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

Abstract

An information processing device (100A) according to the present application comprises: an acquisition unit (133) that acquires an operation load ratio indicating a ratio between a state in which the driving load of a driver is high on a travel route of a target vehicle and a state in which the driving load is low; and an output control unit (134) that outputs the timing at which the driver should take a break by using at least the operation load ratio.

Description

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

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

[0002] In conventional car navigation systems, a typical method of suggesting a break is to simply notify the driver of a message encouraging them to take a break when a set driving time (e.g., two hours) has elapsed. However, depending on the driving conditions, the timing of the suggestion may be too late, resulting in cases where the suggestion is not appropriate.

[0003] On the other hand, a method has been proposed that uses biological information such as drowsiness and fatigue as input and makes recommendations about resting time using a learning model. However, with this method, it is difficult to estimate the timing when a break is necessary when the biological information is not changing.

[0004] Japanese Patent Application Laid-Open No. 2021-149617

[0005] Therefore, the present invention proposes an information processing device, an information processing method, and an information processing program that can notify a driver of an appropriate timing for taking a rest.

[0006] The information processing device described in claim 1 includes an acquisition unit that acquires a driving load ratio that indicates the ratio between a state where the driver's driving load is high and a state where the driving load is low on the driving route of the target vehicle, and an output control unit that uses at least the driving load ratio to output the timing when the driver should take a break.

[0007] The information processing method described in claim 13 is an information processing method executed by an information processing device, and includes an acquisition step of acquiring a driving load ratio indicating the ratio between a state where the driver's driving load is high and a state where the driving load is low on the driving route of the target vehicle, and an output control step of outputting the timing when the driver should take a break using at least the driving load ratio.

[0008] The information processing program described in claim 14 is an information processing program executed by an information processing device, and causes the information processing device to execute an acquisition procedure for acquiring a driving load ratio indicating the ratio between a state in which the driver's driving load is high and a state in which the driver's driving load is low on the driving route of the target vehicle, and an output control procedure for outputting the timing when the driver should take a break using at least the driving load ratio.

[0009] FIG. 1 is a diagram illustrating an example of a system according to a first embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a first server device according to the first embodiment. FIG. 3 is a diagram illustrating an example of the configuration of a second server device. FIG. 4 is a diagram illustrating a specific example of a WL type estimation method. FIG. 5 is a diagram illustrating a specific example (1) of a prediction model generation method. FIG. 6 is a diagram illustrating a specific example (2) of a prediction model generation method. FIG. 7 is a sequence diagram illustrating the procedure of processing performed between server devices included in the system according to the first embodiment. FIG. 8 is a flowchart illustrating the procedure of a rest timing prediction process. FIG. 9 is a diagram illustrating an example of a system according to a second embodiment. FIG. 10 is a diagram illustrating an example of the configuration of a first server device according to the second embodiment. FIG. 11 is a flowchart illustrating the procedure of a range specification process. FIG. 12 is a diagram illustrating a specific example of a range specification process. FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of an information processing device according to an embodiment.

[0010] [Embodiments] Hereinafter, embodiments of the present invention 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 invention are not limited to these embodiments. Furthermore, the same components in the following embodiments will be given the same reference numerals, and duplicated descriptions will be omitted.

[0011] (First embodiment) <1. System configuration> First, the configuration of a system according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of a system according to the first embodiment. Fig. 1 shows a system SyA as an example of a system according to the first embodiment. Information processing according to the first embodiment is realized in the system SyA.

[0012] 1, the system SyA includes an in-vehicle device 10, a first server device 100A, and a second server device 200. The in-vehicle device 10, the first server device 100A, and the second server device 200 are connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection.

[0013] The in-vehicle device 10 may be a dedicated navigation device built into or mounted on the vehicle VEn. For example, the in-vehicle device 10 may be configured with a navigation device and a recording device (drive recorder). As one example, the in-vehicle device 10 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 10 may be a single device that has both a navigation function and a recording function.

[0014] The in-vehicle device 10 also includes various sensors. For example, the in-vehicle device 10 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 10 may also have a function of providing dialogue and information to assist driving based on sensor information acquired by the various sensors.

