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

Abstract

To implement scheduling to output content at the appropriate time. [Solution] The information processing device (100) according to the present application includes: an estimation unit (132) that estimates the type of driving load for each road section included in the planned driving route, which is the route the vehicle is scheduled to travel; a generation unit (133) that generates information indicating change points where the type of driving load changes along the planned driving route, based on the type of driving load for each road section; a determination unit (135) that determines whether or not the current type of driving load corresponding to the vehicle has changed, based on the vehicle's driving status; and a detection unit (134) that detects changes in the driving scene, based on the vehicle's driving status. The generation unit (133) regenerates information indicating change points when the current type of driving load corresponding to the vehicle has changed, and the determination unit (135) determines whether or not the current type of driving load corresponding to the vehicle has changed, based on whether or not a change in the driving scene has been detected.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Conventionally, techniques for providing information by voice while interacting with a user have been proposed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the above conventional techniques have room for improvement in realizing scheduling for outputting content at an appropriate timing. For example, in the above conventional techniques, the driving margin is determined based on the current driving situation, and content is output when the driving margin is high.

[0005] In this way, when content is output based on the driving margin corresponding to the current driving situation, a problem arises in that the output of content may be postponed or interrupted in accordance with changes in the driving margin, making it impossible to output the content at the required timing.

[0006] Accordingly, appropriate scheduling of the output timing is required so that content can be output at an appropriate timing, but the above conventional techniques merely control content output in accordance with the current driving margin.

[0007] The present invention has been made in view of the above, and proposes an information processing device, an information processing method, and an information processing program that can realize scheduling for outputting content at an appropriate time. [Means for solving the problem]

[0008] The information processing device according to claim 1 includes: an estimation unit that estimates the type of driving load for each road section included in the planned driving route, which is the route the vehicle is scheduled to travel; a generation unit that generates information indicating change points in the planned driving route where the type of driving load changes, based on the type of driving load for each road section; a determination unit that determines whether or not the current type of driving load corresponding to the vehicle has changed, based on the vehicle's driving status; and a detection unit that detects changes in the driving scene, wherein the generation unit regenerates the information indicating change points if the current type of driving load corresponding to the vehicle has changed, and the determination unit determines whether or not the current type of driving load corresponding to the vehicle has changed, based on whether or not a change in the driving scene has been detected.

[0009] The information processing method described in claim 13 is an information processing method performed by an information processing device, comprising: an estimation step of estimating the type of driving load for each road section included in a planned driving route, based on a planned driving route which is a route on which a vehicle is scheduled to travel; a generation step of generating information indicating change points in which the type of driving load changes along the planned driving route, based on the type of driving load for each road section; a determination step of determining whether or not the current type of driving load corresponding to the vehicle has changed, based on the vehicle's driving status; and a detection step of detecting a change in the driving scene, based on the vehicle's driving status, wherein the generation step regenerates the information indicating the change points if the current type of driving load corresponding to the vehicle has changed, and the determination step determines whether or not the current type of driving load corresponding to the vehicle has changed, based on whether or not a change in the driving scene has been detected.

[0010] The information processing program described in claim 14 is an information processing program executed by an information processing device, which causes the information processing device to execute: an estimation procedure for estimating the type of driving load for each road section included in a planned driving route, which is the route a vehicle is scheduled to travel; a generation procedure for generating information indicating change points in the planned driving route where the type of driving load changes, based on the type of driving load for each road section; a determination procedure for determining whether the current type of driving load corresponding to the vehicle has changed, based on the vehicle's driving status; and a detection procedure for detecting changes in the driving scene, based on the vehicle's driving status. The generation procedure regenerates the information indicating change points if the current type of driving load corresponding to the vehicle has changed, and the determination procedure determines whether the current type of driving load corresponding to the vehicle has changed, based on whether a change in the driving scene has been detected. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 shows an example of a system according to an embodiment. [Figure 2] Figure 2 is a schematic diagram showing an example of server device operation. [Figure 3] Figure 3 is a schematic diagram showing an example of the operation of an in-vehicle device. [Figure 4] Figure 4 shows an example of the configuration of a server device and an in-vehicle device according to the embodiment. [Figure 5] Figure 5 shows a specific example of a method for estimating WL types. [Figure 6] Figure 6 shows a specific example of a method for generating change point information. [Figure 7] Figure 7 is a flowchart showing the procedure for generating change point information. [Figure 8] Figure 8 is a flowchart showing the processing procedures for regenerating change point information and distributing change point information. [Figure 9] Figure 9 is a flowchart showing the scheduling process. [Figure 10]FIG. 10 is a diagram illustrating a specific example of scheduling processing according to an embodiment. [Figure 11] FIG. 11 is a hardware configuration diagram illustrating an example of a computer that implements the functions of a server apparatus. [Description of Embodiments]

[0012] [Embodiment] Hereinafter, the embodiment of the present invention will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present invention are not limited by this embodiment. Further, in the following embodiments, the same reference numerals are assigned to the same components, and duplicate descriptions are omitted.

[0013] [1. System Configuration] First, the configuration of the system according to the embodiment will be described using FIG. 1. FIG. 1 is a diagram illustrating an example of the system according to the embodiment. FIG. 1 shows a system 1 as an example of the system according to the embodiment. The information processing according to the embodiment may be implemented in the system 1.

[0014] As shown in FIG. 1, the system 1 may include a cloud system 10 and an in-vehicle apparatus 200. Further, the cloud system 10 and the in-vehicle apparatus 200 may be communicably connected via a network N by wire or wirelessly.

[0015] The cloud system 10 includes a server apparatus 100, which is a central apparatus responsible for the information processing according to the embodiment. The server apparatus 100 is an apparatus that estimates the type of driving load for each road section included in the planned travel route that is the route on which the vehicle VE is scheduled to travel. Further, the server apparatus 100 generates information indicating a change point where the type of driving load changes on the planned travel route, based on the current position of the vehicle VE, the distance of each road section, and the type of driving load estimated for each road section.

[0016] Further, the server device 100 determines whether or not the current type of driving load of the driver of the vehicle VE has changed based on the traveling situation of the vehicle, and when the type of driving load has changed, regenerates information indicating the change point. Then, the server device 100 distributes the regenerated information indicating the change point to the in-vehicle device 200.

[0017] The in-vehicle device 200 may be a dedicated navigation device built in or mounted on the vehicle VE. For example, the in-vehicle device 200 may be composed of a navigation device and a recording device. As an example of this, the in-vehicle device 200 may be a composite device in which a navigation device and a recording device independent of each other are communicably connected. As another example, the in-vehicle device 200 may be a single device having a navigation function and a recording function.

[0018] Further, the in-vehicle device 200 may be provided with various sensors. For example, the in-vehicle device 200 may be provided with various sensors such as a camera, an acceleration sensor, a gyro sensor, a GPS (Global Positioning System) sensor, and an atmospheric pressure sensor. For this reason, the in-vehicle device 200 may also have a function of performing interaction and providing information for supporting driving based on sensor information acquired by the various sensors.

