JP2020160736A - Vehicle parking/stopping hours prediction device, vehicle parking/stopping hours prediction method, and program - Google Patents

Abstract

To enable predicting parking/stopping hours of a vehicle even if there is no uniformity in utilizing the vehicle by users.SOLUTION: A vehicle parking/stopping hours prediction device (1A) predicts hours for which a vehicle is parked/stopped, and comprises: an identifying section (11) for, concerning a vehicle being parked/stopped, identifying a user utilizing the vehicle next; a status acquisition section (12) for, from an external device used by the user, acquiring status data identifying present status of the user; and a prediction section (13) for, using the status data, predicting parking/stopping hours of the vehicle from the present moment to a timing when the user starts to drive next.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to a parking / stopping time prediction device for predicting the parking / stopping time of a vehicle, a parking / stopping time prediction method, and a program.

Patent Document 1 discloses a control device that appropriately manages the timing of processing related to the update of a control program in an ECU (Electronic Control Unit). This control device predicts the parking / stopping time of the vehicle and controls the processing related to the update of the control program. Specifically, the predicted time is obtained by using the information representing the state of the vehicle to be acquired as the information for prediction and the running model obtained based on the accumulation of the running state of the vehicle as the predicted condition stored in advance.

Japanese Patent No. 6358286

The present disclosure provides a parking / stopping time prediction device, a parking / stopping time prediction method, and a program for predicting the parking / stopping time of a vehicle even when the behavior of the user is not uniform.

The parking / stopping time prediction device of the present disclosure is a parking / stopping time prediction device that predicts the time when a vehicle is parked / stopped, and is a specific unit that identifies a user who will use the vehicle next for the parked vehicle. , The parking / stopping time from the present until the next timing when the user starts using the state acquisition unit that acquires the state data that identifies the current state of the user from the external device used by the user and the state data. It is provided with a prediction unit for predicting.

These general and specific aspects may be realized by systems, methods, and computer programs, and combinations thereof.

According to the parking / stopping time prediction device, the parking / stopping time prediction method, and the program of the present disclosure, the parking / stopping time of the vehicle can be predicted based on the information received from the vehicle and the terminal used by the user.

FIG. 5 is a block diagram illustrating a configuration of a parking / stopping time prediction system including a parking / stopping time prediction device according to the first embodiment. This is an example of reservation data used by the parking / stopping time prediction device of FIG. This is an example of user data used by the parking / stopping time prediction device of FIG. It is a schematic diagram explaining the positional relationship between a vehicle and each user. It is a flowchart explaining the process in the parking stop time prediction apparatus of FIG. It is a block diagram explaining the structure of the parking / stopping time prediction system including the parking / stopping time prediction device which concerns on Embodiment 2. FIG. It is a flowchart explaining the process in the parking stop time prediction apparatus of FIG. It is a flowchart explaining the process in the parking stop time prediction apparatus which concerns on Embodiment 3. It is a block diagram explaining the structure of the parking / stopping time prediction system including the parking / stopping time prediction device which concerns on Embodiment 4. FIG. It is a flowchart explaining the process in the parking stop time prediction apparatus of FIG. It is a block diagram explaining the structure of the parking / stopping reservation system including the parking / stopping time prediction device which concerns on another embodiment.

[Knowledge on which this disclosure is based]
In recent years, various computer controls have been used in automobiles. A plurality of programs are stored in an in-vehicle device such as an ECU, and such a program may need to be updated. In such a case, in the past, it was brought to a dealer or the like to update it as needed, but in recent years, by connecting the in-vehicle device to the Internet, it has become possible for the user to update it more frequently. .. However, during the program update, the program control by the in-vehicle device is stopped, so that the automobile cannot be used. Therefore, avoid executing the program update while driving, and update the program when the car is parked or stopped. In such a case, it is necessary to predict the parking / stopping time of the car.

If there is only one car user, it is easy to identify the behavior pattern, so it is easy to predict the parking / stopping time. However, in the case of share cars, which have become widespread in recent years, there are many users, and the purpose and method of use of each user is different, so even if the same user is owned by an individual, it is compared. Therefore, it is difficult to predict the parking / stopping time because the total number of times of use may be small or the purpose of use of each person may not be the same each time.

The present disclosure provides a parking / stopping time prediction device for predicting the parking / stopping time of an automobile, a parking / stopping time prediction method, and a program. Specifically, a parking / stopping time prediction device that can predict the parking / stopping time of a vehicle even when there is no uniformity in the use of the vehicle, such as when there are multiple users of the vehicle. A parking / stopping time prediction method and a program are provided.

[Embodiment]
Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. However, in the detailed description, unnecessary parts of the description of the prior art and substantially the same configuration may be omitted. This is for the sake of simplicity. In addition, the following description and accompanying drawings are disclosed so that those skilled in the art can fully understand the present disclosure, and are not intended to limit the subject matter of the claims.

The parking / stopping time prediction device, the parking / stopping time prediction method, and the program according to the present disclosure predict the parking / stopping time of the vehicle.
In the present disclosure, "parking and stopping" means "parking" and "stopping" of a vehicle, and means that the vehicle is simply stopped in a parking space without being driven or the like. Therefore, the "parking / stopping time" means the time when the vehicle is stopped in a parking space or the like without being driven.
Further, in the present disclosure, the "use" of the vehicle includes "driving", and it is described as using the vehicle for movement even when the user himself / herself does not drive. Therefore, the "user" includes the "driver" of the vehicle, and is described as a person who uses the vehicle for movement even if the driver is not the driver of the vehicle. Further, "driving" includes "driving" and refers to a case where the vehicle on which the user rides is moving.
Further, in the present disclosure, the "state" of the user shall be the "position", "behavior", "activity", etc. of the user. For example, the state data representing the user's state is the user's position information received by the GPS receiver, or the scheduled data of the user's action specified from the user's schedule.

