WO2024166411A1

WO2024166411A1 - Information processing device, information processing method, and information processing program

Info

Publication number: WO2024166411A1
Application number: PCT/JP2023/023180
Authority: WO
Inventors: 幸秀 ▲高▼垣; 太郎長▲瀬▼; 良平加川; 友二伊藤
Original assignee: パイオニア株式会社
Priority date: 2023-02-10
Filing date: 2023-06-22
Publication date: 2024-08-15

Abstract

An information processing device (100) according to the present disclosure comprises: a distribution unit (136) that distributes, to a target vehicle traveling in a prescribed road section, first timing information that indicates a timing related to an output of content and that is generated on the basis of a section average speed, which is an average speed of a vehicle in the prescribed road section; and a calculation unit (137) that, after the first timing information has been distributed, calculates a predicted average speed on the basis of the section average speed and a cumulative average speed, which is an average speed based on travel records at a prescribed time point while the target vehicle is traveling in the prescribed road section, the predicted average speed being an average speed of the target vehicle in the prescribed road section that is predicted at the prescribed time point. In addition, the distribution unit determines, on the basis of prescribed information based on the section average speed, whether or not to distribute second timing information that indicates a timing related to the output of the content and that is newly generated using the predicted average speed.

Description

Information processing device, information processing method, and information processing program

This disclosure relates to an information processing device, an information processing method, and an information processing program.

　Conventionally, methods have been proposed to calculate appropriate average values (e.g., average link travel time or average vehicle speed) based on the link attributes of road links.

JP 2007-241813 A

However, the above conventional technologies do not necessarily enable content to be output at the appropriate time.

For example, the above-mentioned conventional technology appropriately averages link travel times and vehicle speeds by taking into account factors that cause changes in vehicle speed, such as traffic lights, railroad crossings, and toll booths. However, simple averaging does not necessarily result in content being output at the appropriate time. Furthermore, the above-mentioned conventional technology does not even consider the idea of outputting content.

The present disclosure has been made in consideration of the above, and proposes an information processing device, an information processing method, and an information processing program that are capable of outputting content at appropriate timing.

The information processing device according to claim 1 includes a distribution unit that distributes to a target vehicle traveling on a specific road section first timing information indicating a timing for outputting content, the first timing information being generated based on a section average speed, which is the average speed of the vehicle on the specific road section, and a calculation unit that calculates a predicted average speed, which is the average speed of the target vehicle on the specific road section predicted at the specific time point based on a cumulative average speed, which is the average speed based on the driving performance of the target vehicle at a specific time point while the target vehicle is traveling on the specific road section, and the section average speed after the first timing information is distributed, and the distribution unit determines whether or not to distribute second timing information indicating a timing for outputting the content, which is newly generated using the predicted average speed, based on the specified information based on the section average speed.

The information processing method according to claim 12 is an information processing method executed by an information processing device, and includes a distribution step of distributing, to a target vehicle traveling on a specific road section, first timing information indicating a timing related to output of content, the first timing information being generated based on a section average speed, which is the average speed of the vehicle on the specific road section, and a calculation step of calculating, after the first timing information is distributed, a predicted average speed, which is the average speed of the target vehicle on the specific road section predicted at the specific time point based on a cumulative average speed, which is the average speed based on the driving performance of the target vehicle at the specific time point while the target vehicle is traveling on the specific road section, and the section average speed, and the distribution step determines whether or not to distribute second timing information indicating a timing related to output of the content, which is newly generated using the predicted average speed, based on the specified information based on the section average speed.

The information processing program according to claim 13 is an information processing program executed by an information processing device, and causes the information processing device to execute a delivery procedure for delivering first timing information indicating a timing for outputting content, the first timing information being generated based on a section average speed, which is the average speed of the vehicle in a specified road section, to a target vehicle traveling on the specified road section, and a calculation procedure for calculating a predicted average speed, which is the average speed of the target vehicle in the specified road section predicted at the specified time point, based on a cumulative average speed, which is the average speed based on the driving performance of the target vehicle at a specified time point while the target vehicle is traveling on the specified road section, and the section average speed, after the first timing information is delivered, and the delivery procedure determines whether or not to deliver second timing information indicating a timing for outputting the content, which is newly generated using the predicted average speed, based on specified information based on the section average speed.

FIG. 1 is a diagram illustrating an example of a system according to an embodiment. FIG. 2 is a schematic diagram illustrating an example of the operation of the server device. FIG. 3 is a schematic diagram showing an example of the operation of the in-vehicle device. FIG. 4 is a diagram illustrating an example of the configuration of the server device and the in-vehicle device according to the embodiment. FIG. 5 is a diagram showing a specific example of a method for estimating the WL type. FIG. 6 is a diagram showing a specific example of a method for calculating a predicted average speed. FIG. 7 is a flowchart showing a timing information processing procedure when the vehicle VE is traveling on the leading link. FIG. 8 is a flowchart showing a timing information processing procedure when the vehicle VE is traveling on the n-th link. FIG. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the server device 100.

[Embodiment]
Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings. Note that the information processing device, information processing method, and information processing program according to the present disclosure are not limited to the embodiment. In addition, the same components in the following embodiments are given the same reference numerals, and duplicated descriptions are omitted.

1. Introduction
Navigation systems that guide users (drivers) to their destinations generally employ a method of predicting (estimating) the time of arrival at the destination based on the average time required to travel a link and the average speed on the link.

However, this method is not suitable for providing content that takes into account the driving load (called "workload"). Specifically, when providing content that takes into account the driving load, it is necessary to output content from the in-vehicle device at an appropriate time so as not to disturb the driver, but simply using various average values in the link, as in conventional methods, may not be able to output content at the appropriate time.

For example, vehicle driving is heavily influenced by individual differences and changes in road conditions, so if the average speed deviates from what is expected, a discrepancy will occur between the actual time and the predicted time. In this way, when using the statistical average speed on a link, the accuracy of predicting the time when a vehicle will reach a specific point is not high, and there is a problem that content is output at inappropriate times that could be an obstacle to the driver.

Another method that could be considered would be to output content after a certain grace period (buffer) has been set in anticipation of a discrepancy between the actual time and the predicted time, but this method could result in the content being output in an unpredictable manner.

On the other hand, in order to improve prediction accuracy, it is possible to, for example, re-predict each time the vehicle's driving conditions change, rather than using the statistical average speed on the link. However, in cases where a configuration is adopted in which prediction result information is distributed from the cloud to the in-vehicle device, the increase in the number of distributions leads to an increase in operating costs, which becomes a problem.

This disclosure proposes a new technology that solves the above problem. Specifically, in this disclosure, when sufficient driving history by the vehicle has not been secured and, for example, there is a long remaining distance from the current position on the driving route to a specific point (for example, a change point where the type of driving load changes), the arrival time is predicted using a statistical average speed corresponding to the driving scene. On the other hand, once sufficient driving history has been secured, the cumulative average speed according to the driving history is apportioned to the statistical average speed corresponding to the driving scene, and the apportioned result is determined as the average speed to be used in predicting the arrival time.

In other words, this disclosure is a measure that makes a plausible prediction when sufficient driving history is not secured, and as the vehicle approaches a specific point and driving history is secured, distributes the cumulative average speed proportionately to absorb the difference with the actual arrival time. As a result, while the number of times prediction result information is distributed is reduced when the vehicle is away from the specific point, highly accurate prediction result information regarding the arrival time is distributed when the vehicle approaches the specific point. Furthermore, since the in-vehicle device can use the highly accurate prediction result information, it becomes possible to output content at an appropriate timing. Below, information processing according to an embodiment of the present disclosure will be specifically described.

2. System Configuration
First, the configuration of a system according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of a system according to an embodiment. Fig. 1 shows a system 1 as an example of a system according to an embodiment. Information processing according to an embodiment may be realized in the system 1.

As shown in FIG. 1, the system 1 may include a cloud system 10 and an in-vehicle device 200. The cloud system 10 and the in-vehicle device 200 may be connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection.

