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

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

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Conventionally, methods have been proposed for calculating appropriate average values ​​(for example, average link travel time or average vehicle speed) based on link attributes of road links. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-241813 Summary of the Invention [Problem to be solved by the invention]

[0004] However, with the above-mentioned conventional techniques, it is not always possible to output content at an appropriate timing.

[0005] For example, in the above-mentioned conventional technology, link travel times and vehicle speeds are appropriately averaged by taking into consideration factors that cause changes in vehicle speed, such as traffic lights, railroad crossings, and toll booths, but simply averaging does not necessarily result in content being output at the appropriate time. Moreover, the above-mentioned conventional technology does not even consider the idea of ​​outputting content.

[0006] The present disclosure has been made in view 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.

[0007] The information processing device described in claim 1 comprises a distribution unit that distributes first timing information indicating the 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 unit that calculates 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 the specified time point while traveling on the specified road section, and the section average speed after the first timing information is distributed, and the distribution unit determines whether to distribute second timing information indicating the timing for outputting the content, which is newly generated using the predicted average speed, based on specified information based on the section average speed.

[0008] The information processing method described in claim 12 is an information processing method executed by an information processing device, and includes a distribution step of distributing first timing information indicating the 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 step of calculating, after the first timing information is distributed, a predicted average speed, which is the average speed of the target vehicle in the specified road section predicted at the specified time, based on a cumulative average speed, which is the average speed based on the driving performance of the target vehicle at the specified time while traveling on the specified road section, and the section average speed, wherein the distribution step determines whether to distribute second timing information indicating the timing for outputting the content, which is newly generated using the predicted average speed, based on specified information based on the section average speed.

[0009] The information processing program described in claim 13 is an information processing program executed by an information processing device, and causes the information processing device to execute a distribution procedure of distributing first timing information indicating the timing of content output, 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 of calculating, after the first timing information is distributed, a predicted average speed, which is the average speed of the target vehicle in the specified road section predicted at the specified time, based on a cumulative average speed, which is the average speed based on the driving performance of the target vehicle at the specified time while traveling on the specified road section, and the section average speed, and the distribution procedure determines whether to distribute second timing information indicating the timing of content output, which is newly generated using the predicted average speed, based on specified information based on the section average speed. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a system according to an embodiment. [Figure 2] FIG. 2 is a schematic diagram illustrating an example of the operation of the server device. [Figure 3] FIG. 3 is a schematic diagram illustrating an example of the operation of the in-vehicle device. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of the server device and the in-vehicle device according to the embodiment. [Figure 5] FIG. 5 is a diagram showing a specific example of a method for estimating the WL type. [Figure 6] FIG. 6 is a diagram showing a specific example of a method for calculating a predicted average speed. [Figure 7] FIG. 7 is a flowchart showing the timing information processing procedure when the vehicle VE is traveling on the leading link. [Figure 8] FIG. 8 is a flowchart showing the timing information processing procedure when the vehicle VE is traveling on the n-th link. [Figure 9]FIG. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the server device 100. DETAILED DESCRIPTION OF THE INVENTION

[0011] [Embodiment] Hereinafter, embodiments 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 these embodiments. Furthermore, the same components in the following embodiments will be designated by the same reference numerals, and duplicated descriptions will be omitted.

[0012] 1. Introduction Navigation systems that guide users (drivers) to their destinations generally use a method of predicting (estimating) the time of arrival at the destination based on the average time required to travel along a link and the average speed on the link.

[0013] However, such a 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 required that the content be output from the in-vehicle device at an appropriate timing so as not to disturb the driver. However, simply using various average values ​​in the link, as in the conventional method, may not be able to output the content at an appropriate timing.

[0014] For example, because vehicle driving is greatly affected by individual differences and changes in road conditions, if the average speed deviates from the expected average speed, a discrepancy will occur between the actual time and the predicted time. In this way, when using the statistical average speed of a link, the accuracy of predicting the time when a vehicle will arrive at a specific point is not high, and there is a problem that content may be output at an inappropriate time that could interfere with the driver.

[0015] Another possible method is to output content after setting a specific grace period (buffer) in anticipation of a discrepancy between the actual time and the predicted time, but this method may result in the opportunity to output content being lost.

[0016] On the other hand, in order to improve prediction accuracy, it is possible to re-predict the speed of the vehicle every time the driving situation changes, rather than using the statistical average speed of 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 increases the operational costs, which becomes a problem.

[0017] This disclosure proposes a new technology to solve the above problem. Specifically, in this disclosure, when sufficient driving history data of the vehicle is not available 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, after sufficient driving history data is available, the cumulative average speed corresponding to the driving history is prorated to the statistical average speed corresponding to the driving scene, and the prorated result is determined as the average speed used to predict the arrival time.

[0018] That is, the present disclosure provides a measure to make a plausible prediction when sufficient driving history is not yet available, and then, as the vehicle approaches a specific point and driving history is acquired, to proportionally distribute the cumulative average speed to absorb the difference with the actual arrival time. As a result, while the number of times that information on the prediction result is distributed is reduced when the vehicle is away from the specific point, highly accurate information on the prediction result regarding the arrival time is distributed when the vehicle approaches the specific point. Furthermore, since the in-vehicle device can use the highly accurate information on the prediction result, it can output content at an appropriate time. Below, a specific description of information processing according to an embodiment of the present disclosure is provided.