In the present disclosure, a "share car" is a car used in so-called car sharing in which a car is shared among registered members. Therefore, the person who uses this share car is mainly a member, but a large number of members registered in advance can be a user. Therefore, since there are a plurality of users, it is difficult to predict the parking / stopping time only from the past traveling information.

In the present disclosure, an "owner car" is a car owned by an individual. The person who uses this owner car is mainly the owner, but the owner may also use it. Therefore, when there are a plurality of users, it is difficult to predict the parking / stopping time of the automobile only from the past driving information without considering the user information and the like.

<Embodiment 1>
《Parking / stopping time prediction system》
The parking / stopping time prediction system according to the embodiment of the present disclosure acquires data acquired from an in-vehicle device installed in a vehicle that is a target of prediction of parking / stopping time, or from a user terminal used by a user of the vehicle. Use the data to predict the parking and stopping time of the vehicle. Specifically, the parking / stopping time is predicted from the position of the in-vehicle device and the position of the user. For example, it is possible to determine whether or not to execute the update of the application program used in the in-vehicle device according to the time predicted by the parking / stopping time prediction device.

Here, it is assumed that the vehicle whose parking / stopping time is predicted may be used by a plurality of users registered in advance. For example, this vehicle is a "share car" as described above. Alternatively, even if it is an "owner's car", it shall include a car that may be used by a plurality of people belonging to a group such as a family registered in advance as needed.

As shown in FIG. 1, the parking / stopping time prediction system 100A uses a parking / stopping time prediction device 1A, an in-vehicle device 2 installed in a vehicle whose parking / stopping time is predicted, and a vehicle whose parking / stopping time is predicted. The user terminal 3 owned by the user is connected to the user terminal 3 via the network 5. Further, as shown in FIG. 1, when the vehicle is a shared car, the reservation management device 4 that manages the reservation data related to the reservation of the shared car is connected via the network 5. In the example shown in FIG. 1, the number of the in-vehicle device 2, the user terminal 3, and the reservation management device 4 connected to the parking / stopping time prediction device 1A is not limited. For example, the parking / stopping time prediction device 1A can be connected to a plurality of in-vehicle devices 2. Further, the parking / stopping time prediction device 1A can be connected to each user terminal 3 owned by a plurality of users who use each of the in-vehicle devices 2.

<< In-vehicle device >>
The in-vehicle device 2 is installed in a vehicle whose parking / stopping time is predicted. For example, the in-vehicle device 2 has a communication interface (I / F) 21 that realizes transmission / reception of data with another device via the network 5, and a GPS receiver 22 that acquires the position information of the own vehicle from the GPS system. The authentication unit 23 used to authenticate whether the user using the vehicle is an appropriate user, the program used to control the in-vehicle device 2, various data required by the in-vehicle device 2, and the like are stored. A storage unit 24 for executing a program and a control unit 25 for executing a program stored in the storage unit 24 are provided.

If the vehicle is an owner car, the authentication unit 23 is not essential, but in the case of a shared car, it is common that a user authentication process is required. Therefore, when the vehicle is a share car, it is considered that the in-vehicle device 2 has the authentication unit 23. The authentication unit 23 is, for example, an acquisition means for reading the user information stored in the IC chip when the IC card in which the IC chip is embedded is held over, and whether or not the acquired user information is user information registered in advance. The vehicle can be used only when the user is a user registered in advance, including a determination means for determining whether or not the vehicle is used. Further, for example, the vehicle key in which the user information is stored or the information output from the smartphone that transmits the user information may be acquired.

In response to the request from the parking / stopping time prediction device 1A, the in-vehicle device 2 transmits the position information of the own vehicle received by the GPS receiver 22 to the parking / stopping time prediction device 1A via the communication I / F 21.

The storage unit 24 is a storage medium such as a RAM or ROM that stores programs and various data used to execute controls required by the vehicle. Further, the control unit 25 is a controller such as a CPU that reads and executes a program stored in the storage unit 24. Note that FIG. 1 shows one storage unit 24 and one control unit 25 for convenience, but is not limited thereto. Specifically, in order to execute various controls in the vehicle, the in-vehicle device 2 includes a plurality of computers each having a pair of storage units 24 and a control unit 25, and each computer has a different program stored in the storage unit 24. Is conceivable to be executed by the corresponding control unit 25. Specifically, a part of the ECU mounted on the vehicle is a storage unit 24 and a control unit 25 shown in FIG. For example, each ECU independently executes control of the accelerator, brake, steering wheel operation, vehicle interior lighting, headlight on / off, audio system, car navigation system, and the like.

《User terminal》
The user terminal 3 is, for example, an information processing device used by the user. For example, the user terminal 3 includes a communication I / F 31 that realizes transmission / reception of data with another device via a network, a GPS receiver 32 that acquires the position information of the own terminal from a GPS system, and a user terminal 3. It is provided with a storage unit 33 which is a storage medium for storing a program used for controlling the user terminal 3 and various data required by the user terminal 3, and a control unit 34 which is a controller for executing a program stored in the storage unit 33. ..

In response to a request from the parking / stopping time prediction device 1A, the user terminal 3 transmits the position information of the own terminal received by the GPS receiver 32 to the parking / stopping time prediction device 1A via the communication I / F 31.