The cloud system 10 includes a server device 100, which is a central device responsible for information processing according to an embodiment of the present disclosure.

The server device 100 acquires, for example from a database, the section average speed, which is the average speed obtained statistically for the road section on which the target vehicle VE is traveling, and generates first timing information indicating the timing of the output of the content based on the acquired section average speed. The server device 100 then distributes the first timing information to the in-vehicle device 200 possessed by the target vehicle VE.

After distributing the first timing information, the server device 100 calculates the cumulative average speed, which is the average speed at a given time point, based on the driving performance at the given time point while the target vehicle VE is traveling on the road section. The server device 100 then calculates the predicted average speed, which is the average speed predicted at the given time point, based on the cumulative average speed and the section average speed, and is the average speed of the target vehicle VE on the road section on which the target vehicle VE is traveling.

Then, the server device 100 determines whether or not to distribute the second timing information indicating the timing of the output of the content, which is newly generated using the predicted average speed instead of the average speed of the section, based on predetermined information based on the average speed of the section.

If the server device 100 determines to distribute the second timing information, it generates second timing information indicating the timing of the output of the content based on the predicted average speed. Then, the server device 100 distributes the second timing information to the in-vehicle device 200 of the target vehicle VE.

Note that, according to the above, the target vehicle VE is a vehicle to which content is output, and may be any vehicle equipped with the in-vehicle device 200. In the following embodiment, the target vehicle VE is abbreviated to "vehicle VE", and the driver of the target vehicle VE is referred to as "driver D". Furthermore, the content referred to here may be content output by voice, such as guidance content related to navigation or warnings, recommended content that suggests spots (e.g., tourist spots, stores, etc.) and various events that are considered to be useful to the user, or other content related to various news and everyday conversations.

The in-vehicle device 200 may be a dedicated navigation device built into or mounted on the vehicle VE. For example, the in-vehicle device 200 may be composed of a navigation device and a recording device. As one example, the in-vehicle device 200 may be a composite device in which a navigation device and a recording device that are independent of each other are connected so as to be able to communicate with each other. As another example, the in-vehicle device 200 may be a single device having a navigation function and a recording function.

The in-vehicle device 200 may also be equipped with various sensors. For example, the in-vehicle device 200 may be equipped with various sensors such as a camera, an acceleration sensor, a gyro sensor, a GPS (Global Positioning System) sensor, and an air pressure sensor. For this reason, the in-vehicle device 200 may also have a function of providing dialogue and information to assist driving based on sensor information acquired by the various sensors.

In addition, the in-vehicle device 200 can use not only the sensors provided in the device itself, but also sensor information detected by sensors provided in the vehicle VE itself as a safe driving system.

In addition, by installing specific application software into a portable terminal device (e.g., a smartphone, tablet terminal, notebook PC, PDA, etc.) that a user uses on a daily basis, the portable terminal device can be made to operate in the same manner as the in-vehicle device 200.

3. Overview of the Server Device
Next, an operation example of the server device 100 will be described with reference to Fig. 2. Fig. 2 is a schematic diagram showing an operation example of the server device 100. According to the example of Fig. 2, the cloud system 10 may include the server device 100 having a workload estimation engine E, a situation grasping engine 231, a guidance information DB, and an application MA.

Note that the workload (WL) referred to here indicates the driving load and may include both the driver's sense of burden (which can also be considered the degree of difficulty) and the driving load set for a road section.

Types of driving load include, for example, "BUSY," "IDEAL," and "FREE," and indicate that the load on the driver on a road section designated as "BUSY" is above a standard (i.e., driving difficulty is high), the load on the driver on a road section designated as "FREE" is below a standard (i.e., driving difficulty is low or not high), and the load on the driver on a road section designated as "IDEAL" is medium (i.e., driving difficulty is normal or not high).

Furthermore, the degree of difficulty for the driver may be expressed numerically as the driver's sense of burden, and can be defined as follows:

For example, a level of difficulty of "1" corresponds to a driving load type of "BUSY_MAX" and is a road section where all general drivers have to be careful when driving, and it is defined that the in-vehicle device 200 should only issue a warning notification on such road sections.

The level of difficulty "0.80" corresponds to a driving load type of "BUSY+", and is a road section where more than 60% of general drivers have to be careful when driving. It is defined that in such road sections, the in-vehicle device 200 should only issue warning notifications and caution notifications.

The level of difficulty "0.60" corresponds to a "BUSY" type of driving load, and is a road section where more than 20% of general drivers have to be careful when driving. It is defined that in such road sections, the in-vehicle device 200 should only issue warning notifications, caution notifications, and important notifications.

The level of difficulty "0.50" corresponds to the driving load type "IDEAL", and it is defined that in the relevant road section, the in-vehicle device 200 may also speak content other than guidance-related information (warning notifications, caution notifications, important notifications).

The level of difficulty "0.25" corresponds to the driving load type "FREE," and is defined as a road section that more than 50% of average drivers would find monotonous and boring, and in which a variety of content should be spoken.

Note that the types of driving load are not necessarily limited to the above examples ("BUSY_MAX", "BUSY+", "BUSY", "IDEAL", and "FREE"). In the following embodiment, the types of driving load are expressed as "WL types" and will be explained using "BUSY" and "FREE". The criteria and reference values shown above are merely examples and may be any values.

Here, we will also explain road sections. For example, a road section means a section between characteristic points of a road, and is called a link. Characteristic points of a road are intersections, corners, dead ends, etc., and are called nodes. In other words, a link means a road section that is set based on a specific rule. In other words, a link means a unit that divides a recorded section of a movement history based on a specific rule.

Following the above example, in the following embodiment, a road section is represented as a link, and a connection point between road sections is represented as a node. For example, the server device 100 has a map information storage unit 121 (FIG. 4), which includes road data that represents a road network as a combination of nodes and links, facility data, and object information around the road. The object information includes information on obstacles that exist temporarily, as well as features such as signs such as road signs, road markings such as stop lines, road dividing lines such as center lines, and structures along the road. Obstacles refer to factors that impede the passage of pedestrians and bicycles, such as puddles, sunken parts of the road, fallen objects, and drains (including parts blocked by nets). The object information may include highly accurate point cloud information of objects to be used for vehicle position estimation, etc. In addition, in the map information storage unit 121, links may be identified by link IDs.

The situation assessment engine 231 is a cloud service that collects situation information, including analysis results obtained by analyzing sensor information obtained by sensors possessed by the in-vehicle device 200, or the operating status of various applications installed in the in-vehicle device 200, and distributes the accumulated information as situation information. In the example of FIG. 2, the situation assessment engine 231 is included in the cloud system 10, but the in-vehicle device 200 may have the situation assessment engine 231.

The guidance information DB stores guidance information used to guide the driver along a route that has been set according to the driver's destination, or guidance information used to guide the driver along a route that has been reset (rerouted) when the target vehicle VE deviates from the set route.

The application MA has the function of distributing the results processed by the workload estimation engine E to the information matching engine 232 of the in-vehicle device 200.

From here on, a specific example of the operation of the workload estimation engine E will be described. The workload estimation engine E performs information processing according to an embodiment of the present disclosure.

First, the workload estimation engine E acquires situation information from the situation grasping engine 231, and estimates the type of driving load (WL type) of the vehicle VE at the current time based on the acquired situation information (step S21). For example, the workload estimation engine E may estimate the degree of difficulty (driving load) of the driver D on the road section on which the vehicle VE is currently traveling, i.e., the current link, as the WL type of the vehicle VE at the current time.

The workload estimation engine E also estimates the type of future driving load (WL type) of the vehicle VE (step S22). Specifically, the workload estimation engine E estimates (predicts) the WL type for each link included in the planned driving route based on the planned driving route, which is the route along which the vehicle VE is scheduled to travel.

