JP7552264B2 - Information processing device, control method, and mobile terminal - Google Patents

Description

The present invention relates to an information processing device, a control method, and a mobile terminal.

Systems have been developed to predict the congestion situation in a certain place or area. For example, Patent Document 1 discloses a system for predicting the future number of visitors and vehicles at a resort.

JP 2003-6376 A

There is a demand for systems that can predict congestion conditions more accurately.

One aspect of the present invention aims to accurately grasp the congestion situation in a certain place or area.

In order to solve the above problem, an information processing device according to one aspect of the present invention is an information processing device mounted on a vehicle, and includes a navigation information acquisition unit that acquires information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle, an arrival probability calculation unit that calculates the arrival probability of the vehicle at the destination depending on the state of the occupants of the vehicle or the environment around the vehicle, and a transmission unit that transmits the travel time and information related to the arrival probability to a management device that manages the arrival status of multiple vehicles.

In order to solve the above problem, a control method for an information processing device according to one aspect of the present invention is a control method for an information processing device mounted on a vehicle, and includes a navigation information acquisition step of acquiring information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle, an arrival probability calculation step of calculating the probability of the vehicle arriving at the destination depending on the state of the occupants of the vehicle or the environment around the vehicle, and a transmission step of transmitting the travel time and information related to the arrival probability to a management device that manages the arrival status of multiple vehicles.

The above configuration and processing allows the travel time and arrival probability of a vehicle to be transmitted to the management device. This allows the management device to know when and with what probability a vehicle will arrive at a particular destination. Therefore, the management device can accurately predict the arrival status of a vehicle at a certain location or region at a future time.

The information processing device may include a travel time correction unit that corrects the travel time to the destination acquired by the navigation information acquisition unit according to the state of the passenger.

The above configuration allows the travel time to the destination to be calculated more accurately and transmitted to the management device. Therefore, the management device can more accurately predict the arrival status of a vehicle at a certain location or area at a future time.

The information processing device may include a biometric information acquisition unit that acquires biometric information of an occupant of the vehicle from a biometric sensor mounted on the vehicle, and a state identification unit that identifies a state of the occupant from the biometric information. The arrival probability calculation unit of the information processing device may calculate the arrival probability based on the state of the occupant identified by the state identification unit.

The above configuration allows the probability of arrival at the destination to be calculated more accurately and transmitted to the management device. Therefore, the management device can more accurately predict the arrival status of a vehicle at a certain location or area at a future time.

In the information processing device, the state of the passenger includes at least one of the passenger's emotions and physical condition, the biometric information acquisition unit periodically acquires the biometric information, the state identification unit periodically estimates at least one of the passenger's emotional category and physical condition, and if the estimation result shows that a certain emotion has continued for a predetermined first time or more, the emotion is identified as the passenger's emotion, and if the estimation result shows that a certain physical condition has continued for a predetermined second time or more, the physical condition is identified as the passenger's physical condition.

The above configuration allows the passenger's condition to be accurately identified from biometric information.

The information processing device may include an environmental information acquisition unit that acquires environmental information, which is information about the environment around the vehicle, and in the information processing device, the arrival probability calculation unit may calculate the arrival probability based on the environmental information.

The above configuration allows the probability of arrival at the destination to be calculated more accurately and transmitted to the management device. Therefore, the management device can more accurately predict the arrival status of a vehicle at a certain location or area at a future time.

The information processing device may include an image acquisition unit that acquires an image captured by an onboard camera, a passenger number determination unit that determines the number of passengers in the vehicle from the captured image, and an expected number of arrivals calculation unit that calculates the expected number of arrivals by multiplying the arrival probability by the number of passengers. The transmission unit may also transmit the expected number of arrivals to the management device as information related to the arrival probability.

According to the above configuration, the number of people expected to arrive can be managed by the management device. Therefore, the management device can accurately predict the number of people expected to arrive at a certain location or area at a future time.

In order to solve the above problem, an information processing device according to one aspect of the present invention is an information processing device mounted on a vehicle, and includes a navigation information acquisition unit that acquires information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle, a biometric information acquisition unit that acquires biometric information of an occupant of the vehicle from a biometric sensor mounted on the vehicle, a status identification unit that identifies the status of the occupant from the biometric information, a travel time correction unit that corrects the travel time to the destination acquired by the navigation information acquisition unit according to the status of the occupant, and a transmission unit that transmits the corrected travel time to a management device that manages the arrival status of multiple vehicles.

In order to solve the above problem, a control method for an information processing device according to one aspect of the present invention is a control method for an information processing device mounted on a vehicle, and includes a navigation information acquisition step of acquiring information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle, a biometric information acquisition step of acquiring biometric information of an occupant of the vehicle from a biometric sensor mounted on the vehicle, a status identification step of identifying the status of the occupant from the biometric information, a travel time correction step of correcting the travel time to the destination acquired in the navigation information acquisition step according to the status of the occupant, and a transmission step of transmitting the corrected travel time to a management device that manages the arrival status of multiple vehicles.

The above configuration and processing allows a travel time corrected according to the passenger's condition to be transmitted to the management device. This allows a more accurate travel time to be notified to the management device. Therefore, the management device can accurately predict the vehicle's arrival status at a certain location or area at a future time.

In the information processing device, the state of the occupant includes at least one of an emotion and a physical condition of the occupant, the biological information acquisition unit periodically acquires the biological information,
The condition identification unit may periodically estimate at least one of the occupant's emotional category and physical condition, and if the result of the estimation shows that a certain emotion has continued for a predetermined first time or more, identify the emotion as the occupant's emotion, and if the result of the estimation shows that a certain physical condition has continued for a predetermined second time or more, identify the physical condition as the occupant's physical condition.

The above configuration allows the passenger's condition to be accurately identified from biometric information.

The information processing device may include an arrival probability calculation unit that calculates the probability of the vehicle arriving at the destination depending on the state of the passenger, and the transmission unit of the information processing device may transmit the travel time and information related to the arrival probability to the management device.

According to the above configuration, in addition to the travel time, the arrival probability is also transmitted to the management device. Therefore, the management device can more accurately predict the arrival status of a vehicle at a certain location or area at a future time.

The information processing device may include an environmental information acquisition unit that acquires environmental information, which is information about the environment around the vehicle, and the arrival probability calculation unit may calculate the arrival probability based on the environmental information.

The above configuration allows the arrival probability to be calculated taking into account the environment around the vehicle. Therefore, a more accurate arrival probability can be calculated.

The information processing device may include an image acquisition unit that acquires an image captured by an onboard camera, a passenger number determination unit that determines the number of passengers in the vehicle from the captured image, and an expected number of arrivals calculation unit that calculates the expected number of arrivals by multiplying the arrival probability by the number of passengers, and the transmission unit may transmit the expected number of arrivals to the management device as information related to the arrival probability.

According to the above configuration, the number of people expected to arrive can be managed by the management device. Therefore, the management device can accurately predict the number of people expected to arrive at a certain location or area at a future time.

The information processing device may include a condition acquisition unit that acquires search conditions, which are information indicating a specific location or area, from the management device, and a target determination unit that determines that the vehicle is a search target vehicle if the destination matches the specific location or area, and the transmission unit of the information processing device may transmit the travel time when the target determination unit determines that the vehicle is a search target vehicle.

According to the above configuration, the travel time of vehicles that match the search target is transmitted to the management device. Therefore, the load of processing information on vehicles that are not the search target can be reduced on the management device.

In order to solve the above problem, a mobile terminal according to one aspect of the present invention is a mobile terminal carried by a vehicle occupant, and includes a location information acquisition unit that periodically acquires location information indicating the current location of the mobile terminal, an arrival determination unit that determines whether the passenger has arrived at a specific location among one or more locations registered in advance based on the location information, a stay time prediction unit that predicts the passenger's stay time at the specific location when the arrival determination unit determines that the passenger has arrived at the specific location, a status information acquisition unit that acquires status information indicating the passenger's status from an information processing device mounted on the vehicle, a stay time correction unit that corrects the stay time predicted by the stay time prediction unit based on the status information, and a mobile terminal communication unit that transmits the corrected stay time to a management device that manages the number of people staying at the specific location, and is characterized in that in the information processing device, the status of the passenger is identified based on biometric information acquired from a biometric sensor mounted on the vehicle.

According to the above configuration, when a passenger arrives at a specific location, the mobile terminal can predict the length of stay at the specific location. Furthermore, the mobile terminal can correct the predicted length of stay depending on the passenger's condition. Therefore, the length of stay of the passenger can be accurately determined. Then, the management device can manage the number of people staying based on the accurately determined length of stay.

In the mobile terminal, the stay time prediction unit may predict the occupant's stay time at the specific location based on schedule information including a schedule indicating the date and time, location, and content of the occupant's activities.

The above configuration allows for more accurate prediction of dwell time based on passenger schedules.

The activity date and time may include the scheduled start date and time and the scheduled end date and time of the schedule, and in the mobile device, when another schedule in a different location is set immediately after the schedule that matches the current time, the stay time prediction unit may predict the stay time according to the scheduled start date and time of the other schedule.

The passenger can be predicted to be moving to the location where the other schedule is to be carried out, i.e., the location of the activity, at the scheduled start date and time of the other schedule. Therefore, with the above configuration, the scheduled start date and time of the other schedule can be taken into account to more accurately predict the duration of the passenger's stay at a specific location.

The schedule information may include a past schedule, and in the mobile device, when the schedule information includes a past schedule in which the same location as the specific location is the activity location, the stay time prediction unit may predict the stay time of the passenger based on the past stay time at the activity location.

According to the above configuration, if a passenger has visited the same location in the past, the passenger's stay time is predicted taking into account the past stay time. Therefore, the passenger's stay time at a specific location can be predicted more accurately.