[0019] Further, the in-vehicle device 200 can use not only the sensors provided in its own device but also sensor information detected by sensors provided in the vehicle VE itself as a safe driving system.

[0020] Further, the driver can make this portable terminal device operate in the same manner as the in-vehicle device 200 by installing predetermined application software in a portable terminal device that is used on a daily basis (for example, a smartphone, a tablet terminal, a notebook PC, a PDA, etc.).

[0021] [2. Overview of Server Device] Next, an example of the operation of the server device 100 will be explained using Figure 2. Figure 2 is a schematic diagram showing an example of the operation of the server device 100. According to the example in Figure 2, the cloud system 10 may include a server device 100 having a workload estimation engine E, a situation awareness engine 231, a guidance information DB, and an application MA.

[0022] In this context, workload (WL) refers to the driving load and may encompass both the driver's perceived burden (which can also be called the degree of difficulty) and the driving load defined for the road section.

[0023] The categories of driving load include, for example, "BUSY," "IDEAL," and "FREE." Road sections designated as "BUSY" indicate that the burden on the driver is above the standard (i.e., the driving difficulty is high), road sections designated as "FREE" indicate that the burden on the driver is below the standard (i.e., the driving difficulty is low or not high), and road sections designated as "IDEAL" indicate that the burden on the driver is in the middle (i.e., the driving difficulty is normal or not high).

[0024] Furthermore, the degree of driver difficulty can be expressed numerically as the driver's perceived burden, and can be defined as follows:

[0025] For example, difficulty level "1" corresponds to the driving load type "BUSY_MAX," which is a road section where all typical drivers need to be careful while driving, and in such road sections, the in-vehicle device 200 is defined to only issue warning notifications.

[0026] A difficulty level of "0.80" corresponds to a driving load category of "BUSY+", representing a road section where more than 60% of typical drivers need to be extra careful while driving. In such road sections, the in-vehicle device 200 is defined to only issue warning and cautionary notifications.

[0027] A difficulty level of "0.60" corresponds to a driving load category of "BUSY," representing a road section where more than 20% of typical drivers need to be extra careful while driving. In such road sections, the in-vehicle device 200 is defined to only issue warning, caution, and important notifications.

[0028] A difficulty level of "0.50" corresponds to a driving load category of "IDEAL," and in such road sections, the in-vehicle device 200 is defined as being able to speak content other than guidance systems (warning notifications, caution notifications, important notifications).

[0029] A difficulty level of "0.25" corresponds to a driving load category of "FREE," and is defined as a road section that more than 50% of typical drivers would find monotonous and boring, requiring the use of a variety of content.

[0030] Note that the type of operating load is not necessarily limited to the examples above ("BUSY_MAX", "BUSY+", "BUSY", "IDEAL", "FREE"). In the following embodiments, the type of operating load will be referred to as "WL type", and "BUSY" and "FREE" will be used for explanation. Also, the criteria and reference values ​​shown above are examples and may be any values.

[0031] Here, we will also explain road sections. For example, a road section refers to a section between characteristic points on a road and is called a link. Characteristic points on a road include intersections, corners, dead ends, etc., and are called nodes. In other words, a link refers to a road section that is defined according to a predetermined rule. To put it another way, a link refers to a unit that divides the recording section of the movement history according to a predetermined rule.

[0032] Following the example above, in the following embodiment, road sections are represented as links, and connection points between road sections are represented as nodes. For example, the server device 100 has a map information storage unit 121 (Figure 4), which includes road data, facility data, and object information around the roads, representing the road network as a combination of nodes and links. The object information includes road signs and other signs, road markings such as stop lines, road lane markings such as center lines, structures along the roads, and other geographical features, as well as information on temporarily existing obstacles. Obstacles refer to things that hinder the passage of pedestrians and cyclists, such as puddles, sinkholes in the road, fallen objects, and drainage ditches (including parts blocked by nets). The object information may also include high-precision point cloud information of objects used for self-vehicle position estimation, etc. Furthermore, in the map information storage unit 121, links may be identified by link IDs.

[0033] The situation awareness engine 231 is a cloud service that collects situation information, including analysis results obtained by analyzing sensor information acquired by sensors in the in-vehicle device 200, or the operating status of various applications installed in the in-vehicle device 200, and distributes the accumulated information as situation information. In the example in Figure 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.

[0034] The guidance information database stores guidance information used for guiding drivers along routes set according to their destination, or guidance information used for guiding drivers along routes that have been rerouted due to the vehicle's VE (Vehicle Elevator) deviating from the set route.

[0035] The application MA has the function of distributing the results processed by the workload estimation engine E to the information matching engine 232 of the in-vehicle device 200.

[0036] From here, we will explain a specific example of operation by the workload estimation engine E. The workload estimation engine E performs information processing according to the embodiment.

[0037] First, the workload estimation engine E obtains situation information from the situation awareness engine 231 and estimates the type of driving load (WL type) of vehicle VE at the present 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 (let's call them driver D) on the road section that vehicle VE is currently traveling on, i.e., the current link, as the WL type of vehicle VE at the present time.

[0038] Furthermore, the workload estimation engine E estimates the type of future operating 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 route, which is the route that the vehicle VE is scheduled to travel.

[0039] For example, the workload estimation engine E may compare the planned route with map data (map information storage unit 121) to which each link is associated with a WL type, and predict the WL type for each link included in the planned route.

[0040] Here, if driver D specifies a destination, the workload estimation engine E can set the route to the destination as the planned route by creating a route plan that satisfies this destination. In such cases, the workload estimation engine E may refer to the guidance information DB and predict the WL type based on unique information that cannot be obtained from map data that only associates WL types. For example, by referring to the guidance information DB, the workload estimation engine E may be able to detect the attributes of the first link, which is the link that vehicle VE is currently traveling on, and the attributes of the second link, which is located in the direction of travel of vehicle VE and connected to the first link. For example, if the workload estimation engine E can refer to the guidance information DB and detect the attribute "wide road" for the first link and the attribute "narrow road" for the second link, it may predict the WL type based on a comparison of the attributes "wide road" for the first link and "narrow road" for the second link.

[0041] Furthermore, if driver D has not specified a destination and route setting based on the destination is not possible, the workload estimation engine E does not need to perform the process of predicting the WL type for each link included in the planned route. On the other hand, if route setting is not possible in this way, the workload estimation engine E may predict the route based on the vehicle VE's driving history and set the predicted route as the planned route.

[0042] Furthermore, in step S22, the workload estimation engine E also calculates the estimated time when the vehicle VE will arrive at a node included in the planned route, based on the estimation result (prediction result). The estimated time is an example of information indicating a change point where the WL type changes (change point information), and details will be described later.

[0043] Returning to the explanation in step S21, the workload estimation engine E monitors the changes in the current WL type (driver D's workload) of the vehicle VE while the vehicle VE is in motion, based on the vehicle VE's driving conditions (situation information obtained from the information matching engine 232).