[0015] Furthermore, the in-vehicle device 10 can use not only the sensors provided in the device itself, but also sensor information detected by sensors provided in the vehicle VEn itself as a safe driving system.

[0016] In addition, users can install specific application software into a portable terminal device (e.g., a smartphone, tablet terminal, notebook PC, PDA, etc.) that they use on a daily basis, and cause this portable terminal device to operate in the same way as the in-vehicle device 10.

[0017] In this embodiment, the term "user" refers to a person directly involved with the vehicle VEn (for example, the driver of the vehicle VEn or a passenger other than the driver). In addition, when a vehicle VEn is to be uniquely identified, an arbitrary common value is substituted for "n," and the vehicle is referred to as, for example, vehicle VE1, vehicle VE2, etc.

[0018] The first server device 100A is a central device responsible for information processing according to the first embodiment. For example, the first server device 100A calculates a driving load ratio based on the type of driving load (workload) using the driving time of a target vehicle (referred to as a "target vehicle VEx") that is the subject of a proposal among the vehicles VEn. The first server device 100A then inputs the driving load ratio into a prediction model that statistically models the driving time that an unspecified number of drivers have traveled until they actually take a break, thereby predicting the timing at which the driver DX of the target vehicle VEx should take a break. The prediction result is controlled to be output from the in-vehicle device 10 of the target vehicle VEx.

[0019] Here, the workload (hereinafter abbreviated as "WL") will be described. WL 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.

[0020] The types of driving load, i.e., WL types, include, for example, "BUSY," "IDEAL," and "FREE," and indicate that the burden on the driver in a road section designated as "BUSY" is above a standard (i.e., the driving difficulty is high), the burden on the driver in a road section designated as "FREE" is below a standard (i.e., the driving difficulty is low or not high), and the burden on the driver in a road section designated as "IDEAL" is medium (i.e., the driving difficulty is normal or not high).

[0021] 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:

[0022] For example, a level of difficulty "1" corresponds to a WL type "BUSY_MAX", which is a road section where all ordinary drivers have to be careful when driving, and it is defined that in such road sections, the in-vehicle device 10 should only issue a warning notification.

[0023] A severity level of "0.80" corresponds to a WL type of "BUSY+", which 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 10 should only issue warning notifications and caution notifications.

[0024] A severity level of "0.60" corresponds to a WL type of "BUSY," and is a road section where more than 20% of average drivers have to be careful when driving. It is defined that in such road sections, the in-vehicle device 10 should only issue warning notifications, caution notifications, and important notifications.

[0025] The severity level "0.50" corresponds to the WL type "IDEAL", and it is defined that in this road section, the in-vehicle device 10 may also utter content other than guidance-related content (warning notification, caution notification, important notification).

[0026] A difficulty level of "0.25" corresponds to a WL type of "FREE", and is defined as a road section that more than 50% of general drivers may find monotonous and boring, and for which various content should be spoken.

[0027] Note that the WL type is not necessarily limited to the above examples ("BUSY_MAX", "BUSY+", "BUSY", "IDEAL", and "FREE"). Furthermore, in the following embodiments, "BUSY" and "FREE" are mainly used as WL types. Furthermore, the criteria and reference values ​​shown above are merely examples and may be any values. For example, "BUSY" is an example of a first type indicating that the WL is higher than the reference value. Furthermore, "FREE" is an example of a second type indicating that the WL is lower than the reference value.

[0028] Next, road sections will also be described. For example, a road section refers to a section between characteristic points on a road, and is called a link. The characteristic points on 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.

[0029] Following the above example, in the following embodiment, a road section is represented as a link, and a connection point between links is represented as a node. For example, the first server device 100A has a map information storage unit 121 ( FIG. 2 ), 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 also include high-precision point cloud information of objects used for estimating the vehicle's position, etc. In addition, in the map information storage unit 121, links may be identified by link IDs.

[0030] Returning to Figure 1, the second server device 200 generates a prediction model that statistically models the driving time an unspecified number of drivers have traveled until they actually take a break, and provides the generated prediction model to the first server device 100A.

[0031] The management operator may be the same or different between the first server device 100A and the second server device 200. Each of the first server device 100A and the second server device 200 corresponds to an example of an information processing device according to the first embodiment, but the first server device 100A and the second server device 200 may be integrated into a single server device. When the first server device 100A and the second server device 200 are implemented as a single server device, this single server device corresponds to the information processing device according to the first embodiment.