[0019] [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.

[0020] 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 in a wired or wireless manner so as to be able to communicate with each other.

[0021] The cloud system 10 includes a server device 100 that is a central device responsible for information processing according to an embodiment of the present disclosure.

[0022] The server device 100 acquires, for example, from a database, an average section speed, which is a statistically obtained average speed, for the road section on which the target vehicle VE is traveling, and generates first timing information indicating the timing for outputting the content based on the acquired average section speed.The server device 100 then distributes the first timing information to the in-vehicle device 200 of the target vehicle VE.

[0023] After distributing the first timing information, the server device 100 calculates the cumulative average speed, which is the average speed at a predetermined time point, based on the driving performance of the target vehicle VE at that time while the target vehicle VE is traveling through the road section. Then, based on the cumulative average speed and the section average speed, the server device 100 calculates the predicted average speed, which is the average speed of the target vehicle VE at the predetermined time point, on the road section through which the target vehicle VE is traveling.

[0024] Then, the server device 100 determines whether or not to distribute the second timing information, which indicates the timing of content output and is newly generated using a predicted average speed rather than the average speed of the section, based on predetermined information based on the average speed of the section.

[0025] When determining to distribute the second timing information, the server device 100 generates the second timing information indicating the timing of content output based on the predicted average speed, and distributes the second timing information to the in-vehicle device 200 of the target vehicle VE.

[0026] 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 embodiments, the target vehicle VE is abbreviated as "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 (for example, tourist spots, stores, etc.) or various events that are considered to be useful to the user, or other content related to various news or everyday conversations.

[0027] 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 configured with 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 from each other are connected to each other so that they can communicate with each other. As another example, the in-vehicle device 200 may be a single device that has a navigation function and a recording function.

[0028] The in-vehicle device 200 may also include various sensors. For example, the in-vehicle device 200 may include 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.

[0029] Furthermore, the on-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.

[0030] 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.

[0031] [3. Server Device Overview] Next, an example of the operation of the server device 100 will be described with reference to Fig. 2. Fig. 2 is a schematic diagram illustrating an example of the operation 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 assessment engine 231, a guidance information DB, and an application MA.

[0032] The workload (WL) referred to here indicates the driving load, and may include both the driver's sense of burden (which can also be said to be the degree of difficulty) and the driving load determined for a road section.

[0033] 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., the driving difficulty is high), the load on the driver on a road section designated as "FREE" is below a standard (i.e., the driving difficulty is low or not high), and the load on the driver on a road section designated as "IDEAL" is medium (i.e., the driving difficulty is normal or not high).

[0034] The degree of difficulty for the driver may be expressed as a numerical value representing the sense of burden felt by the driver, and can be defined as follows:

[0035] For example, the level of difficulty "1" corresponds to the driving load type "BUSY_MAX", which is a road section where all general drivers have to be careful when driving, and it is defined that in such road sections, the in-vehicle device 200 should only issue a warning notification.

[0036] The level of difficulty "0.80" corresponds to the driving load type "BUSY+", and is a road section where more than 60% of average drivers have to be careful when driving, and it is defined that in such road sections, the in-vehicle device 200 should only issue warning notifications and caution notifications.

[0037] The level of difficulty "0.60" corresponds to the driving load type "BUSY", and is a road section where more than 20% of average drivers have to be careful when driving, and it is defined that in such road sections, the in-vehicle device 200 should only issue warning notifications, caution notifications, and important notifications.

[0038] The degree of difficulty "0.50" corresponds to the type of driving load "IDEAL", and it is defined that in the relevant road section, the in-vehicle device 200 may also utter content other than guidance-related content (warning notification, caution notification, important notification).

[0039] The difficulty level "0.25" corresponds to the driving load type "FREE," and is defined as a road section that more than 50% of general drivers may find monotonous and boring, and various content should be spoken.

[0040] The types of driving loads are not necessarily limited to the above examples ("BUSY_MAX", "BUSY+", "BUSY", "IDEAL", and "FREE"). In the following embodiments, the types of driving loads are expressed as "WL types", and will be described using "BUSY" and "FREE". The criteria and reference values ​​shown above are merely examples, and may be any values.

[0041] Here, road sections will also be explained. For example, a road section refers to a section between characteristic points of a road, and is called a link. The characteristic points of a road are intersections, corners, dead ends, etc., and are called nodes. In other words, a link refers to a road section that is set based on a predetermined rule. In other words, a link refers to a unit obtained by dividing a recorded section of a travel history based on a predetermined rule.

[0042] 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 representing a road network as a combination of nodes and links, facility data, and object information around the road. The object information includes information on features such as signs such as road signs, road markings such as stop lines, road dividing lines such as center lines, and roadside structures, as well as information on temporary obstacles. Obstacles refer to factors that impede the passage of pedestrians and bicycles, such as puddles, depressions in the road, fallen objects, and drainage ditches (including those blocked by mesh). The object information may include high-precision point cloud information of objects used for estimating the vehicle's position, etc. Furthermore, in the map information storage unit 121, links may be identified by link IDs.