[0044] As a result, if the workload estimation engine E determines, for example, that the current WL type of vehicle VE has changed compared to the WL type estimated at a previous timing, it recalculates the estimated time of arrival of vehicle VE at a node included in the planned route. In other words, if the workload estimation engine E determines that the current WL type of vehicle VE has changed, it recalculates the change point information. Then, the workload estimation engine E distributes the recalculated change point information to application MA (step S23). Application MA distributes the change point information obtained from the workload estimation engine E to the in-vehicle device 200. Specifically, application MA distributes the change point information obtained from the workload estimation engine E to the information matching engine 232.

[0045] [3. Overview of In-Vehicle Equipment] Next, an example of the operation of the in-vehicle device 200 will be explained using Figure 3. Figure 3 is a schematic diagram showing an example of the operation of the in-vehicle device 200. According to the example in Figure 3, the in-vehicle device 200 has an information matching engine 232.

[0046] First, the information matching engine 232 receives WL information (step S31-1). The WL information here refers to change point information distributed by the server device 100, that is, information on the expected time when the vehicle VE will arrive at a node included in the planned route.

[0047] Next, the information matching engine 232 searches for links from among the links included in the planned travel route that it determines can output content via voice (speech-permitted links) (step S31-2). For example, the information matching engine 232 may search for links from among the links included in the planned travel route that are estimated to have a WL type of "FREE" as links that it determines can output content via voice.

[0048] Furthermore, the information matching engine 232 also determines, in a phase separate from steps S31-1 and S31-2, whether or not it has received output request information. The output request information referred to here is 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 to the information matching engine 232 that includes output conditions (time conditions for permitting output, or geographical conditions for permitting output) and the content to be output.

[0049] When the information matching engine 232 receives output request information (step S32-1), it estimates the time required to play the content included in the output request information (step S32-2). For example, the information matching engine 232 may estimate the time required based on the playback time of the content included in the output request information.

[0050] Next, the information matching engine 232 determines whether the content included in the output request information can be output based on the speech-permitted link searched in step S31-2 and the estimated time in step S32-2 (step S33). For example, if the time length of the speech-permitted link 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.

[0051] Next, the information matching engine 232 performs control processing to resolve the output request information (step S34). The control processing to resolve the output request information is a basic process performed by the information matching engine 232, and refers to scheduling that determines the timing of content output so as to satisfy the output conditions included in the output request information.

[0052] If the content output timing is determined as a result of scheduling by the information matching engine 232, the content will be output as audio from the speaker SP (Figure 4) of the in-vehicle device 200.

[0053] [4. Functional Configuration] From here, we will describe an example configuration of the server device 100 and the in-vehicle device 200 using Figure 4. Figure 4 is a diagram showing an example configuration of the server device 100 and the in-vehicle device 200 according to the embodiment.

[0054] (Server device 100) First, let's describe an example of the configuration of the server device 100. As shown in Figure 4, the server device 100 has a communication unit 110, a storage unit 120, and a control unit 130.

[0055] (Communications Department 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wireless connection and performs, for example, sending and receiving information with the in-vehicle device 200.

[0056] (Storage unit 120) The storage unit 120 is implemented by, for example, a semiconductor memory element such as RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or optical disc. The storage unit 120 may store, for example, data or programs related to information processing according to the embodiment. Also, according to the example in Figure 4, the storage unit 120 may have a map information storage unit 121 and a control result storage unit 122.

[0057] (Map information storage unit 121) The map information storage unit 121 stores map data used for estimating the WL type. This map data includes road data, which represents the road network using combinations of nodes and links. Links are managed by link IDs and may be associated with WL type and link length.

[0058] (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, future WL type, change point information, etc.).

[0059] (Regarding the control unit 130) The control unit 130 is implemented by a CPU (Central Processing Unit) or MPU (Micro Processing Unit), etc., which executes various programs (for example, information processing programs according to the embodiment) stored in the storage device inside the server device 100 using RAM as the working area. Alternatively, the control unit 130 can be implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0060] As shown in Figure 4, the control unit 130 is equipped with a workload estimation engine E, which includes an acquisition unit 131, an estimation unit 132, a generation unit 133, a detection unit 134, a determination unit 135, and a distribution unit 136, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the workload estimation engine E is not limited to the configuration shown in Figure 4, and other configurations are also possible as long as they perform the information processing described later. Furthermore, the connection relationships of each processing unit in the workload estimation engine E are not limited to the connection relationships shown in Figure 4, and other connection relationships are also possible.

[0061] (Acquisition part 131) The acquisition unit 131 acquires information indicating the travel route of vehicle VE. For example, the acquisition unit 131 may acquire information indicating the travel route of vehicle VE, which is the route that vehicle VE is scheduled to travel.

[0062] For example, if driver D specifies a destination, the acquisition unit 131 sets the route to the destination as the planned driving route based on a route plan that satisfies this destination. As a result, the acquisition unit 131 acquires information indicating the set planned driving route.

[0063] Furthermore, if driver D has not specified a destination and it is not possible to set a route according to the destination, the acquisition unit 131 may predict a driving route based on the vehicle VE's driving history and set the predicted driving route as the planned driving route. In this case, the acquisition unit 131 acquires information indicating the predicted planned driving route.

[0064] (Estimation part 132) The estimation unit 132 estimates the WL type. For example, the estimation unit 132 estimates the driver D's difficulty level (driving difficulty) on the road section currently being traveled by the vehicle VE, i.e., the current link, as the current WL type of the vehicle VE. For example, the estimation unit 132 may acquire situation information from the situation awareness engine 231 and estimate the driver D's difficulty level based on the acquired situation information. Alternatively, the estimation unit 132 may compare the current link with map data to which the WL type is associated for each link, and estimate the driver D's difficulty level based on the WL type associated with the current link.

[0065] Furthermore, the estimation unit 132 estimates the WL type for each link included in the planned route, which is the route that the vehicle VE is scheduled to travel, as the future WL type for the vehicle VE. For example, the estimation unit 132 may predict the WL type for each link included in the planned route by comparing the planned route with map data on which the WL type is associated with each link.

[0066] Furthermore, the estimation unit 132 may estimate the WL type without relying on map data to which WL types are 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 to be "BUSY" for links where a tendency for sudden braking is obtained as a result of analyzing the driving history. Alternatively, the estimation unit 132 may 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 links with sharp curves or steep gradients have a high driving difficulty and estimate the WL type to be "BUSY".

[0067] Furthermore, the estimation unit 132 may estimate the WL type using the guidance information DB. For example, suppose the estimation unit 132 obtains route guidance corresponding to the planned route from the guidance information DB, and the route guidance states, "The road will narrow from point XX ahead." In this case, the estimation unit 132 can obtain the attribute "wide road" for the first link, which is the link that vehicle VE is currently traveling on, and the attribute "narrow road" for the second link, which is located in the direction of vehicle VE's travel and connected to the first link, and may predict the WL type based on a comparison of these attributes. For example, since "narrow roads" are generally considered to be more difficult to drive on than "wide roads," the estimation unit 132 can estimate the WL type "FREE" for the first link, which is the link that vehicle VE is currently traveling on, and estimate the WL type "BUSY" for the second link, which is located in the direction of vehicle VE's travel and connected to the first link.