[0032] <2. Functional Configuration> Next, an example configuration of the first server device 100A and the second server device 200 will be described.

[0033] [First Server Device 100A] Fig. 2 is a diagram showing an example of the configuration of the first server device 100A according to the first embodiment. As shown in Fig. 2, the first server device 100A includes a communication unit 110, a storage unit 120A, and a control unit 130A.

[0034] 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 between, for example, the second server device 200 and the in-vehicle device 10.

[0035] [Storage Unit 120A] The storage unit 120A is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120A may store, for example, data and programs related to the information processing according to the embodiment. Furthermore, according to the example of FIG. 2, the storage unit 120A may include a map information storage unit 121, a WL type estimation result storage unit 122, and a ratio information storage unit 123.

[0036] [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.

[0037] [WL Type Estimation Result Storage Unit 122] The WL type estimation result storage unit 122 stores the WL type estimated for each link included in (constituting) the travel route.

[0038] [Ratio Information Storage Unit 123] The ratio information storage unit 123 stores a driving load ratio indicating the ratio between a high driving load state and a low driving load state.

[0039] [Control Unit 130A] The control unit 130A is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), etc., executing various programs (e.g., the information processing program according to the embodiment) stored in a storage device inside the first server device 100A using RAM as a work area. The control unit 130A is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0040] As shown in Fig. 2, the control unit 130A has an estimation unit 131, a calculation unit 132, an operating load ratio acquisition unit 133, an output control unit 134, and a search unit 135, and realizes or executes the functions and actions of the information processing described below. Note that the internal configuration of the control unit 130A is not limited to the configuration shown in Fig. 2, and may be other configurations as long as they perform the information processing described below. Furthermore, the connection relationships between the processing units of the control unit 130A are not limited to the connection relationships shown in Fig. 2, and may be other connection relationships.

[0041] [Estimation Unit 131] The estimation unit 131 estimates a WL type for each link included in a predetermined driving route. For example, the estimation unit 131 estimates a WL type for each link included in the driving route of the target vehicle VEx. The driving route of the target vehicle VEx may be an actual driving route traveled by continuous driving from the start of driving of the target vehicle VEx to the present time, or may be a planned driving route that the driver DX plans to drive in the target vehicle VEx. Note that the start of driving here means starting driving from a state of a long-term stay with a specific purpose (e.g., stopping at a home or a rest area), excluding a temporary stop on the road (e.g., stopping at a traffic light), and can also be rephrased as engine start. For example, the estimation unit 131 can distinguish between a temporary stop on the road and a long-term stay with a specific purpose based on the stop duration. The planned driving route may be, for example, a route that is the result of a search that is searched to satisfy a search condition based on information (e.g., a destination) input to the in-vehicle device 10, or a route that is directly input by the user as a route that is planned to be traveled from now on. Alternatively, the planned driving route may be a route to a destination that is predicted from the usual driving history of the driver DX.

[0042] In addition, the estimation unit 131 estimates the WL type for each link included in the driving route indicated by the driving history of an unspecified number of vehicles VEn, which is the driving history of continuous driving from engine start (start of driving) to engine stop (end of driving).

[0043] In the following description, driving from engine start (start of driving) to engine stop (end of driving) may be referred to as "one trip." Continuous driving refers to driving without a break. For this reason, for example, the driving route indicated by the driving record of continuous driving from engine start to engine stop means the driving route for one trip that does not include breaks, and does not include temporary stops on the road (for example, stopping at traffic lights).

[0044] Here, the estimation unit 131 can detect, for example, engine stoppage for a rest period (for example, engine stoppage for 10 to 20 minutes) based on data representing the behavior of the vehicle VEn, and can extract the driving period from engine start to engine stoppage for a rest period as one trip.

