 8, two parking areas 51a and 51b are obtained, and two corresponding travel areas 52a and 52b are obtained, respectively.

[0037] Generally, when parking a vehicle, the vehicle moves to the parking position at a certain speed, but when parking the vehicle by changing direction, the vehicle speed is sufficiently low. Therefore, the parking area 51 and the driving area 52 may be distinguished from each other by taking into account not only the position coordinates of the vehicle 3 but also the vehicle speed in the off-road driving data.

[0038] Furthermore, the shape of the entire parking lot can be estimated by combining the one or more parking areas 51 thus obtained with one or more driving areas 52. In the example of Figure 8, the overall shape of the parking lot is roughly the shape shown by the dashed line 48. Once the parking lot area has been estimated in this way, the parking lot area estimation process ends.

[0039] As described above, according to this embodiment, the parking lot area can be estimated based on the probe data of multiple vehicles, i.e., the driving history. In addition to the overall shape of the parking lot area, it is also possible to distinguish between the driving area and the parking area within the parking lot area.

[0040] [Estimation of congestion level] Next, a method for estimating the congestion level of a parking lot based on the distribution of parking positions within the parking lot area is described. By dividing vehicle parking positions by time period, the distribution of parking positions for each time period can be obtained. Basically, when a parking lot is empty, drivers tend to park their vehicles in the most convenient areas within the parking lot. For example, in parking lots attached to facilities such as stores, parking spaces often fill up starting from the areas closest to the facility. On the other hand, when a parking lot is congested, the most convenient areas are often already filled, forcing drivers to park their vehicles in other areas, i.e., randomly occurring empty spaces. As a result, vehicle parking positions tend to be highly random and dispersed throughout the parking lot. Therefore, by analyzing the distribution of vehicle parking positions within the parking lot, if vehicle parking positions are concentrated in a certain area, it can be estimated that the parking lot is empty. On the other hand, if vehicle parking positions are dispersed throughout the parking lot, it can be estimated that the parking lot is congested.

[0041] 9 is a flowchart of a process for analyzing the congestion level based on the parking position of a vehicle. This process is performed by the server 10. First, the server 10 receives probe data from the navigation device 20 of the vehicle 3 (step S41).

[0042] Next, the server 10 acquires the coordinates of the parking positions of the vehicles 3 for each time period from the probe data (step S42). For example, the server 10 divides a day into three periods: morning, afternoon, and night, and acquires the coordinates of the parking positions of the vehicles for each time period. The server 10 then calculates the concentration of parking positions based on the acquired coordinates of the parking positions, and estimates the congestion level for each time period (step S43). The process then ends. In this way, the congestion level of the parking lot for each time period can be obtained.

[0043] Next, a method for calculating the concentration degree of parking positions will be described. The concentration degree of parking positions can be calculated by any one of the following first to third methods.

[0044] (First method) The first method for calculating the concentration level uses a standard deviation ellipse. Specifically, the server 10 calculates a standard deviation ellipse based on the coordinates of the parking locations obtained in step S42 and calculates a predetermined percentage of the area. For example, the server 10 calculates 90% of the area of the standard deviation ellipse. Essentially, if parking locations are concentrated, the area of the standard deviation ellipse is small, and if parking locations are dispersed, the area of the standard deviation ellipse is large. Therefore, the server 10 compares the predetermined percentage of the area of the standard deviation ellipse with a predetermined threshold. If the percentage is smaller than the threshold, the server 10 estimates that the concentration level is high, i.e., the parking lot is empty. If the percentage is larger than the threshold, the server 10 estimates that the concentration level is low, i.e., the parking lot is crowded. In this way, the server 10 can estimate the congestion level for each time period based on the coordinates of the parking locations for each time period.

[0045] In the above example, one threshold value was used to classify the congestion level of a parking lot into two levels, "empty" and "crowded," but two or more threshold values may be used to classify the congestion level of a parking lot into three or more levels. For example, two threshold values may be used to classify the congestion level of a parking lot into three levels, "empty," "normal," and "crowded."

[0046] Note that the method of using the area of the standard deviation ellipse is a technique for calculating the concentration of parking positions in one location throughout an entire parking lot, so it cannot be applied as is when there are multiple concentration points throughout the entire parking lot. However, if the entire parking lot is divided into multiple parking areas and the area of the standard deviation ellipse is calculated for each parking area, it can be applied to cases where parking positions are concentrated in multiple locations throughout the entire parking lot.

[0047] (Second method) The second method for calculating concentration uses the nearest neighbor distance method, a spatial analysis method. The nearest neighbor distance method classifies point distributions as dispersed or concentrated, and calculates the "average nearest neighbor distance W" from the distribution of multiple points. Here, the average nearest neighbor distance W is the average value of the distance from each point to the nearest point, and is calculated using the following formula:

number

[0048] Here, as a classification criterion for each distribution, we consider the case where points are randomly distributed on a plane of area S (following a uniform Poisson distribution). In this case, the expected value of the average nearest neighbor distance W is

number

[0049] So,

number

[0050] (Third Method) The third method for calculating concentration uses the K-function method, a spatial analysis method. The K-function method is a method for identifying distributions that cannot be distinguished using the nearest neighbor distance method, and calculates the K-function K(h) from the distribution of multiple points using the following formula:

number

[0051] The K-function can be used to determine the scale (spatial range) at which points are concentrated or dispersed. If the points are randomly distributed, the expected value of the K-function is

number

[0052] Therefore, on the scale of a circle of radius h,

number

[0053] In the above example, a day is divided into morning, noon, and night as time periods, but the application of the present invention is not limited to this. For example, a day may be divided into day and night, or into hourly periods. A week may also be divided into seven days from Monday to Sunday. Furthermore, a year may be divided into months, or into four seasons. In the present invention, the concept of a "time period" includes not only division of a period into hours, but also division into days of the week or months.

[0054] Furthermore, in the above example, the congestion level is estimated for each time period, but the congestion level may be estimated without limiting the time period. [Explanation of symbols]

[0055] 3 vehicles 10 Servers 12 Control Unit 13 Map DB 14 Probe DB 20 Navigation devices

Claims

[Claim 1] an acquisition unit that acquires location information including a parking location from a mobile object; an estimation unit that estimates parking lot information regarding parking lots present in the off-road area based on position information of a plurality of mobile objects that have entered the off-road area; Equipped with The estimation unit is an information processing device that estimates the overall congestion level of the parking lot based on the parking location.

Images