[0068] (Generation unit 133) The generation unit 133 generates information indicating change points in the planned route where the WL type changes, based on the current position of the vehicle VE, the distance of each link included in the planned route, and the WL type for each link.

[0069] For example, if different WL types are estimated between adjacent links included in the planned travel route, the generation unit 133 may generate distance information indicating the distance from the vehicle VE's current position to the connection point, i.e., the node, where the adjacent links are connected, as information indicating a change point.

[0070] Furthermore, if different WL types are estimated between adjacent links included in the planned travel route, the generation unit 133 may generate time information indicating the expected time when the vehicle VE will reach the node where the adjacent links are connected, based on the vehicle VE's current position, as information indicating a change point.

[0071] Furthermore, the information indicating the point of change may also include type information indicating the WL type of the link on which the vehicle VE is currently traveling.

[0072] (Detection unit 134) The detection unit 134 detects changes in the driving scene based on the driving conditions of the vehicle VE. For example, the detection unit 134 detects changes in the driving behavior of the vehicle VE as changes in the driving scene. As an example, the detection unit 134 may detect changes in the driving behavior of the vehicle VE such as a significant decrease in speed, a significant increase in speed, a temporary stop, a complete stop, slow driving, a curve, a sudden start, a sudden brake, a sudden steering, an impact, etc.

[0073] Furthermore, the detection unit 134 may detect whether or not the vehicle VE has entered a point corresponding to a change point where the WL type changes, as a change in the driving scene. Specifically, when different WL types are estimated between adjacent links, the detection unit 134 detects whether or not the vehicle VE has entered a node, which is a connection point where adjacent links are connected.

[0074] Furthermore, the detection unit 134 may detect changes in the driving scene by comparing the attributes of the first link on which the vehicle VE is currently traveling with the attributes of the second link located in the direction of travel of the vehicle VE and connected to the first link, as changes in the driving scene. For example, the detection unit 134 may detect entry from a narrow road to a wide road, entry from a wide road to a narrow road, and entry from a road outside the residential area to a road within the residential area.

[0075] Furthermore, the detection unit 134 may detect, as a change in the driving scene, whether or not vehicle VE has entered an area corresponding to a predetermined feature point located in the first link on which vehicle VE is currently traveling, among the links included in the planned driving route. A feature point here refers to, for example, an intersection, merging point, junction, toll booth, level crossing, etc.

[0076] Furthermore, the detection unit 134 may also detect, for example, whether the road on which vehicle VE is currently traveling is a highway, or whether the road on which vehicle VE is currently traveling is a road with the same characteristics and no changes in attributes (for example, a straight road).

[0077] (Judgment unit 135) The determination unit 135 determines whether the current WL type of vehicle VE has changed based on the vehicle VE's driving conditions. For example, the determination unit 135 may determine whether the driver D's difficulty level has changed on the link that vehicle VE is currently traveling on, as the current WL type of vehicle VE. For example, the determination unit 135 may determine whether the driver D's difficulty level has changed on the link that vehicle VE is currently traveling on, based on whether the detection unit 134 has detected a change in the driving scene.

[0078] For example, if the determination unit 135 detects that the driving behavior of vehicle VE has changed, it may determine that the driver D's level of difficulty has changed.

[0079] Furthermore, if the determination unit 135 detects that the vehicle VE has entered a point (node) corresponding to a change point where the WL type changes, it may determine that the driver D's level of difficulty has changed.

[0080] Furthermore, if the determination unit 135 detects a change in attributes between the attributes of the first link on which vehicle VE is currently traveling and the attributes of the second link located in the direction of travel of vehicle VE and connected to the first link, it may determine that the driver D's level of difficulty has changed.

[0081] Furthermore, if the determination unit 135 detects that vehicle VE has entered an area corresponding to a predetermined characteristic point, it may determine that the driver D's level of distress has changed.

[0082] Furthermore, if the generation unit 133 determines that the current WL type of vehicle VE has changed (i.e., the driver D's difficulty level has changed), it regenerates the change point information (for example, the estimated time when vehicle VE will arrive at a node included in the planned route) that has been generated in advance based on the estimation results by the estimation unit 132.

[0083] (Distribution Section 136) If the current WL type of the vehicle VE changes, the distribution unit 136 distributes information to the on-board device 200 indicating the point in the planned route where the WL type changes.

[0084] (Summary regarding server device 100) The detection unit 134 detected a change in the driving scene, and the determination that driver D's level of difficulty had changed suggests that there may be an error in the change point information (predicted time) that had been generated. If the content output timing is determined based on change point information containing this error, content that should be output via a WL type "FREE" link may be output via a WL type "BUSY" link, potentially interfering with driver D's driving. There is also a risk that content output may be delayed or interrupted.

[0085] Therefore, if the generation unit 133 determines that the driver D's level of fatigue has changed in accordance with the change in the driving scene, it recalculates the change point information. As a result, it becomes possible to perform scheduling on the in-vehicle device 200 using highly accurate change point information with reduced errors, thereby enabling scheduling to output content at the appropriate timing.

[0086] Furthermore, in order to enable the in-vehicle device 200 to perform scheduling using highly accurate change point information with reduced errors, it is preferable to generate the latest change point information as needed, regardless of changes in the driving scene, and distribute the generated change point information to the in-vehicle device 200. However, in such a case, the number of times the server device 100 distributes change point information increases, which creates a problem in that it is not possible to meet the need to output content at the appropriate timing while keeping the amount of communication low. Therefore, the server device 100 actually performs recalculation only when it is determined that the driver D's level of fatigue has changed in accordance with changes in the driving scene, that is, when the need to recalculate the change point information has increased, and distributes the change point information obtained from the recalculation to the in-vehicle device 200. As a result, the server device 100 can meet the above needs.

[0087] (In-vehicle device 200) Next, we will explain an example of the configuration of the in-vehicle device 200 using Figure 4. As shown in Figure 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.

[0088] (Mike MC) The microphone (MC) is a sound collection device that collects sounds generated inside the vehicle (VE). For example, the microphone (MC) collects speech sounds made by the driver (D).

[0089] (Speaker SP) The speaker SP corresponds to an output device that outputs various information via sound. For example, the speaker SP outputs content information in accordance with the output control by the control unit 230.

[0090] (Sensor SC) Sensor SC detects various information related to vehicle VE and transmits the detected sensor information to the situation awareness engine 231.

[0091] (Application AP) An application AP is an application that provides content. For example, an application AP sends output request information to the information matching engine 232, which includes output conditions (time conditions for allowing output, or geographical conditions for allowing output) and the content to be output. Although not shown in Figure 1, system 1 may further include an application server that controls the application AP.