The schedule information may include a schedule for group behavior, and in the mobile device, the activity date and time of the schedule for the group behavior may include a meeting date and time and a disbanding or ending date and time, and the stay time prediction unit may predict the stay time based on the meeting date and time and the disbanding or ending date and time when the schedule that matches the current time is a schedule for group behavior.

The schedule for group activities is determined in advance, with a meeting date and time (i.e., a start date and time) and a breakup or end date and time. With the above configuration, the length of stay of passengers is predicted based on such predetermined dates and times, making it possible to predict the length of stay more accurately.

According to one aspect of the present invention, it is possible to accurately grasp the congestion situation in a certain place or area.

1 is a diagram illustrating an overview of a management system according to a first embodiment. FIG. FIG. 2 is a diagram showing a configuration of a main part of a management system. FIG. 2 is a diagram illustrating an example of a data structure of an emotion database. FIG. 4 is a diagram illustrating an example of a data structure of a physical condition database. 10 is a flowchart illustrating an example of a processing flow of a management system according to the first embodiment. 13 is a flowchart showing the flow of a condition match determination process. 13 is a flowchart showing the flow of a state identification process. 13 is a flowchart showing the flow of a state identification process. 13 is a flowchart showing the flow of an arrival probability calculation process. 13 is a flowchart showing an example of the flow of a counting process. FIG. 11 is a diagram showing an example of image data showing a compilation result. FIG. 11 is a diagram showing an example of image data showing a compilation result. FIG. 11 is a diagram showing an example of image data showing a compilation result. FIG. 11 is a block diagram showing a configuration of a main part of a management system according to a second embodiment. FIG. 11 is a block diagram showing a configuration of a main part of a management system according to a third embodiment. 13 is a flowchart showing another example of the condition match determination process. FIG. 13 is a block diagram showing a configuration of a main part of a management system according to a fourth embodiment. 13 is a flowchart showing an example of a processing flow of a mobile terminal according to a fourth embodiment. 13 is a flowchart showing an example of the flow of a stay time prediction process. 13 is a flowchart showing an example of the flow of a stay time correction process. FIG. 13 is a diagram illustrating an example of a relationship between facility classification, customer demographics, and a stay time coefficient. 13 is a flowchart showing an example of the flow of a second counting process.

[Embodiment 1]
In places where many people come by car, such as private cars, it is important to accurately predict the number of vehicles that will arrive at a certain time in order to grasp, for example, the availability of parking spaces. However, since the destination, travel route, and travel time of vehicles are not fixed, it has been difficult to predict the arrival status in the first place. The management system 500 according to this embodiment aims to accurately predict the arrival status of vehicles at a certain place or area at a future time.

<System Overview>
(Configuration Overview)
1 is a diagram showing a schematic overview of a management system 500 according to this embodiment. The management system 500 is a system that predicts a congestion state at a specific place or area by acquiring and aggregating a travel time (i.e., a required time) required for a vehicle 9 heading for a specific place or area to arrive at the specific place or area. The management system 500 may also be a system that acquires from the vehicles 9 a value of a probability (arrival probability) that each vehicle 9 will arrive at the specific place or area at a future time, and predicts the congestion state by taking the arrival probability into account.

The management system 500 includes at least a terminal device 3, a cloud server (management device) 2, and a number of vehicles 9. As will be described in more detail below, the vehicles 9 include an information processing device 1, a car navigation system (navigation device) 4, and a biosensor 6. The cloud server 2 communicates with the terminal device 3 and the vehicles 9 (more precisely, the information processing device 1 of the vehicles 9).

The terminal device 3 is an input interface for specifying a location or area for which congestion status is to be predicted. The terminal device 3 also serves as an output interface for presenting the aggregation results of the cloud server 2 to a user. The terminal device 3 is realized, for example, by a personal computer or a smartphone.

The cloud server 2 receives a location or area designation from the terminal device 3, and acquires the travel time of each vehicle 9 from a vehicle 9 that matches the designation to the designated location or designated area. The cloud server 2 may also acquire the arrival probability of the vehicle 9 that matches the designation to the designated location or designated area. The cloud server 2 also aggregates the acquired travel times (and arrival probability) for each predetermined time segment (i.e., unit time) and transmits the aggregation result to the terminal device 3.

The vehicle 9 is a vehicle equipped with an information processing device 1 and a car navigation system 4, which will be described later. The specific configuration of the vehicle 9 is not particularly limited, but for example, the vehicle 9 may be a passenger car. Furthermore, in this embodiment, the vehicle 9 is not a means of transportation that travels along a specific route, such as a bus, but a vehicle that is driven by a passenger of the vehicle 9 who decides on a destination by himself/herself. However, the vehicle 9 may also include vehicles that travel along a fixed route, such as a bus or a train.

(Overview of processing flow)
A flow of the management system 500 will be briefly described below. A user who wishes to predict the congestion situation performs an input operation on the terminal device 3 to specify a specific location or area. In the example of Fig. 1, the user performs an input operation to specify the facility name "ABC Theme Park". The terminal device 3 identifies the specified location or area from the input operation, and transmits information indicating the specified location and the specified area to the cloud server 2.

In other words, the terminal device 3 supplies the cloud server 2 with search conditions for identifying, from among the countless vehicles 9, the target vehicles 9 for which travel times (and arrival probabilities) should be compiled. Hereinafter, the locations and areas specified by the user are also referred to as "search conditions." Specifically, the search conditions may be any of the following: a location name, a facility name, the name of an event held at a specific location, an address, latitude and longitude, a city/town/village name, an area name, etc.

The cloud server 2, which has received information indicating the search conditions, transmits a response request so that vehicles 9 that match the search conditions respond. The response request includes at least information indicating the search conditions. The vehicle 9 that receives the response request transmits the travel time (and arrival probability) to the cloud server 2 if the vehicle is heading toward the location or area specified in the search conditions. In the case of FIG. 1, the vehicles 9 surrounded by dotted lines are heading toward the specified location, i.e., ABC Theme Park. Therefore, these vehicles 9 transmit their travel time (and arrival probability) to the cloud server 2.

The cloud server 2 receives travel times (and arrival probabilities) from each vehicle 9 and tallys up the information from each vehicle 9 for each predetermined time interval (i.e., unit time). This makes it possible to identify the number of vehicles 9 scheduled to arrive at a specified location or area at a certain time (number of vehicles scheduled to arrive). The cloud server 2 transmits the tally up results to the terminal device 3. Note that the representation format of the tally up results and the format in which the tally up results are transmitted to the terminal device 3 are not particularly limited. In this embodiment, as shown in FIG. 1, the cloud server 2 transmits an image in which the tally up results are represented in a graph format to the terminal device 3.

The terminal device 3 displays the counted results received from the cloud server 2. This allows the user of the terminal device 3 to view the number of vehicles scheduled to arrive per specified time slot in the location or area that the user has specified.

<Main components>
Fig. 2 is a diagram showing the main configuration of the management system 500. In the example of Fig. 2, an external device 5 and a biosensor 6 are also shown, but the external device 5 and the biosensor 6 are not essential components of the management system 500. In Fig. 2 and subsequent figures, a case will be described in which the information processing device 1 of the vehicle 9 transmits information on both the travel time and the arrival probability to the cloud server 2.

(Terminal device 3)
The terminal device 3 includes a terminal communication unit 31, a terminal storage unit 32, a terminal input unit 33, a terminal control unit 30, and a terminal display unit 34.

The terminal communication unit 31 communicates between the terminal device 3 and the cloud server 2. For example, the terminal communication unit 31 transmits information indicating the search conditions input from the terminal control unit 30 to the cloud server 2. Also, for example, the terminal communication unit 31 receives information indicating the tally results from the cloud server 2 and outputs it to the terminal control unit 30. In this embodiment, the terminal communication unit 31 receives image data indicating the tally results from the cloud server 2.

The terminal storage unit 32 is a storage device that stores various data necessary for the operation of the terminal device 3. The terminal input unit 33 is an input interface for the terminal device 3, and accepts input operations for specifying search conditions from the user of the terminal device 3.

The terminal control unit 30 controls the terminal device 3 in an integrated manner. The terminal control unit 30 acquires the user's operation contents from the terminal input unit 33, and identifies search conditions from the operation contents. The terminal control unit 30 outputs the identified search conditions to the terminal communication unit 31. The terminal control unit 30 also causes the terminal display unit 34 to display image data indicating the tally results input from the terminal communication unit 31.

The terminal display unit 34 is an output interface of the terminal device 3, and displays various information. The terminal display unit 34 displays information indicating the aggregation results acquired by the terminal control unit 30 from the cloud server 2. The terminal display unit 34 may also display an image to guide the input operation for specifying search conditions according to the control of the terminal control unit 30.

(Cloud Server 2)
The cloud server 2 includes a server communication unit 21 , a server control unit 20 , and a server storage unit 22 .

The server communication unit 21 communicates between the cloud server 2 and the terminal device 3, and between the cloud server 2 and the information processing device 1. The server communication unit 21 receives information indicating search conditions from the terminal device 3 and outputs the information to the server control unit 20. The server communication unit 21 also transmits information indicating the aggregation results input from the server control unit 20 to the terminal device 3. The server communication unit 21 also transmits a response request input from the server control unit 20 to the information processing device 1. The server communication unit 21 also receives information indicating travel time and arrival probability from the information processing device 1 and outputs it to the server control unit 20.

The server control unit 20 performs overall control of the cloud server 2. The server control unit 20 functions as a vehicle identification unit 201, a request creation unit 202, a counting unit 203, and a display data creation unit 204 by executing a program included in the second program data 222 described below.

The vehicle identification unit 201 identifies vehicles 9 (registered vehicles) registered in the management system 500. The method of identifying registered vehicles is not particularly limited, but for example, the vehicle identification unit 201 may identify registered vehicles by referring to vehicle registration information stored in the memory unit 12. When the vehicle identification unit 201 is able to identify one or more vehicles 9, it notifies the request creation unit 202 that a vehicle 9 has been identified. Note that when a vehicle 9 cannot be identified, the vehicle identification unit 201 may end the process without providing such notification.