[0045] As another example, the estimation unit 131 may extract, as one trip, a journey from engine start (start of travel) to a rest point where the driver of the vehicle VEn took a rest. For example, the estimation unit 131 generates, based on the travel history of the vehicle VEn, stay point information indicating stay points of the vehicle VEn, stay time information indicating stay times at the stay points, and traveling direction change information indicating changes in the traveling direction of the vehicle VEn between three consecutive stay points on the travel route of the vehicle VEn. Then, the estimation unit 131 may estimate a rest point where the driver of the vehicle VEn took a rest based on the stay point information, stay time information, and traveling direction change information. As an example, the estimation unit 131 may determine whether the stay time at a stay point is within a predetermined time range (e.g., 10 to 20 minutes), and estimate a stay point where the stay time is determined to be within the predetermined time range as a rest point. In addition, the estimation unit 131 may estimate as a rest point a stay point where it is determined that the vehicle VEn is moving away from the departure point based on the angular relationship established between the three stay points: the primary stay point where the vehicle VEn first arrives, the secondary stay point where the vehicle VEn next arrives, and the tertiary stay point where the vehicle VEn last arrives.

[0046] The estimation unit 131 also stores the estimation result of the WL type in the WL type estimation result storage unit 122 .

[0047] Here, a method for estimating the WL type will be described with reference to FIG. 4 . FIG. 4 is a diagram showing a specific example of the method for estimating the WL type. FIG. 4 shows a scene in which the WL type is estimated for an arbitrary travel route RT. The arbitrary travel route RT may be an actual travel route traveled by continuous driving of the target vehicle VEx from the start of travel to the present time, or may be a planned travel route that the driver DX plans to travel in the target vehicle VEx. Furthermore, the arbitrary travel route RT may be a travel route for one trip, not including rest stops, of a single vehicle VEn selected from an unspecified number of vehicles VEn. In other words, the method for estimating the WL type of a link included in the travel route is the same regardless of the travel route.

[0048] For example, if the driving route RT is the actual driving route traveled by continuous driving from the start of driving of the target vehicle VEx to the present time, position PT1 is the point where the target vehicle VEx started driving, and position PT2 is the current location of the target vehicle VEx.

[0049] As another example, if the travel route RT is a travel route for one trip by a single vehicle VEn that does not include a rest stop, position PT1 is the point where the engine of vehicle VEn is started, and position PT2 is the point where vehicle VEn is staying.

[0050] 4A, the estimation unit 131 compares the traveling route RT with map data in which a WL type is associated with each link, and associates a link ID (link_id) with each link included in the traveling route RT. FIG. 4A shows an example in which the estimation unit 131 divides the traveling route RT 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 RT.

[0051] Also, in FIG. 4(a), node ND01 is shown as information on the connection point where the link identified by link ID "100" (link 100) and the link identified by link ID "101" (link 101) are connected.

[0052] 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.

[0053] Furthermore, node ND23 is indicated as information on the connection point where the link identified by link ID "102" (link 102) and the link identified by link ID "103" (link 103) are connected.

[0054] Furthermore, node ND34 is indicated as information on the connection point where the link identified by link ID "103" (link 103) and the link identified by link ID "104" (link 104) are connected.

[0055] 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.

[0056] The estimation unit 131 may also calculate the distance (len) of each link by referring to map data. Fig. 4(a) shows an example in which the estimation unit 131 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.

[0057] FIG. 4( b) shows an example in which the estimation unit 131 refers to map data in which a WL type is associated with each link, and estimates the WL type of link 100 as “FREE,” the WL type of link 101 as “FREE,” 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.”

[0058] In the following, a link whose WL type is estimated to be "BUSY" may be referred to as a "BUSY section," a link whose WL type is estimated to be "FREE" may be referred to as a "FREE section," and a link whose WL type is estimated to be "IDEAL" may be referred to as an "IDEAL section."

[0059] 4 illustrates an example in which the estimation unit 131 estimates the WL type of a link by comparing the link included in the travel route RT with map data in which a WL type is associated with each link. However, the estimation unit 131 may estimate the WL type of each link based on the link type (link_kind) of the link included in the travel route R or the road type (road_kind) of the travel route RT. 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.

[0060] [Calculation unit 132] Returning to FIG. 2 , the calculation unit 132 calculates a first cumulative time, which is the cumulative time that the target vehicle VEx has traveled through links included in the travel route of the target vehicle VEx, for which the WL type is estimated to be "BUSY" (a first type indicating that the WL is higher than a reference value). Furthermore, the calculation unit 132 calculates a second cumulative time, which is the cumulative time that the target vehicle VEx has traveled through links included in the travel route of the target vehicle VEx, for which the WL type is estimated to be "FREE" (a second type indicating that the WL is lower than a reference value). This point will be described using the example of FIG. 4(b).