For example, the workload estimation engine E may compare the planned driving route with map data (map information storage unit 121) in which a WL type is associated with each link, and predict the WL type for each link included in the planned driving route.

Next, the workload estimation engine E executes processing related to generating and distributing timing information as information processing according to an embodiment of the present disclosure (step S23). Below, the information processing according to an embodiment of the present disclosure will be described in more detail using the example of FIG. 2.

Figure 2 shows links L1 and L2 that are adjacent to each other and are estimated to have different WL types among the links that make up the planned driving route RTx, which is the route that vehicle VE1 (an example of a vehicle VE) is scheduled to travel, and shows a scene immediately after vehicle VE1 starts traveling along link L1 toward link L2.

Also, in the example of FIG. 2, the WL type of link L1 is estimated to be "FREE," and the WL type of link L2 is estimated to be "BUSY." Furthermore, in the example of FIG. 2, position PTx1 is the current position of vehicle VE1 at this point in time, and position PTx2 corresponds to the connection point where link L1 and link L2 are connected. Also, for this reason, link L1, which includes position PTx1, can be said to be the current link (current road section) along which vehicle VE1 is currently traveling. On the other hand, link L2 can be said to be the future link (future road section) along which vehicle VE1 is scheduled to travel in the future.

Here, immediately after vehicle VE1 starts traveling on link L1, i.e., when vehicle VE1 is traveling at position PT1, the traveling history of vehicle VE1 on link L1 has not been sufficiently accumulated. In such a case, workload estimation engine E acquires, for example, from a database, the section average speed, which is the average speed obtained statistically for link L1, and generates first timing information indicating the timing of content output based on the acquired section average speed.

More specifically, the workload estimation engine E calculates the estimated time TM1 at which the vehicle VE1 will arrive at the connection point PTx2 based on the average section speed corresponding to link L1 and the distance from the current position PTx1 to the connection point PTx2. The workload estimation engine E also generates a time range including the estimated time TM1 as first timing information, and distributes the generated first timing information to the in-vehicle device 200 of the vehicle VE1. Here, the first timing information may be information indicating a period of several tens of seconds including the estimated time TM1, and may be defined as a period during which content output is recommended.

The section average speed may be a speed obtained by simply averaging the driving records of various vehicles VE that have a driving history of link L1, or may be a speed obtained by averaging the driving records in driving situations corresponding to link L1 (e.g., occurrence of a large decrease in speed, a large increase in speed, temporary stop, stop, slow driving, curves, sudden acceleration, sudden braking, sudden steering, impact, etc.).

Returning to FIG. 2, the workload estimation engine E monitors changes in the driving scene of the vehicle VE1, and detects a change in the driving scene after the first timing information is delivered. In this way, when the driving scene changes, the workload estimation engine E determines that the WL type of the vehicle VE1 at the current time has changed. For example, the workload estimation engine E may determine that the degree of difficulty of the driver D on the link L1 on which the vehicle VE1 is currently traveling has changed, as the WL type of the vehicle VE1 at the current time.

In the example of FIG. 2, the workload estimation engine E determines that the current WL type of the vehicle VE1 has changed when the vehicle VE1 passed through position PTx3. In this case, the workload estimation engine E acquires the driving performance at the time when the vehicle VE1 reached position PTx3. For example, the workload estimation engine E acquires, as the driving performance, the distance from position PTx1 to position PTx3 and the time required to reach position PTx3 from position PTx1. Then, the workload estimation engine E calculates, based on the driving performance, the cumulative average speed, which is the average speed at the time when the vehicle VE1 reached position PTx3.

In the above example, the workload estimation engine E calculates the cumulative average speed using the driving performance at the time when it is determined that the WL type has changed. However, the workload estimation engine E may also calculate the cumulative average speed, for example, using the driving performance at each time a predetermined period has elapsed since the vehicle VE1 started traveling on link L1, or each time a predetermined distance has been traveled since the vehicle VE1 started traveling on link L1.

Returning to FIG. 2, the workload estimation engine E calculates a predicted average speed, which is the average speed currently predicted for link L1 and is the average speed of vehicle VE1, based on the cumulative average speed and the above-mentioned section average speed. The method for calculating the predicted average speed will be explained in detail later.

The workload estimation engine E also generates new second timing information indicating the timing of content output based on the predicted average speed. Specifically, the workload estimation engine E calculates the predicted time TM2 at which the vehicle VE1 will arrive at the connection point PTx2 based on the predicted average speed and the distance from the current position PTx3 to the connection point PTx2. The workload estimation engine E then generates a time range including the predicted time TM2 as the second timing information.

Next, the workload estimation engine E determines whether or not to deliver the second timing information based on the result of comparing the first timing information with the second timing information. Note that the workload estimation engine E may also determine whether or not to deliver the second timing information based on the result of comparing the section average speed with the predicted average speed.

If the workload estimation engine E determines that the second timing information should be distributed, it distributes the second timing information to the in-vehicle device 200 of the vehicle VE1. Here, the second timing information may be information indicating a period of several tens of seconds including the predicted time TM2, and may be defined as a period during which content output is recommended.

4. Overview of the in-vehicle device
Next, an example of the operation of the in-vehicle device 200 will be described with reference to Fig. 3. Fig. 3 is a schematic diagram showing an example of the operation of the in-vehicle device 200. According to the example of Fig. 3, the in-vehicle device 200 has an information matching engine 232.

First, the information matching engine 232 receives timing information distributed from the server device 100 (step S31). The information matching engine 232 receives the first timing information and the second timing information.

The information matching engine 232 also determines whether output request information has been received in a phase other than step S31. The output request information referred to here is output request information sent by various applications installed in the in-vehicle device 200. For example, an application that provides content related to tourist information may send output request information to the information matching engine 232 that includes output conditions (time conditions that permit output, or geographical conditions that permit output) and the content to be output.

When the information matching engine 232 receives output request information (step S32-1), it estimates the time required to play the content included in the output request information (step S32-2). For example, the information matching engine 232 may estimate the time required based on the playback time length of the content included in the output request information.

Then, the information matching engine 232 determines whether or not the content included in the output request information can be output based on the timing information received in step S31 and the required time estimated in step S32-2 (step S33). For example, if the time range indicated by the timing information is sufficiently longer than the required time, the information matching engine 232 may determine that the content included in the output request information can be output.

Then, the information matching engine 232 performs scheduling based on the timing information to determine the time to output the content (step S34). Furthermore, when the content output timing is determined as a result of scheduling by the information matching engine 232, the content is output as audio from the speaker SP (FIG. 4) of the in-vehicle device 200 in accordance with this output timing.

5. Functional Configuration
From here, a configuration example of the server device 100 and the in-vehicle device 200 will be described with reference to Fig. 4. Fig. 4 is a diagram showing a configuration example of the server device 100 and the in-vehicle device 200 according to the embodiment.

(Server device 100)
First, a description will be given of an example of the configuration of the server device 100. As shown in FIG.

(Communication unit 110)
The communication unit 110 is realized by, for example, a network interface card (NIC) etc. The communication unit 110 is connected to a network N by wire or wirelessly, and transmits and receives information to and from the in-vehicle device 200, for example.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 may store, for example, data and programs related to the information processing according to the embodiment. According to the example of FIG. 4, the storage unit 120 may include a map information storage unit 121 and a control result storage unit 122.

(Map information storage unit 121)
The map information storage unit 121 stores map data used for estimating the WL type. The map data includes road data in which a road network is represented by a combination of nodes and links. The links are managed by link IDs, and may be associated with the WL type and the link length.

(Control result storage unit 122)
The control result storage unit 122 may store information obtained by the workload estimation engine E (for example, the current WL type, the future WL type, change point information, etc.).