[0043] The situation awareness engine 231 is a cloud service that collects situation information including analysis results obtained by analyzing sensor information obtained by sensors included in the in-vehicle device 200 or operation statuses 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 awareness engine 231 is included in the cloud system 10, but the in-vehicle device 200 may have the situation awareness engine 231.

[0044] The guidance information DB stores guidance information used to guide the driver along a route set according to the driver's destination, or guidance information used to guide the driver along a route that has been re-set (rerouted) when the target vehicle VE deviates from the set route.

[0045] The application MA has a function of distributing the results of processing by the workload estimation engine E to the information matching engine 232 of the in-vehicle device 200.

[0046] Next, 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.

[0047] 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.

[0048] Furthermore, the workload estimation engine E 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 that is the route along which the vehicle VE is scheduled to travel.

[0049] 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.

[0050] Next, the workload estimation engine E executes processing related to generation and distribution of timing information as information processing according to an embodiment of the present disclosure (step S23). The information processing according to an embodiment of the present disclosure will be described in more detail below using the example of FIG.

[0051] 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 vehicle VE) is scheduled to travel, and shows a scene immediately after vehicle VE1 starts traveling on link L1 toward link L2.

[0052] 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 time, and position PTx2 corresponds to the connection point where link L1 and link L2 are connected. Also, from this perspective, link L1 including 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.

[0053] Here, immediately after the vehicle VE1 starts traveling on the link L1, i.e., when the vehicle VE1 is traveling at the position PT1, the travel history of the vehicle VE1 on the link L1 has not been sufficiently accumulated. In such a case, the workload estimation engine E acquires, for example, from a database, the section average speed, which is the average speed statistically obtained for the link L1, and generates first timing information indicating the timing for outputting the content based on the acquired section average speed.

[0054] 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 the 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.

[0055] The average section 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 (for example, a large decrease in speed, a large increase in speed, a temporary stop, a stop, slow driving, a curve, a sudden start, a sudden braking, a sudden turn, an impact, etc.).

[0056] Returning to FIG. 2, it is assumed that 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 distributed. When the driving scene changes in this way, the workload estimation engine E determines that the WL type of the vehicle VE1 at the current time point has changed. For example, the workload estimation engine E may determine that the level 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 point.

[0057] In the example of FIG. 2, it is assumed that the workload estimation engine E determines that the current WL type of the vehicle VE1 has changed when the vehicle VE1 passes through position PTx3. In this case, the workload estimation engine E acquires the driving history at the time when the vehicle VE1 reaches position PTx3. For example, the workload estimation engine E acquires, as the driving history, the distance from position PTx1 to position PTx3 and the time required to travel from position PTx1 to position PTx3. Then, based on the driving history, the workload estimation engine E calculates the cumulative average speed, which is the average speed of the vehicle VE1 at the time when it reaches position PTx3.

[0058] In the above example, the workload estimation engine E calculates the cumulative average speed using the driving record 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 using the driving record at each time when a predetermined period has elapsed since the vehicle VE1 started traveling on link L1, or each time when the vehicle VE1 has traveled a predetermined distance since it started traveling on link L1.

[0059] 2, the workload estimation engine E calculates a predicted average speed, which is the average speed of the vehicle VE1 that is currently predicted for the link L1, based on the cumulative average speed and the section average speed. The method for calculating the predicted average speed will be described in detail later.

[0060] Furthermore, the workload estimation engine E newly generates second timing information indicating timing related to the output of the content 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. Then, the workload estimation engine E generates a time range including the predicted time TM2 as the second timing information.

[0061] Next, the workload estimation engine E determines whether to distribute the second timing information based on a comparison result between the first timing information and the second timing information. Note that the workload estimation engine E may also determine whether to distribute the second timing information based on a comparison result between the section average speed and the predicted average speed.

[0062] When determining to distribute the second timing information, the workload estimation engine E 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.

[0063] [4. Overview of the on-board 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.

[0064] 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.

[0065] The information matching engine 232 also determines whether output request information has been received in a phase separate from step S31. The output request information here refers to output request information transmitted by various applications installed in the in-vehicle device 200. For example, an application that provides content related to tourist information may transmit output request information including output conditions (time conditions that permit output or geographical conditions that permit output) and content to be output to the information matching engine 232.

[0066] 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 duration of the content included in the output request information.

[0067] Next, 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.

[0068] Then, the information matching engine 232 performs scheduling to determine the time to output the content based on the timing information (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.

[0069] [5. Functional Configuration] Next, 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.

[0070] (Server device 100) First, a description will be given of an example of the configuration of the server device 100. As shown in Fig. 4, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0071] (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 the network N by wire or wirelessly, and transmits and receives information to and from the in-vehicle device 200, for example.

[0072] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) 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. Furthermore, 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.

[0073] (Map information storage unit 121) The map information storage unit 121 stores map data used to estimate the WL type. The map data includes road data that represents a road network using a combination of nodes and links. Links are managed by link IDs, and may be associated with WL types and link lengths.

[0074] (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.).