The request creation unit 202 creates a response request to be sent to the vehicle 9. The request creation unit 202 creates a response request that includes information indicating the search conditions input to the server control unit 20. The request creation unit 202 outputs the created response request to the server communication unit 21.

The tallying unit 203 tallys up information received from other devices. For example, the tallying unit 203 in this embodiment calculates the number of vehicles scheduled to arrive in each unit time when the future time is divided into predetermined time segments, based on the travel time and arrival probability received from each vehicle 9. The tallying unit 203 outputs the tallying results to the display data creation unit 204.

The display data creation unit 204 creates data to be sent to the terminal device 3 based on the tallying result of the tallying unit 203. In this embodiment, the display data creation unit 204 creates image data to be displayed on the terminal display unit 34 of the terminal device 3. The display data creation unit 204 outputs the created image data to the server communication unit 21.

The server storage unit 22 is a storage device that stores various data necessary for the operation of the cloud server 2. The server storage unit 22 stores second program data 222. The second program data 222 is a data group that compiles programs and libraries for the server control unit 20 to realize various functions. The server storage unit 22 may also store data, etc., that is necessary for the request creation unit 202 of the server control unit 20 to create a response request. For example, the server storage unit 22 may also store formulas, etc. used during the tallying process in the tallying unit 203. For example, the server storage unit 22 may also store templates, etc., that are necessary for the display data creation unit 204 to create image data. For example, the server storage unit 22 may also store
In addition, the server control unit 20 may store in the server storage unit 22 travel status data 221 that summarizes the travel time and arrival probability received from each vehicle 9 via the server communication unit 21 for each vehicle 9 .

(Vehicle 9)
The vehicle 9 includes a car navigation system 4, a biosensor 6, and an information processing device 1. In addition to the above, the vehicle 9 may also include various mechanisms necessary for the operation of the vehicle.

(Car navigation system 4)
The car navigation system 4 is a navigation device attached to the vehicle 9. The car navigation system 4 is connected to the information processing device 1 by wire or wirelessly. The car navigation system 4 includes a navigation communication unit 41, a navigation storage unit 42, a navigation input unit 43, a navigation display unit 44, and a navigation control unit 40. In addition to the above, the car navigation system 4 may include other components necessary as a navigation device, as appropriate.

The navigation communication unit 41 communicates between the car navigation 4 and the information processing device 1. The navigation communication unit 41 receives various requests from the navigation information acquisition unit 108 and outputs them to the navigation control unit 40. The navigation communication unit 41 also transmits information indicating the destination (destination information) and travel time input from the navigation control unit 40 to the navigation information acquisition unit 108 of the information processing device 1. In this embodiment, "travel time" refers to the time required to arrive at the destination from the current position of the vehicle 9.

The navigation memory unit 42 is a storage device that stores various data necessary for the operation of the car navigation system 4. The navigation input unit 43 is an input interface for the car navigation system 4, and accepts input operations such as specifying a destination by a passenger in the vehicle 9. The navigation display unit 44 is an output interface for the car navigation system 4, and displays guidance images and the like to the destination to a passenger in the vehicle 9.

The navigation control unit 40 performs overall control of the car navigation system 4. For example, the navigation control unit 40 sets the destination of the vehicle 9 based on input operations received via the navigation input unit 43, and calculates the travel route and travel time from the current position of the vehicle 9 to the destination. The current position of the vehicle 9 may be identified using a GPS receiver built into or external to the car navigation system 4.

In addition, the navigation control unit 40 outputs destination information to the navigation communication unit 41 in response to a request from the information processing device 1. In addition, the navigation control unit 40 outputs travel time to the navigation communication unit 41 in response to a request from the information processing device 1.

(Biosensor 6)
The biosensor 6 is a sensor for measuring bioinformation of an occupant of the vehicle 9. The "biometric information" may be, for example, an image or video showing the face and body of the occupant, the occupant's heart rate, the occupant's body temperature, etc. The biosensor 6 is realized, for example, by a camera that captures the interior of the vehicle, a heart rate sensor that measures the occupant's heart rate, a thermograph, etc.

The biosensor 6 starts measuring the biometric information at a predetermined timing. For example, the biosensor 6 may start measuring the biometric information when the vehicle 9 is unlocked or the engine is started. The biosensor 6 outputs the measurement result, i.e., the biometric information, to the biometric information acquisition unit 103 of the information processing device 1.

(Information processing device 1)
The information processing device 1 includes a communication unit (transmission unit) 11, a control unit 10, and a storage unit 12. The communication unit 11 performs communication between the information processing device 1 and the cloud server 2, and between the information processing device 1 and an external device 5.

The control unit 10 performs overall control of the information processing device 1. By executing a program included in the first program data 125 described below, the control unit 10 functions as a condition acquisition unit 101, a target determination unit 102, a biometric information acquisition unit 103, a state identification unit 104, a travel time correction unit 105, an arrival probability calculation unit 106, an environmental information acquisition unit 107, and a navigation information acquisition unit 108.

The condition acquisition unit 101 receives a response request that the information processing device 1 receives from the cloud server 2. The condition acquisition unit 101 outputs information indicating the search conditions that is included in the response request to the target determination unit 102.

The target determination unit 102 executes a condition match determination process based on the search conditions. The condition match determination process is a process for determining whether or not the vehicle 9 is a vehicle that matches the search conditions (i.e., a vehicle to be searched). The specific steps of the condition match determination process will be described later. If the condition match determination process determines that the vehicle 9 is not a search target vehicle, the target determination unit 102 creates a notification (non-target notification) indicating that the vehicle 9 is not a search target vehicle, and outputs it to the communication unit 11. On the other hand, if the target determination unit 102 determines that the vehicle 9 is a search target vehicle, the target determination unit 102 instructs the state identification unit 104 to execute a state identification process and instructs the travel time correction unit 105 to correct the travel time.

The biometric information acquisition unit 103 acquires biometric information from the biometric sensor 6. The biometric information acquisition unit 103 sequentially transmits the acquired biometric information to the condition identification unit 104.

The condition identification unit 104 estimates and identifies the condition of the occupant of the vehicle 9. The type of "occupant condition" is not particularly limited as long as it is a condition that can be estimated and identified from biometric information. In this embodiment, the condition identification unit 104 estimates and identifies the occupant's emotional category (emotion category) and the degree of physical condition of the occupant as the occupant's condition. The condition identification unit 104 may estimate the condition each time biometric information is input from the biometric information acquisition unit 103.

The emotions of the passenger can be estimated from an image or video of the passenger captured by a camera. Any conventional method may be used as the emotion estimation method. In this embodiment, emotions are classified into six categories: "happiness," "anger," "sadness," "pleasure," "discomfort," and "excitement."

The physical condition of the passenger can be estimated from the passenger's body temperature measured by thermography. In this embodiment, the physical condition is classified into five levels, from level 1 to level 5. In this classification, level 1 indicates the worst physical condition and level 5 indicates the best physical condition.

The state identification unit 104 may compile the state of the passenger estimated as described above into state log data 122, which is time-series log data, and store it in the memory unit 12. The state log data 122 is created for each passenger.

When the state identification unit 104 receives an instruction from the target determination unit 102, it further executes a state identification process. The state identification process is a process for identifying the current state of the passenger from the progress of the state estimation results in the state identification unit 104. Details of the state identification process will be described later. The state identification unit 104 outputs information indicating the identified state to the travel time correction unit 105 and the arrival probability calculation unit 106.

The environmental information acquisition unit 107 acquires environmental information from the external device 5 via the communication unit 11. The environmental information is information about the environment around the vehicle 9. For example, the environmental information may be information about the current or future weather in the area in which the vehicle 9 is traveling. Also, for example, the environmental information may be information indicating the presence or absence of construction, congestion, and traffic accidents on the road on which the vehicle 9 is traveling or on roads surrounding the road.

The environmental information acquisition unit 107 outputs the acquired environmental information to the travel time correction unit 105 and the arrival probability calculation unit 106. The environmental information acquisition unit 107 may acquire environmental information from the external device 5 at a predetermined timing or periodically, and store the acquired environmental information in the storage unit 12 as environmental information 121. In this case, the travel time correction unit 105 and the arrival probability calculation unit 106 acquire environmental information by referring to the environmental information 121 in the storage unit 12 as necessary.

The travel time correction unit 105 executes a travel time correction process. The travel time correction process is a process of correcting the travel time acquired from the car navigation system 4 according to at least one of the environmental information and the state of the passengers. The travel time correction process will be described in detail later. The travel time correction unit 105 outputs the corrected travel time to the communication unit 11.

The arrival probability calculation unit 106 executes an arrival probability calculation process. The arrival probability calculation process is a process for calculating the arrival probability based on at least one of the environmental information and the state of the passengers. Details of the arrival probability calculation process will be described later. The arrival probability calculation unit 106 outputs the calculated arrival probability of the vehicle 9 at the destination to the communication unit 11.

The navigation information acquisition unit 108 is a communication interface between the information processing device 1 and the car navigation system 4. The navigation information acquisition unit 108 requests destination information and travel time from the car navigation system 4. Furthermore, when the navigation information acquisition unit 108 acquires destination information from the car navigation system 4, it outputs the destination information to the target determination unit 102. Furthermore, when the navigation information acquisition unit 108 acquires travel time from the car navigation system 4, it outputs the travel time to the travel time correction unit 105.

The memory unit 12 stores various data necessary for the operation of the information processing device 1. The memory unit 12 stores environmental information 121, status log data 122, emotion DB 123, physical condition DB 124, and first program data 125. The memory unit 12 may also store vehicle registration information (not shown). The vehicle registration information is information about the vehicles 9 registered for use in the management system 500 (e.g., identification information for each vehicle 9, and information necessary for communication with each vehicle 9). The first program data 125 is a data group that brings together programs and libraries for the control unit 10 to realize various functions.