(Regarding the control unit 130)
The control unit 130 is realized by a central processing unit (CPU), a micro processing unit (MPU), or the like executing various programs (e.g., the information processing program according to the embodiment) stored in a storage device inside the server device 100 using a RAM as a working area. The control unit 130 is also realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

As shown in FIG. 4, the control unit 130 is equipped with a workload estimation engine E, which includes an acquisition unit 131, an estimation unit 132, a detection unit 133, a determination unit 134, a generation unit 135, a delivery unit 136, and a calculation unit 137, and realizes or executes the functions and actions of the information processing described below. Note that the internal configuration of the workload estimation engine E is not limited to the configuration shown in FIG. 4, and may be other configurations as long as they perform the information processing described below. Furthermore, the connection relationships of each processing unit in the workload estimation engine E are not limited to the connection relationships shown in FIG. 4, and may be other connection relationships.

(Acquisition unit 131)
The acquisition unit 131 acquires information indicating a driving route of the vehicle VE. For example, the acquisition unit 131 may acquire information on a planned driving route, which is a route along which the vehicle VE is scheduled to travel, as the information indicating the driving route of the vehicle VE.

For example, when the driver D specifies a destination, the acquisition unit 131 sets a route to the destination as a planned driving route based on a route plan that satisfies the destination. As a result, the acquisition unit 131 acquires information indicating the set planned driving route.

In addition, if the driver D has not specified a destination and it is not possible to set a route according to the destination, the acquisition unit 131 may predict a driving route based on the driving history of the vehicle VE, and set the predicted driving route as the planned driving route. In this case, the acquisition unit 131 acquires information indicating the predicted planned driving route.

(Estimation unit 132)
The estimation unit 132 estimates the WL type. For example, the estimation unit 132 estimates the degree of difficulty (driving difficulty) of the driver D in the road section on which the vehicle VE is currently traveling, i.e., the current link, as the WL type of the vehicle VE at the current time. For example, the estimation unit 132 may acquire situation information from the situation grasping engine 231, and estimate the degree of difficulty of the driver D based on the acquired situation information. Furthermore, the estimation unit 132 may compare the current link with map data in which a WL type is associated with each link, and estimate the degree of difficulty of the driver D based on the WL type associated with the current link.

The estimation unit 132 also estimates the WL type for each link included in the planned driving route, which is the route along which the vehicle VE is scheduled to travel, as the future WL type for the vehicle VE. For example, the estimation unit 132 may compare the planned driving route with map data in which a WL type is associated with each link, and predict the WL type for each link included in the planned driving route.

The estimation unit 132 may estimate the WL type without relying on map data in which a WL type is associated with each link. For example, the estimation unit 132 may statistically estimate the WL type based on the driving history of each link. As an example, the estimation unit 132 may estimate the WL type as "BUSY" for a link that shows a tendency for sudden braking as a result of analyzing the driving history. The estimation unit 132 may also estimate the WL type based on the driving difficulty calculated from the attributes of the link. For example, the estimation unit 132 may determine that a link with a sharp curve or a steep gradient has a high driving difficulty and estimate the WL type as "BUSY."

(Detection Unit 133)
The detection unit 133 detects a change in the driving scene based on the driving conditions of the vehicle VE. For example, the detection unit 133 detects a change in the driving behavior of the vehicle VE as a change in the driving scene. As an example, the detection unit 133 may detect a significant decrease in speed, a significant increase in speed, a temporary stop, a stop, slow driving, a curve, a sudden start, a sudden brake, an abrupt steering wheel, an impact, and the like as a change in the driving behavior of the vehicle VE.

The detection unit 133 may also detect, as a change in the driving scene, whether the vehicle VE has entered a point corresponding to a change point where the WL type changes. Specifically, when different WL types are estimated between adjacent links, the detection unit 133 detects whether the vehicle VE has entered a node that is a connection point where the adjacent links are connected.

The detection unit 133 may also detect, as a change in the driving scene, a change in attributes based on a comparison between the attributes of a first link, among the links included in the planned driving route, along which the vehicle VE is currently traveling, and the attributes of a second link that is located in the traveling direction of the vehicle VE and is connected to the first link. For example, the detection unit 133 may detect entry from a narrow road to a wide road, entry from a wide road to a narrow road, and entry from a road outside the living area to a road within the living area.

The detection unit 133 may also detect, as a change in the driving scene, whether the vehicle VE has entered an area corresponding to a specific characteristic point that exists in a first link on which the vehicle VE is currently traveling, among the links included in the planned driving route. The characteristic point referred to here is, for example, an intersection, a junction, a fork, a toll booth, a railroad crossing, etc.

In addition to the above examples, the detection unit 133 may also detect, for example, whether the road on which the vehicle VE is currently traveling is an expressway, or whether the road on which the vehicle VE is currently traveling is a road with the same tendency (for example, a straight road) with no change in attributes.

(Determination unit 134)
The determination unit 134 determines whether or not the WL type of the vehicle VE at the current time has changed based on the driving conditions of the vehicle VE. For example, the determination unit 134 may determine whether or not the degree of difficulty for the driver D on the link on which the vehicle VE is currently traveling has changed, as the WL type of the vehicle VE at the current time. For example, the determination unit 134 may determine whether or not the degree of difficulty for the driver D on the link on which the vehicle VE is currently traveling has changed, based on whether or not a change in the driving scene has been detected by the detection unit 133.

For example, if the determination unit 134 detects that the driving behavior of the vehicle VE has changed, it may determine that the degree of difficulty of the driver D has changed.

In addition, the determination unit 134 may determine that the degree of difficulty of the driver D has changed if it detects that the vehicle VE has entered a location (node) corresponding to a change point where the WL type changes.

In addition, the determination unit 134 may determine that the degree of difficulty of the driver D has changed if an attribute change is detected between the attribute of a first link along which the vehicle VE is currently traveling and the attribute of a second link that is located in the traveling direction of the vehicle VE and is connected to the first link.

In addition, if the determination unit 134 detects that the vehicle VE has entered an area corresponding to a specified characteristic point, it may determine that the degree of difficulty of the driver D has changed.

(Generation unit 135)
The generating unit 135 generates timing information that indicates timing related to the output of the content.

The generating unit 135 generates first timing information indicating the timing of content output based on the section average speed, which is the average speed of the vehicle VE on a specific link. For example, when different WL types are estimated between adjacent links included in the planned driving route, the generating unit 135 calculates the predicted time for the vehicle VE to arrive at a connection point where the adjacent links are connected from the current position of the vehicle VE. The generating unit 135 then generates a time range including the predicted time as the first timing information.

In addition, when it is determined that the WL type of the vehicle VE has changed while the vehicle VE is traveling on a link included in the planned travel route, the generation unit 135 generates the first timing information based on the average section speed corresponding to the current link (current road section), which is the link that includes the current position of the vehicle VE.

Specifically, when it is determined that the WL type has changed and the vehicle VE enters a link for which a WL type different from that of the link on which the vehicle has been traveling is estimated, the generation unit 135 generates the first timing information based on the average section speed corresponding to the entered link as the current link. More specifically, when the vehicle VE enters a future link via a connection point that connects adjacent links for which a WL type different from that of the link on which the vehicle has been traveling is estimated, the generation unit 135 generates the first timing information based on the average section speed corresponding to the entered link.

In this way, immediately after the vehicle VE leaves the link on which it has been traveling and enters a link for which a WL type different from that of the link on which it has been traveling has been estimated, the vehicle VE's travel history for the entered link has not been sufficiently accumulated. Therefore, when the vehicle VE enters a link for which a WL type different from that of the link on which it has been traveling has been estimated, the generation unit 135 generates the first timing information based on the section average speed, which is the average speed obtained statistically for the entered link.

On the other hand, at a specific point in time after the first timing information is distributed, the accumulation of the vehicle VE's driving performance for the current link is progressing. Therefore, in such a case, the generation unit 135 calculates the predicted time at which the vehicle VE will reach the start point of the future link that is included in the planned driving route and for which a different WL type than the current link is estimated. For example, the generation unit 135 calculates the predicted time at which the vehicle VE will reach the start point based on the predicted average speed that is predicted as the average speed of the vehicle VE on the current link. Then, the generation unit 135 generates a time range including the predicted time as the second timing information.