[0075] (Regarding the control unit 130) The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs (for example, the information processing program according to the embodiment) stored in a storage device inside the server device 100 using RAM as a work area. The control unit 130 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0076] 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 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 between the processing units included in the workload estimation engine E are not limited to the connection relationships shown in Fig. 4, and may be other connection relationships.

[0077] (Acquisition part 131) The acquisition unit 131 acquires information indicating a travel route of the vehicle VE. For example, the acquisition unit 131 may acquire information on a planned travel route, which is a route along which the vehicle VE is scheduled to travel, as the information indicating the travel route of the vehicle VE.

[0078] For example, when the driver D specifies a destination, the acquisition unit 131 sets a route to the destination as a planned driving route according to a route plan that satisfies the destination. As a result, the acquisition unit 131 acquires information indicating the set planned driving route.

[0079] Furthermore, when 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 travel route based on the travel history of the vehicle VE, and set the predicted travel route as the planned travel route. In this case, the acquisition unit 131 acquires information indicating the predicted planned travel route.

[0080] (Estimation part 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 recognition 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.

[0081] Furthermore, the estimation unit 132 estimates the WL type for each link included in the planned driving route, which is a route along which the vehicle VE is scheduled to travel, as the future WL type of the vehicle VE. For example, the estimation unit 132 may compare the planned driving route with map data in which the WL type is associated with each link, and predict the WL type for each link included in the planned driving route.

[0082] 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 has been found to have 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."

[0083] (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 braking, an abrupt turn, an impact, etc. as a change in the driving behavior of the vehicle VE.

[0084] Furthermore, the detection unit 133 may detect, as a change in the traveling scene, whether or not the vehicle VE has entered a point corresponding to a change point at which the WL type changes. Specifically, when different WL types are estimated between adjacent links, the detection unit 133 detects whether or not the vehicle VE has entered a node that is a connection point where the adjacent links are connected.

[0085] Furthermore, the detection unit 133 may detect, as a change in the driving scene, an attribute change based on a comparison between the attribute of a first link, of links included in the planned driving route, on 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. 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, or entry from a road outside the living area to a road within the living area.

[0086] Furthermore, the detection unit 133 may detect, as a change in the traveling scene, whether the vehicle VE has entered an area corresponding to a predetermined characteristic point that exists in a first link on which the vehicle VE is currently traveling among the links included in the planned traveling route. The characteristic point here is, for example, an intersection, a junction, a fork, a toll booth, a railroad crossing, etc.

[0087] 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 a highway, 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.

[0088] (Judgment unit 134) The determination unit 134 determines whether the WL type of the vehicle VE at the current time has changed based on the driving situation of the vehicle VE. For example, the determination unit 134 may determine whether the degree of difficulty for the driver D on the link on which the vehicle VE is currently driving has changed, as the WL type of the vehicle VE at the current time. For example, the determination unit 134 may determine whether the degree of difficulty for the driver D on the link on which the vehicle VE is currently driving has changed based on whether the detection unit 133 has detected a change in the driving scene.

[0089] For example, when it is detected that the driving behavior of the vehicle VE has changed, the determination unit 134 may determine that the degree of difficulty of the driver D has changed.

[0090] Furthermore, the determination unit 134 may determine that the degree of difficulty of the driver D has changed when it is detected that the vehicle VE has entered a point (node) corresponding to a change point where the WL type changes.

[0091] In addition, the judgment unit 134 may determine that the degree of difficulty for the driver D has changed if an attribute change is detected between the attribute of the first link on which the vehicle VE is currently traveling and the attribute of the second link located in the direction of travel of the vehicle VE and connected to the first link.

[0092] Furthermore, when it is detected that the vehicle VE has entered an area corresponding to a predetermined characteristic point, the determination unit 134 may determine that the degree of difficulty of the driver D has changed.

[0093] (Generation unit 135) The generation unit 135 generates timing information that indicates timing related to the output of the content.

[0094] The generation unit 135 generates first timing information indicating timing for outputting content 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 generation 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. Then, the generation unit 135 generates a time range including the predicted time as the first timing information.

[0095] 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 traveling route, the generation unit 135 generates first timing information based on the section average speed corresponding to the current link (current road section), which is the link that includes the current position of the vehicle VE.

[0096] Specifically, when it is determined that the WL type has changed and the vehicle VE enters a link whose WL type is estimated to be different from the link on which the vehicle VE has been traveling, the generation unit 135 generates first timing information based on the average section speed corresponding to the destination link as the current link. More specifically, when the vehicle VE enters a future link via a connection point connecting adjacent links whose WL types are estimated to be different from each other, the generation unit 135 generates first timing information based on the average section speed corresponding to the destination link.

[0097] In this way, immediately after the vehicle VE leaves the link on which it has been traveling and enters a link for which a different WL type is estimated to exist than the link on which it has been traveling, the travel history of the vehicle VE for the link it is entering is not sufficiently accumulated. Therefore, when the vehicle VE enters a link for which a different WL type is estimated to exist than the link on which it has been traveling, the generation unit 135 generates the first timing information based on the section average speed, which is the average speed statistically obtained for the link it is entering.