The user terminal 3 may be, for example, an information processing device carried by the user, such as a smartphone, or a wearable terminal. Therefore, the user does not have to have the user terminal 3 exclusively for the parking / stopping time prediction system 100A, and the program corresponding to the parking / stopping time prediction system 100A is stored in the storage unit 33 of the information processing device that the user has conventionally used. The user terminal 3 can be realized by storing the above. In the parking / stopping time prediction system 100A, it is preferable that the user holds the user terminal 3 when moving. This is because by holding and moving the user terminal 3, accurate position information of the user can be provided to the parking / stopping time prediction device 1A, and accurate parking / stopping time can be predicted.

<< Reservation management device >>
The reservation management device 4 is used when the vehicle is a shared car, and is an information processing device such as a server device that stores reservation data related to reservations for using the vehicle. For example, the reservation management device 4 is stored in the communication I / F 41, a storage unit 42 which is a storage medium for storing various data such as a program for executing the reservation management device and reservation data, and a storage unit 42. It includes a control unit 43, which is a controller that executes control for reservation management by executing a program.

In the reservation management device 4, for example, the communication I / F 41 receives a vehicle usage reservation request from the user terminal 3. As a result, the control unit 43 refers to the reservation data stored in the storage unit 42, determines whether or not the reservation is possible, and adds the usage reservation to the reservation data if the reservation is possible. Further, the control unit 43 notifies the user terminal 3 of the result of whether or not the reservation is possible via the communication I / F 41.

As shown by an example in FIG. 2, the reservation data D4 is data that associates the identification information of the user who is the reservation person with the reservation date and time when the use is reserved for the target vehicle. In the example of the reservation data D4 shown in FIG. 2, "User 1" makes a reservation from "7:00 to 19:00 on March 2", and "User 2" makes a reservation from "7:00 to 12:00 on March 3". , It can be seen that "User 3" made a reservation from "13:00 to 23:00 on March 3".

"network"
The network 5 does not limit the format of the protocol as long as the parking / stopping time prediction system 100A can transmit and receive data, but since the in-vehicle device 2 and the user terminal 3 are mobile objects, wireless communication is performed at least in part. It is preferable to use.

《Parking / stopping time prediction device》
As shown in FIG. 1, the parking / stopping time prediction device 1A is an information processing device such as a server device including a control unit 10, a storage unit 20, a communication interface (I / F) 30, and the like. The parking / stopping time prediction device 1A predicts the parking / stopping time of the vehicle by using the data acquired from the in-vehicle device 2, the user terminal 3, and the reservation management device 4.

The control unit 10 is a controller that controls the entire parking / stopping time prediction device 1A. For example, the control unit 10 realizes processing as the specific unit 11, the state acquisition unit 12, the prediction unit 13, and the determination unit 14 by reading and executing the parking / stopping time prediction program P stored in the storage unit 20. .. Further, the control unit 10 is not limited to one that realizes a predetermined function by the cooperation of hardware and software, and may be a hardware circuit specially designed to realize a predetermined function. That is, the control unit 10 can be realized by various processors such as a CPU, MPU, GPU, FPGA, DSP, and ASIC.

The storage unit 20 is a recording medium for recording various information. The storage unit 20 is realized by, for example, a RAM, a ROM, a flash memory, an SSD (Solid State Device), a hard disk, another storage device, or a combination thereof as appropriate. In addition to the parking / stopping time prediction program P executed by the control unit 10, various information acquired for parking / stopping time prediction is stored in the storage unit 20. Further, the storage unit 20 stores the user data D11 and the access data D12.

The parking / stopping time prediction device 1A may be realized by a plurality of information processing devices. Further, a part of the data stored in the storage unit 24 may be stored in an external storage device and read from the external storage device for use.

The user data D11 is data in which a user who may use the vehicle is registered. The user data D11 is, for example, as shown in FIG. 3, data in which vehicle identification information is associated with identification information of a user who may use the vehicle. In the example shown in FIG. 3, "Vehicle 1" may be used by "User 11" and "User 12", and "Vehicle 2" is "User 21", "User 22" and "User 23". It can be seen that the "vehicle 3" may be used by the "user 31", the "user 32", the "user 33" and the "user 34". For example, when the vehicle is a shared car, a plurality of people who are users of the shared car are registered in advance as user data D11. When the vehicle is an owner car used by a family of a plurality of people, it is conceivable to register a plurality of people who use the vehicle in advance as user data D11.

The access data D12 is data in which the access destination of the in-vehicle device 2 installed in the vehicle and the access destination of the user terminal 3 of the user who may use the vehicle are registered. The access data D12 includes, for example, data in which vehicle identification information is associated with IP address data which is an access destination of the in-vehicle device 2 installed in the vehicle, and identification information of a user who may use the vehicle. Includes data associated with the IP address or the like that is the access destination of the user terminal 3 owned by the user.

The communication I / F 30 is an interface circuit (module) for enabling data communication with the in-vehicle device 2, the user terminal 3, and the reservation management device 4. The parking / stopping time prediction device 1A acquires various data from the in-vehicle device 2, the user terminal 3, and the reservation management device 4 via the communication I / F30.

The identification unit 11 identifies a user who will use the vehicle next for the parked and stopped vehicle. Here, the timing at which the specifying unit 11 identifies the user who will use the vehicle next is, for example, the timing when the program used in the in-vehicle device 2 needs to be updated. The timing at which the program needs to be updated can be specified, for example, by periodically checking from the in-vehicle device 2 to the server that provides the program whether or not the program needs to be updated. Alternatively, the timing may be specified by a method of notifying the in-vehicle device 2 of the occurrence of the need to update the program from the server that provides the program. Further, at this timing, the vehicle is not driven and is parked and stopped. Therefore, for example, the specific unit 11 starts access to the target vehicle at the timing when the program needs to be updated, and when the vehicle is in use, the vehicle does not proceed to the user's specific process. The process may be interrupted until the vehicle is parked or stopped. The specific unit 11 can request the data necessary for identifying the user from the state acquisition unit 12 and acquire the data from the state acquisition unit 12.