(Distribution unit 136)
The distribution unit 136 distributes timing information indicating timing related to the output of the content to the in-vehicle device 200 of the vehicle VE. For example, when the generation unit 135 generates first timing information, the distribution unit 136 distributes the first timing information to the in-vehicle device 200. Furthermore, when the generation unit 135 generates second timing information, the distribution unit 136 distributes the second timing information to the in-vehicle device 200.

The distribution unit 136 also determines whether to distribute the second timing information based on specified information based on the section average speed. For example, when a comparison result between the first timing information and the second timing information satisfies a specified condition, the distribution unit 136 determines to distribute the second timing information generated by the generation unit 135. As another example, the distribution unit may determine to distribute the second timing information when a comparison result between a value based on the section average speed and a value based on the predicted average speed satisfies a specified condition. The predicted average speed is calculated by the calculation unit 137.

(Calculation unit 137)
After distributing the first timing information, the calculation unit 137 calculates an accumulated average speed, which is the average speed at a predetermined time point, based on the driving performance at the predetermined time point while the vehicle VE is traveling on the current link. Then, the calculation unit 137 calculates a predicted average speed, which is the average speed predicted at the predetermined time point and is the average speed of the vehicle VE predicted for the current link, based on the accumulated average speed and the section average speed corresponding to the current link.

For example, when it is determined that the WL type has changed while the vehicle VE is traveling on the current link, the calculation unit 137 calculates the cumulative average speed, which is the average speed of the vehicle VE at the current time, based on the driving performance at the current time when it is determined that the WL type has changed, that is, the driving performance for the current link.

As another example, the calculation unit 137 may calculate a cumulative average speed, which is the average speed of the vehicle VE at the current time, based on the driving performance for the current link, which is the driving performance when a predetermined period of time has elapsed while the vehicle VE is traveling on the current link, or when the vehicle VE has traveled a predetermined distance on the current link.

Then, the calculation unit 137 calculates the predicted average speed, which is the average speed predicted for the current link at the current time, based on the cumulative average speed and the section average speed corresponding to the current link, and is the average speed of the vehicle VE.

For example, the calculation unit 137 calculates the predicted average speed by performing a correction calculation to correct the section average speed to approach the cumulative average speed using the ratio of the actual driving performance to the distance of the current link as a coefficient indicating the influence of the cumulative average speed on the section average speed. More specifically, the calculation unit 137 calculates the predicted average speed by adding together a first average speed, which is an average speed obtained by correcting the cumulative average speed with a first coefficient that is the ratio of the actual driving performance to the distance of the current link, and a second average speed, which is an average speed obtained by correcting the average value of the cumulative average speed and the section average speed with a second coefficient that is the ratio of the remaining distance to the current link, which is the distance obtained by subtracting the actual driving performance from the current link.

(In-vehicle device 200)
Continuing to use Fig. 4, a configuration example of the in-vehicle device 200 will be described. As shown in Fig. 4, the in-vehicle device 200 has a microphone MC, a speaker SP, a sensor SC, an application AP, a communication unit 210, a storage unit 220, and a control unit 230.

(Mike MC)
The microphone MC is a sound collecting device that collects sounds generated within the vehicle VE. For example, the microphone MC collects speech generated by the driver D.

(Speaker SP)
The speaker SP corresponds to an output device that outputs various information by sound. For example, the speaker SP outputs content information in accordance with output control by the control unit 230.

The sensor SC detects various information related to the vehicle VE and transmits the detected sensor information to the situation assessment engine 231.

(Application AP)
The application AP is an application that provides content. For example, the application AP transmits output request information including output conditions (time conditions for permitting output or geographic conditions for permitting output) and content to be output to the information matching engine 232. Although not shown in FIG. 1, the system 1 may further include an application server that controls the application AP.

(Memory unit 220)
The storage unit 220 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 220 may store, for example, data and programs related to the information processing according to the embodiment. According to the example of FIG. 4, the storage unit 220 may include a user information storage unit 221 and a content storage unit 222.

(User information storage unit 221)
The user information storage unit 221 stores various information related to a user (e.g., a driver D) of the vehicle VE. The user information storage unit 221 may store a user of the vehicle VE, or may store information related to the vehicle VE. The driving history may further be stored.

(Content storage unit 222)
The content storage unit 222 stores the content provided by the application AP.

(Regarding the control unit 230)
The control unit 230 is realized by a CPU, an MPU, or the like executing various programs (e.g., the information processing program according to the embodiment) stored in a storage device inside the in-vehicle device 200 using a RAM as a working area. The control unit 230 is also realized by an integrated circuit such as an ASIC or an FPGA.

As shown in FIG. 4, the control unit 230 has a situation understanding engine 231, an information matching engine 232, and an output control unit 233, and realizes or executes the information processing functions and actions described below. Note that the internal configuration of the control unit 230 is not limited to the configuration shown in FIG. 4, and may be other configurations as long as they perform the information processing described below. Also, the connection relationships between the processing units in the control unit 230 are not limited to the connection relationships shown in FIG. 4, and may be other connection relationships.

(Situation awareness engine 231)
The situation recognition engine 231 identifies the situation related to the vehicle VE based on the sensor information. For example, the situation recognition engine 231 identifies the situation of the vehicle VE by detecting the voice, the state, the behavior of the vehicle VE, etc. inside the vehicle VE. Then, the situation recognition engine 231 outputs situation information indicating the identified situation to the information matching engine 232.

(Information Matching Engine 232)
The information matching engine 232 searches for links (speech-permitted links) that can be determined to allow output of content by voice from among the links included in the planned driving route.

In addition, when the information matching engine 232 receives output request information, it estimates the time required to play the content included in the output request information.

The information matching engine 232 also determines whether the content included in the output request information can be output based on the speech allowable link and the required time.

In addition, the information matching engine 232 executes a scheduling process that determines the output timing of the content so as to satisfy the output conditions included in the output request information.

(Output control unit 233)
The output control unit 233 controls the content to be output from the speaker SP at the timing scheduled by the information matching engine 232 .

6. WL type estimation method
From here, the WL type estimation method will be specifically described with reference to Fig. 5. Fig. 5 is a diagram showing a specific example of the WL type estimation method. In Fig. 5, the WL type estimation method will be described using an example in which a travel route RT1 is plotted according to a route plan that satisfies the destination.

As shown in FIG. 5, the travel route RT1 is a route connecting the starting point PT1 and the destination point PT2, and the scene in which the WL type estimation process is started at a predetermined time point while the vehicle VE1 is traveling on the travel route RT1 is shown.

Here, in the example of FIG. 5(a), the estimation unit 132 compares the travel route RT1 with map data in which a WL type is associated with each link, and links each link that constitutes the travel route RT1 with a link ID (link_id). FIG. 5(a) shows an example in which the estimation unit 132 divides the travel route RT1 into five links by linking link ID "100", link ID "101", link ID "102", link ID "103", link ID "104", and link ID "105" to the travel route RT1.

In addition, in FIG. 5(a), node ND01 is shown as information on the connection point where the link identified by link ID "100" (link 100) and the link identified by link ID "101" (link 101) are connected.

In addition, node ND12 is shown as information on the connection point where the link identified by link ID "101" (link 101) and the link identified by link ID "102" (link 102) are connected.

In addition, node ND23 is shown as information on the connection point where the link identified by link ID "102" (link 102) and the link identified by link ID "103" (link 103) are connected.

In addition, node ND34 is shown as information on the connection point where the link identified by link ID "103" (link 103) and the link identified by link ID "104" (link 104) are connected.

In addition, node ND45 is shown as information on the connection point where the link identified by link ID "104" (link 104) and the link identified by link ID "105" (link 105) are connected.

The estimation unit 132 may also refer to the map data and calculate the distance (len) of each link. FIG. 5(a) shows an example in which the estimation unit 132 calculates the distance "100" of link 100, the distance "200" of link 101, the distance "300" of link 102, the distance "100" of link 103, the distance "500" of link 104, and the distance "200" of link 105.

