Information processing device, information processing method, and program

Abstract

To estimate an installation rate of on-board devices on vehicles.SOLUTION: An information processing device groups a plurality of pieces of location information acquired from a plurality of on-board devices, and estimates the installation ratio of the on-board devices in parking lots of facilities corresponding to the generated groups, based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles in the generated groups.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a vehicle equipped with an on-board device. [Background technology]

[0002] There is a technology that collects data from on-board devices installed in multiple vehicles and provides information to users. In this regard, for example, Patent Document 1 discloses a system that calculates traffic volume in a predetermined section based on the results of communication between ground equipment and on-board devices. In this system, the percentage of vehicles equipped with on-board devices is estimated, and the traffic volume is determined using the results of this estimation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-016569 Summary of the Invention [Problem to be solved by the invention]

[0004] With the development of machine learning, it is expected that there will be more and more situations in which data collected from vehicles will be utilized.

[0005] The present disclosure aims to estimate the installation rate of in-vehicle devices in vehicles. [Means for solving the problem]

[0006] One aspect of an embodiment of the present disclosure is an information processing device having a control unit that performs the following: grouping multiple pieces of location information obtained from multiple vehicle-mounted devices; and estimating the installation ratio of the vehicle-mounted devices in the parking lots of the facilities corresponding to the generated group based on facility data, which is data regarding the parking lot capacity of each of the multiple facilities, and the number of vehicles included in the generated group.

[0007] One aspect of an embodiment of the present disclosure is an information processing method including the steps of grouping multiple pieces of location information obtained from multiple vehicle-mounted devices, and estimating the installation ratio of the vehicle-mounted devices in the parking lots of the facilities corresponding to the generated group based on facility data, which is data regarding the parking lot capacity of each of the multiple facilities, and the number of vehicles included in the generated group.

[0008] Another aspect of the present invention is a program for causing a computer to execute the above-described method, or a computer-readable storage medium that non-transitoryly stores the program. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to estimate the installation rate of in-vehicle devices in vehicles. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 10 is a diagram illustrating a method for calculating the installation rate of an in-vehicle device. [Figure 2] 1 is a schematic diagram of a vehicle system according to a first embodiment. [Figure 3] 1 is a system configuration diagram of an in-vehicle device according to a first embodiment; [Figure 4] FIG. 3 is a diagram illustrating vehicle data transmitted from a vehicle. [Figure 5] FIG. 2 is a system configuration diagram of a server device according to the first embodiment. [Figure 6] 10 shows a specific example of a process for associating location information with facilities. [Figure 7] 10 shows an example of facility data stored in a server device. [Figure 8] An example of estimating when a parking lot is full. [Figure 9] FIG. 4 is a sequence diagram of processing between the in-vehicle device and the server device. [Figure 10] 5 is a flowchart of a process executed by a server device in the first embodiment. [Figure 11] 10 shows an example of result data generated by the server device. [Figure 12] FIG. 10 is a schematic diagram of a vehicle system according to a second embodiment. [Figure 13] 10 is an example of vacancy data used in the second embodiment. [Figure 14] FIG. 10 is a system configuration diagram of a server device according to a second embodiment. [Figure 15] FIG. 10 is a schematic diagram of a vehicle system according to a third embodiment. [Figure 16] FIG. 10 is a system configuration diagram of a server device according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] There are services that generate traffic information and provide it to users based on data transmitted from multiple vehicles. For example, it is possible to estimate the volume of traffic passing through a specific section based on data transmitted from vehicles in that section. Such data is generated and transmitted by an in-vehicle device having communication capabilities.

[0012] On the other hand, not all vehicles are equipped with on-board devices. For example, even if data is transmitted from 100 on-board devices per unit time in a certain section, it does not necessarily mean that 100 vehicles passed through. For example, if half of the passing vehicles do not have on-board devices, the actual traffic volume will be doubled. In this way, in order to accurately determine traffic volume, it is necessary to estimate what percentage of all vehicles are equipped with an on-board device (hereinafter referred to as the installation ratio).

[0013] The installation rate of the in-vehicle device can be roughly estimated based on, for example, the penetration rate of the in-vehicle device. However, the penetration rate of the in-vehicle device can vary greatly depending on the region. For example, the penetration rate may be relatively high in metropolitan areas, while it may be low in rural areas. Therefore, if the installation rate is assumed to be uniform, accurate traffic information may not be calculated. The information processing device according to the present disclosure solves this problem.