(Emotion DB and physical condition DB)
3 is a diagram showing an example of the data structure of emotion DB 123. Emotion DB 123 is a database that specifies the arrival probability correction coefficient (arrival probability coefficient) and the travel time correction coefficient (travel time coefficient) corresponding to each passenger emotion classification category. Various information specified in emotion DB 123 may be added, changed, or deleted.

Figure 4 is a diagram showing an example of the data structure of the physical condition DB 124. The physical condition DB 124 is a database that specifies the arrival probability coefficient and travel time coefficient corresponding to each passenger's physical condition category (physical condition level). The various information specified in the physical condition DB 124 may be added, changed, or deleted.

(External device 5)
The external device 5 is a device that provides information related to the environment around the vehicle 9. The external device 5 may be, for example, a server that provides a weather forecast service for each region, or traffic congestion conditions for each road, etc. Note that there may be a plurality of external devices 5, each of which communicates with at least one of the information processing device 1 and the car navigation system 4.

<<Processing flow of the entire system>>
5 is a flowchart showing an example of the processing flow of the management system 500. A user inputs information specifying a location or area for which congestion status is to be checked as a search condition via the terminal input unit 33 of the terminal device 3. The terminal input unit 33 accepts an operation to input the search condition (S100), and the terminal control unit 30 acquires the operation content from the terminal input unit 33. The terminal control unit 30 identifies the search condition from the operation content, and transmits information indicating the search condition to the cloud server 2 via the terminal communication unit 31 (S102).

The server control unit 20 of the cloud server 2 receives information indicating the search conditions via the server communication unit 21 (S104). The vehicle identification unit 201 identifies one or more registered vehicles. The request creation unit 202 creates a response request for the registered vehicles. The response request created by the request creation unit 202 includes at least information indicating the search conditions received by the cloud server 2 from the terminal device 3. The server communication unit 21 transmits the response request created by the request creation unit 202 to each registered vehicle (S108).

On the other hand, in the vehicle 9, the biometric information of the occupant of the vehicle 9 is measured periodically using the biometric sensor 6 from a predetermined timing such as when the vehicle 9 is unlocked or the engine is started. The biometric information acquisition unit 103 of the information processing device 1 periodically acquires the biometric information (S110) and estimates the state of the occupant (e.g., emotion and physical condition) from the biometric information (S112).

The environmental information acquisition unit 107 of the information processing device 1 communicates with the external device 5 at a predetermined timing via the communication unit 11 to acquire environmental information (S114). For example, when the engine of the vehicle 9 is started, the environmental information acquisition unit 107 acquires from the external device 5 weather forecast information for the area in which the vehicle 9 is located, or information indicating the traffic congestion situation on the road on which the vehicle 9 is traveling or is scheduled to travel.

In this state, the communication unit 11 of the information processing device 1 receives a response request from the cloud server 2 (S116). The communication unit 11 outputs the received response request to the condition acquisition unit 101. The condition acquisition unit 101 outputs information indicating the search conditions included in the response request to the target determination unit 102. The target determination unit 102 executes a condition match determination process based on the search conditions (S118).

(Condition Matching Determination Process)
6 is a flowchart showing the flow of the condition match determination process performed by the object determination unit 102. When the object determination unit 102 acquires information indicating the search conditions, the object determination unit 102 checks whether the power of the car navigation system 4 is ON via the navigation information acquisition unit 108 (S200). Note that this process is not essential.

If the car navigation system is powered on (YES in S200), the target determination unit 102 requests destination information from the car navigation system 4 via the navigation information acquisition unit 108 (S202). If the destination information can be acquired from the car navigation system 4 in response to the request (YES in S204), the target determination unit 102 determines whether the destination indicated by the destination information matches the search conditions (S206). For example, if a location is specified as a search condition, the target determination unit 102 may determine whether the specified location matches the destination. In this case, if the specified location matches the destination, the target determination unit 102 determines that the destination matches the search conditions, and if they do not match, the target determination unit 102 determines that the destination does not match the search conditions. Also, for example, if a region is specified as a search condition, the target determination unit 102 may determine whether the destination is included in the specified region. In this case, if the destination is included in the specified region, the target determination unit 102 determines that the destination matches the search conditions, and if not, the target determination unit 102 determines that the destination does not match the search conditions.

If the destination matches the search conditions (YES in S206), the target determination unit 102 determines that the vehicle 9 is a vehicle to be searched for (S208). On the other hand, if the destination does not match the search conditions (NO in S206), the target determination unit 102 determines that the vehicle 9 is a vehicle not to be searched for (S210).

In addition, even if the car navigation system 4 is not turned on (NO in S200) and if destination information is not obtained from the car navigation system 4 (NO in S204), the target determination unit 102 determines that the vehicle 9 is not a vehicle to be searched for (S210). This is because when the car navigation system 4 is not turned on and when a destination is not set in the car navigation system 4, the destination of the vehicle 9 is unknown.

Referring again to FIG. 5, if the result of the condition match determination process (S118) is that the vehicle 9 is not a vehicle to be searched for (NO in S120), the target determination unit 102 creates a non-target notification and outputs it to the communication unit 11. The communication unit 11 transmits the non-target notification to the cloud server 2 (S128).

On the other hand, if the result of the condition match determination process (S118) is that the vehicle 9 is the vehicle to be searched for (YES in S120), the target determination unit 102 instructs the state identification unit 104 to perform state identification process, the travel time correction unit 105 to perform travel time correction process, and the arrival probability calculation unit 106 to perform arrival probability calculation process. The target determination unit 102 executes the state identification process (S122) and outputs the identification result to the arrival probability calculation unit 106. The travel time correction unit 105 executes travel time correction process (S124). The arrival probability calculation unit 106 executes arrival probability calculation process (S126). The state identification process and the travel time correction process may be performed in any order. The state identification process and the travel time correction process may be performed in parallel. The travel time correction process and the arrival probability calculation process may be performed in parallel.

(State Identification Processing)
FIG. 7 is a flowchart showing the flow of the state identification process performed by the state identification unit 104. Note that the example of FIG. 7 describes an example in which both the emotion and physical condition of the passenger are identified. When the state identification unit 104 receives an instruction from the target determination unit 102, it refers to the emotion estimation results up to now and determines whether the same emotion has continued for a predetermined first time or more (S300). If it has continued (YES in S300), the state identification unit 104 determines the latest estimation result, i.e., the continuing emotion, as the current emotion (S302). On the other hand, if the same emotion has not continued for the predetermined first time or more (NO in S300), the state identification unit 104 determines the emotion that has continued for a second time or more in the past as the current emotion (S304).

The same applies to physical condition. The condition identification unit 104 refers to the previous estimated physical condition results and determines whether the same physical condition level has continued for a predetermined second time or more (S306). If it has continued (YES in S306), the condition identification unit 104 determines the latest estimated result, i.e., the ongoing physical condition level, as the current physical condition level (S308). On the other hand, if the same physical condition level has not continued for the predetermined second time or more (NO in S306), the condition identification unit 104 determines the emotion that has continued in the past for the second time or more as the current emotion (S310).

The past estimation results of emotions and physical conditions may be stored by the condition identification unit 104 for a certain period of time, or may be identified by referring to the condition log data 122. The first and second times may be the same length or different lengths. For example, the first and second times may be about five minutes. In this way, by identifying the current condition of the occupant as a state that has continued for a certain period of time, rather than an estimation result at a single current point, it is possible to prevent the accuracy of state identification from decreasing due to temporary changes in biometric information, etc.

<Travel time calculation process>
8 is a flowchart showing the flow of the state specification process performed by the travel time correction unit 105. The travel time correction unit 105 acquires the travel time from the car navigation system 4 via the navigation information acquisition unit 108 (S400). The travel time correction unit 105 corrects the acquired travel time according to the environmental information input from the environmental information acquisition unit 107 (S402).

For example, if the environmental information indicates that the weather around vehicle 9 is bad, such as heavy rain, heavy snow, or a typhoon, the travel time correction unit 105 may multiply the travel time by a travel time coefficient greater than 1. As a result, when the weather around vehicle 9 is bad, the travel time is corrected to a longer time than before correction. Also, for example, if the environmental information indicates that construction, congestion, or a traffic accident is occurring on the road on which vehicle 9 is traveling or on roads surrounding the road, the travel time correction unit 105 may multiply the travel time by a travel time coefficient greater than 1. As a result, when construction, congestion, or a traffic accident is occurring around vehicle 9, the travel time is corrected to a longer time than before correction.

The arrival probability calculation unit 106 further corrects the travel time corrected in S402 according to the state of the passenger (S404). For example, the arrival probability calculation unit 106 refers to the emotion DB 123 and the physical condition DB 124, and calculates a travel time coefficient corresponding to the passenger's overall state by multiplying a travel time coefficient corresponding to the emotion of the passenger identified by the state identification unit 104 by a travel time coefficient corresponding to the physical condition. The arrival probability calculation unit 106 then multiplies the calculated travel time coefficient by the travel time corrected in S402 to calculate the corrected travel time of the vehicle 9.

<Arrival probability calculation process>
9 is a flowchart showing the flow of the arrival probability calculation process executed by the arrival probability calculation unit 106. The arrival probability calculation unit 106 refers to the emotion DB 123 and reads out an arrival probability coefficient (X) corresponding to the emotion of the passenger identified by the state identification unit 104 (S500). Next, the arrival probability calculation unit 106 refers to the physical condition DB 124 and reads out an arrival probability coefficient (Y) corresponding to the physical condition of the passenger identified by the state identification unit 104 (S502). Next, the arrival probability calculation unit 106 calculates an arrival probability coefficient (Z) according to the environmental information.