[0061] In describing the processing of the calculation unit 132, the travel route RT is assumed to be an actual travel route traveled by continuous driving of the target vehicle VEx from the start of travel to the present time. In this example, the target vehicle VEx starts travel from position PT1 and is currently traveling to position PT2 by continuously driving without a break. In this case, the calculation unit 132 calculates a first cumulative time by accumulating the travel time required for travel on each link (specifically, link 100, link 101, link 102, and link 104) for which the WL type is estimated to be "BUSY." The calculation unit 132 also calculates a second cumulative time by accumulating the travel time required for travel on each link (specifically, link 103 and link 105) for which the WL type is estimated to be "FREE." For example, the calculation unit 132 can calculate the travel time based on the travel history of the target vehicle VEx and sensor information acquired from the in-vehicle device 10.

[0062] The calculation unit 132 also calculates a driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, and transmits the calculated driving load ratio to the driving load ratio acquisition unit 133 .

[0063] The calculation unit 132 may calculate a cumulative distance instead of a cumulative time. Similarly, using the example of FIG. 4B , the calculation unit 132 may calculate a first cumulative distance by integrating the distances of each link whose WL type is estimated to be “BUSY” (specifically, link 100, link 101, link 102, and link 104). The calculation unit 132 may calculate a second cumulative distance by integrating the distances of each link whose WL type is estimated to be “FREE” (specifically, link 103 and link 105). In this example, the calculation unit 132 calculates a driving load ratio, which is the ratio between the first cumulative distance and the second cumulative distance, and transmits the driving load ratio to the driving load ratio acquisition unit 133.

[0064] [Driving Load Ratio Acquisition Unit 133] The driving load ratio acquisition unit 133 acquires a driving load ratio indicating the ratio between a state in which the driving load of the driver DX is high and a state in which the driving load is low on the driving route of the target vehicle VEx.

[0065] Specifically, the driving load ratio acquisition unit 133 acquires a driving load ratio indicating the ratio between the first cumulative time calculated by the calculation unit 132 (information indicating a state in which the driving load is high) and the second cumulative time calculated by the calculation unit 132 (information indicating a state in which the driving load is low).

[0066] As another example, the driving load ratio acquisition unit 133 acquires a driving load ratio indicating a ratio between the first cumulative distance calculated by the calculation unit 132 (information indicating a state in which the driving load is high) and the second cumulative distance calculated by the calculation unit 132 (information indicating a state in which the driving load is low). The driving load ratio is stored in the ratio information storage unit 123.

[0067] The driving load ratio acquisition unit 133 may also perform a process of calculating a predicted driving time of the target vehicle VEx based on the driving load ratio and the prediction model generated by the second server device 200. Specifically, the driving load ratio acquisition unit 133 predicts the time for which the target vehicle VEx will be continuously driven (the time for which the driver DX will drive continuously without taking a break) based on the driving load ratio and the prediction model. A method for generating the prediction model will be described later.

[0068] [Output control unit 134] The output control unit 134 predicts the timing when the driver DX should take a rest based on the predicted driving time calculated by the driving load ratio acquisition unit 133. Specifically, the output control unit 134 predicts the timing when the driver DX should take a rest based on the difference between the driving time of the target vehicle VEx and the predicted driving time. For example, the output control unit 134 predicts the timing when the driver DX should take a rest based on the difference between the predicted driving time and the driving time calculated for the actual driving route traveled by continuous driving of the target vehicle VEx from the start of driving to the present time as the driving time of the target vehicle VEx.

[0069] The timing when driver DX should take a break may be expressed in terms of time, such as "you should take a break at x:00", or in terms of distance, such as "you should take a break after traveling △ meters".