[0092] (Storage unit 220) The storage unit 220 is implemented by, for example, a semiconductor memory element such as RAM or flash memory, or a storage device such as a hard disk or optical disc. The storage unit 220 may store, for example, data or programs related to information processing according to the embodiment. Also, according to the example in Figure 4, the storage unit 220 may have a user information storage unit 221 and a content storage unit 222.

[0093] (User information storage unit 221) The user information storage unit 221 stores various information about the user (for example, driver D) of vehicle VE. The user information storage unit 221 may store the user of vehicle VE, and may also store the vehicle VE's driving history.

[0094] (Content storage unit 222) The content storage unit 222 stores the content provided by the application AP.

[0095] (Regarding the control unit 230) The control unit 230 is implemented by a CPU, MPU, etc., which executes various programs (for example, information processing programs according to this embodiment) stored in the memory device inside the in-vehicle device 200 using RAM as a working area. Alternatively, the control unit 230 can be implemented by an integrated circuit such as an ASIC or FPGA.

[0096] As shown in Figure 4, the control unit 230 includes a situation awareness engine 231, an information matching engine 232, and an output control unit 233, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the control unit 230 is not limited to the configuration shown in Figure 4, and other configurations are also possible as long as they perform the information processing described later. Furthermore, the connection relationships of the various processing units in the control unit 230 are not limited to the connection relationships shown in Figure 4, and other connection relationships are also possible.

[0097] (Situation awareness engine 231) The situation awareness engine 231 identifies the situation regarding the vehicle VE based on sensor information. For example, the situation awareness engine 231 identifies the situation of the vehicle VE by detecting sounds, appearances, and behavior within the vehicle VE. The situation awareness engine 231 then outputs situation information indicating the identified situation to the information matching engine 232.

[0098] (Information matching engine 232) The information matching engine 232 searches for links included in the planned travel route that it determines can output content via voice (speech-permitted links).

[0099] Furthermore, when the information matching engine 232 receives output request information, it estimates the time required to play the content included in the output request information.

[0100] Furthermore, the information matching engine 232 determines whether or not the content included in the output request information can be output based on the speech permission link and the required time.

[0101] Furthermore, the information matching engine 232 performs scheduling processing to determine the timing of content output in order to satisfy the output conditions included in the output request information.

[0102] (Output control unit 233) The output control unit 233 controls the output of content from the speaker SP at timings scheduled by the information matching engine 232.

[0103] [5. Estimation Method for WL Type] From here, we will explain the WL type estimation method in detail using Figure 5. Figure 5 is a diagram showing a specific example of the WL type estimation method. In Figure 5, we will explain the WL type estimation method using a scenario in which the driving route RT1 is drawn by a route plan that satisfies the destination as an example.

[0104] Furthermore, as shown in Figure 5, the travel route RT1 is the route connecting the departure point PT1 and the destination PT3, and the WL type estimation process starts when the vehicle VE is located at position PT2. In this example, position PT2 corresponds to the current location of the vehicle VE.

[0105] Here, as shown in the example in Figure 5(a), the estimation unit 132 compares the driving route RT1 with map data to which the WL type is associated for each link, and associates a link ID (link_id) with each link that makes up the driving route RT1. Figure 5(a) shows an example in which the estimation unit 132 associates link IDs "100", "101", "102", "103", "104", and "105" with the driving route RT1, thereby dividing the driving route RT1 into six links.

[0106] Furthermore, Figure 5(a) shows node ND01 as information about the connection point where the link identified by link ID "100" (link 100) and the link identified by link ID "101" (link 101) are connected.

[0107] Furthermore, node ND12 is shown as information about 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.

[0108] Furthermore, node ND23 is shown as information about the connection point where the link identified by link ID "102" (link 102) and the link identified by link ID "103" (link 103) are connected.

[0109] Furthermore, node ND34 is shown as information about the connection point where the link identified by link ID "103" (link 103) and the link identified by link ID "104" (link 104) are connected.

[0110] Furthermore, node ND45 is shown as information about 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.

[0111] Furthermore, the estimation unit 132 may also refer to map data and calculate the distance (len) of each link. Figure 5(a) shows an example in which the estimation unit 132 calculates the distance of link 100 as "100", the distance of link 101 as "200", the distance of link 102 as "300", the distance of link 103 as "100", the distance of link 104 as "500", and the distance of link 105 as "200".

[0112] In this state, the estimation unit 132 may estimate the WL type for the links including the vehicle VE's current location PT2, namely link 102 on which the vehicle VE is currently traveling, and links 103, 104, and 105 which the vehicle VE is scheduled to travel after link 102, as shown in Figure 5(b). Figure 5(b) shows an example in which the estimation unit 132 refers to map data to which the WL type is associated for each link and estimates the WL type for link 102 to be "FREE", for link 103 to be "BUSY", for link 104 to be "FREE", and for link 105 to be "BUSY".

[0113] Furthermore, although not shown in Figure 5(b), the estimation unit 132 may also estimate the degree of difficulty for driver D on link 102, which vehicle VE is currently traveling on, as the current WL type of vehicle VE.

[0114] [6. Methods for generating change point information] Next, using Figure 6, we will specifically explain a generation method for generating information indicating change points where the WL type changes. Figure 6 is a diagram showing a specific example of a change point information generation method. In Figure 6, we will explain the change point information generation method using the same scenario as in Figure 5 as an example.

[0115] First, as shown in Figure 6(a), the generation unit 133 detects nodes that connect links of different WL types as change points where the WL type changes. Referring to the example in Figure 5, node ND23 is the node that connects link 102 of WL type "FREE" and link 103 of WL type "BUSY". Node ND34 is the node that connects link 103 of WL type "BUSY" and link 104 of WL type "FREE". Node ND45 is the node that connects link 104 of WL type "FREE" and link 105 of WL type "BUSY". Therefore, the generation unit 133 detects nodes ND23, ND34, and ND45 as change points where the WL type changes, as nodes that connect links of different WL types. Hereafter, node ND23 may be referred to as change point CP23, node ND34 as change point CP34, and node ND45 as change point CP45.

[0116] Next, the generation unit 133 obtains the distance (link length information) of each link that includes the change points (CP23, CP34, CP45), as shown in Figure 6(b). Since the links that include the change points (CP23, CP34, CP45) are links 102, 103, 104, and 105, the generation unit 133 obtains the distance of link 102 as "300", the distance of link 103 as "100", the distance of link 104 as "500", and the distance of link 105 as "200".

[0117] Next, as shown in Figure 6(c), the generation unit 133 calculates the distance (dist) from node ND12, which is included in link 102 on which vehicle VE is currently traveling, to the change points (CP23, CP34, CP45). Since the distance of link 102 is "300", the generation unit 133 calculates the distance "300" from node ND12 to change point CP23.