Finally, the arrival probability calculation unit 106 multiplies each arrival probability and determines the arrival probability of vehicle 9 (S506). In other words, the arrival probability calculation unit 106 determines X*Y*Z to be the arrival probability of vehicle 9. Note that the processes of S500 to S504 may be performed in any order.

Returning to FIG. 5, the explanation will be given. Once the corrected travel time and the arrival probability of vehicle 9 are calculated, the control unit 10 outputs the corrected travel time and the arrival probability of vehicle 9 to the communication unit 11. The communication unit 11 transmits the corrected travel time and the arrival probability of vehicle 9 to the cloud server 2 (S128).

The cloud server 2 receives the corrected travel time and the arrival probability of the vehicle 9, or a non-target notification, from the vehicle 9 (S130). When the cloud server 2 receives the corrected travel time and the arrival probability of the vehicle 9, it may store this information in the server storage unit 22 as travel status data 221.

Then, the aggregation unit 203 of the cloud server 2 performs aggregation processing using the corrected travel time and the arrival probability of the vehicle 9 obtained from the vehicle 9 that sent the response request.

<<Aggregation process>>
10 is a flowchart showing an example of the flow of the tallying process. First, the tallying unit 203 divides the arrival probability acquired from each vehicle 9 into categories according to the length of the travel time associated with the arrival probability (S600). Next, the tallying unit 203 sums up the vehicle arrival probability (i.e., the number of vehicles 9 scheduled to arrive) for each time segment (S602). Finally, the display data creation unit 204 creates a graph showing the number of vehicles scheduled to arrive for each time segment (S604).

<Example of tally results>
11 to 13 are diagrams showing an example of image data indicating the tallying results generated by the display data generating unit 204 after the tallying process by the tallying unit 203. That is, it can be said that Fig. 11 to 13 are diagrams showing an example of the search results displayed on the terminal display unit 34 of the terminal device 3.

Figure 11 is a graph showing the results of dividing the time elapsed from the current time (future time) into specified time segments and tallying up the number of vehicles scheduled to arrive per unit time. Also, Figure 12 is a graph showing the results of dividing future time into specified time slots and tallying up the number of vehicles scheduled to arrive per unit time. As shown in the figure, by dividing the future time and showing the number of vehicles scheduled to arrive, it is possible to accurately predict, for example, the number of arriving vehicles in the future.

When multiple locations are input as search conditions, the tallying unit 203 may calculate tallying results for a specific time period and for each location. The display data creation unit 204 may then create image data showing the number of vehicles scheduled to arrive for each location at a specific time, and transmit the image data to the terminal device 3.

Figure 13 is a graph showing the number of vehicles scheduled to arrive at multiple locations within a specified period of time. In this way, by comparing the congestion situations at multiple locations at the same time, for example, a user of terminal device 3 can compare the congestion situations at each location.

[Embodiment 2]
The management system according to the present invention may include an on-board camera as the biosensor 6. The biometric information acquisition unit 103 may include an image acquisition unit that acquires an image captured by the on-board camera. The information processing device 1 may include a passenger number determination unit that determines the number of passengers in the vehicle 9 from the captured image, and an expected number of arrivals calculation unit that calculates the expected number of arrivals by multiplying the arrival probability by the number of passengers. The communication unit 11 may transmit the expected number of arrivals to the cloud server 2 as information related to the arrival probability.

The second embodiment will be described below. Note that components having the same functions as those described in the first embodiment will be denoted by the same reference numerals, and their description will not be repeated.

Figure 14 is a block diagram showing the main components of the management system 600 according to this embodiment. In this embodiment, the biometric sensor 6 includes at least an on-board camera. The biometric information acquisition unit 103 acquires images captured by the on-board camera showing passengers as biometric information. The biometric information acquisition unit 103 also transmits the acquired images to the passenger number determination unit 109. The memory unit 12 also stores first program data 126. The first program data 126 is a data group that compiles programs and libraries for the control unit 10 according to this embodiment to realize various functions.

The control unit 10 of the information processing device 1 according to this embodiment executes a program included in the first program data 126, thereby functioning as each unit of the control unit 10 according to the first embodiment, as well as the passenger number determination unit 109 and the arrival number calculation unit 110. The passenger number determination unit 109 determines the number of passengers in the vehicle 9 from an image captured by the onboard camera. The passenger number determination unit 109 transmits the determined number of passengers to the arrival number calculation unit 110.

The number of arrivals calculation unit 110 calculates the number of expected arrivals per vehicle 9 by multiplying the arrival probability of vehicle 9 calculated by the arrival probability calculation unit 106 by the number of passengers identified by the number of passengers identification unit 109. For example, if the arrival probability of vehicle 9 calculated by the arrival probability calculation unit 106 is 0.9 and the number of passengers is 2, the number of arrivals calculation unit 110 calculates the expected number of arrivals to be 0.9 x 2 = 1.8. The control unit 10 outputs the expected number of arrivals to the communication unit 11 together with the arrival probability or instead of the arrival probability. The communication unit 11 transmits the expected number of arrivals to the cloud server 2.

Then, the counting unit 203 of the cloud server 2 counts the number of people expected to arrive, instead of the number of vehicles expected to arrive. The counting method is the same as that for the number of vehicles expected to arrive shown in embodiment 1.

By the above process, the information processing device 1 can notify the cloud server 2 of the number of people expected to arrive. Therefore, the cloud server 2 can count not only the number of vehicles but also the number of people expected to arrive. This allows for more accurate prediction of congestion.

[Embodiment 3]
In the management system of the present invention, when the destination information cannot be obtained from the car navigation system 4, the target determination unit 102 may obtain information indicating the plans of the passengers (schedule information) from the terminal device (mobile terminal 7) of the passengers in the vehicle 9, and determine whether the schedule information matches the search information.

<Main components>
15 is a block diagram showing the configuration of the main parts of a management system 700 according to this embodiment. The management system 700 includes a mobile terminal 7 in addition to the various devices included in the management system 500.

The mobile terminal 7 is a terminal device carried by a passenger of the vehicle 9. The mobile terminal 7 includes a mobile terminal control unit 70, a mobile terminal communication unit 71, a mobile terminal storage unit 72, a GPS receiver 74, and a timing unit 75. The mobile terminal 7 may include an input interface and an output interface such as a touch panel 73, but the touch panel 73 is not a required component.

In this embodiment, if the navigation information acquisition unit 108 is unable to acquire destination information from the car navigation system 4, it notifies the target determination unit 102 of the failure to acquire destination information. When the target determination unit 102 is notified of the failure to acquire destination information, it requests the mobile terminal 7 via the communication unit 11 for the passenger's schedule information.

The mobile terminal communication unit 71 communicates between the mobile terminal 7 and the information processing device 1. The mobile terminal communication unit 71 receives requests from the information processing device 1 and sends them to the mobile terminal control unit 70. The mobile terminal communication unit 71 may also be connectable to the Internet. It may also access a server (not shown) that is also connected to the Internet to exchange information. For example, the mobile terminal communication unit 71 may be able to access a server that stores information related to group tours from a travel agency. For example, the mobile terminal communication unit 71 may obtain schedule information (schedule information 721) of a user (passenger) that is stored in the cloud or on a specific server by accessing the Internet. The mobile terminal communication unit 71 may also communicate with the information processing device 1 via the Internet.

The GPS receiver 74 receives GPS (Global Positioning System) signals transmitted from artificial satellites at a predetermined time interval. The GPS signals contain location information indicating the longitude and latitude of the location where the vehicle 9 is located. The GPS receiver 74 supplies the location information to the mobile terminal control unit 70. The clock unit 75 keeps track of the current time and supplies it to the mobile terminal control unit 70.

The mobile terminal control unit 70 performs overall control of the mobile terminal 7. The mobile terminal control unit 70 executes a program included in the third program data 720 (described below) to realize various functions as the mobile terminal control unit 70. When the mobile terminal control unit 70 receives a request from the information processing device 1, it reads out schedule information 721 from the mobile terminal storage unit 72.

In addition, the mobile device control unit 70 may refer to or obtain the user's (passenger's) schedule information (schedule information 721) stored in the cloud or on a specific server by accessing the Internet via the mobile device communication unit 71, rather than obtaining the schedule information 721 from the mobile device storage unit 72. In this case, the process flow is the same, except that the information reference source and information acquisition source are different.

The mobile terminal storage unit 72 stores third program data 720 and schedule information 721. The third program data 720 is a data group that compiles programs and libraries for the mobile terminal control unit 70 according to this embodiment to realize various functions. The schedule information 721 is information that includes one or more schedules, each of which is data that compiles the activity date and time (scheduled start date and time and scheduled end date and time of the activity), the activity location, and the activity content of the user of the mobile terminal 7, i.e., the passenger. The schedule information 721 may store not only future schedules, but also past schedules. In other words, the scheduled start time and scheduled end time of the activity may be past dates and times. In addition, each schedule in the schedule information 721 may include a purchase history of admission tickets for admission to various facilities (i.e., activity locations) related to the schedule.

For example, the schedule may include not only the passenger's personal plans and behavioral history, but also a schedule of group activities. For example, a schedule of group activities may include a schedule of a group tour in which the passenger is participating. If it is a group tour schedule, the meeting date and time and the disbanding or end date and time for each schedule may also be included in the schedule. In this case, it is desirable that each schedule is stored in the schedule information 721 in such a way that it can be distinguished as to whether it is a group tour schedule or an individual schedule.