[0014] An information processing device according to one aspect of the present disclosure has a control unit that performs the following: grouping multiple pieces of location information obtained from multiple vehicle-mounted devices; and estimating the installation ratio of the vehicle-mounted devices within the parking lots of the facilities corresponding to the generated group based on facility data, which is data regarding the parking lot capacity of each of the multiple facilities, and the number of vehicles included in the generated group.

[0015] An in-vehicle device is a device that is installed in a vehicle and provides predetermined information. The control unit collects a plurality of pieces of location information and groups the collected pieces of location information. The groups may be generated by, for example, clustering. This allows for the generation of one or more groups, each of which includes one or more vehicles.

[0016] The control unit may also associate each of the created groups with one of a plurality of facilities, for example, based on data defining the positional relationship between the facility and its parking lot. Furthermore, the control unit estimates the installation ratio of the in-vehicle device in the parking lot based on data (facility data) relating to the capacity of the parking lot owned by the facility.

[0017] FIG. 1 is a schematic diagram of a parking lot corresponding to a certain facility. The parking lot can accommodate 10 cars. Black circles represent vehicles equipped with an on-board device, and white circles represent vehicles without an on-board device. In this case, let us assume that the parking lot is full at a certain time. If there are five in-vehicle devices that have transmitted location information from within the parking lot, it can be estimated that the installation rate of in-vehicle devices in that parking lot is 50%. Also, if there are two in-vehicle devices that have transmitted location information from within the parking lot, it can be estimated that the installation rate of in-vehicle devices in that parking lot is 20%.

[0018] In this way, if the capacity of a parking lot (e.g., the number of cars that can be accommodated) and the number of onboard devices that have transmitted location information within the parking lot are known, it becomes possible to estimate the installation ratio of onboard devices within the parking lot.

[0019] In order to make an accurate estimation, it is preferable to identify the timing when the parking lot is filled. This is because when the parking lot is full, it can be estimated that the number of vehicles parked there is approximately the same as the parking lot's capacity. The timing when the parking lot is filled may be identified based on information sent from the parking lot management device, information on the Internet, etc. Furthermore, it may be estimated that the parking lot is full when the number of vehicles in the group reaches a plateau.

[0020] The control unit may output the installation rate of the in-vehicle device in association with each facility, thereby making it possible to compile data on the installation rate of the in-vehicle device for each region. Furthermore, the control unit may calculate the total number of vehicles in the parking lot based on the estimated installation ratio. If the installation ratio of on-board devices in a certain facility is known, it becomes possible to estimate the total number of vehicles in the parking lot (including vehicles not equipped with on-board devices).

[0021] Specific embodiments of the present disclosure will be described below with reference to the accompanying drawings. Unless otherwise specified, the hardware configuration, module configuration, functional configuration, etc. described in each embodiment are not intended to limit the technical scope of the disclosure to those configurations.

[0022] (First embodiment) An overview of a vehicle system according to a first embodiment will be described with reference to Fig. 2. The vehicle system according to this embodiment includes a vehicle 10 equipped with an on-vehicle device 100, and a server device 200. The vehicle system may include multiple vehicles 10 (and on-vehicle devices 100).

[0023] The vehicle 10 (on-vehicle device 100) is configured to acquire location information and transmit the acquired location information to the server device 200. In this embodiment, the on-vehicle device 100 transmits the location information to the server device 200 when the vehicle 10 has finished traveling. The timing when the vehicle's traveling system has stopped (the system power supply has been cut off), for example.

[0024] The server device 200 groups the position information acquired from a plurality of vehicles (on-board devices 100) by parking lot, and estimates the installation ratio of on-board devices within the parking lot based on the generated groups.

[0025] Each element that makes up the system will be explained. The vehicle 10 is a connected car having a communication function with an external network. The vehicle 10 includes an in-vehicle device 100.

[0026] The in-vehicle device 100 is a computer mounted on a vehicle. The in-vehicle device 100 may be a device that provides information to vehicle occupants (for example, a car navigation device), or may be another electronic control unit (ECU). The in-vehicle device 100 may also be a data communication module (DCM) having a communication function. The in-vehicle device 100 has a function of performing wireless communication with an external network. The in-vehicle device 100 may have a function of downloading traffic information, road map data, music, video images, etc. by communicating with the external network of the vehicle 10. The in-vehicle device 100 may also be a device that can be linked to a smartphone or the like.