[0070] [Searching Unit 135] The searching unit 135 searches for a rest spot that the target vehicle VEx can reach by the time it is time to take a rest. For example, the searching unit 135 searches for a rest spot within a range of a predicted driving distance, which is the distance the target vehicle VEx is predicted to travel until the time it is time to take a rest. The searching unit 135 may also calculate a predicted arrival time, which is the time when the target vehicle VEx is predicted to arrive at the rest spot. In this case, the output control unit 134 controls output so that proposal information suggesting the rest spot, including information indicating the content of the rest spot and the predicted arrival time at the rest spot, is output from the in-vehicle device 10.

[0071] As another example, when the planned driving route of the target vehicle VEx is known in advance and a rest time is predicted for this planned driving route, the search unit 135 may estimate points on the planned driving route that the target vehicle VEx may reach at the time the driver DX will take a rest, and search for rest spots in areas corresponding to the estimated points. Note that the planned driving route referred to here may be a guided route searched based on information input by the driver DX (e.g., a destination input before departure). Furthermore, the time the driver DX will take a rest may be predicted based on a prediction result that predicts the time when the driver DX should take a rest. Furthermore, when a request operation by the driver DX (e.g., a request for a rest spot) is accepted, the search unit 135 may perform a search process for rest spots using such a guided route and output the search results to the output control unit 134.

[0072] For example, the output control unit 134 outputs proposal information that suggests rest spots along with a planned driving route, which is a guidance route. With such advance proposals, the driver DX can determine rest spots in advance, for example, at the time of setting the route before departure, and therefore can make an appropriate driving plan before departure.

[0073] Furthermore, according to the above example, the search unit 135 may also perform general route guidance processing, specifically, route search to a destination using search conditions. The search conditions and the destination are input into the in-vehicle device 10 by the user.

[0074] [Second Server Device] Next, Fig. 3 is a diagram showing an example of the configuration of the second server device 200. As shown in Fig. 3, the second server device 200 has a communication unit 210, a storage unit 220, and a control unit 230.

[0075] The communication unit 210 is realized by, for example, a NIC etc. The communication unit 210 is connected to the network N by wire or wirelessly, and transmits and receives information between, for example, the first server device 100A and the in-vehicle device 10.

[0076] [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. 3 , the storage unit 220 may include a WL type estimation result storage unit 221 and a prediction model storage unit 222.

[0077] [Control Unit 230] The control unit 230 is realized by a CPU, an MPU, or the like using RAM as a work area to execute various programs (e.g., the information processing program according to the embodiment) stored in a storage device inside the second server device 200. The control unit 230 is also realized by an integrated circuit such as an ASIC or an FPGA.

[0078] As shown in Fig. 3, the control unit 230 has a WL type acquisition unit 231, a prediction model generation unit 232, and a transmission unit 233, and realizes or executes the functions and actions of the information processing described below. Note that the internal configuration of the control unit 230 is not limited to the configuration shown in Fig. 3, and may be other configurations as long as they perform the information processing described below. Furthermore, the connection relationships between the processing units of the control unit 230 are not limited to the connection relationships shown in Fig. 3, and may be other connection relationships.

[0079] [WL type acquisition unit 231] The WL type acquisition unit 231 acquires the WL type estimated by the estimation unit 131. Specifically, the WL type acquisition unit 231 acquires information on the WL type estimated for each link included in a travel route for one trip, which is indicated by the travel history of an unspecified number of vehicles VEn, and does not include rest stops. The acquired WL type is stored in the WL type estimation result storage unit 221.

[0080] [Prediction model generation unit 232] The prediction model generation unit 232 generates a prediction model that predicts the driving time for continuous driving based on the WL type estimated for each link included in the driving route of one trip, which does not include breaks, which is the driving route indicated by the driving history of an unspecified number of vehicles VEn.

[0081] Specifically, the prediction model generation unit 232 calculates a model ratio, which is the ratio of links included in a travel route for one trip that does not include rest breaks, for which the WL type is estimated to be "BUSY" (a first type indicating that the WL is higher than a reference value), to links for which the WL type is estimated to be "FREE" (a second type indicating that the WL is lower than a reference value). The travel route for one trip that does not include rest breaks treated here may be common to all vehicles VEn, or may be different between all vehicles VEn.

[0082] Furthermore, the prediction model generation unit 232 calculates, for each model ratio, an average running time, which is the average of the time that the vehicle VEn is continuously driven. Then, the prediction model generation unit 232 generates a prediction model based on the model ratio and the average running time.