The mobile terminal control unit 70 transmits the read schedule information 721 to the information processing device 1 via the mobile terminal communication unit 71. The mobile terminal control unit 70 may read and transmit all schedules in the schedule information 721, or may extract schedules within a predetermined range (for example, within 24 hours) from the current time and transmit them to the information processing device 1. When transmitting the schedule information 721 to the information processing device 1, the mobile terminal control unit 70 may calculate the travel distance from the current position to the action location of each schedule to be transmitted, and transmit information indicating the travel distance to the information processing device 1. For example, the travel distance from the current position to the action location can be calculated by the following method. That is, the mobile terminal control unit 70 identifies the current position based on the position information supplied from the GPS receiver 74. Then, the mobile terminal control unit 70 identifies the position of the action location indicated by each schedule by accessing a map service on the Internet via the mobile terminal communication unit 71. The mobile terminal control unit 70 calculates the travel distance from the current position and the position of the action location. In addition, the mobile device control unit 70 may calculate the travel time from the current location to the activity location of each schedule based on the travel distance and transmit this to the information processing device 1.

The object determination unit 102 acquires the schedule information 721 via the communication unit 11. The object determination unit 102 identifies one schedule that has the action date and time closest to the current time, and determines the action location indicated in the schedule as the destination. Note that if the action location cannot be identified from the schedule, the object determination unit 102 may determine that the vehicle 9 does not match the search conditions.

When the destination is identified from the schedule information in this way, the travel time correction unit 105 may execute the following process instead of the process shown in S400 of FIG. 8 in the travel time correction process. That is, the travel time correction unit 105 may acquire the travel time from the mobile terminal 7. The travel time correction unit 105 may also acquire the travel distance from the mobile terminal 7 to the destination and calculate the travel time based on the travel distance. The travel time correction unit 105 may also calculate the travel distance and travel time to the destination by itself. For example, the travel time correction unit 105 can calculate the travel distance from the distance between the destination identified from the schedule information acquired by the target determination unit 102 and the current position of the vehicle 9. The current position of the vehicle 9 may be acquired, for example, from a GPS receiver installed in the vehicle 9, a car navigation system 4, or the like.

<Condition matching determination process>
Fig. 16 is a flowchart showing another example of the condition match determination process. S700 to S708 in Fig. 16 are similar to S200 to S208 in Fig. 6, and therefore the description will not be repeated. In the condition match determination process according to this embodiment, when destination information cannot be acquired from the car navigation system 4 (NO in S704), or when the acquired destination information does not match the search condition (NO in S706), the target determination unit 102 searches for the mobile terminal 7 via the communication unit 11 (S710). When the mobile terminal 7 can be identified (YES in S710), the target determination unit 102 requests schedule information from the passenger's terminal device via the communication unit 11 (S712), and acquires the schedule information from the mobile terminal 7 (S714).

The target determination unit 102 determines whether the acquired schedule information matches the search conditions (S716). If it matches (YES in S716), the target determination unit 102 determines that the vehicle 9 is a vehicle to be searched (S708). On the other hand, if it does not match (NO in S716), the target determination unit 102 determines that the vehicle 9 is not a vehicle to be searched (S718).

According to the above process, even if the destination is not set in the car navigation system 4, the destination of the vehicle 9 can be inferred from the passengers' schedule, and it can be determined whether the vehicle 9 is a search target.

The mobile device storage unit 72 does not have to store the schedule information 721. If the mobile device storage unit 72 does not store the schedule information 721, the mobile device control unit 70 may obtain the schedule information 721 from another server or the like by accessing the Internet, as in the third embodiment.

[Embodiment 4]
If it were possible to predict how many people would be staying in a certain place at a certain time, it would be possible to more accurately grasp or predict the congestion situation in a specific place such as a facility. However, it has been difficult to predict when people staying in a certain place will leave.

The management system 800 according to this embodiment aims to accurately grasp the number of people staying at a location or area on the cloud server 2.

<Main components>
17 is a block diagram showing a main configuration of a management system 800 according to this embodiment. The management system 800 includes an information processing device 1, a cloud server 2, a biosensor 6, and a mobile terminal 7. The management system 800 may also include a terminal device 3.

The memory unit 12 of the information processing device 1 according to this embodiment includes condition log data 122, an emotion DB 123, a physical condition DB 124, and first program data 127. The first program data 127 is a data group that compiles programs and libraries for the control unit 10 according to this embodiment to realize various functions. The control unit 10 according to this embodiment functions as a biometric information acquisition unit 103 and a condition identification unit 104 by executing a program included in the first program data 127.

The mobile terminal 7 according to this embodiment includes a mobile terminal control unit 70, a mobile terminal communication unit 71, and a mobile terminal storage unit 72. The mobile terminal 7 may also include a touch panel 73.

The mobile device communication unit 71 communicates between the mobile device 7 and the cloud server 2 and the information processing device 1. The mobile device communication unit 71 receives status information from the information processing device 1 and outputs it to the status information acquisition unit 704. The mobile device communication unit 71 transmits to the cloud server 2 the arrival time of the user of the mobile device 7 (i.e., the passenger) at a specific location and a predicted value of the stay time at the specific location.

The mobile device storage unit 72 stores data necessary for the operation of the mobile device 7. The mobile device storage unit 72 stores schedule information 721, a registration location DB 722, stay time coefficient data 723, and third program data 724. The third program data 724 is a data group that brings together programs and libraries for the mobile device control unit 70 according to this embodiment to realize various functions. Note that the schedule information 721 and the stay time coefficient data 723 are not essential data.

The schedule information 721 includes information similar to the schedule information 721 according to the third embodiment. If the mobile device storage unit 72 does not include the schedule information 721, the mobile device control unit 70 may obtain the schedule information 721 from another server by accessing the Internet, as in the third embodiment.

Registered location DB722 is a database that stores, as one record, data that associates identification information of a pre-registered location, information indicating the location of the location (e.g., latitude and longitude), and the average stay time at the location. Registered location DB722 stores one or more records. In other words, registered location DB722 registers one or more specific locations. Note that a "specific location" refers to, for example, a facility, a landmark building, a specific area such as a city or street, etc.

In this embodiment, unless otherwise specified, a specific location is a certain facility. That is, in this embodiment, the registered location DB722 contains data that associates identification information of a certain facility, information indicating the location of the facility, and the average stay time at the facility.

The dwell time coefficient data 723 is data for identifying a coefficient to be multiplied by the dwell time in the dwell time correction process described below. The dwell time coefficient data 723 will be described in detail later.

The mobile terminal control unit 70 executes the program included in the third program data 724 to function as a location information acquisition unit 701, an arrival determination unit 702, a stay time prediction unit 703, a status information acquisition unit 704, and a stay time correction unit 705.

The location information acquisition unit 701 acquires location information from the GPS receiver 74. The location information acquisition unit 701 supplies the acquired location information to the arrival determination unit 702.

The arrival determination unit 702 determines whether the mobile terminal 7 and the passenger carrying the mobile terminal 7 have arrived at a specific location based on the location information.

When the arrival determination unit 702 determines that a passenger has arrived at a specific location, the stay time prediction unit 703 executes a stay time prediction process to predict the stay time of the passenger. The stay time prediction process will be described in detail later. The stay time prediction unit 703 outputs the predicted stay time to the status information acquisition unit 704. Hereinafter, unless otherwise specified, "stay time" refers to the predicted time predicted by the stay time prediction unit 703, not the actual stay time of the passenger.

The status information acquisition unit 704 acquires status information from the information processing device 1. The status information acquisition unit 704 outputs the status information to the dwell time correction unit 705. "Status information" is information that indicates the status of a passenger. The status information is created by the status identification unit 104 of the information processing device 1.

In this embodiment, the state identification unit 104 estimates the degree of well-being of the occupant as the state of the occupant. However, the state information may also include information indicating the emotions of the occupant. The classification of the emotions of the occupant and the degree of well-being of the occupant's physical condition may be the same as the classification described in embodiment 1.

The stay time correction unit 705 executes a stay time correction process to correct the stay time based on various information. The stay time correction process will be described in detail later.

The cloud server 2 according to this embodiment includes a counting unit 203 and a display data creation unit 204. The counting unit 203 according to this embodiment counts information received from the mobile terminal 7. The counting process of the counting unit 203 according to this embodiment will be described in detail later. The counting unit 203 outputs the counting result to the display data creation unit 204.

The display data creation unit 204 creates data to be sent to the terminal device 3 based on the tallying result of the tallying unit 203. In this embodiment, the display data creation unit 204 creates image data to be displayed on the terminal display unit 34 of the terminal device 3. The display data creation unit 204 outputs the created image data to the server communication unit 21.

<Processing flow>
18 is a flowchart showing a process flow of the mobile terminal 7 according to this embodiment. The position information acquisition unit 701 of the mobile terminal 7 acquires position information indicating the current position of the mobile terminal 7 at a predetermined time interval (S800). The position information acquisition unit 701 outputs the acquired position information to the arrival determination unit 702.

The arrival determination unit 702 determines whether the user of the mobile terminal 7, i.e., the passenger, has arrived at any specific location among the pre-registered locations based on the location information (S802). If the arrival determination unit 702 determines that the passenger has not arrived (NO in S802), the mobile terminal 7 waits until the location information acquisition unit 701 acquires location information again. On the other hand, if the arrival determination unit 702 determines that the passenger has arrived at a specific location (YES in S802), the arrival determination unit 702 notifies the stay time prediction unit 703 of information indicating the location where the passenger has arrived.

When the stay time prediction unit 703 receives the notification from the arrival determination unit 702, it executes a stay time prediction process (S804). The stay time prediction unit 703 outputs the predicted stay time to the stay time correction unit 705.

When the stay time is input from the state information acquisition unit 704, the stay time correction unit 705 executes a stay time correction process (S806). The stay time correction unit 705 transmits the current time (i.e., the arrival time) provided by the timing unit 75 and the corrected stay time to the cloud server 2 via the mobile device communication unit 71 (S808).

<<Stay Time Prediction Processing>>
19 is a flowchart showing an example of the flow of the staying time prediction process. When the staying time prediction unit 703 receives a notification from the arrival determination unit 702, it reads out at least a part of the schedule information 721 stored in the mobile device storage unit 72 (S810). Alternatively, the staying time prediction unit 703 may download a part or all of the passenger's schedule information from an external server via the mobile device communication unit 71.