In this state, as shown in FIG. 5(b), it is assumed that the vehicle VE1 is traveling on the link 102. In this example, the estimation unit 132 may estimate the WL type for the link 102 on which the vehicle VE1 is currently traveling, and for the

links

103, 104, and 105 which the vehicle VE1 is scheduled to travel after the link 102. FIG. 5(b) shows an example in which the estimation unit 132 refers to map data in which the WL type is associated with each link, and estimates the WL type of link 102 as "FREE", the WL type of link 103 as "BUSY", the WL type of link 104 as "FREE", and the WL type of link 105 as "BUSY".

Although not shown in FIG. 5(b), the estimation unit 132 may also estimate the degree of difficulty of the driver D on the link 102 on which the vehicle VE1 is currently traveling as the WL type of the vehicle VE1 at the current time.

In the above example, the estimation unit 132 estimates the WL type of the link by comparing the map data in which the WL type is associated with each link with the links that make up the travel route RT1. However, the estimation unit 132 may estimate the WL type of the link on which the vehicle VE1 is traveling based on the link type (link_kind) of the link on which the vehicle VE1 is traveling, or the road type (road_kind) of the travel route RT1.

For example, the link on which the vehicle VE1 is traveling changes according to the traveling of the vehicle VE1, so the estimation unit 132 may estimate the WL type of the current link each time the link changes. Furthermore, the link type here refers to classification information such as a main line, a connecting road, etc. Furthermore, the road type refers to classification information such as an expressway, a national road, a narrow street, etc.

[7. Calculation method of predicted average speed]
Next, a method for calculating the predicted average speed will be specifically described with reference to Fig. 6. Fig. 6 is a diagram showing a specific example of a method for calculating the predicted average speed. Fig. 6 shows an example of calculating the predicted average speed, in which the first link (leading link) for which the predicted average speed is calculated among the links constituting the travel route RT1 (Fig. 5) is set as link 104. Note that the example in Fig. 6 corresponds to a process in which the second timing information is distributed in accordance with a comparison result between the section average speed and the predicted average speed.

Here, node ND34 is a connection point that connects link 103 with a WL type of "BUSY" and link 104 with a WL type of "FREE". In other words, node ND34 is a connection point that connects links whose WL types are estimated to be different from each other. Also, node ND45 is a connection point that connects link 104 with a WL type of "FREE" and link 105 with a WL type of "BUSY". In other words, node ND45 is also a connection point that connects links whose WL types are estimated to be different from each other. As an example, the predicted average speed may be calculated for links whose WL types are estimated to be different from each other and the connection points that connect these links.

In this state, FIG. 6(a) shows a scene immediately after vehicle VE1 starts traveling along link 104, i.e., when it is located at node ND34. In this example, link 104 is the current link of vehicle VE1.

At the time when vehicle VE1 starts traveling on link 104, the travel history of vehicle VE1 on link 104 has not been sufficiently accumulated. In such a case, the generation unit 135 obtains, for example, from a database, section average speed LV1, which is the average speed obtained statistically for link 104, and generates first timing information indicating the timing related to the output of the content based on section average speed LV1.

More specifically, the generation unit 135 calculates the predicted time TM1 at which the vehicle VE1 will reach node ND45 based on the average section speed LV1 corresponding to link 104 and the distance from node ND34, which is the current position of the vehicle VE1, to node ND45, which is the next connection point. The generation unit 135 also generates a time range including the predicted time TM1 as first timing information, and the distribution unit 136 distributes the first timing information to the in-vehicle device 200 of the vehicle VE1.

Here, the detection unit 133 detects a change in the driving scene based on the driving conditions of the vehicle VE1. Furthermore, the determination unit 134 continuously monitors whether the WL type of the vehicle VE1 at the current time has changed based on whether a change in the driving scene has been detected.

Now, moving on to the example of FIG. 6(b), the detection unit 133 detects a change in the driving scene of the vehicle VE1 when the vehicle VE1 has traveled 100 m on the link 104, and as a result, the determination unit 134 determines that the WL type of the vehicle VE1 has changed at the present time when the vehicle VE1 has traveled 100 m on the link 104. In this case, the calculation unit 137 acquires the driving performance at the present time when the vehicle VE1 has traveled 100 m on the link 104. For example, the calculation unit 137 acquires, as the driving performance, the driving distance "100 m" from the node ND34 to the current position PT3 and the time it took for the vehicle VE1 to reach the current position PT3 from the node ND34. Then, the calculation unit 137 calculates the cumulative average speed SV1, which is the average speed at the present time when the vehicle VE1 reached the current position PT3, based on the driving performance.

Then, the calculation unit 137 calculates a predicted average speed PV1, which is the average speed predicted for link 104 at the current time (the current time when vehicle VE1 has reached current position PT3) based on the cumulative average speed SV1 and the section average speed LV1, and is the average speed of vehicle VE1.

In this example where the leading link is link 104, the calculation unit 137 calculates the predicted average speed PV1 using calculation formula (1) shown in FIG. 6. According to calculation formula (1), the predicted average speed PV1 of the leading link is calculated by adding together a first average speed, which is an average speed obtained by correcting the cumulative average speed SV1 by a first coefficient that is the ratio of the traveled distance D2 to the distance D1 of the current link, and a second average speed, which is an average speed obtained by correcting the average value of the cumulative average speed SV1 and the section average speed LV1 by a second coefficient that is the ratio of the remaining distance D3 (the distance obtained by subtracting the traveled distance D2 from the distance D1 of the current link) to the current link.

Therefore, in the example of FIG. 6(b), the calculation unit 137 calculates the predicted average speed PV1 corresponding to the leading link 104 by applying the distance "500 m" of the current link 104 as the distance D1 of the current link, the distance "100 m" from node ND34 to the current position PT3 as the traveled distance D2 for the distance D1 of the current link, and the distance "400 m" from the current position PT3 to node ND45 as the remaining distance D3 for the current link to the calculation formula (1).

When the distribution unit 136 calculates the predicted average speed PV1, it compares the section average speed LV1 with the predicted average speed PV1, as shown in FIG. 6(c). If the speed difference between the section average speed LV1 and the predicted average speed PV1 is equal to or greater than a threshold (Yes), the distribution unit 136 determines that it is OK to distribute second timing information indicating the timing of content output, the second timing information being newly generated using the predicted average speed PV1. On the other hand, if the speed difference between the section average speed LV1 and the predicted average speed PV1 is less than the threshold (No), the distribution unit 136 determines not to distribute the second timing information.

In addition, the distribution unit 136 may compare the arrival time calculated based on the section average speed LV1 with the arrival time calculated based on the predicted average speed PV1, rather than simply comparing the section average speed LV1 with the predicted average speed PV1. The arrival time here is the predicted time at which the vehicle VE1 will arrive at node ND45.

If it is determined that the second timing information may be distributed, the generation unit 135 calculates the predicted time TM2 at which the vehicle VE1 will reach node ND45 based on the distance "400 m" from the current position PT3 to node ND45 and the predicted average speed PV1. The generation unit 135 also generates a time range including the predicted time TM2 as the second timing information, and the distribution unit 136 distributes the second timing information to the in-vehicle device 200 of the vehicle VE1.

Here, when the vehicle VE1 is traveling on the nth link (n≧2), which is the second or subsequent link following the leading link 104, the calculation unit 137 may use calculation formula (2) to calculate the predicted average speed PVn corresponding to the nth link. According to calculation formula (2), the predicted average speed PVn corresponding to the nth link is calculated by correcting the cumulative average speed SVn corresponding to the nth link with a coefficient calculated as the ratio of the section average speed LVn of the nth link to the section average speed LV1 of the leading link.