[0083] For example, the prediction model generation unit 232 generates a prediction model based on a three-dimensional graph that three-dimensionally represents the relationship between the model ratio and the average driving time.

[0084] Here, a method for generating a prediction model will be described with reference to Fig. 5 and Fig. 6. Fig. 5 is a diagram showing a specific example (1) of the method for generating a prediction model. Fig. 6 is a diagram showing a specific example (2) of the method for generating a prediction model.

[0085] 5 , the prediction model generation unit 232 aggregates, for each model ratio, the time that the vehicle VEn corresponding to that model ratio has been continuously driven. The model ratio may be the ratio between the distance of a link whose WL type is estimated to be "BUSY" and the distance of a link whose WL type is estimated to be "FREE" among the links included in a travel route for one trip that does not include a break. Alternatively, the model ratio may be the ratio between the cumulative time that the vehicle VEn has traveled through links whose WL type is estimated to be "BUSY" and the cumulative time that the vehicle VEn has traveled through links whose WL type is estimated to be "FREE" among the links included in a travel route for one trip that does not include a break.

[0086] As an example, suppose that a model ratio, which is the ratio of busy sections to free sections, of "100:0" is calculated based on the results of estimating the WL type for one trip of vehicle VE11. Furthermore, suppose that a model ratio, which is the ratio of busy sections to free sections, of "100:0" is also calculated based on the results of estimating the WL type for one trip of vehicle VE12. In this example, the prediction model generation unit 232 aggregates the travel time required to travel one trip of the travel route for each vehicle VEn, such as vehicle VE11 and vehicle VE12, that share a common model ratio of "100:0," as shown in FIG. 5( a). For example, the prediction model generation unit 232 can calculate the travel time based on the travel history and sensor information of vehicle VEn acquired from the on-board device 10.

[0087] Figure 5(a) shows an example in which the prediction model generation unit 232 calculates "TM11" as the travel time required for vehicle VE11 to travel the travel route for one trip, and calculates "TM12" as the travel time required for vehicle VE12 to travel the travel route for one trip.

[0088] In this state, the prediction model generation unit 232 aggregates the travel time "TM11" and the travel time "TM12", etc., and calculates the average travel time AV1.

[0089] As another example, suppose that a model ratio, which is the ratio between busy sections and free sections, of "90:10" is calculated based on the results of estimating the WL type for one trip of vehicle VE21. Furthermore, suppose that a model ratio, which is the ratio between busy sections and free sections, of "90:10" is also calculated based on the results of estimating the WL type for one trip of vehicle VE22. In this example, the prediction model generation unit 232 tallies the travel time required to travel the travel route for one trip for each vehicle VEn, such as vehicle VE21 and vehicle VE22, that share a common model ratio of "90:10," as shown in FIG. 5( b).

[0090] Figure 5 (b) shows an example in which the prediction model generation unit 232 calculates "TM21" as the travel time required for vehicle VE21 to travel the travel route for one trip, and calculates "TM22" as the travel time required for vehicle VE22 to travel the travel route for one trip.

[0091] In this state, the prediction model generation unit 232 aggregates the travel time "TM21" and the travel time "TM22", etc., and calculates the average travel time AV2.

[0092] As yet another example, suppose that a model ratio, which is the ratio of busy sections to free sections, of "80:20" is calculated based on the results of estimating the WL type for one trip of vehicle VE31. Furthermore, suppose that a model ratio, which is the ratio of busy sections to free sections, of "80:20" is also calculated based on the results of estimating the WL type for one trip of vehicle VE32. In this example, the prediction model generation unit 232 tallies the travel time required to travel the travel route for one trip for each vehicle VEn, such as vehicle VE31 and vehicle VE32, that share a common model ratio of "80:20," as shown in FIG. 5( c).

[0093] Figure 5 (c) shows an example in which the prediction model generation unit 232 calculates "TM31" as the travel time required for vehicle VE31 to travel the travel route for one trip, and calculates "TM32" as the travel time required for vehicle VE32 to travel the travel route for one trip.

[0094] In this state, the prediction model generation unit 232 aggregates the travel time "TM31" and the travel time "TM32", etc., and calculates the average travel time AV3.