[0118] Furthermore, the generation unit 133 adds the distance of link 102 ("300") and the distance of link 103 ("100") to calculate the distance from node ND12 to change point CP34 ("400"). The generation unit 133 adds the distance of link 102 ("300"), the distance of link 103 ("100") and the distance of link 104 ("500") to calculate the distance from node ND12 to change point CP45 ("900").

[0119] As shown in Figure 6(d), the generation unit 133 calculates the distance (dist) from the vehicle VE's current location PT2 to the change points (CP23, CP34, CP45). In the example in Figure 5, the vehicle VE's current location PT2 corresponds to a position 120m away from node ND12. Therefore, the generation unit 133 subtracts "120" from the distance "300" from node ND12 to change point CP23 to calculate the distance "180" from the current location PT2 to change point CP23.

[0120] Furthermore, the generation unit 133 calculates the distance from the current location PT2 to the change point CP34, which is "280", by subtracting "120" from the distance "400" from node ND12 to change point CP34. The generation unit 133 also calculates the distance from the current location PT2 to change point CP45, which is "780", by subtracting "120" from the distance "900" from node ND12 to change point CP45.

[0121] Here, the generation unit 133 defines the distance from the current location PT2 to the change point CP23 as "180", the distance from the current location PT2 to the change point CP34 as "280", and the distance from the current location PT2 to the change point CP45 as "780", as information indicating a change point.

[0122] Specifically, the generation unit 133 generates change point information indicating change point CP23, with a distance of "180" from the current location PT2 to change point CP23. The generation unit 133 also generates change point information indicating change point CP34, with a distance of "280" from the current location PT2 to change point CP34. Furthermore, the generation unit 133 generates change point information indicating change point CP45, with a distance of "780" from the current location PT2 to change point CP45.

[0123] As shown in Figure 6(e), the generation unit 133 calculates the estimated time when vehicle VE will reach the change points (CP23, CP34, CP45) based on the distance (dist) from the vehicle VE's current location PT2 to the change points (CP23, CP34, CP45) and the vehicle VE's speed. Figure 6(e) shows an example in which the generation unit 133 calculates the estimated time "TM23" when vehicle VE will reach change point CP23, the estimated time "TM34" when vehicle VE will reach change point CP34, and the estimated time "TM45" when vehicle VE will reach change point CP45.

[0124] Here, the generation unit 133 defines the predicted times "TM23", "TM34", and "TM45" as information indicating change points. Specifically, the generation unit 133 generates the predicted time "TM23" as change point information indicating change point CP23. The generation unit 133 also generates the predicted time "TM34" as change point information indicating change point CP34. The generation unit 133 also generates the predicted time "TM45" as change point information indicating change point CP45.

[0125] [7. Procedure for generating change point information] Figure 7 is a flowchart illustrating the procedure for generating change point information. Figure 7 illustrates the procedure for generating change point information as described using Figures 5 and 6.

[0126] First, the acquisition unit 131 acquires information indicating the travel route of vehicle VE (step S701). For example, as information indicating the travel route of vehicle VE, the acquisition unit 131 acquires information on the planned travel route, which is the route that vehicle VE is scheduled to travel.

[0127] The estimation unit 132 determines whether it is time to estimate the WL type (step S702). For example, the estimation unit 132 may determine that it is time to estimate the WL type if a certain period of time has elapsed since the vehicle VE started moving, or if the vehicle VE has traveled a certain distance since the vehicle VE started moving.

[0128] If it is not yet time to estimate the WL type (step S702; No), the estimation unit 132 waits until it can determine that it is time to estimate the WL type.

[0129] On the other hand, when it is time to estimate the WL type (step S702; Yes), the estimation unit 132 estimates the difficulty level of driver D on the link that vehicle VE is currently traveling on (driving difficulty level) based on the situation information obtained from the situation awareness engine 231 (step S703).

[0130] Furthermore, the estimation unit 132 predicts the WL type for each link included in the planned route (step S704). For example, the estimation unit 132 may compare the planned route with map data and estimate the WL type for each link that includes the current location of vehicle VE, i.e., the link that vehicle VE is currently traveling on, and for links that vehicle VE is scheduled to travel on after that link. The estimation unit 132 may also define the estimated WL type for the link that vehicle VE is currently traveling on as the difficulty level for driver D.

[0131] Next, the generation unit 133 determines, based on the estimation result obtained in step S704, whether or not there are nodes connecting links of different WL types (step S705). If there are no nodes connecting links of different WL types (step S705; No), the process proceeds to step S702.

[0132] On the other hand, if there are nodes connecting links of different WL types (step S705; Yes), the generation unit 133 detects them as change points where the WL type changes (step S706). A change point where the WL type changes is an example of a change point where the WL type changes.

[0133] Then, the generation unit 133 generates change point information based on the change point information (step S707). For example, the generation unit 133 calculates the distance from the current location to the change point based on the current location of the vehicle VE and the distance of the link connected by the change point where the WL type changes, and generates the calculated distance as change point information. The generation unit 133 also calculates the estimated time when the vehicle VE will arrive at the change point based on the current location of the vehicle VE and the distance of the link connected by the change point where the WL type changes, and generates the calculated estimated time as change point information.

[0134] Furthermore, the generation unit 133 registers the generated change point information in the control result storage unit 122 (step S708).

[0135] [8. Regeneration and distribution of change point information] Figure 8 is a flowchart showing the processing procedures for regenerating change point information and distributing change point information.

[0136] As shown in Figure 8, the status monitoring engine 231 of the on-board device 200 may periodically transmit status information obtained from sensor information, etc., to the server device 100 (step S801). The status information includes various information regarding the driving status of the vehicle VE.

[0137] The detection unit 134 of the server device 100 receives the status information transmitted by the status awareness engine 231 (step S802).

[0138] Then, the detection unit 134 identifies the driving scene of the vehicle VE based on the situation information and detects changes in the driving scene based on the driving scenes before and after (step S803).

[0139] For example, the detection unit 134 detects changes in the driving behavior of the vehicle VE as a change in the driving scene. As an example, the detection unit 134 may detect changes in the driving behavior of the vehicle VE such as a significant decrease in speed, a significant increase in speed, a temporary stop, a complete stop, slow driving, a curve, a sudden start, a sudden brake, a sudden steering, an impact, etc.

[0140] Furthermore, the detection unit 134 may detect whether or not the vehicle VE has entered a change point where the WL type changes, as a change in the driving scene. For example, using the example in Figure 6, the detection unit 134 may detect whether or not the vehicle VE has entered the change point CP23 (node ​​ND23).

[0141] Furthermore, the detection unit 134 may detect attribute changes as a change in the driving scene, based on a comparison between the attributes of the first link on which the vehicle VE is currently traveling and the attributes of the second link located in the direction of travel of the vehicle VE and connected to the first link, among the links included in the planned driving route. For example, in the example of Figure 5, suppose the attribute of link 102 on which the vehicle VE is currently traveling is "wide road," and the attribute of link 103, which is connected to link 102, is "narrow road," and it is predetermined in the guidance information DB that the guidance point on link 102 will notify that the attribute has changed from "wide road" to "narrow road." In this case, the detection unit 134 can detect the attribute change from "wide road" to "narrow road" based on the guidance information registered in the guidance information DB, and when the vehicle VE reaches the guidance point, it may detect that the driving scene has changed.