[0027] The in-vehicle device 100 can be configured as a general-purpose computer. That is, the in-vehicle device 100 can be configured as a computer having a processor such as a CPU or a GPU, a main memory such as a RAM or a ROM, and an auxiliary memory such as an EPROM, a hard disk drive, or a removable medium. The auxiliary memory stores an operating system (OS), various programs, various tables, etc., and by executing the programs stored therein, various functions that match predetermined purposes, as described below, can be realized. However, some or all of the functions may be realized by hardware circuits such as an ASIC or an FPGA.

[0028] FIG. 3 is a diagram showing in detail the components of the in-vehicle device 100 included in the vehicle system according to this embodiment. The in-vehicle device 100 includes a control unit 101 , a storage unit 102 , a communication unit 103 , a wireless communication unit 104 , and a location information acquisition unit 105 .

[0029] The control unit 101 is a calculation unit that executes a predetermined program to realize various functions of the in-vehicle device 100. The control unit 101 may be realized by, for example, a CPU or the like. The control unit 101 is configured to have a data transmission unit 1011 as a functional module. The functional module may be realized by executing a stored program by a CPU.

[0030] The data transmission unit 1011 acquires the location information of the vehicle itself via the location information acquisition unit 105 (described later) at a predetermined timing, and transmits the location information to the server device 200 as vehicle data. In this embodiment, the predetermined timing is the timing when the vehicle's driving system is shut down. In other words, the predetermined timing can be the timing when the vehicle 10 arrives at the destination. Figure 4 shows an example of vehicle data. As shown in the figure, the vehicle data includes fields for a vehicle ID, date and time information, location information, and status. The vehicle ID field stores an identifier that uniquely identifies a vehicle. The date and time information field stores the date and time when the vehicle data was generated. The position information field stores position information (for example, latitude and longitude) acquired by the position information acquisition unit 105. The status field stores data relating to the state of the vehicle.

[0031] In this example, the data transmission unit 1011 transmits the vehicle data when the vehicle's driving system is shut down. However, the vehicle data may be transmitted periodically while the vehicle is traveling. In this case, information regarding the state of the vehicle's driving system may be stored in the status field. This allows the server device 200 to identify the timing when the vehicle's driving system is shut down.

[0032] The storage unit 102 is a means for storing information, and is configured with storage media such as RAM, a magnetic disk, a flash memory, etc. The storage unit 102 stores various programs executed by the control unit 101, data used by the programs, etc.

[0033] The communication unit 103 is a communication interface that connects the in-vehicle device 100 to a bus of an in-vehicle network. It is a base.

[0034] The wireless communication unit 104 includes an antenna and a communication module for wireless communication. The antenna is an antenna element for inputting and outputting wireless signals. In this embodiment, the antenna is suitable for mobile communication (e.g., mobile communication such as 3G, LTE, or 5G). The antenna may be configured to include multiple physical antennas. For example, when performing mobile communication using radio waves in high frequency bands such as microwaves or millimeter waves, multiple antennas may be distributed and arranged to stabilize communication. The communication module is a module for performing mobile communication.

[0035] The location information acquisition unit 105 includes a GPS antenna and a positioning module for determining location information. The GPS antenna is an antenna that receives positioning signals transmitted from positioning satellites (also called GNSS satellites). The positioning module is a module that calculates location information based on the signals received by the GPS antenna.

[0036] Next, the server device 200 will be described. The server device 200 collects location information from the vehicle 10 (on-vehicle device 100) and estimates the installation ratio of the on-vehicle device based on the collected location information.

[0037] FIG. 5 is a diagram showing in detail the components of the server device 200 included in the vehicle system according to this embodiment.

[0038] The server device 200 can be configured as a general-purpose computer. That is, the server device 200 can be configured as a computer having a processor such as a CPU or GPU, a main storage device such as a RAM or ROM, and an auxiliary storage device such as an EPROM, a hard disk drive, or removable media. The auxiliary storage device stores an operating system (OS), various programs, various tables, etc., and the programs stored therein are loaded into a working area of ​​the main storage device and executed. By controlling each component through the execution of the programs, various functions consistent with a predetermined purpose can be realized, as described below. However, some or all of the functions may be realized by hardware circuits such as ASICs or FPGAs.