For example, the identification unit 11 is a user who uses the vehicle from the reservation data D4 which is stored in the reservation management device 4 and which is associated with the identification information of the user who has reserved the use of the vehicle and the time information when the user uses the vehicle. To identify. The reservation data D4 is data that associates the identification information of the user who is the reservation person with the reservation time, for example, as described above with reference to FIG. The specific unit 11 extracts the person who made the reservation at the current time from the reservation data D4. Further, when the reservation person is not registered at the current time, the specific unit 11 extracts the latest future reservation person. Specifically, if it is currently "10 o'clock on March 2nd", "User 2" is specified as the next user to use the vehicle. Also, if it is currently "12:30 on March 2nd", "User 3" is specified as the next user to use the vehicle.

Further, for example, the identification unit 11 may identify the user by using the authentication information acquired from the authentication device provided in the vehicle. For example, when the in-vehicle device 2 is provided with the authentication unit 23 which is a user authentication device, the user who uses the vehicle is specified from the user information acquired by the authentication unit 23. Specifically, in the case of a shared car that is charged according to the usage time and that the vehicle authentication unit 23 executes the vehicle rental processing and return processing, the period until the rental processing is performed and the return processing is performed. , The user in use can be identified as the user who will use the vehicle next.

Further, for example, the specific unit 11 acquires the user data D11 from the storage unit 20, and the position information is used as the state data of each user from an external device used by each user registered in the user data D11 in the state acquisition unit 12. Request acquisition. Then, the specific unit 11 obtains the distance between the vehicle and the user from the acquired position information of each user and the position information of the vehicle acquired from the vehicle, and from the result, selects the user who has the closest distance to the vehicle. Identify as the next user to use.

An example of specifying a user who will use the vehicle 2 next in the case of the user data D11 shown in FIG. 3 will be described. The specific unit 11 extracts "user 21", "user 22", and "user 23" from the user data D11 as users who may use the vehicle 2. Further, the identification unit 11 extracts the access destination of the in-vehicle device 2 installed in the vehicle 2 and the access destination of the terminal owned by each user 21 to 23 from the access data D12. After that, the specific unit 11 accesses the in-vehicle device 2 of the vehicle 2 to acquire the position information of the vehicle 2, requests the state acquisition unit 12, and requests the position information of the user terminal 3 which is the state data of each user 21 to 23. To get.

FIG. 4 shows an example of the positional relationship of each position information acquired by the specific unit 11. Further, the specific unit 11 obtains the distances L1 to L3 between the vehicle and each user 21 to 23. In FIG. 4, it is assumed that the distance L1 is the shortest. After that, the identification unit 11 identifies the user (user 21 in the example of FIG. 4) having the closest distance between the position of the vehicle and the position of the user as the user who will use the vehicle next. In the example shown in FIG. 4, the straight line distance is simply used, but the moving distance may be a moving distance in consideration of the moving path instead of the simple straight line distance. In other words, the distance may be calculated in consideration of the distance, not the distance in a straight line.

The state acquisition unit 12 acquires state data that identifies the current state of the user from the user terminal 3 used by the user. For example, the state acquisition unit 12 acquires the current position information as the user's state data. Specifically, the state acquisition unit 12 acquires the position information of the user terminal 3 held by the user, and uses this as the current position information of the user. Further, the state acquisition unit 12 provides the acquired state data to the specific unit 11 as needed.

The prediction unit 13 uses the state data to predict the parking / stopping time, which is the time from the present to the timing when the user starts to use the next time. Specifically, the prediction unit 13 takes time for the user to move to the vehicle from the current position information of the user acquired as state data and the current position information of the vehicle acquired from the vehicle (hereinafter, if necessary). (Let's call it "travel time"), and predict the parking and stopping time. For example, the prediction unit 13 acquires the current position information of the vehicle. Further, the prediction unit 13 specifies a movement route from the current position of the user and the position information of the vehicle to the movement of the user from the current position to the vehicle, and moves the time when the user moves on the movement route by walking. Time. Further, the prediction unit 13 sets the obtained travel time as the parking / stopping time.

The travel time specified by the prediction unit 13 is not necessarily based on walking, but is, for example, the shortest time when the travel route exceeds a predetermined distance or when another vehicle such as a train, a bus, or a taxi is used. May be good.

The determination unit 14 determines whether or not the program used for executing the in-vehicle device 2 provided in the vehicle can be updated according to the time predicted by the prediction unit 13. Specifically, the time predicted by the prediction unit 13 and the scheduled time required for updating the program (hereinafter, referred to as “update time” as necessary) are compared, and the prediction unit 13 compares the time. Whether or not the program can be updated is determined according to whether or not the program update is completed within the time until the predicted vehicle is used next time. For example, the parking / stopping time prediction device 1A acquires this "update time" from an external device (not shown) in advance and stores it in the storage unit 20.

If the update time is shorter than the parking / stopping time, the determination unit 14 determines that the program can be updated because the update of the program is completed by the time the user starts using the vehicle. On the other hand, if the update time is longer than the parking / stopping time, it is determined that the program update is not possible because the program update is not completed by the time the user starts using the vehicle. The parking / stopping time prediction device 1A outputs the determination result of the determination unit 14 to, for example, an external device (not shown) that updates the program of the in-vehicle device such as the ECU.

In this way, the parking / stopping time prediction device 1A can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, the parking / stopping time prediction device 1A can determine whether or not the program can be updated from the predicted parking / stopping time.