Therefore, for example, when vehicle VE1 has passed link 104 and is traveling on second link 105, calculation unit 137 applies the section average speed LV1 of the first link 104, the section average speed LV2 of the second link 105, and the cumulative average speed SV2 corresponding to the second link 105 to calculation formula (2) to calculate a predicted average speed PV2 corresponding to the second link 105.

8. Timing Information Distribution Procedure
The processing procedure for distributing timing information will be described with reference to Fig. 7 and Fig. 8. Fig. 7 describes the processing procedure when the vehicle VE is traveling on the first link. Fig. 8 describes the processing procedure when the vehicle VE is traveling on the n-th link, which is the second or subsequent link following the first link.

8-1. Timing information distribution procedure (1)
FIG. 7 is a flowchart showing a timing information processing procedure when the vehicle VE is traveling on the leading link.

First, the acquisition unit 131 acquires information indicating the driving route of the vehicle VE (step S701). For example, the acquisition unit 131 acquires information on a planned driving route, which is a route along which the vehicle VE is scheduled to travel, as information indicating the driving route of the vehicle VE.

The generation unit 135 determines whether the vehicle VE has started traveling on the leading link, which is the first link for which the predicted average speed is calculated (step S702). The leading link is, for example, a link having a connection point that connects links for which different WL types are estimated, and may be determined from among the links that make up the planned traveling route of the vehicle VE.

If the vehicle VE has not yet started traveling along the leading link (step S702; No), the generation unit 135 waits until the vehicle VE starts traveling along the leading link.

On the other hand, when the vehicle VE starts traveling on the leading link (step S702; Yes), the generation unit 135 generates first timing information indicating the timing related to the output of the content based on the average section speed LV1 corresponding to the leading link (step S703). Specifically, the generation unit 135 calculates the predicted time TM1 at which the vehicle VE will finish traveling on the leading link based on the average section speed LV1 and the distance D1 of the leading link, and generates a time range including the predicted time TM1 as the first timing information.

The distribution unit 136 also distributes the first timing information to the in-vehicle device 200 of the vehicle VE (step S704).

In this state, the determination unit 134 determines whether the traveling of the vehicle VE satisfies a predetermined condition (step S705). For example, the determination unit 134 may determine whether the WL type of the vehicle VE (the degree of difficulty of the driver D) has changed based on the detection result by the detection unit 133 (detection result of a change in the traveling scene). As another example, the determination unit 134 may repeatedly determine whether a predetermined period has elapsed since the vehicle VE started traveling on the leading link, or whether the vehicle VE has traveled a predetermined distance since starting traveling on the leading link.

While the traveling of the vehicle VE does not satisfy the predetermined condition (step S705; No), the determination unit 134 waits until it can determine that the traveling of the vehicle VE satisfies the predetermined condition.

On the other hand, if the calculation unit 137 determines that the travel of the vehicle VE meets the predetermined condition (step S705; Yes), it acquires the travel performance of the vehicle VE traveling along the leading link up to the current time (step S706). For example, the calculation unit 137 acquires, as the travel performance, the travel distance D2 to the current position on the leading link and the time required to reach the current position. The calculation unit 137 may also acquire the remaining distance D3, which is calculated by subtracting the travel distance D2 from the distance D1 of the leading link.

Then, the calculation unit 137 calculates the cumulative average speed SV1, which is the average speed at the time when the vehicle VE reached the current position on the leading link (the current position determined to satisfy the predetermined condition), based on the driving history (step S707).

Then, the calculation unit 137 calculates a predicted average speed PV1, which is the average speed currently predicted for the leading link and is the average speed of the vehicle VE, based on the cumulative average speed SV1 and the section average speed LV1 (step S708). For example, the calculation unit 137 calculates the predicted average speed PV1 by applying the cumulative average speed SV1, the section average speed LV1, the distance D1 of the leading link, the traveled distance D2, and the remaining distance D3 to the calculation formula (1) described in FIG. 6.

The generating unit 135 generates second timing information indicating the timing of the output of the content based on the predicted average speed PV1 (step S709). Specifically, the generating unit 135 calculates the predicted time TM2 at which the vehicle VE will finish traveling the leading link based on the predicted average speed PV1 and the remaining distance D3, and generates a time range including the predicted time TM2 as the second timing information.

The distribution unit 136 compares the first timing information with the second timing information and determines whether there is a deviation between the two timings that is equal to or exceeds a threshold (step S710).

If it is determined that the deviation is equal to or greater than the threshold (step S710; Yes), the distribution unit 136 distributes the second timing information to the in-vehicle device 200 of the vehicle VE (step S711).

In this state, the calculation unit 137 determines whether the vehicle VE has started traveling on the nth link (n≧2), which is the second or subsequent link following the leading link (step S712). If the vehicle VE has not started traveling on the nth link (step S712; No), the process returns to step S705. Also, as shown in FIG. 7, if it is determined that the deviation is less than the threshold (step S710; No), the process may return to step S705.

8-2. Timing information distribution procedure (2)
FIG. 8 is a flowchart showing a timing information processing procedure when the vehicle VE is traveling on the n-th link.

When the vehicle VE starts traveling on the nth link (step S712; Yes), the determination unit 134 determines whether the traveling of the vehicle VE satisfies a predetermined condition (step S801). For example, the determination unit 134 may determine whether the WL type of the vehicle VE (the degree of difficulty of the driver D) has changed based on the detection result by the detection unit 133 (detection result of a change in the traveling scene). As another example, the determination unit 134 may repeatedly determine whether a predetermined period has elapsed since the vehicle VE started traveling on the nth link, or whether the vehicle VE has traveled a predetermined distance since starting traveling on the nth link.

While the traveling of the vehicle VE does not satisfy the predetermined condition (step S801; No), the determination unit 134 waits until it can determine that the traveling of the vehicle VE satisfies the predetermined condition.

On the other hand, if the calculation unit 137 determines that the travel of the vehicle VE satisfies the predetermined condition (step S801; Yes), it acquires the travel performance of the vehicle VE traveling along the n-th link up to the current time (step S802). For example, the calculation unit 137 acquires, as the travel performance, the travel distance to the current position on the n-th link and the time required to reach the current position.

Then, the calculation unit 137 calculates the cumulative average speed SVn, which is the average speed at the time when the vehicle VE reaches the current position on the nth link (the current position that is determined to satisfy the specified condition), based on the driving history (step S803).

Then, the calculation unit 137 calculates the predicted average speed PVn corresponding to the nth link by correcting the cumulative average speed SVn based on the section average speed LV1 of the first link and the section average speed LVn of the nth link (step S804). For example, the calculation unit 137 calculates the predicted average speed PVn by applying the section average speed LV1, the section average speed LVn, and the cumulative average speed SVn to the calculation formula (2) described in FIG. 6.

The generating unit 135 generates second timing information indicating the timing of the output of the content based on the predicted average speed PVn (step S805). Specifically, the generating unit 135 calculates the predicted time TM2 at which the vehicle VE finishes traveling the nth link based on the predicted average speed PVn, and generates a time range including the predicted time TM2 as the second timing information.

The distribution unit 136 compares the first timing information with the second timing information and determines whether there is a difference between the two timings that is equal to or greater than a threshold (step S806). Note that the first timing information here is a time range that includes the predicted time TM1 at which the vehicle VE will finish traveling the nth link, and the predicted time TM1 is calculated based on the average section speed LVn.

If it is determined that the deviation is equal to or greater than the threshold (step S806; Yes), the distribution unit 136 distributes the second timing information to the in-vehicle device 200 of the vehicle VE (step S807).

In this state, the calculation unit 137 determines whether the vehicle VE has started traveling on the next n-th link (step S808). If the vehicle VE has not started traveling on the next n-th link (step S808; No), the process returns to step S801. Also, as shown in FIG. 8, if the speed difference between the section average speed LVn and the predicted average speed PVn is less than the threshold (step S806; No), the process may also return to step S801.

On the other hand, if the vehicle VE starts traveling on the next n-th link (step S808; Yes), the calculation unit 137 repeats the process from step S802.