When the stay time prediction unit 703 obtains the schedule information, it identifies a schedule that matches the current time (S812). The stay time prediction unit 703 then determines whether the activity location indicated by the identified schedule matches the specific location where the arrival determination unit 702 has determined that the user has arrived (S814). If they do not match (NO in S814), the stay time prediction unit 703 executes the process of S828 described below.

On the other hand, if there is a match, the stay time prediction unit 703 determines whether or not the schedule identified in S812 is a schedule for group activity (for example, a group tour in the example of FIG. 19) (S816). For example, the stay time prediction unit 703 refers to the schedule information 721 to determine whether or not the schedule identified in S812 is a schedule for a group tour. If it is a schedule for a group tour (YES in S816), the stay time prediction unit 703 predicts the stay time from the meeting time and the disbanding time (or end time) in the schedule (S818). For example, the stay time prediction unit 703 may predict the stay time to be the time from the current time to the disbanding time (or end time).

If the schedule identified in S812 is not a group tour schedule (NO in S816), the stay time prediction unit 703 refers to the schedule information 721 to determine whether or not a schedule in a different location is set immediately after the schedule identified in S812 (S820). Specifically, the stay time prediction unit 703 determines whether or not a schedule in a different location from the activity location of the schedule identified in S812 has been registered. Hereinafter, the "schedule in a different location immediately after the schedule identified in S812" will be referred to simply as the "later schedule."

If a later schedule is set (YES in S820), the stay time prediction unit 703 predicts the passenger's stay time from the scheduled start date and time of the later schedule (S822). The stay time prediction unit 703 may, for example, set the exit time to a time that is a predetermined travel time back from the scheduled start date and time of the later schedule, and predict the stay time to be the time from the current time to the exit time.

If no future schedule has been set (NO in S820), the stay time prediction unit 703 determines whether the schedule information 721 includes a past schedule in which the location of the activity is the same as the specific location notified by the arrival determination unit 702. In other words, the stay time prediction unit 703 determines whether the passenger has previously visited the location where the passenger is currently located (S824).

If the user has visited the venue in the past (YES in S824), the stay time prediction unit 703 predicts the current stay time from the stay time of the previous visit (S826). Note that if the user has visited the venue multiple times in the past, the stay time of the previous visits may be averaged, and this average value may be used as the current stay time.

If the passenger has not visited the venue before (NO in S824), the stay time prediction unit 703 refers to the registered location DB 722 and predicts the average stay time of the passenger at the location notified by the arrival determination unit 702 (S828).

Note that among the processes in FIG. 19, processes other than S810 to S814 and S828 are not essential. For example, one or more of the determinations of S816, S820, and S824 may be omitted.

<<Adjustment of Stay Time>>
FIG. 20 is a flowchart showing an example of the flow of the stay time correction process.

The state information acquisition unit 704 requests state information from the information processing device 1 and acquires state information in response to the request (S840). The state information acquisition unit 704 outputs the state information to the stay time correction unit 705.

The staying time correction unit 705 corrects the staying time according to the status information (S842). For example, the staying time correction unit 705 multiplies the staying time by a staying time coefficient according to the degree of the passenger's physical condition. Specifically, the status information acquisition unit 704 may multiply the staying time by the following staying time coefficients according to the level of the passenger's physical condition.
Physical condition level 1: Stay time coefficient = 0.7
Physical condition level 2: Stay time coefficient = 0.8
Physical condition level 3: Stay time coefficient = 0.9
Physical condition level 4 or higher: Stay time coefficient = 1
Next, the staying time correction unit 705 identifies the customer demographics of the passengers (S844). Specifically, the staying time correction unit 705 acquires an image of the passengers and accompanying persons from a camera mounted on the vehicle 9, and analyzes the image to identify the number of passengers and accompanying persons, as well as the age (or classification as adult or child) and gender of each person. Then, based on the identified number of persons, age, and gender, the relationship between the passengers and accompanying persons may be classified as the customer demographics. Note that, if there are no accompanying persons, the customer demographics may be identified based on the age, gender, etc. of the passengers.

The process of S844 may be executed in advance before executing the process of S800 in FIG. 18. For example, the customer demographics may be identified at the timing when the vehicle 9 arrives at the destination and stops. In this case, the control unit 10 identifies that the vehicle 9 has stopped at the destination, for example, from information from various mechanisms of the vehicle 9. Alternatively, the control unit 10 identifies the movement of the passengers (and accompanying persons) from images of the biosensor 6 or a camera mounted on the vehicle 9, and determines that the vehicle 9 has stopped at the destination when the passengers (and accompanying persons) are moving to disembark.

When the customer demographic is identified in advance in this manner, if the passenger does not arrive at any of the pre-registered locations within a predetermined time (e.g., 15 minutes) after identifying the customer demographic, the stay time correction unit 705 does not need to execute the stay time correction process (S848 described below) using the identified customer demographic. This makes it possible to prevent, for example, a customer demographic identified in the past from being mistakenly used to correct the stay time.

In addition, the stay time correction unit 705 identifies the classification of the facility by referring to the registered location DB 722 for the location (e.g., facility) where the passenger arrived, as identified by the arrival determination unit 702.

Next, the stay time correction unit 705 corrects the stay time corrected in S842 according to the customer demographic and facility classification (S848). For example, the stay time correction unit 705 may refer to the stay time coefficient data 723 to identify a stay time coefficient by which to further multiply the stay time corrected in S842.

<<Stay Time Coefficient Data 723>>
Fig. 21 is a diagram showing an example of the stay time coefficient data 723. As shown in the figure, the stay time coefficient data 723 is data indicating a stay time coefficient for each facility classification and each customer demographic. Note that the format of the stay time coefficient data 723 is not limited to that shown in Fig. 21.

Note that among the processes in FIG. 20, steps S844, S846, and S848 are not essential. Therefore, some or all of these three processes may be omitted.

<<Aggregation process>>
FIG. 22 is a flowchart showing an example of the flow of the tallying process performed by the tallying unit 203. First, the tallying unit 203 calculates the scheduled exit time of each user of the mobile terminal 7 (i.e., each passenger) from the arrival time and stay time included in the arrival notice acquired from each mobile terminal 7 (S900). The tallying unit 203 then calculates the number of people staying in each period when the time is divided into a predetermined period (e.g., one hour) (S902). Here, the number of people staying is equal to the number of arrival notices indicating a time when the arrival time plus the stay time is later than the end time of each period. For example, the number of people staying in the period from 13:00 to 13:59 on a certain day is indicated by the number of arrival notices indicating a time when (arrival time + stay time) is later than 13:59. Finally, the display data creation unit 204 creates a graph showing the number of people staying in each period (S904).

By using the above process, the cloud server 2 can accurately tally up the change in the number of visitors over time at a particular location. The terminal device 3 can then receive the tally up graph from the cloud server 2 and display it, thereby showing the searcher the change in the number of visitors over time at a particular location.

[Modification 1]
In the fourth embodiment, the schedule information 721 may not include information indicating whether each schedule is a group activity schedule. In this case, the stay time prediction unit 703 may make the determination in S816 in FIG. 19 based on information acquired from a server of a travel agency that is affiliated with the management system 800.

Specifically, the stay time prediction unit 703 first identifies personal information of the user (i.e., the passenger) from the user information in the mobile device storage unit 72. Personal information includes, for example, name, address, and date of birth. The stay time prediction unit 703 then connects to the Internet via the mobile device communication unit 71 and searches for information related to tours stored on a travel agency's server on the Internet that matches the user's personal information. For example, the stay time prediction unit 703 searches to see if anyone with the same name and date of birth as the user is planning to participate in the tour.

If there is a plan to join a tour, the stay time prediction unit 703 determines whether the destination of the planned tour matches the activity location indicated in the schedule identified in S812. If they match, the stay time prediction unit 703 determines that the schedule identified in S812 is a group tour schedule (YES in S816). On the other hand, if there is no plan to join a tour, or if the destination of the planned tour does not match the activity location indicated in the schedule identified in S812, the stay time prediction unit 703 determines that the schedule identified in S812 is not a group tour schedule (NO in S816).

[Modification 2]
In the first to fourth embodiments, some of the various processes executed by the control unit 10 of the information processing device 1 may be executed by the server control unit 20 of the cloud server 2. Also, some of the various processes executed by the server control unit 20 may be executed by the control unit 10.

In this case, data required for various processes is transmitted and received between the information processing device 1 and the cloud server 2 as appropriate. The first program data 125, 126, or 127 may include programs and libraries required for the processes handled by the control unit 10 among the processes executed by the control unit 10 and the server control unit 20. The second program data 222 or 223 may include programs and libraries required for the processes handled by the server control unit 20 among the processes executed by the control unit 10 and the server control unit 20.

[Modification 3]
In the third and fourth embodiments, some of the various processes executed by the control unit 10 of the information processing device 1 may be executed by the mobile terminal control unit 70 of the mobile terminal 7. Also, some of the various processes executed by the mobile terminal control unit 70 may be executed by the control unit 10.

In this case, data required for various processes is transmitted and received between the information processing device 1 and the mobile terminal 7 as appropriate. The first program data 125 or 127 may include programs and libraries required for the processes handled by the control unit 10 among the processes executed by the control unit 10 and the mobile terminal control unit 70. The third program data 720 or 724 may include programs and libraries required for the processes handled by the mobile terminal control unit 70 among the processes executed by the control unit 10 and the mobile terminal control unit 70.

[Software implementation example]
The control blocks of the information processing device 1, the cloud server 2, the terminal device 3, and the car navigation system 4 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software.