《How to predict parking / stopping time》
Next, a parking / stopping time prediction method executed by the parking / stopping time prediction device 1A will be described with reference to the flowchart shown in FIG.
The parking / stopping time prediction device 1A determines whether or not the vehicle is in use at a predetermined timing (S1).

When the vehicle is not in use (NO in S1), the parking / stopping time prediction device 1A identifies the user who will use the vehicle next (S2).
Subsequently, the parking / stopping time prediction device 1A acquires the position information of the user specified in step S2 (S3).
Further, the parking / stopping time prediction device 1A acquires the position information of the vehicle (S4).

The parking / stopping time prediction device 1A calculates the distance between the user and the vehicle from the user's position information acquired in step S3 and the vehicle position information acquired in step S4 (S5).
The parking / stopping time prediction device 1A predicts the parking / stopping time using the distance calculated in step S5 (S6).

The parking / stopping time prediction device 1A compares the parking / stopping time obtained in step S6 with the update time (S7).
When the parking / stopping time is longer than the update time (YES in S7), the parking / stopping time prediction device 1A determines that the program can be updated (S8).
On the other hand, when the parking / stopping time is shorter than the update time (NO in S7), the parking / stopping time prediction device 1A determines that the program cannot be updated (S9).
The parking / stopping time prediction device 1A outputs the determination results of steps S8 and S9 to, for example, an external device (not shown) that updates the program of the in-vehicle device such as the ECU.

In this way, the parking / stopping time prediction device 1A can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, the parking / stopping time prediction device 1A can determine whether or not the program can be updated from the predicted parking / stopping time.

<Embodiment 2>
《Parking / stopping time prediction system》
The parking / stopping time prediction system 100B according to the second embodiment shown in FIG. 6 includes a map management device 6 as compared with the parking / stopping time prediction system 100A described above using FIG. 1, and is provided with a parking / stopping time prediction device 1B. The map information acquisition unit 15 is different in that the map information acquisition unit 15 acquires map information from the map management device 6. The parking / stopping time prediction device 1B predicts the parking / stopping time by using the map information acquired from the map management device 6. This map information includes information about the location where the user exists. The same configuration as the parking / stopping time prediction system 100A described above in the parking / stopping time prediction system 100B will not be described.

《Map management device》
The map management device 6 is an information processing device such as a server that stores map data. For example, the map management device 6 is stored in the communication I / F 61, a storage unit 62 which is a storage medium for storing various data such as a program for executing management of map information and map data, and a storage unit 62. The control unit 63 is a controller that executes control for providing map information in response to a request by executing a program. The map data stored in the storage unit 62 of the map management device 6 includes, for example, information on facilities existing at each position specified by the latitude and longitude included in the map. The map data also includes information about the user's staying time at each facility. Specifically, it may include information on the staying time for each time and each day of the week and information on the current staying time in consideration of the congestion situation.

In the map management device 6, for example, the communication I / F 41 receives a request for map information from the parking / stopping time prediction device 1B. The request includes, for example, location information identified by latitude and longitude. As a result, the control unit 63 refers to the map data stored in the storage unit 62 and acquires the facility information of the corresponding position. This facility information can include information used to identify the staying time of the user. Further, the control unit 63 transmits the acquired facility information as map information to the parking / stopping time prediction device 1B via the communication I / F61.

《Parking / stopping time prediction device》
When the state acquisition unit 12 acquires the current position information of the user, the map information acquisition unit 15 transmits a request for transmitting the map information of the position specified by the position information to the map management device 6 to manage the map. The map information is acquired from the device 6.

The prediction unit 13 specifies the staying time of the user from the map information acquired by the map information acquisition unit 15, and predicts the parking / stopping time according to the staying time. For example, when the map information includes the average staying time of the user at the facility where the user is currently staying, the prediction unit 13 can use the average staying time as the parking / stopping time. Further, for example, the prediction unit 13 may set the parking / stopping time as the time obtained by adding the moving time from the user's current position to the vehicle to the average staying time at the facility. Further, when the map information acquisition unit 15 acquires only the type of facility as map information, for example, the parking / stopping time prediction device 1B stores the type of facility in the storage unit 20 in advance and the staying time at that facility. You may predict the staying time by using the associated data. Alternatively, when data on the user's past behavior history can be used, the prediction unit 13 makes a prediction using the user's past staying time at the same facility and the average time thereof, which is linked to the map information.

In this way, the parking / stopping time prediction device 1B can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, since the parking / stopping time prediction device 1B also considers the staying time, it is possible to predict the parking / stopping time more accurately than obtaining only from the position information. Further, the parking / stopping time prediction device 1B can determine whether or not the program can be updated from the predicted parking / stopping time.

《How to predict parking / stopping time》
Next, a parking / stopping time prediction method executed by the parking / stopping time prediction device 1A will be described with reference to the flowchart shown in FIG. Further, the above-described processing in the flowchart of FIG. 5 is assigned the same number and the description thereof will be omitted.

The parking / stopping time prediction device 1A acquires map information according to the position information acquired in step S3 (S11).
Further, the parking / stopping time prediction device 1A predicts the staying time of the user according to the map information acquired in step S11 (S12).

After that, the parking / stopping time prediction device 1A predicts the parking / stopping time using the distance calculated in step S5 and the staying time predicted in step S12 (S6).

In this way, the parking / stopping time prediction device 1A can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, since the parking / stopping time prediction device 1B also considers the staying time, it is possible to predict the parking / stopping time more accurately than obtaining only from the position information. Further, the parking / stopping time prediction device 1B can determine whether or not the program can be updated from the predicted parking / stopping time.