[0142] Furthermore, the detection unit 134 may detect, as a change in the driving scene, whether or not the vehicle VE has entered an area corresponding to a predetermined feature point in the link on which the vehicle VE is currently driving among the links included in the planned driving route.

[0143] Next, the determination unit 135 determines whether the current WL type of vehicle VE has changed based on the detection result by the detection unit 134 (step S804). For example, the determination unit 135 may determine whether the difficulty level of driver D on the link that vehicle VE is currently traveling on has changed as the current WL type of vehicle VE.

[0144] If it is determined that there is no change in the current WL type of the vehicle VE (step S804; No), the process returns to step S803.

[0145] On the other hand, if the generation unit 133 determines that the vehicle VE has changed to the current WL type (step S804; Yes), it recognizes that there is a greater need to update the change point information that has already been generated in the flow described in Figure 7, and regenerates the change point information (step S805).

[0146] For example, if the detection unit 134 detects a change in the driving scene, the generation unit 133 recognizes that there is a high probability that the vehicle VE speed has changed between the time the change point information (predicted time) was calculated using the flow described in Figure 7 and the present time when the change in the driving scene has been detected (i.e., there is a high probability that the predicted time has changed (an error has occurred)), and that a recalculation of the predicted time is necessary.

[0147] Here, we will show an example of a method for recalculating the predicted time, using the case where the detection unit 134 detects that the vehicle VE has stopped as a change in the driving scene, and the determination unit 135 determines that the vehicle VE's current WL type has changed. For example, the generation unit 133 predicts the amount of time the vehicle VE is stopped. For example, the generation unit 133 may predict the amount of time the vehicle VE is stopped based on statistical information at the location where the vehicle VE stopped. For example, if the generation unit 133 predicts a stopping time of "30 seconds" for the vehicle VE, it recalculates the predicted time by adding "30 seconds" to the predicted time calculated in the flow described in Figure 7.

[0148] Returning to the explanation of Figure 8, the distribution unit 136 distributes the change point information regenerated in step S805 to the in-vehicle device 200 (step S806). For example, the distribution unit 136 may transmit the recalculated result of the estimated time when the vehicle VE will arrive at the change point as the regenerated change point information.

[0149] The information matching engine 232 of the in-vehicle device 200 receives change point information distributed by the distribution unit 136 (step S807). Then, based on the change point information, the information matching engine 232 performs scheduling to determine the time at which content should be output (step S808).

[0150] The output control unit 233 controls the output of content from the speaker SP at timings scheduled by the information matching engine 232 (step S809).

[0151] [9. Scheduling Procedure] Next, the scheduling procedure by the in-vehicle device 200 will be explained using Figure 9. Figure 9 is a flowchart of the scheduling procedure.

[0152] The information matching engine 232 determines whether or not it has received output request information from the application AP (step S901). The output request information may include output conditions (time conditions for allowing output, or geographical conditions for allowing output) and the content to be output. If the information matching engine 232 has not received output request information (step S901; No), it waits until it can receive output request information.

[0153] On the other hand, if the information matching engine 232 receives output request information (step S901; Yes), it determines whether or not there is a link among the links included in the planned route that is estimated to have a WL type of "FREE" (step S902). This process corresponds to searching among the links included in the planned route for links that can output content by voice (speech-permitted links). If, as a result of the search, the information matching engine 232 finds no links that are estimated to have a WL type of "FREE" (step S902; No), it may terminate the scheduling process.

[0154] On the other hand, if the information matching engine 232 finds a link whose WL type is estimated to be "FREE" as a result of the search (step S902; Yes), it estimates the time required to play the content based on the playback time of the content included in the output request information (step S903). The time required here is the time required from the start to the end of content playback.

[0155] Furthermore, the information matching engine 232 determines whether the content included in the output request information can be output for a link that is estimated to have a WL type of "FREE" and based on the required time (step S904). For example, if the time length of a link that is estimated to have a WL type of "FREE" 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.

[0156] The information matching engine 232 may terminate the scheduling process if output is not possible (step S904; No). On the other hand, if output is possible (step S904; Yes), the information matching engine 232 determines the time at which the content is scheduled to be output based on the change point information (step S905). For example, the information matching engine 232 determines the time at which the content is scheduled to be output in such a way that the output conditions included in the output request information are met.

[0157] Here, we will explain in more detail the scheduling process for determining the time at which content will be output, using Figure 10. Figure 10 is a diagram showing a specific example of the scheduling process according to the embodiment.

[0158] As shown in the example in Figure 10, when vehicle VE is traveling at position PT2, the processing performed calculates "14:15" as change point information indicating change point CP23, and "14:16" as change point information indicating change point CP34.

[0159] Furthermore, as shown in the example in Figure 10, the generation unit 133 recalculates the change point information when the vehicle VE moves and the current time becomes "14:14," updating the estimated time when the vehicle VE reaches change point CP23 to "14:17" and the estimated time when the vehicle VE reaches change point CP34 to "14:18."

[0160] In this example, the distribution unit 136 distributes change point information indicating a predicted time of "14:17" and change point information indicating a predicted time of "14:18" to the in-vehicle device 200.

[0161] Here, if the information matching engine 232 receives output request information for content C1 and output request information for content C2, it proceeds with processing as described in Figure 9. For example, if the information matching engine 232, after considering the output conditions for content C1, determines that content C1 can be output at link 102, it may determine the time at which content C1 will be output to ensure that playback of content C1 is completed by the expected time of "14:17", as shown in Figure 10.

[0162] Furthermore, if the information matching engine 232 determines, after considering the output conditions for content C2, that content C2 can be output at link 104, it may determine the time to output content C2 as a time after the expected time "14:18" such that playback of content C2 is completed before vehicle VE finishes passing through link 104, as shown in Figure 10.

[0163] [10. Variations] In the above embodiment, an example was shown in which the information matching engine 232 schedules content to be output when the vehicle VE travels on a link where the WL type is estimated to be "FREE". However, if the importance of output is defined for the content, the information matching engine 232 may also schedule content to be output when the vehicle VE travels on a link where the WL type is estimated to be "BUSY", depending on the importance. For example, the information matching engine 232 may schedule content with an estimated importance of "low" that is estimated to have no impact even if driver D misses it to be output when the vehicle VE travels on a link where the WL type is estimated to be "FREE". On the other hand, the information matching engine 232 preferably schedules content with an estimated importance of "high" that is beneficial and should not be missed by driver D to be output when the vehicle VE travels on a link where the WL type is estimated to be "FREE". Furthermore, if a link where the WL type is estimated to be "BUSY" is included in driver D's living area, the information matching engine 232 may also schedule content with an estimated importance of "high" to be output when the vehicle VE travels on a link with a WL type of "BUSY".