[9. Removal of restrictions]
In the above embodiment, the processes described as being performed by the server device 100 may be performed by the in-vehicle device 200. Specifically, a series of processes related to the generation and distribution of timing information described as the information processing according to the embodiment of the present disclosure may be performed by the in-vehicle device 200.

10. Hardware Configuration
The above-described server device 100 (an example of an information processing device) may be realized, for example, by a computer 1000 having a configuration as shown in Fig. 9. Fig. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the server device 100. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, a HDD 1400, a communication interface (I/F) 1500, an input/output interface (I/F) 1600, and a media interface (I/F) 1700.

The CPU 1100 operates based on the programs stored in the ROM 1300 or the HDD 1400, and controls each component. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, and programs that depend on the hardware of the computer 1000, etc.

HDD 1400 stores programs executed by CPU 1100 and data used by such programs. Communication interface 1500 receives data from other devices via a specified communication network and sends it to CPU 1100, and transmits data generated by CPU 1100 to other devices via the specified communication network.

The CPU 1100 controls an output device such as a display and an input device such as a keyboard via the input/output interface 1600. The CPU 1100 acquires data from the input device via the input/output interface 1600. The CPU 1100 also outputs generated data to the output device via the input/output interface 1600.

The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

For example, when the computer 1000 functions as the server device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a specified communication network.

11. Other
Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Furthermore, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. For example, some or all of the processing described as being performed by the server device 100 may be configured to be performed on the in-vehicle device 200 side.

In addition, the above embodiments can be combined as appropriate to the extent that the processing content is not contradictory.

　Although several embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be embodied in other forms that incorporate various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the "present invention" section.

REFERENCE SIGNS LIST 1 System 100 Server device 120 Storage unit 121 Map information storage unit 122 Control result storage unit 130 Control unit 131 Acquisition unit 132 Estimation unit 133 Detection unit 134 Determination unit 135 Generation unit 136 Distribution unit 137 Calculation unit 200 In-vehicle device 231 Situation grasping engine 232 Information matching engine 233 Output control unit

Claims

a distribution unit that distributes first timing information indicating a timing related to an output of a content, the first timing information being generated based on a section average speed that is an average speed of a vehicle in a predetermined road section, to a target vehicle traveling on the predetermined road section;
a calculation unit that calculates a predicted average speed, which is an average speed of the target vehicle in the specified road section predicted at the predetermined time point, based on a cumulative average speed, which is an average speed based on a driving record at a predetermined time point while the target vehicle is traveling in the specified road section, and the section average speed after the first timing information is distributed;
Equipped with
The information processing device is characterized in that the distribution unit determines whether or not to distribute second timing information indicating timing regarding the output of the content, the second timing information being newly generated using the predicted average speed, based on specified information based on the section average speed.
an estimation unit that estimates a type of driving load for each road section included in a planned travel route, the road being a route that the target vehicle is scheduled to travel;
a generating unit configured to generate, when different types of the driving load are estimated between adjacent road sections among road sections included in the planned travel route, the first timing information indicating a range based on a predicted time for the target vehicle to arrive at a connection point where the adjacent road sections are connected from a current position of the target vehicle, based on an average section speed corresponding to the road section including the current position,
The information processing device according to claim 1 , wherein the distribution unit distributes the first timing information generated by the generation unit to the target vehicle.
A detection unit that detects a change in a driving scene based on a driving situation of the target vehicle;
and a determination unit that determines whether a type of a driving load of the target vehicle at the current time has changed based on whether a change in the driving scene has been detected,
The information processing device according to claim 2, characterized in that when it is determined that a type of driving load of the target vehicle has changed while the target vehicle is traveling on a road section included in the planned driving route, the generation unit generates the first timing information based on the section average speed corresponding to a current road section, which is a road section that includes a current position of the target vehicle.
The information processing device according to claim 3, characterized in that when it is determined that the type of driving load has changed and the target vehicle has entered a road section in which the type of driving load is estimated to be different from the road section on which the target vehicle has been traveling until now, the generation unit generates the first timing information based on the section average speed corresponding to the road section to be entered as the current road section.
The information processing device according to claim 3, characterized in that when it is determined that the type of driving load has changed while the target vehicle is traveling on the current road section, the calculation unit calculates a predicted average speed, which is the average speed of the target vehicle in the current road section predicted at the current time when it is determined that the type of driving load has changed, based on a cumulative average speed, which is an average speed based on the driving performance for the current road section at the current time when it is determined that the type of driving load has changed, and the section average speed corresponding to the current road section.
The information processing device according to claim 3, characterized in that the calculation unit calculates a predicted average speed, which is an average speed of the target vehicle in the current road section predicted at the current time, based on a cumulative average speed, which is an average speed based on driving performance for the current road section at a point when a predetermined period has elapsed while the target vehicle is traveling on the current road section or at a point when the target vehicle has traveled a predetermined distance on the current road section, and the section average speed corresponding to the current road section.
The information processing device according to claim 5 or 6, characterized in that the calculation unit calculates the predicted average speed by a correction calculation that uses a ratio of the actual driving performance to the distance of the current road section as a coefficient indicating a degree of influence of the cumulative average speed on the section average speed, and corrects the section average speed to approach the cumulative average speed.
The information processing device according to claim 7, characterized in that the calculation unit calculates the predicted average speed by adding together a first average speed, which is an average speed obtained by correcting the cumulative average speed by a first coefficient that is a ratio of the actual driving performance to the distance of the current road section, and a second average speed, which is an average speed obtained by correcting an average value of the cumulative average speed and the section average speed by a second coefficient that is a ratio of the remaining distance for the current road section, which is the distance obtained by subtracting the actual driving performance from the current road section.
the predetermined information includes the first timing information;
The information processing device according to claim 1, characterized in that the distribution unit determines to distribute the second timing information newly generated using the predicted average speed when a comparison result between the first timing information and the second timing information satisfies a predetermined condition.
the predetermined information includes the section average speed and the predicted average speed,
The information processing device according to claim 1, characterized in that the distribution unit determines to distribute the second timing information newly generated using the predicted average speed when a comparison result between a value based on the section average speed and a value based on the predicted average speed satisfies a predetermined condition.
The information processing device according to claim 5 or 6, characterized in that, when it is determined that the second timing information is to be distributed, the generation unit generates the second timing information indicating a range based on a predicted time for the target vehicle to arrive at the connection point based on a predicted average speed, which is an average speed of the target vehicle in the current road section.
An information processing method executed by an information processing device,
a distribution step of distributing, to a target vehicle traveling on a predetermined road section, first timing information indicating a timing related to an output of a content, the first timing information being generated based on a section average speed, which is an average speed of a vehicle on the predetermined road section;
a calculation step of calculating a predicted average speed, which is an average speed of the target vehicle in the specified road section predicted at the predetermined time point, based on a cumulative average speed, which is an average speed based on a driving record of the target vehicle at the predetermined time point while the target vehicle is traveling in the specified road section, and the section average speed, after the first timing information is distributed;
Including,
The information processing method, characterized in that the distribution step includes determining whether or not to distribute second timing information indicating timing regarding the output of the content, the second timing information being newly generated using the predicted average speed, based on specified information based on the section average speed.
An information processing program executed by an information processing device,
a delivery procedure for delivering first timing information indicating a timing related to an output of a content, the first timing information being generated based on a section average speed, which is an average speed of a vehicle in a predetermined road section, to a target vehicle traveling on the predetermined road section;
a calculation step of calculating a predicted average speed, which is an average speed of the target vehicle in the specified road section predicted at a predetermined time point, based on a cumulative average speed, which is an average speed based on a driving record of the target vehicle at a predetermined time point while the target vehicle is traveling in the specified road section, and the section average speed, after the first timing information is distributed;
causing the information processing device to execute the above steps;
The information processing program includes a step of determining whether or not to distribute second timing information, which indicates timing regarding the output of the content and is newly generated using the predicted average speed, based on predetermined information based on the section average speed.