<Embodiment 3>
《Parking / stopping time prediction system》
Since the parking / stopping time prediction system according to the third embodiment has the same configuration as the parking / stopping time prediction system 100A described above with reference to FIG. 1, it will be described with reference to FIG. In the first embodiment, the parking / stopping time prediction device 1A predicts the parking / stopping time by using the distance between the vehicle and the user. On the other hand, in the third embodiment, the parking / stopping time prediction device 1A predicts the parking / stopping time by using the state of the user obtained by using the user terminal 3.

《Parking / stopping time prediction device》
The state acquisition unit 12 acquires the state obtained in response to the execution of the application on the user terminal 3. For example, the state acquisition unit 12 acquires the user's schedule information obtained by the calendar function. Further, for example, the state acquisition unit 12 acquires the sleep state or the like obtained by the user's alarm setting set in the user terminal 3. Further, for example, the state acquisition unit 12 acquires an exercise state such as ranking, walking, or swimming obtained by setting the distance and time of the exercise target.

The prediction unit 13 predicts the parking / stopping time by using the state obtained by the state acquisition unit 12. For example, when the state acquired by the state acquisition unit 12 is the schedule information and the time information is included, the prediction unit 13 may set the parking / stopping time until the end of this time. Specifically, when the schedule of the meeting is registered as the user's schedule from 13:00 to 15:00 and the current time is 14:00, the prediction unit 13 may set the parking / stopping time from 14:00 to 15:00. Further, the prediction unit 13 may use the time including the movement time from the user's current position to the vehicle as the parking / stopping time.

Further, if the state acquired by the state acquisition unit 12 is the alarm information, the prediction unit 13 may set the standby state until the time when the user sets the alarm. Specifically, when the user's alarm is set at 15:00 and the current time is 14:00, the prediction unit 13 may set the parking / stopping time from 14:00 to 15:00. Further, the prediction unit 13 may use the time including the movement time from the user's current position to the vehicle as the parking / stopping time.

Further, if the state acquired by the state acquisition unit 12 is the target distance for running, the prediction unit 13 predicts the time from the start of running to the present to the time until the target distance is reached, and the present The parking / stopping time may be up to the predicted time.

Further, the state acquisition unit 12 may acquire the state of the user terminal 3 itself and the state of another device that realizes data communication with the user terminal 3 by executing the application through the user terminal 3. For example, it is conceivable to use the usage state of a device such as a smart home appliance associated with the user terminal 3.

In this way, the parking / stopping time prediction device 1A can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, since the parking / stopping time prediction device 1A according to the third embodiment specifically considers the state of the user behavior, it is possible to predict the parking / stopping time more accurately than obtaining only from the position information. Further, the parking / stopping time prediction device 1A can determine whether or not the program can be updated from the predicted parking / stopping time.

《How to predict parking / stopping time》
Next, a parking / stopping time prediction method executed by the parking / stopping time prediction device 1A will be described with reference to the flowchart shown in FIG. Further, the above-described processing in the flowchart of FIG. 5 is assigned the same number and the description thereof will be omitted.

The parking / stopping time prediction device 1A acquires the state of the user specified in step S2 (S21). This state is, for example, a state obtained by an application executed on the user terminal 3.

The parking / stopping time prediction device 1A predicts the end time of the user's state acquired in step S21 (S22). For example, when the user's schedule information is acquired in step S21, the end time of the current schedule is predicted.

After that, the parking / stopping time prediction device 1A predicts the parking / stopping time using the time predicted in step S22 (S6).

In this way, the parking / stopping time prediction device 1A can predict the parking / stopping time of the vehicle by using the position of the user and the position of the vehicle even when the vehicle is used by a large number of users. ..

In this way, the parking / stopping time prediction device 1A can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, since the parking / stopping time prediction device 1A according to the third embodiment specifically considers the state of the user behavior, it is possible to predict the parking / stopping time more accurately than obtaining only from the position information. Further, the parking / stopping time prediction device 1A can determine whether or not the program can be updated from the predicted parking / stopping time.

<Embodiment 4>
《Parking / stopping time prediction system》
The parking / stopping time prediction system 100C according to the fourth embodiment shown in FIG. 9 is different from the parking / stopping time prediction system 100A described above with reference to FIG. 1 in that the reservation management device 4 is not provided. Therefore, the parking / stopping time prediction device 1C obtains the parking / stopping time for the plurality of users, and selects the shortest parking / stopping time among the obtained plurality of the obtained parking / stopping time.

The identification unit 11 reads the user data D11 from the storage unit 20, and identifies a plurality of users registered in the user data D11 as candidates for the user who will use the vehicle next. In the case of the above-mentioned example in FIG. 3, for the "vehicle 2", the specifying unit 11 specifies "user 21", "user 22", and "user 23" as candidates.

The state acquisition unit 12 acquires state data for all the users specified by the specific unit 11 as candidates. For example, the state acquisition unit 12 acquires the addresses of the user terminals 3a to 3c owned by the user 21, the user 22, and the user 23 from the access data D12, and acquires the position information of the user terminals 3a to 3b.

The prediction unit 13 predicts the time until the next start of use for each candidate user, and sets the shortest time among the predicted plurality of times as the prediction result. For example, the prediction unit 13 acquires the current position information of the vehicle from the in-vehicle device 2 and obtains the travel time to the vehicle for each of the user terminals 3a to 3c. In addition, the prediction unit 13 obtains the parking / stopping time according to the travel time obtained for each user. Then, the prediction unit 13 determines the user with the shortest parking / stopping time as the user who will use the vehicle next, and sets the parking / stopping time obtained for this user as the determination target in the determination unit 14.

In addition, in the prediction of the parking / stopping time for each user by the prediction unit 13, only the distance between the vehicle and the user is used, and the staying time specified by using the map information as described above in the second embodiment. Or the state obtained according to the execution of the application on the user terminal 3 as described above in the third embodiment may be considered.