[0039] The server device 200 includes a control unit 201 , a storage unit 202 , and a communication unit 203 . The control unit 201 is an arithmetic unit that controls the server device 200. The control unit 201 can be realized by an arithmetic processing unit such as a CPU. The control unit 201 is configured to have, as functional modules, a data collection unit 2011, an association unit 2012, and an estimation unit 2013. Each functional module may be realized by executing a stored program by a CPU.

[0040] The data collection unit 2011 collects vehicle data from a plurality of vehicles 10 (on-vehicle devices 100) and stores the collected vehicle data as vehicle data 202B in the storage unit 102, which will be described later.

[0041] The associating unit 2012 performs processing to group the collected pieces of location information and associate the group with one of the facilities. The group generation can be performed using a known method such as clustering, for example. Additionally, the association of groups with facilities can be based on data describing the geographic location of parking spaces at each facility.

[0042] 6 is a schematic diagram illustrating the association process. First, the association unit 2012 performs the following steps: As shown in Fig. 1, location information transmitted from multiple vehicles (i.e., the location where the vehicle finished traveling) is collected, and this location information is grouped according to a predetermined rule. The grouping can be performed by, for example, a clustering process.

[0043] The associating unit 2012 then associates each of the generated groups with a facility, as shown in (B). Specifically, based on data describing the geographical location of each facility's parking lot, it determines which parking lot a group corresponds to and identifies the facility associated with that parking lot. For example, if a parking lot is associated with store A and a group is located inside that parking lot, it can determine that the group corresponds to store A.

[0044] The estimation unit 2013 estimates the installation ratio of the in-vehicle device in the parking lot based on the result of the association. The specific method will be described later.

[0045] The storage unit 202 is configured to include a main storage device and an auxiliary storage device. The main storage device is a memory in which the programs executed by the control unit 201 and the data used by the control programs are expanded. The auxiliary storage device is a device in which the programs executed by the control unit 201 and the data used by the control programs are stored.

[0046] The storage unit 202 also stores facility data 202A and vehicle data 202B. Facility data 202A is a collection of data related to multiple facilities under the management of the system. Fig. 7 shows an example of facility data 202A. As shown in the figure, the facility data includes a facility identifier (facility ID), facility location information, parking lot location information, and the parking lot's capacity (number of cars that can be parked). The parking lot location information may specify an area where vehicles can be parked, or may be single location information that represents the parking lot. The vehicle data 202B is a collection of a plurality of vehicle data transmitted from the in-vehicle device 100. The vehicle data 202B stores the plurality of vehicle data described with reference to FIG.

[0047] The communication unit 203 is a communication interface for connecting the server device 200 to a network. The communication unit 203 includes, for example, a network interface board and a wireless communication interface for wireless communication.

[0048] 3 and 5 are merely examples, and all or part of the illustrated functions may be performed using dedicated circuits. Furthermore, programs may be stored or executed using a combination of a main memory device and an auxiliary memory device other than those illustrated.

[0049] Next, a processing method by which the estimation unit 2013 estimates the installation ratio of in-vehicle devices in the parking lot of a given facility will be described. The installation ratio of on-board devices among multiple vehicles parked in a certain parking lot can be calculated by dividing the number A of vehicles that have transmitted vehicle data from within the parking lot by the total number B of vehicles parked in the parking lot. Here, the above A can be obtained by counting the number of vehicles included in the group corresponding to the target parking lot among the multiple groups generated by the association unit 2012. Furthermore, the above B can be calculated based on the facility data 202A (reference numeral 701 in FIG. 7) stored in the storage unit 202.

[0050] Here, the parking capacity defined in the facility data is the maximum number of vehicles that can be parked, so if the parking lot is not full, the loading ratio cannot be calculated correctly. Therefore, in this embodiment, the target parking lot is determined based on the vehicle data transmitted from the vehicle 10. Estimate the timing when the parking lot becomes full.

[0051] FIG. 8(A) is a diagram showing the change over time in the number of vehicles included in a group corresponding to a certain parking lot (i.e., the number of vehicles equipped with the on-board device 100). In this example, the number of vehicles peaks out at the timing indicated by reference numeral 801 and does not increase any further. Therefore, it can be assumed that the parking lot is full at this timing. The following three examples can be given as conditions for determining that the parking lot is full. (1) The increase in the number of vehicles stopped at a certain value. (2) From now on, the number of vehicles does not exceed the above value. (3) The number of vehicles has not decreased during the specified period thereafter. That is, if the number of vehicles remains at a value that is thought to be the upper limit for a certain period of time, it can be determined that the parking lot is full.