[0098] On the other hand, at a specific point in time after the first timing information is distributed, the accumulation of the vehicle VE's travel history 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 arrive at the start point of a future link that is included in the planned travel route and is estimated to have a different WL type from the current link. For example, the generation unit 135 calculates the predicted time at which the vehicle VE will arrive at the start point based on the predicted average speed 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.

[0099] (Distribution Section 136) The distribution unit 136 distributes timing information indicating timing related to the output of 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.

[0100] Furthermore, the distribution unit 136 determines whether to distribute the second timing information based on predetermined 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 predetermined condition, the distribution unit 136 determines to distribute the second timing information generated by the generation unit 135. As another example, 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 distribution unit 136 may determine to distribute the second timing information. The predicted average speed is calculated by the calculation unit 137.

[0101] (Calculation unit 137) After distributing the first timing information, the calculation unit 137 calculates the cumulative average speed, which is the average speed at a predetermined time point, based on the travel record at that time 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 cumulative average speed and the section average speed corresponding to the current link.

[0102] 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 time when it is determined that the WL type has changed, that is, the driving performance for the current link.

[0103] 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 at the point when a predetermined period has elapsed while the vehicle VE is traveling on the current link, or at the point when the vehicle VE has traveled a predetermined distance on the current link.

[0104] Then, the calculation unit 137 calculates a predicted average speed, which is the average speed predicted for the current link at the current time and is the average speed of the vehicle VE, based on the cumulative average speed and the section average speed corresponding to the current link.

[0105] 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.

[0106] (In-vehicle device 200) Continuing with reference to Fig. 4, an example configuration of the in-vehicle device 200 will be described. As shown in Fig. 4, the in-vehicle device 200 includes 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.

[0107] (Mike MC) The microphone MC is a sound collecting device that collects sounds generated inside the vehicle VE. For example, the microphone MC collects the speech of the driver D.

[0108] (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.

[0109] The sensor SC detects various types of information related to the vehicle VE and transmits the detected sensor information to the situation recognition engine 231.

[0110] (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.

[0111] (Storage unit 220) The storage unit 220 is realized by, for example, a semiconductor memory element such as RAM or flash memory, or a storage device such as a hard disk or optical disk. The storage unit 220 may store, for example, data and programs related to the information processing according to the embodiment. Furthermore, 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.

[0112] (User information storage unit 221) The user information storage unit 221 stores various information related to a user (for example, a driver D) of the vehicle VE. The user information storage unit 221 may store the user related to the vehicle VE, and may further store the driving history of the vehicle VE.

[0113] (Content storage unit 222) The content storage unit 222 stores the content provided by the application AP.

[0114] (Regarding the control unit 230) The control unit 230 is realized by a CPU, an MPU, or the like executing various programs (for example, the information processing program according to the embodiment) stored in a storage device inside the in-vehicle device 200 using a RAM as a work area. The control unit 230 is also realized by an integrated circuit such as an ASIC or an FPGA.

[0115] 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 functions and actions of information processing 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. Furthermore, the connection relationship between the processing units included in the control unit 230 is not limited to the connection relationship shown in Fig. 4, and may be other connection relationships.

[0116] (Situational 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 sounds, conditions, 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.

[0117] (Information Matching Engine 232) The information matching engine 232 searches for links (speech-permitted links) that can be determined to allow content to be output by voice from among the links included in the planned driving route.

[0118] Furthermore, when the information matching engine 232 receives output request information, it estimates the time required to play back the content included in the output request information.

[0119] Furthermore, the information matching engine 232 determines whether or not the content included in the output request information can be output based on the utterance allowable link and the required time.

[0120] Furthermore, the information matching engine 232 executes a scheduling process to determine the output timing of the content so as to satisfy the output conditions included in the output request information.

[0121] (output control unit 233) The output control unit 233 controls the speaker SP so that the content is output at the timing scheduled by the information matching engine 232.

[0122] [6. Estimation method for WL type] From here, the method for estimating the WL type will be specifically explained using Fig. 5. Fig. 5 is a diagram showing a specific example of the method for estimating the WL type. In Fig. 5, the method for estimating the WL type will be explained using an example in which a travel route RT1 is plotted based on a route plan that satisfies the destination.

[0123] Also, as shown in Figure 5, the driving route RT1 is a route connecting a departure point PT1 and a destination point PT2, and a scene is shown in which the WL type estimation process is started at a predetermined time point while the vehicle VE1 is traveling on the driving route RT1.

[0124] 5(a), the estimation unit 132 compares the traveling route RT1 with map data in which a WL type is associated with each link, and associates a link ID (link_id) with each link that makes up the traveling route RT1. FIG. 5(a) shows an example in which the estimation unit 132 divides the traveling route RT1 into five links by associating link ID "100," link ID "101," link ID "102," link ID "103," link ID "104," and link ID "105" with the traveling route RT1.

[0125] Also, in FIG. 5(a), node ND01 is shown as information on the connection point where the link identified by the link ID "100" (link 100) and the link identified by the link ID "101" (link 101) are connected.

[0126] Furthermore, node ND12 is indicated 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.

[0127] Furthermore, node ND23 is indicated as information on the connection point where the link identified by the link ID "102" (link 102) and the link identified by the link ID "103" (link 103) are connected.