As a result, the parking / stopping time prediction device 1C can determine the position of the user and the position of the vehicle even when the use of the vehicle by the users is not uniform, such as when the vehicle is used by a large number of users. It can be used to predict the parking / stopping time of a vehicle. Further, since the parking / stopping time prediction device 1C predicts the parking / stopping time from the time candidates obtained for a plurality of users, the parking / stopping time can be predicted even when the user cannot be accurately identified. Further, the parking / stopping time prediction device 1C can determine whether or not the program can be updated from the predicted parking / stopping time.

《How to predict parking / stopping time》
Next, a parking / stopping time prediction method executed by the parking / stopping time prediction device 1C will be described with reference to the flowchart shown in FIG. Further, the above-described processing in the flowchart of FIG. 5 is assigned the same number and the description thereof will be omitted.

The parking / stopping time prediction device 1C identifies a plurality of users who may use the target vehicle (S31).
Subsequently, the parking / stopping time prediction device 1C acquires the position information of the plurality of users specified in step S31 (S32).
Further, the parking / stopping time prediction device 1C acquires the position information of the vehicle (S4).

The parking / stopping time prediction device 1C calculates the distance between each user and the vehicle from the position information of each user acquired in step S32 and the position information of the vehicle acquired in step S4 (S34).
The parking / stopping time prediction device 1C predicts the parking / stopping time for each user by using the distance calculated in step S34 (S35).

The parking / stopping time prediction device 1C selects the shortest predicted parking / stopping time among the parking / stopping times predicted in step S35 (S36).
After that, the parking / stopping time prediction device 1C compares the parking / stopping time predicted in step S36 with the update time (S7).

In this way, the parking / stopping time prediction device 1C can be used for the user's position and the vehicle position even when the user's use of the vehicle is not uniform, such as when the vehicle is used by a large number of users. Can be used to predict the parking and stopping time of the vehicle. Further, since the parking / stopping time prediction device 1C predicts the parking / stopping time from the time candidates obtained for a plurality of users, the parking / stopping time can be predicted even when the user cannot be accurately identified. Further, the parking / stopping time prediction device 1C can determine whether or not the program can be updated from the predicted parking / stopping time.

[Effects and supplements]
As described above, the above-described embodiment has been described as an example of the technology disclosed in this application. However, the technique in the present disclosure is not limited to this, and can be applied to embodiments in which changes, replacements, additions, omissions, etc. are made as appropriate. Therefore, other embodiments will be illustrated below.

<< Other Embodiments >>
(1) Further, as shown in FIG. 11, the parking / stopping time prediction system 200 may have the parking / stopping time prediction device 2A itself as an in-vehicle device. In this case, the user data D11, the access data D12, and the parking / stopping time prediction program P are stored in the storage unit 24 in the vehicle-mounted device. Further, the control unit 25 in the vehicle-mounted device reads and executes the parking / stopping time prediction program P to execute the processing as the specific unit 11, the state acquisition unit 12, the prediction unit 13, and the determination unit 14. Note that the user data D11 and the access data D12 are not stored in the storage unit 24, and may be read from an external storage device.
(2) A learning unit and a user information storage unit are provided, and behavior patterns related to a specific user are stored and stored in the user information storage unit, and the learning unit learns general behavior patterns from the behavior patterns of many users. It is possible to improve the accuracy of predicting the parking / stopping time in the behavior pattern of. As a result, it is possible to improve the accuracy of predicting the parking / stopping time, and it is possible to obtain high prediction accuracy especially when a certain user uses another vehicle. Specifically, the staying time of the past user when using the facility, the duration of the user's behavior, and the like can be used for learning.

<< Outline of Embodiment >>
(1) The parking / stopping time prediction device of the present disclosure is a parking / stopping time prediction device that predicts the time when a vehicle is parked / stopped, and specifies a user who will use the vehicle next for the parked vehicle. From the present to the timing when the user starts to use the status data, the status acquisition unit that acquires the status data that identifies the user's current status from the specific unit and the external device used by the user, and the status data. It is equipped with a prediction unit that predicts parking and stopping times.

As a result, even when the use of the vehicle by the users is not uniform, such as when the vehicle is used by a large number of users, the position of the user and the position of the vehicle are used to park and stop the vehicle. You can predict the time.

(2) In the parking / stopping time prediction device of (1), the specific unit is stored in the reservation data storage unit, and the identification information of the user who has reserved the use of the vehicle is associated with the time information when this user uses the vehicle. The user may be identified from the reservation data to be obtained.

As a result, the user of the vehicle can be identified from the reservation status of the vehicle.

(3) In the parking / stopping time reservation device of (1), the specific unit may acquire authentication information from the user authentication device provided in the vehicle and specify the user.

As a result, the user of the vehicle can be identified from the authentication information of the vehicle.

(4) In the parking / stopping time reservation device of (1), the specific unit acquires user data from the user data storage unit that stores user data in which a user who may use the vehicle is registered, and converts the user data into user data. The vehicle is used by the user who has the closest distance between the vehicle and the user, which is obtained from the position information of each user acquired from the external device used by each registered user and the position information of the vehicle acquired from the vehicle. It may be specified as a user.

Thereby, the user of the vehicle can be identified from the position information of the vehicle and the user.

(5) In the parking / stopping time prediction device of (1), the state data is the user's current position information, and the prediction unit uses the vehicle's current position information acquired from the vehicle and the user's current position. The travel time required for the user to move to the vehicle may be predicted from the information, and the parking / stopping time may be predicted from the travel time.

As a result, the parking / stopping time can be predicted from the position information of the vehicle and the user.