[0052] For example, if the target parking lot has a capacity of 100 vehicles, it can be estimated that approximately 100 vehicles are parked in the parking lot during the period indicated by the hatching. In this case, the number of vehicles in the group during the period indicated by the hatching is obtained. The number of vehicles in the group is the same as the number of vehicles that transmitted vehicle data from the in-vehicle device 100. For example, if the group contains 30 vehicles, it can be estimated that 30 of the 100 vehicles are equipped with the in-vehicle device 100 (i.e., the installation rate is 30%).

[0053] Next, a flowchart of the process executed by each device will be described. 9 is a sequence diagram of a process for transmitting and receiving vehicle data between the in-vehicle device 100 and the server device 200. The process shown in the figure is repeatedly executed at a predetermined interval while the vehicle 10 is traveling.

[0054] First, in step S11, the data transmission unit 1011 acquires the position information of the vehicle via the position information acquisition unit 105. Note that, if a configuration is adopted in which data other than the position information is transmitted to the server device 200, other sensor data may be acquired in this step. In step S12, the data transmission unit 1011 determines whether or not the vehicle 10 has finished traveling. The end of the vehicle 10's traveling can be determined, for example, based on data transmitted by an electronic control unit (ECU) that controls the vehicle platform. For example, if data indicating that an operation to shut off the system power supply of the vehicle 10 has been performed is detected from the in-vehicle network, this step will be determined as positive. If the determination is positive in this step, the process proceeds to step S13. If the determination is negative in this step, the process returns to step S11.

[0055] In step S13, the data transmission unit 1011 generates vehicle data and transmits it to the server device 200. As shown in Fig. 4, the vehicle data includes the location information acquired in step S11. In step S14, the server device 200 (data collection unit 2011) receives the vehicle data transmitted from the in-vehicle device 100 and stores it in the storage unit 202. As a result, the storage unit 202 of the server device 200 accumulates the position information received from a plurality of vehicles as needed.

[0056] Next, a description will be given of the processing executed by the server device 200. Fig. 10 is a flowchart of the processing executed by the server device 200. The processing shown in the figure starts when a sufficient amount of vehicle data has been stored in the storage unit 202.

[0057] First, in step S21, the association unit 2012 clusters a plurality of pieces of location information included in the collected vehicle data using predetermined parameters. Multiple clusters containing each of these will be generated. Note that if the vehicle data spans multiple days, processing will be performed for any one of the dates. This is because if data from multiple dates is mixed, it will be impossible to correctly identify the timing when the parking lot was occupied.

[0058] The processes of steps S22 to S25 are executed sequentially for each of the generated clusters. In step S22, the associating unit 2012 associates the cluster to be processed with one of the facilities. Specifically, the unit compares the representative location of the cluster with the location information of the parking lot for each facility included in the facility data 202A, and associates the facility if both are within a predetermined threshold. Note that the association may be performed by other methods. For example, if the area of ​​the parking lot is known, it may be determined whether the location information of all vehicles included in the target cluster is within that area.

[0059] Next, in step S23, the estimation unit 2013 acquires the time transition of the number of vehicles included in the target cluster over a day, thereby obtaining data such as that shown in FIG. In step S24, the estimation unit 2013 determines whether it is possible to identify the timing when the parking lot corresponding to the target cluster became full. Whether there is a timing when the parking lot became full can be determined, for example, by the above-mentioned conditions (1) to (3). For example, in the example shown in FIG. 8(A), it can be evaluated that the parking lot was full during the period indicated by hatching.

[0060] If the timing when the parking lot became full can be identified in step S24, the process proceeds to step S25. If the timing when the parking lot became full cannot be identified, the process ends and the next cluster is selected. In step S25, the estimation unit 2013 calculates the loading ratio of the in-vehicle device 100 in the target cluster, and stores the calculated ratio in association with the facility. For example, if 30 vehicles belong to the corresponding cluster at a time when the parking lot of facility A is full and the parking lot has a capacity of 100 vehicles, a loading ratio of 30% is calculated and associated with facility A.