[0164] [11. Hardware Configuration] The server device 100 (an example of an information processing device) described above may be implemented by a computer 1000 having a configuration such as that shown in Figure 11. Figure 11 is a hardware configuration diagram showing an example of a computer that implements the functions of the server device 100. The computer 1000 has a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

[0165] The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400, controlling various components. The ROM 1300 stores boot programs executed by the CPU 1100 when the computer 1000 starts up, as well as programs that depend on the computer 1000's hardware.

[0166] The HDD1400 stores programs executed by the CPU1100, as well as data used by such programs. The communication interface1500 receives data from other devices via a predetermined communication network and sends it to the CPU1100, and transmits data generated by the CPU1100 to other devices via the predetermined communication network.

[0167] The CPU 1100 controls output devices such as displays and input devices such as keyboards via the input / output interface 1600. The CPU 1100 acquires data from input devices via the input / output interface 1600. The CPU 1100 also outputs the generated data to output devices via the input / output interface 1600.

[0168] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0169] For example, when the computer 1000 functions as a server device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 by executing programs loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be obtained from other devices via a predetermined communication network.

[0170] [12. Other] Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0171] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. For example, some or all of the processing described as being performed by the server device 100 may be configured to be performed on the in-vehicle device 200 side.

[0172] Furthermore, the above embodiments can be combined as appropriate, provided that the processing content is not contradictory.

[0173] Although some embodiments of the present application have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, including the embodiments described in the section on the present invention. [Explanation of symbols]

[0174] 1 System 100 Server Devices 120 Storage section 121 Map Information Storage Unit 122 Control Result Storage Unit 130 Control Unit 131 Acquisition Department 132 Estimation Department 133 Generation part 134 Detection unit 135 Judgment section 136 Distribution Department 200 On-vehicle equipment 231 Situation Awareness Engine 232 Information Integrity Engine 233 Output Control Unit

Claims

1. An estimation unit that estimates the type of driving load for each road section included in the planned route, based on the planned route which is the route the vehicle is scheduled to travel, A generation unit that generates information indicating change points in the planned route where the type of driving load changes, based on the type of driving load for each road section. A determination unit that determines whether or not the type of current driving load corresponding to the vehicle has changed based on the vehicle's driving conditions, A detection unit that detects changes in the driving scene based on the driving conditions of the aforementioned vehicle, It has, The generation unit regenerates information indicating the change point when the type of current driving load corresponding to the vehicle changes. Based on whether or not a change in the driving scene has been detected, the determination unit determines whether or not the type of current driving load corresponding to the vehicle has changed. An information processing device characterized by the following:

2. The generation unit, when different types of driving load are estimated between adjacent road sections within the road section, generates distance information indicating the distance from the vehicle's current position to the connection point where the adjacent road sections are connected, based on the vehicle's current position and the distance between each of the road sections, as information indicating the change point. The information processing apparatus according to feature 1.

3. The generation unit, when different types of driving load are estimated between adjacent road sections within the road section, generates time information indicating the estimated time at which the vehicle will arrive at the connection point where the adjacent road sections are connected, based on the vehicle's current position and the distance between each of the road sections, as information indicating the change point. The information processing apparatus according to feature 1.

4. The information indicating the change point includes type information indicating the type of driving load in the current road section, including the vehicle's current position. The information processing apparatus according to feature 1.

5. The change in the type of operating load is a change from a type indicating that the operating load is high compared to a reference value to a type indicating that the operating load is low compared to a reference value, or a change from a type indicating that the operating load is low compared to a reference value to a type indicating that the operating load is high compared to a reference value. The information processing apparatus according to feature 1.

6. The detection unit detects a change in the driving behavior of the vehicle as a change in the driving scene, If the determination unit detects that the driving behavior of the vehicle has changed, it determines that the type of driving load corresponding to the vehicle at that time has changed. The information processing apparatus according to feature 1.

7. The detection unit detects whether the vehicle has entered a point corresponding to a change point in the type of driving load, as a change in the driving scene. If the determination unit detects that the vehicle has entered the location, it determines that the type of current driving load corresponding to the vehicle has changed. The information processing apparatus according to feature 1.

8. The detection unit detects, as a change in the driving scene, an attribute change based on a comparison between a first road attribute, which is the attribute of the first road section on which the vehicle is currently traveling, and a second road attribute, which is the attribute of the second road section located in the direction of travel of the vehicle and connected to the first road section. When the determination unit detects the attribute change, it determines that the type of current driving load corresponding to the vehicle has changed. The information processing apparatus according to feature 1.

9. The detection unit detects whether the vehicle has entered an area corresponding to a predetermined characteristic point in the first road section on which the vehicle is currently traveling, as a change in the driving scene. If the determination unit detects that the vehicle has entered the area, it determines that the type of current driving load corresponding to the vehicle has changed. The information processing apparatus according to feature 1.

10. The generation unit predicts the duration for which the changed driving scene continues based on statistical information regarding the changed driving scene, and regenerates information indicating the change point using the predicted duration. The information processing apparatus according to feature 1.

11. A determination unit determines the time at which the content is scheduled to be output, based on the information indicating the change point and the playback time of the content output by sound. Furthermore, it has The information processing apparatus according to feature 1.

12. The determination unit determines, based on the relationship between the type of operating load and the importance level assigned to the content, which content should be output at the scheduled time. The information processing apparatus according to feature 11.

13. An information processing method performed by an information processing device, An estimation step of estimating the type of driving load for each road section included in the planned route, based on the planned route which is the route the vehicle is scheduled to travel, A generation step that generates information indicating change points in the planned route where the type of driving load changes, based on the type of driving load for each road section. A determination step of determining whether the type of current driving load corresponding to the vehicle has changed based on the vehicle's driving conditions, A detection step for detecting changes in the driving scene based on the driving conditions of the aforementioned vehicle, Includes, The generation process, if the type of current driving load corresponding to the vehicle changes, regenerates the information indicating the change point. The determination step determines whether the type of current driving load corresponding to the vehicle has changed, based on whether or not a change in the driving scene has been detected. An information processing method characterized by the following:

14. An information processing program executed by an information processing device, An estimation procedure for estimating the type of driving load for each road section included in the planned route, based on the planned route which is the route the vehicle is scheduled to travel, A generation procedure for generating information indicating change points in the planned route where the type of driving load changes, based on the type of driving load for each road section, A determination procedure for determining whether the type of current driving load corresponding to the vehicle has changed based on the vehicle's driving conditions, A detection procedure for detecting changes in the driving scene based on the driving conditions of the aforementioned vehicle, The information processing device is made to execute the above, The generation procedure, if the type of current driving load corresponding to the vehicle changes, regenerates the information indicating the change point. The determination procedure determines whether the type of current driving load corresponding to the vehicle has changed, based on whether or not a change in the driving scene has been detected. Information processing program.

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