[0128] Furthermore, node ND34 is indicated as information on the connection point where the link identified by the link ID "103" (link 103) and the link identified by the link ID "104" (link 104) are connected.

[0129] Furthermore, node ND45 is indicated 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.

[0130] The estimation unit 132 may also calculate the distance (len) of each link by referring to map data. 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.

[0131] In this state, it is assumed that the vehicle VE1 is traveling on link 102, as shown in Fig. 5(b). In this example, the estimation unit 132 may estimate the WL types for link 102, which the vehicle VE1 is currently traveling on, and links 103, 104, and 105, which the vehicle VE1 is scheduled to travel on after link 102. Fig. 5(b) shows an example in which the estimation unit 132 refers to map data in which a 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."

[0132] 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 VE is currently traveling as the WL type of the vehicle VE1 at the current time.

[0133] 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.

[0134] For example, the link on which the vehicle VE1 is traveling changes depending on 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. The link type here refers to classification information such as a main road, a connecting road, etc. The road type refers to classification information such as an expressway, a national highway, a narrow street, etc.

[0135] [7. Calculation method for 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, where the first link (leading link) for calculating the predicted average speed among the links constituting the travel route RT1 (Fig. 5) is designated as link 104. The example in Fig. 6 corresponds to a process in which second timing information is distributed in accordance with the result of comparing the section average speed with the predicted average speed.

[0136] Here, node ND34 is a connection point connecting link 103 with a WL type of "BUSY" and link 104 with a WL type of "FREE." That is, node ND34 is a connection point connecting links whose WL types are estimated to be different from each other. Furthermore, node ND45 is a connection point connecting link 104 with a WL type of "FREE" and link 105 with a WL type of "BUSY." That is, node ND45 is also a connection point connecting 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 connecting these links.

[0137] 6(a) shows a scene in which the vehicle VE1 is located at node ND34 immediately after starting to travel along link 104. In this example, link 104 is the current link of the vehicle VE1.

[0138] At the time when the vehicle VE1 starts traveling on the link 104, there is not enough accumulated travel history of the vehicle VE1 on the link 104. In such a case, the generation unit 135 acquires, for example, from a database, an average section speed LV1, which is an average speed statistically obtained for the link 104, and generates first timing information indicating the timing related to the output of the content based on the average section speed LV1.

[0139] More specifically, the generation unit 135 calculates a predicted time TM1 at which the vehicle VE1 will arrive at node ND45, based on the section average speed LV1 corresponding to the link 104 and the distance from node ND34, which is the current location 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.

[0140] 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.

[0141] 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 moment when the vehicle VE1 has traveled 100 m on the link 104. In this case, the calculation unit 137 acquires the driving history at the moment when the vehicle VE1 has traveled 100 m on the link 104. For example, the calculation unit 137 acquires, as the driving history, the driving distance "100 m" from the node ND34 to the current position PT3 and the time required 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 of the vehicle VE1 at the moment when it reached the current position PT3, based on the driving history.

[0142] Then, based on the cumulative average speed SV1 and the section average speed LV1, 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), and is the average speed of vehicle VE1.

[0143] 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 for the current link (the distance obtained by subtracting the traveled distance D2 from the distance D1 of the current link).

[0144] Therefore, in the example of Figure 6(b), the calculation unit 137 calculates the predicted average speed PV1 corresponding to the leading link 104 by applying, to calculation formula (1), the distance of the current link 104, "500 m," as the distance D1 of the current link, the distance traveled from node ND34 to the current position PT3, "100 m," as the traveled distance D2 for the distance D1 of the current link, and the distance from the current position PT3 to node ND45, "400 m," as the remaining distance D3 for the current link.

[0145] When the predicted average speed PV1 is calculated, the distribution unit 136 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 second timing information indicating the timing related to content output, newly generated using the predicted average speed PV1, may be distributed. 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.

[0146] 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 refers to the predicted time at which the vehicle VE1 will arrive at node ND45.

[0147] If it is determined that the second timing information may be distributed, the generation unit 135 calculates a predicted time TM2 at which the vehicle VE1 will arrive at 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.

[0148] Here, when the vehicle VE1 is traveling on an n-th 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 n-th link. According to calculation formula (2), the predicted average speed PVn corresponding to the n-th link is calculated by correcting the cumulative average speed SVn corresponding to the n-th link with a coefficient obtained as the ratio of the section average speed LVn of the n-th link to the section average speed LV1 of the leading link.

[0149] Therefore, for example, when vehicle VE1 has passed link 104 and is traveling on the second link 105, the calculation unit 137 calculates the predicted average speed PV2 corresponding to the second link 105 by applying 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).

[0150] [8. Timing information distribution procedure] The processing procedure for distributing timing information will be described with reference to Figures 7 and 8. Figure 7 describes the processing procedure when the vehicle VE is traveling on the first link. Figure 8 describes the processing procedure when the vehicle VE is traveling on the nth link, which is the second or subsequent link following the first link.

[0151] [8-1. Timing information distribution procedure (1)] FIG. 7 is a flowchart showing the timing information processing procedure when the vehicle VE is traveling on the leading link.