[0061] When the processing is completed, the estimation unit 2013 outputs the calculation result in association with the facility. The output may be performed via an input / output unit (e.g., a display), a network, or a storage medium. FIG. 11 shows an example of data output by the estimation unit 2013. In the illustrated example, the estimation unit 2013 generates and outputs data (result data) that records the facility identifier, date, parking lot capacity, number of vehicles equipped with the on-vehicle device 100, and installation ratio.

[0062] In this example, the processing is performed for a single date, but the processing described above may be performed for multiple dates. In this case, the installation rate of the in-vehicle device can be calculated for each date and each facility. In this case, the transition of the installation rate for each date, its average value, etc. may be calculated.

[0063] As described above, the server device according to the first embodiment generates groups corresponding to multiple parking lots by clustering location information collected from multiple vehicles. The server device also calculates the installation rate of in-vehicle devices for each group using the capacity of each parking lot as an index. This makes it possible to calculate the installation rate of in-vehicle devices for each region.

[0064] (Second embodiment) In the first embodiment, the timing when a specific parking lot is filled up is estimated by detecting that the number of vehicles in the parking lot has reached a plateau. As described above, there are cases where it is difficult to evaluate whether the number of vehicles has peaked out. In such cases, it is difficult to determine whether the parking lot is temporarily full or not. Therefore, other data may be used to make the determination regarding fullness. In the second embodiment, the determination regarding fullness is made based on information regarding the congestion status of the parking lot (hereinafter referred to as fullness information) provided by the parking lot management device.

[0065] 12 is a diagram showing an outline of a vehicle system according to the second embodiment. As shown in the figure, the vehicle system according to the second embodiment further includes a management device 300. The management device 300 is a server device that manages parking lots. The management device 300 may manage multiple parking lots. The management device 300 can provide vacancy information at any time in response to a request. The vacancy information is information that indicates whether a parking lot is full or empty. Figure 13 is an example of vacancy information (vacancy data) generated and provided by the management device 300. As shown in the figure, the vacancy data includes a facility identifier (facility ID), date and time information, status ("vacancy available" or "full"), etc.

[0066] FIG. 14 is a schematic diagram of the server device 200 according to the second embodiment. The server device 200 according to the second embodiment further includes a parking lot data acquisition unit 2014. The parking lot data acquisition unit 2014 acquires the above-mentioned vacancy / occupancy data by communicating with a parking lot management device and stores the data in the memory unit 202 (vacancy / occupancy data 202C). The vacancy / occupancy data may be acquired periodically at a predetermined timing, for example, or when necessary. The estimation unit 2013 can determine the status of any parking lot at any timing by referring to the vacancy / occupancy data 202C stored in the memory unit 202.

[0067] In the second embodiment, the estimation unit 2013 determines whether the parking lot is full by referring to the vacancy / non-vacancy data 202C in step S24. This makes it possible to accurately determine when the parking lot is full.

[0068] In this example, the occupancy data indicates whether a specific parking lot is full or not. However, the occupancy data may also indicate the number of vehicles currently parked in the specific parking lot. In this case, it becomes possible to calculate the installation ratio of in-vehicle devices without waiting for the parking lot to become full. For example, if the number of parked vehicles at a certain time is known, the installation ratio of in-vehicle devices can be calculated by obtaining the number of vehicles included in a group at that time. For example, if 40 vehicles are parked in a specific time period in a certain parking lot and 20 vehicles are included in the group corresponding to that time period, the installation ratio of in-vehicle devices can be estimated to be 50%.

[0069] (Third embodiment) The third embodiment is an embodiment in which social media is used to determine when a parking lot will be full. Social media is a two-way communication medium in which users exchange video, audio, text information, and the like.

[0070] 15 is a diagram showing an overview of a vehicle system according to the third embodiment. As shown in the diagram, the vehicle system according to the third embodiment further includes an SNS server 400. The SNS server 400 is a server device that provides a social networking service. The social networking service in this embodiment can accept posts of information about specific facilities and provide the information to other users. The SNS server 400 accepts and publishes posts such as ratings and messages about specific facilities. Hereinafter, data posted by multiple users will be referred to as SNS data.