In the latter case, the information processing device 1, the cloud server 2, the terminal device 3, and the car navigation system 4 are provided with a computer that executes instructions of a program, which is software that realizes each function. This computer is provided with, for example, one or more processors, and a computer-readable recording medium that stores the program. The object of the present invention is achieved by the processor reading the program from the recording medium and executing it in the computer. The processor can be, for example, a CPU (Central Processing Unit). The recording medium can be a "non-transient tangible medium", such as a ROM (Read Only Memory), as well as a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, etc. The computer can also be provided with a RAM (Random Access Memory) that deploys the program. The program can also be supplied to the computer via any transmission medium (such as a communication network or a broadcast wave) that can transmit the program. Note that one aspect of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. The technical scope of the present invention also includes embodiments obtained by appropriately combining the technical means disclosed in different embodiments.

LIST OF SYMBOLS 1 Information processing device 2 Cloud server 3 Terminal device 4 Car navigation 5 External device 6 Biometric sensor 7 Mobile terminal 9 Vehicle 10 Control unit 11 Communication unit 12 Memory unit 20 Server control unit 21 Server communication unit 22 Server memory unit 30 Terminal control unit 31 Terminal communication unit 32 Terminal memory unit 33 Terminal input unit 34 Terminal display unit 40 Navigation control unit 41 Navigation communication unit 42 Navigation memory unit 43 Navigation input unit 44 Navigation display unit 70 Mobile terminal control unit 71 Mobile terminal communication unit 72 Mobile terminal memory unit 101 Condition acquisition unit 102 Target determination unit 103 Biometric information acquisition unit 104 State identification unit 105 Travel time correction unit 106 Arrival probability calculation unit 107 Environmental information acquisition unit 108 Navigation information acquisition unit 109 Number of passengers identification unit 110 Number of arrivals calculation unit 121 Environmental information 122 State log data 201 Vehicle identification unit 202 Request creation unit 203 Aggregation unit 204 Display data creation unit 221 Movement status data 500, 600, 700, 800 Management system 123 Emotion database 124 Physical condition database 701 Position information acquisition unit 702 Arrival determination unit 703 Stay time prediction unit 704 Status information acquisition unit 705 Stay time correction unit 721 Schedule information 722 Registered location database 723 Stay time coefficient data

Claims (19)

An information processing device mounted on a vehicle,
a navigation information acquisition unit that acquires information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle;
an arrival probability calculation unit that calculates an arrival probability that the vehicle will arrive at the destination in the future from a current time ;
a transmission unit that transmits the travel time and information related to the arrival probability to a management device that manages an arrival status of a plurality of vehicles ,
An information processing device characterized in that the arrival probability becomes lower as at least one of the condition, including at least one of the physical condition and emotion of the vehicle's occupants, and the environment, including at least one of the weather and traffic congestion situation around the vehicle, deteriorates . The information processing device according to claim 1, further comprising a travel time correction unit that corrects the travel time to the destination according to the passenger's condition. a biometric information acquiring unit that acquires biometric information of a vehicle occupant from a biometric sensor mounted in the vehicle;
a condition identification unit that identifies a condition of the occupant from the biological information,
3 . The information processing apparatus according to claim 1 , wherein the arrival probability calculation unit calculates the arrival probability based on the state of the passenger identified by the state identification unit. The state of the passenger includes at least one of the passenger's emotion and physical condition,
The biometric information acquisition unit periodically acquires the biometric information,
The state specifying unit is
Periodically estimating at least one of the passenger's emotional category and physical condition;
If a certain emotion continues for a predetermined first time or more as a result of the estimation, the emotion is identified as an emotion of the passenger;
4. The information processing device according to claim 3, wherein, when a certain physical condition continues for a predetermined second time or longer as a result of the estimation, the certain physical condition is identified as the physical condition of the passenger. An environmental information acquisition unit that acquires environmental information that is information about an environment around the vehicle,
5. The information processing apparatus according to claim 1, wherein the arrival probability calculation unit calculates the arrival probability based on the environmental information. an image acquisition unit for acquiring an image captured by an in-vehicle camera;
an occupant number determination unit that determines the number of occupants of the vehicle from the captured image;
and an expected number of arrivals calculation unit that calculates the expected number of arrivals by multiplying the arrival probability by the number of passengers,
6. The information processing apparatus according to claim 1, wherein the transmission unit transmits the number of expected arrivals to the management device as information related to the arrival probability. An information processing device mounted on a vehicle,
a navigation information acquisition unit that acquires information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle;
a biometric information acquiring unit that acquires biometric information of a vehicle occupant from a biometric sensor mounted in the vehicle;
a state identification unit that identifies a state of the occupant of the vehicle, including at least one of a physical condition and an emotion of the occupant , from the biological information;
A travel time correction unit that corrects a travel time to the destination according to a state of the passenger;
a transmission unit that transmits the corrected travel time to a management device that manages the arrival status of a plurality of vehicles ,
The travel time is
The worse the condition of the passenger, the longer the value is corrected.
An information processing device, characterized in that the length is corrected in accordance with the emotion of the occupant . The state of the passenger includes at least one of the passenger's emotion and physical condition,
The biometric information acquisition unit periodically acquires the biometric information,
The state specifying unit is
Periodically estimating at least one of the passenger's emotional category and physical condition;
If a certain emotion continues for a predetermined first time or more as a result of the estimation, the emotion is identified as an emotion of the passenger;
8. The information processing device according to claim 7, wherein, when a certain physical condition continues for a predetermined second time or longer as a result of the estimation, the certain physical condition is identified as the physical condition of the passenger. an arrival probability calculation unit that calculates an arrival probability of the vehicle at the destination according to a state of the passenger;
9. The information processing apparatus according to claim 7, wherein the transmission unit transmits the travel time and information related to the arrival probability to the management apparatus. An environmental information acquisition unit that acquires environmental information that is information about an environment around the vehicle,
The information processing apparatus according to claim 9 , wherein the arrival probability calculation unit calculates the arrival probability based on the environmental information. an image acquisition unit for acquiring an image captured by an in-vehicle camera;
an occupant number determination unit that determines the number of occupants of the vehicle from the captured image;
and an expected number of arrivals calculation unit that calculates the expected number of arrivals by multiplying the arrival probability by the number of passengers,
11. The information processing apparatus according to claim 9, wherein the transmission unit transmits the number of expected arrivals to the management apparatus as information related to the arrival probability. a condition acquisition unit that acquires search conditions, which are information indicating a specific place or area, from the management device;
and a target determination unit that determines that the vehicle is a search target vehicle when the destination matches a specific location or area;
The information processing device according to any one of claims 1 to 11, characterized in that the transmission unit transmits the travel time when the target determination unit determines that the vehicle is a vehicle to be searched for. A method for controlling an information processing device mounted on a vehicle, comprising:
a navigation information acquisition step of acquiring information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle;
an arrival probability calculation step of calculating an arrival probability that the vehicle will arrive at the destination in the future from a current time ;
a transmission step of transmitting the travel time and information related to the arrival probability to a management device that manages the arrival status of a plurality of vehicles ;
A control method characterized in that the arrival probability becomes lower as at least one of the conditions, including at least one of the physical condition and emotion of the vehicle's occupants, and the environment, including at least one of the weather and traffic congestion conditions around the vehicle, deteriorates . A method for controlling an information processing device mounted on a vehicle, comprising:
a navigation information acquisition step of acquiring information indicating a destination and a travel time to the destination from a navigation device mounted on the vehicle;
a biometric information acquiring step of acquiring biometric information of a vehicle occupant from a biometric sensor mounted on the vehicle;
a state identification step of identifying a state of the occupant of the vehicle, the state including at least one of a physical condition and an emotion of the occupant from the biological information;
a travel time correction step of correcting a travel time to the destination according to a state of the passenger;
a transmission step of transmitting the corrected travel time to a management device that manages the arrival status of a plurality of vehicles ;
The travel time is
The worse the condition of the passenger, the longer the value is corrected.
A control method comprising the steps of: correcting the length in response to the emotion of the passenger ; A mobile terminal carried by a vehicle occupant,
a location information acquisition unit that periodically acquires location information indicating a current location of the mobile terminal;
an arrival determination unit that determines whether the passenger has arrived at a specific location among one or more locations registered in advance based on the location information;
a stay time prediction unit that predicts a stay time of the passenger at the specific location when the arrival determination unit determines that the passenger has arrived at the specific location;
a status information acquisition unit that acquires status information indicating a status of the passenger from an information processing device mounted on the vehicle;
a stay time correction unit that corrects the stay time predicted by the stay time prediction unit based on the state information;
a mobile terminal communication unit that transmits the corrected staying time to a management device that manages the number of people staying at the specific location,
In the information processing device, the state of the occupant includes a physical condition of the occupant identified based on biological information acquired from a biological sensor mounted on the vehicle,
A mobile terminal, characterized in that the staying time predicted by the staying time prediction unit becomes shorter when the physical condition of the passenger deteriorates . The mobile terminal according to claim 15 , characterized in that the stay time prediction unit predicts the stay time of the passenger at the specific location based on schedule information including a schedule indicating the date and time, location, and content of the passenger's activity. The action date and time includes a scheduled start date and time and a scheduled end date and time of the schedule,
The mobile terminal according to claim 16, characterized in that, when another schedule in a different location is set immediately after a schedule that matches the current time, the stay time prediction unit predicts the stay time according to a scheduled start date and time of the other schedule. The schedule information includes a past schedule,
The mobile terminal of claim 16 or 17, characterized in that, when the schedule information includes a past schedule in which the same location as the specific location is used as a location of activity, the stay time prediction unit predicts the stay time of the passenger based on the past stay time at the location of activity. The schedule information includes a schedule of group activities;
The activity date and time of the schedule of the group activity includes a meeting date and a disbanding or ending date and time,
The mobile terminal according to any one of claims 16 to 18, characterized in that, when a schedule that matches the current time is a schedule for group activity, the stay time prediction unit predicts the stay time based on the meeting date and time and the disbanding or end date and time.

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