[0152] First, the acquisition unit 131 acquires information indicating a travel route of the vehicle VE (step S701). For example, the acquisition unit 131 acquires information on a planned travel route, which is a route along which the vehicle VE is scheduled to travel, as the information indicating the travel route of the vehicle VE.

[0153] The generation unit 135 determines whether the vehicle VE has started traveling on a 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.

[0154] While the vehicle VE has not yet started traveling on the leading link (step S702; No), the generation unit 135 waits until the vehicle VE starts traveling on the leading link.

[0155] On the other hand, when the vehicle VE starts traveling on the first 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 first link (step S703). Specifically, the generation unit 135 calculates the predicted time TM1 at which the vehicle VE will finish traveling on the first link based on the average section speed LV1 and the distance D1 of the first link, and generates a time range including the predicted time TM1 as the first timing information.

[0156] Furthermore, the distribution unit 136 distributes the first timing information to the in-vehicle device 200 of the vehicle VE (step S704).

[0157] 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 (the 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.

[0158] While the traveling of the vehicle VE does not satisfy the predetermined condition (step S705; No), the determination unit 134 waits until it can be determined that the traveling of the vehicle VE satisfies the predetermined condition.

[0159] On the other hand, if it is determined that the travel of the vehicle VE has satisfied the predetermined condition (step S705; Yes), the calculation unit 137 acquires the travel history 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 history, a 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 a remaining distance D3 obtained by subtracting the travel distance D2 from the distance D1 of the leading link.

[0160] 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 top link (the current position determined to satisfy the predetermined condition), based on the driving history (step S707).

[0161] Next, the calculation unit 137 calculates a predicted average speed PV1, which is the average speed predicted at the current time 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 traveling distance D2, and the remaining distance D3 to the calculation formula (1) described in FIG.

[0162] The generation unit 135 generates second timing information indicating timing related to output of the content based on the predicted average speed PV1 (step S709). Specifically, the generation unit 135 calculates a 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.

[0163] The distribution unit 136 compares the first timing information with the second timing information, and determines whether or not there is a difference between the two timings that is equal to or greater than a threshold (step S710).

[0164] If it is determined that the deviation is equal to or greater than the threshold value (step S710; Yes), the distribution unit 136 distributes the second timing information to the in-vehicle device 200 of the vehicle VE (step S711).

[0165] In this state, the calculation unit 137 determines whether the vehicle VE has started traveling on the n-th 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 n-th 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 also return to step S705.

[0166] [8-2. Timing information distribution procedure (2)] FIG. 8 is a flowchart showing the timing information processing procedure when the vehicle VE is traveling on the n-th link.

[0167] When the vehicle VE starts traveling on the n-th 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 (the 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 n-th link, or whether the vehicle VE has traveled a predetermined distance since starting traveling on the n-th link.

[0168] While the traveling of the vehicle VE does not satisfy the predetermined condition (step S801; No), the determination unit 134 waits until it can be determined that the traveling of the vehicle VE satisfies the predetermined condition.

[0169] On the other hand, when it is determined that the traveling of the vehicle VE satisfies the predetermined condition (step S801; Yes), the calculation unit 137 acquires the traveling performance of the vehicle VE traveling along the n-th link up to the present time (step S802). For example, the calculation unit 137 acquires the traveling distance to the present position on the n-th link and the time required to reach the present position as the traveling performance.

[0170] Then, the calculation unit 137 calculates the cumulative average speed SVn, which is the average speed at the time when the vehicle VE reached the current position on the nth link (the current position determined to satisfy the predetermined condition), based on the driving history (step S803).

[0171] Next, the calculation unit 137 calculates the predicted average speed PVn corresponding to the n-th 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 n-th 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.

[0172] The generation unit 135 generates second timing information indicating timing related to the output of the content based on the predicted average speed PVn (step S805). Specifically, the generation unit 135 calculates a predicted time TM2 at which the vehicle VE will finish traveling the n-th link based on the predicted average speed PVn, and generates a time range including the predicted time TM2 as the second timing information.

[0173] 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 n-th link, and the predicted time TM1 is calculated based on the average section speed LVn.

[0174] If it is determined that the deviation is equal to or greater than the threshold value (step S806; Yes), the distribution unit 136 distributes the second timing information to the in-vehicle device 200 of the vehicle VE (step S807).

[0175] 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.

[0176] On the other hand, if the vehicle VE has started traveling on the next n-th link (step S808; Yes), the calculation unit 137 repeats the processing from step S802.

[0177] [9. Removal of restrictions] In the above embodiment, the processing described as being performed by the server device 100 may be performed by the in-vehicle device 200. Specifically, a series of processing 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.

[0178] [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 configured 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, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0179] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0180] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, etc. The communication interface 1500 receives data from other devices via a predetermined communication network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0181] 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.

[0182] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program or data from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0183] 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 predetermined communication network.

[0184] [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 using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0185] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown. 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, part or all of the processing described as being performed by server device 100 may be configured to be performed on the in-vehicle device 200 side.