[0071] 16 is a schematic diagram of the server device 200 according to the third embodiment. The server device 200 according to the third embodiment further includes an SNS data acquisition unit 2015. The SNS data acquisition unit 2015 acquires information about the target facility from the SNS server 400 and analyzes the information. For example, the SNS data acquisition unit 2015 detects keywords such as "full," "crowded," and "waiting" from the posted message and estimates the timing when the parking lot will be filled.

[0072] For example, if a message is posted about a facility such as "The parking lot was full as soon as we opened at 10:00," it can be determined that the parking lot was full shortly after 10:00. Also, if a message posted around 11:00 mentions the parking lot being full, it can be inferred that the parking lot was full around 11:00. The estimation result is sent to the estimation unit 2013, which then estimates the timing when the parking lot will be filled based on the result.

[0073] For example, in the case of Figure 8(B), it is not possible to determine whether the parking lot was full during the hatched time period. However, if a message such as "The parking lot was full" was posted during the relevant time period, it can be estimated with a high degree of certainty that the parking lot was full.

[0074] In this way, the third embodiment uses messages (word-of-mouth) posted by users who have used the facility to estimate the timing when the parking lot becomes full, thereby improving the accuracy of estimating the installation rate of in-vehicle devices.

[0075] In this embodiment, SNS data is acquired as needed in step S24 and its contents are analyzed, but the analysis of SNS data may be performed in the background. For example, the SNS data acquisition unit 2015 may periodically collect posts about multiple facilities under its management, estimate when a parking lot is full based on the date and content, and reflect the result in the occupancy data 202C. In this case, it is not necessary to acquire SNS data every time in step S24.

[0076] Furthermore, in the case of a facility that does not have data on the parking capacity (reference numeral 701 in FIG. 7) as exemplified in the first embodiment, the parking capacity may be estimated based on information on social media. For example, the parking capacity may be estimated based on a post such as "There are 12 parking spaces, so you can park your car here."

[0077] (Variation) The above-described embodiment is merely an example, and the present disclosure can be modified and implemented as appropriate within the scope that does not deviate from the gist of the disclosure. For example, the processes and means described in this disclosure can be freely combined and implemented as long as no technical contradiction occurs.

[0078] Further, the installation rate of on-board devices calculated for each facility may be used to provide further services. For example, if the installation rate of on-board devices at a certain facility is 20%, the actual number of parked vehicles can be estimated by multiplying the number of vehicles that transmitted vehicle data by five. The server device 200 may provide the estimated number of parked vehicles calculated in this way for each facility in real time.

[0079] Furthermore, a process that has been described as being performed by one device may be shared and executed by multiple devices. Alternatively, a process that has been described as being performed by different devices may be executed by one device. In a computer system, each function may be performed by any hardware. The hardware configuration (server configuration) used to achieve this can be flexibly changed.

[0080] The present disclosure can also be realized by providing a computer program implementing the functions described in the above embodiments to a computer, and having one or more processors in the computer read and execute the program. Such a computer program may be provided to the computer via a non-transitory computer-readable storage medium connectable to the computer's system bus or via a network. Non-transitory computer-readable storage media include, for example, any type of disk, such as a magnetic disk (e.g., a floppy disk, a hard disk drive (HDD), etc.), an optical disk (e.g., a CD-ROM, a DVD disk, a Blu-ray disk), a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic card, a flash memory, an optical card, or any type of medium suitable for storing electronic instructions. [Explanation of symbols]

[0081] 10. Vehicle 100...In-vehicle equipment 200 Server device 300...Management device 400...SNS server 101,201 Control unit 102,202...Storage section 103,203···Communications Department 104 Wireless communication unit 105...Location information acquisition unit

Claims

1. grouping a plurality of pieces of location information acquired from a plurality of in-vehicle devices; estimating the installation ratio of the on-vehicle device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; a control unit that executes the following: the control unit acquires a time-varying change in the number of in-vehicle devices included in the group; the control unit estimates a first time when a parking lot of the facility corresponding to the group becomes full; the control unit determines a time when the number of in-vehicle devices included in the group reaches a ceiling as the first time point; The control unit sets the installation ratio to the ratio of the number of in-vehicle devices included in the group at the first time point to the number of vehicles that can be accommodated in the parking lot.

2. the control unit associates the group with the facility based on location data of parking lots owned by each of the plurality of facilities; The information processing device according to claim 1 .