(6) In the parking / stopping time prediction device of (5), the prediction unit may predict the parking / stopping time according to the staying time of the user specified by the user's position information.

As a result, it is possible to predict the parking / stopping time in consideration of the user's staying time by using the information about the user's staying place.

(7) In the parking / stopping time prediction device of (1), the state data is the schedule information of the user, and the prediction unit may predict the parking / stopping time from the time included in the schedule information.

As a result, the parking / stopping time can be predicted in consideration of the user's schedule.

(8) In the parking / stopping time prediction device of (1), the state data is the usage information of the application used by the user, and the prediction unit can predict the parking / stopping time from the usage information of the application. ..

As a result, it is possible to predict the parking / stopping time in consideration of the user's behavior.

(9) In the parking / stopping time prediction device of (1), the specific unit acquires user data from the user data storage unit that stores user data in which a user who may use the vehicle is registered, and converts the user data into user data. Multiple users associated with the registered vehicle are identified as candidates for the next user to be used, and the prediction unit predicts the time until the next start of use for each candidate user, and the predicted plurality of users. The shortest time in time may be used as the prediction result.

As a result, the parking / stopping time can be predicted even when the user of the vehicle cannot be identified.

(10) The parking / stopping time prediction device of (1) has a determination unit that determines whether or not the program used for executing the in-vehicle device provided in the vehicle can be updated according to the parking / stopping time predicted by the prediction unit. You may also prepare.

As a result, it is possible to update the program only when the program update is completed within the predicted parking / stopping time.

(11) The parking / stopping time prediction method of the present disclosure is a parking / stopping time prediction method for predicting the time when a vehicle is parked / stopped, and specifies a user who will use the vehicle next for the parked vehicle. Using the step, the step of acquiring the state data that identifies the current state of the user from the external device used by the user, and the state data, the time from the present to the timing when the user starts the next use is predicted. Have steps to do.

As a result, even when the use of the vehicle by the users is not uniform, such as when the vehicle is used by a large number of users, the position of the user and the position of the vehicle are used to park and stop the vehicle. You can predict the time.

The parking / stopping time prediction device and the parking / stopping time prediction method described in all claims of the present disclosure are realized by cooperation with hardware resources such as a processor, a memory, and a program.

The parking / stopping time prediction device and the parking / stopping time prediction method of the present disclosure are useful for specifying the timing when updating the program used in the vehicle.

100A, 100B, 100C Parking / stopping time prediction system 1A, 1B, 1C Parking / stopping time prediction device 11 Specific unit 12 State acquisition unit 13 Prediction unit 14 Judgment unit 2 In-vehicle device 3, 3a, 3b, 3c User terminal 4 Reservation management device 5 Network 6 Map management device

Claims (12)

It is a parking / stopping time prediction device that predicts the time when a vehicle is parked / stopped.
For vehicles that are parked and stopped, a specific part that identifies the user who will use the vehicle next,
A state acquisition unit that acquires state data that identifies the current state of the user from an external device used by the user.
Using the state data, a prediction unit that predicts the parking / stopping time from the present to the timing when the user starts using the next time,
A parking / stopping time prediction device equipped with. The specific unit is stored in the reservation data storage unit, and claims to identify the user from the reservation data in which the identification information of the user who has reserved the use of the vehicle and the time information in which the user uses the vehicle are associated with each other. The parking / stopping time prediction device according to 1. The parking / stopping time prediction device according to claim 1, wherein the specific unit acquires authentication information from a user authentication device provided in the vehicle and identifies the user. The specific unit acquires the user data from a user data storage unit that stores user data registered by a user who may use the vehicle, and is an external external device used by each user registered in the user data. A request for identifying a user who has the closest distance between the vehicle and the user, which is obtained from the position information of each user acquired from the device and the position information of the vehicle acquired from the vehicle, as a user who uses the vehicle. Item 1. The parking / stopping time prediction device according to Item 1. The state data is the current position information of the user, and is
The prediction unit predicts the movement time required for the user to move to the vehicle from the current position information of the vehicle acquired from the vehicle and the current position information of the user, and the parking time is used from the movement time. The parking / stopping time prediction device according to any one of claims 1 to 4, which predicts the stop time. The parking / stopping time prediction device according to claim 5, wherein the prediction unit predicts the parking / stopping time according to the staying time of the user specified by the position information of the user. The state data is the schedule information of the user, and is
The parking / stopping time prediction device according to any one of claims 1 to 4, wherein the prediction unit predicts the parking / stopping time from the time included in the schedule information. The state data is usage information of the application used by the user, and is
The parking / stopping time prediction device according to any one of claims 1 to 4, wherein the prediction unit predicts the parking / stopping time from the usage information of the application. The specific unit acquires the user data from a user data storage unit that stores user data registered by a user who may use the vehicle, and is associated with the vehicle registered in the user data. Identify the user as a candidate for the next user to use
The prediction unit predicts the parking / stopping time until the next start of use for each of the candidate users, and sets the shortest time among the predicted plurality of times as the prediction result, according to claims 1 to 8. The parking / stopping time prediction device according to any one. One of claims 1 to 9, further comprising a determination unit for determining whether or not the program used for executing the in-vehicle device provided in the vehicle can be updated according to the parking / stopping time predicted by the prediction unit. The described parking / stopping time prediction device. It is a parking / stopping time prediction method that predicts the time when a vehicle is parked / stopped.
For parked and stopped vehicles, the steps to identify the next user to use the vehicle, and
A step of acquiring state data that identifies the current state of the user from an external device used by the user, and
Using the state data, a step of predicting the parking / stopping time from the present to the timing when the user starts using the next time, and
A parking / stopping time prediction method. A program that causes a computer to execute the authentication method according to claim 11.

Images