[0186] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0187] Although some of the 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 implemented in other forms that include the aspects described in the "present invention" section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0188] 1 System 100 Server device 120 Storage section 121 Map information storage unit 122 Control result memory unit 130 control section 131 Acquisition Department 132 Estimation Department 133 Detector 134 Judgment section 135 Generation part 136 Distribution Department 137 Calculation Unit 200 On-vehicle equipment 231 Situational Awareness Engine 232 Information Matching Engine 233 Output control section

Claims

1. a distribution unit that distributes first timing information indicating timing related to content output, the first timing information being generated based on a section average speed that is an average speed of vehicles in a predetermined road section, to a target vehicle traveling on the predetermined road section; a calculation unit that calculates, after the first timing information is distributed, a predicted average speed that is an average speed of the target vehicle in the predetermined road section that is predicted at the predetermined time point, based on an accumulated average speed that is an average speed based on a driving record of the target vehicle at the predetermined time point while the target vehicle is driving in the predetermined road section, and the section average speed; Equipped with The distribution unit determines whether to distribute second timing information indicating timing related to output of the content, the second timing information being newly generated using the predicted average speed, based on predetermined information based on the section average speed.

1. An information processing device comprising:

2. an estimation unit that estimates a type of driving load for each road section included in a planned driving route, based on the planned driving route being a route that the target vehicle is scheduled to travel; a generating unit that generates, when different types of driving loads are estimated between road sections that are adjacent to each other among road sections included in the planned travel route, the first timing information indicating a range based on an expected 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 that includes the current position; Furthermore, The distribution unit distributes the first timing information generated by the generation unit to the target vehicle.

2. The information processing apparatus according to claim 1, wherein:

3. a detection unit that detects a change in a driving scene based on a driving situation of the target vehicle; a determination unit that determines whether a type of driving load of the target vehicle at the current time has changed based on whether a change in the driving scene has been detected; Furthermore, 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 travel route, the generation unit generates the first timing information based on the section average speed corresponding to a current road section that is a road section including a current position of the target vehicle.

3. The information processing apparatus according to claim 2, wherein:

4. 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, the generation unit generates the first timing information based on the section average speed corresponding to the road section to which the target vehicle has entered as the current road section.

4. The information processing apparatus according to claim 3,

5. When it is determined that the type of driving load has changed while the target vehicle is traveling in 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 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 driving actual data for the current road section at the time when it is determined that the type of driving load has changed, and the section average speed corresponding to the current road section.

4. The information processing apparatus according to claim 3,

6. 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 present time, based on a cumulative average speed, which is an average speed based on the driving performance for the current road section, at a time when a predetermined period has elapsed while the target vehicle is traveling in the current road section or when the target vehicle has traveled a predetermined distance in the current road section, and the section average speed corresponding to the current road section.

4. The information processing apparatus according to claim 3,

7. The calculation unit calculates the predicted average speed by performing a correction calculation using a ratio of the actual travel performance to the distance of the current road section as a coefficient indicating the influence of the cumulative average speed on the section average speed, to correct the section average speed so as to approach the cumulative average speed.

7. The information processing apparatus according to claim 5, wherein the information processing apparatus is a computer.

8. 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.

8. The information processing apparatus according to claim 7,

9. the predetermined information includes the first timing information; 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.

2. The information processing apparatus according to claim 1, wherein:

10. the predetermined information includes the section average speed and the predicted average speed, The distribution unit determines to distribute the second timing information newly generated using the predicted average speed when a comparison result of the comparison between the value based on the section average speed and the value based on the predicted average speed satisfies a predetermined condition.

2. The information processing apparatus according to claim 1, wherein:

11. 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 at which the target vehicle will arrive at the connection point, based on a predicted average speed that is an average speed of the target vehicle in the current road section.

7. The information processing apparatus according to claim 5, wherein the information processing apparatus is a computer.

12. An information processing method executed by an information processing device, a distribution step of distributing first timing information indicating timing related to content output, the first timing information being generated based on a section average speed, which is an average speed of vehicles in a predetermined road section, to a target vehicle traveling on the predetermined road section; a calculation step of calculating, after the first timing information is distributed, a predicted average speed, which is an average speed of the target vehicle in the predetermined road section predicted at the predetermined time, based on a cumulative average speed, which is an average speed based on the driving record of the target vehicle at the predetermined time while the target vehicle is driving in the predetermined road section, and the section average speed; Including, The distributing step includes determining whether or not to distribute second timing information indicating timing related to output of the content, the second timing information being newly generated using the predicted average speed, based on predetermined information based on the section average speed.

1. An information processing method comprising:

13. An information processing program executed by an information processing device, a distribution procedure for distributing first timing information indicating timing related to content output, the first timing information being generated based on a section average speed, which is an average speed of vehicles in a predetermined road section, to a target vehicle traveling on the predetermined road section; a calculation step of calculating, after the first timing information is distributed, a predicted average speed, which is an average speed of the target vehicle in the predetermined road section predicted at the predetermined time point, based on a cumulative average speed, which is an average speed based on the driving record of the target vehicle at the predetermined time point while the target vehicle is driving in the predetermined road section, and the section average speed; causing the information processing device to execute the above; The distribution procedure includes determining whether or not to distribute second timing information indicating timing related to output of the content, the second timing information being newly generated using the predicted average speed, based on predetermined information based on the section average speed. Information processing program.

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