3. The facility data includes the number of parking spaces that each of the plurality of facilities has.

3. The information processing device according to claim 1.

4. The control unit outputs the estimated loading ratio in association with each of the plurality of facilities. The information processing device according to claim 1 .

5. The control unit calculates the total number of vehicles in the parking lot based on the estimated loading ratio. The information processing device according to claim 1 .

6. grouping a plurality of pieces of location information acquired from a plurality of in-vehicle devices; Facility data, which is data on the parking capacity of each of multiple facilities and estimating an installation ratio of the in-vehicle device in a parking lot of the facility corresponding to the group based on the number of vehicles included in the group. a control unit that executes the control unit acquires a time-varying change in the number of in-vehicle devices included in the group; the control unit estimates a first time when a parking lot of the facility corresponding to the group becomes full; the control unit estimates the first time based on parking lot occupancy information of a facility corresponding to the group; The control unit sets the installation ratio to the ratio of the number of in-vehicle devices included in the group at the first time point to the number of vehicles that can be accommodated in the parking lot.

7. grouping a plurality of pieces of location information acquired from a plurality of in-vehicle devices; estimating the installation ratio of the on-vehicle device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; a control unit that executes the following: the control unit acquires a time-varying change in the number of in-vehicle devices included in the group; the control unit estimates a first time when a parking lot of the facility corresponding to the group becomes full; The control unit estimates the first time based on post data to a social media site that mentions a facility corresponding to the group; and The control unit sets the installation ratio to the ratio of the number of in-vehicle devices included in the group at the first time point to the number of vehicles that can be accommodated in the parking lot.

8. grouping a plurality of pieces of location information acquired from a plurality of in-vehicle devices; estimating the installation ratio of the on-vehicle device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; a control unit that executes the following: the control unit acquires a time-varying change in the number of in-vehicle devices included in the group; the control unit estimates a first time when a parking lot of the facility corresponding to the group becomes full; The control unit sets the installation ratio to the ratio of the number of in-vehicle devices included in the group at the first time point to the number of vehicles that can be accommodated in the parking lot.

9. grouping a plurality of pieces of position information acquired from a plurality of in-vehicle devices; a step of estimating the installation ratio of the on-board device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; Including, Obtaining a time-varying change in the number of in-vehicle devices included in the group; Estimating a first time when a parking lot of a facility corresponding to the group becomes full; a timing when the number of in-vehicle devices included in the group reaches a ceiling is defined as the first time; The information processing method defines the installation ratio as a ratio between the parking lot's capacity and the number of in-vehicle devices included in the group at the first time.

10. Correlating the group with the facility based on location data of parking lots owned by each of the plurality of facilities; The information processing method according to claim 9.

11. The facility data includes the number of parking spaces that each of the plurality of facilities has.

11. The information processing method according to claim 9 or 10.

12. grouping a plurality of pieces of position information acquired from a plurality of in-vehicle devices; a step of estimating the installation ratio of the on-board device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; Including, Obtaining a time-varying change in the number of in-vehicle devices included in the group; Estimating a first time when a parking lot of a facility corresponding to the group becomes full; estimating the first time based on social media posting data that mentions a facility corresponding to the group; The information processing method defines the installation ratio as a ratio between the parking lot's capacity and the number of in-vehicle devices included in the group at the first time.

13. grouping a plurality of pieces of position information acquired from a plurality of in-vehicle devices; a step of estimating the installation ratio of the on-board device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; Including, Obtaining a time-varying change in the number of in-vehicle devices included in the group; Estimating a first time when a parking lot of a facility corresponding to the group becomes full; estimating the first time based on parking lot occupancy information of a facility corresponding to the group; The information processing method defines the installation ratio as a ratio between the parking lot's capacity and the number of in-vehicle devices included in the group at the first time.

14. grouping a plurality of pieces of position information acquired from a plurality of in-vehicle devices; a step of estimating the installation ratio of the on-board device in the parking lots of the facilities corresponding to the group based on facility data, which is data on the capacity of the parking lots of each of the plurality of facilities, and the number of vehicles included in the generated group; Including, Obtaining a time-varying change in the number of in-vehicle devices included in the group; Estimating a first time when a parking lot of a facility corresponding to the group becomes full; The information processing method defines the installation ratio as a ratio between the parking lot's capacity and the number of in-vehicle devices included in the group at the first time.

15. A program for causing a computer to execute the information processing method according to any one of claims 9 to 14.

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