[0095] In the examples of Figures 5(a), 5(b), and 5(c), the driving time required to travel the driving route for one trip can be rephrased as the time the vehicle VEn was driven continuously (continuous driving time).

[0096] 5 shows the list of the "continuous driving time" and the "average driving time" for each "model ratio," and the list of the "list LT." The list LT is information that forms the basis of the prediction model, and may be stored in the prediction model storage unit 222.

[0097] We now turn to the explanation of Figure 6. Figure 6 shows a scene in which a prediction model is generated from information included in the list LT. As shown in Figure 6, the prediction model generation unit 232 generates a three-dimensional graph G in which the relationship between the model ratio and the average running time is expressed in three dimensions, with "BUSY" as the x-axis, "FREE" as the y-axis, and "predicted average time" as the z-axis. Specifically, the prediction model generation unit 232 generates the three-dimensional graph G using a combination of "BUSY" / "FREE" / "predicted average time."

[0098] According to the example of the list LT, the prediction model generation unit 232 generates a bar graph in which the average running time "AV1" is fitted to the z-axis for the x- and y-coordinate positions where BUSY "100" and FREE "0" intersect. The prediction model generation unit 232 also generates a bar graph in which the average running time "AV2" is fitted to the z-axis for the x- and y-coordinate positions where BUSY "90" and FREE "10" intersect. The prediction model generation unit 232 also generates a bar graph in which the average running time "AV3" is fitted to the z-axis for the x- and y-coordinate positions where BUSY "80" and FREE "20" intersect. The same process is performed for the other model ratios. As a result, a three-dimensional graph G as shown in FIG. 6( a) is generated.

[0099] The prediction model generation unit 232 generates a prediction model from this three-dimensional graph G. For example, the prediction model generation unit 232 performs statistical processing on the three-dimensional graph G to generate a prediction model M as shown in FIG. 6( b). In a simple example, the prediction model generation unit 232 generates a planar prediction model M using the vertices of the three-dimensional graph G. The x-axis of the prediction model M represents "BUSY", the y-axis represents "FREE", and the z-axis represents "predicted driving time" (estimated driving time). The predicted driving time here refers to the time that the driver DX of the target vehicle VEx is predicted to drive continuously without taking a break.

[0100] Although not shown in Figures 6(a) and 6(b), "IDEAL" may be used on the other y-axis opposite "FREE". Therefore, the driving load ratio is actually the ratio of the cumulative time or distance calculated for each of the "BUSY" section, the "FREE" section, and the "IDEAL" section. However, the proportion of the driving load ratio accounted for by the "IDEAL" section may be treated as the remaining proportion of the proportions accounted for by the "BUSY" section and the "FREE" section.

[0101] [Transmission Unit 233] Returning to Fig. 3 , the transmission unit 233 transmits the prediction model generated by the prediction model generation unit 232 to the first server device 100A. As described above, the driving load ratio acquisition unit 133 acquires the transmitted prediction model and inputs the driving load ratio into the prediction model. Then, the driving load ratio acquisition unit 133 calculates a predicted driving time based on the output result of the prediction model. Furthermore, the output control unit 134 uses the predicted driving time to predict when the driver DX should take a rest.

[0102] Next, a specific example of a method for predicting rest timing will be described using the example of Figure 6(b). For example, if the actual driving route traveled by continuous driving of the target vehicle VEx from the start of driving to the present time includes only "BUSY" sections as links, the driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, is calculated to be "100:0". In this example, the prediction model generation unit 232 inputs the driving load ratio of "100:0" into the prediction model M.

[0103] 6(b) shows an example in which a driving load ratio of "100:0" is input and a predicted driving time of "16 minutes" for the target vehicle VEx is predicted from the output information output by the prediction model M. This corresponds to an estimation that, assuming that the driver DX continues driving continuously with a driving load ratio of "100:0", it would be optimal for him to take a break "16 minutes" after the start of driving, since there is a statistical tendency to take a break at "16 minutes" when the driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, is "100:0" (in other words, when the driver drives continuously through the "BUSY" section).

[0104] For example, if the driving time of the target vehicle VEx from the start of driving to the present time due to continuous driving is "10 minutes," the output control unit 134 calculates "6 minutes," which is the difference between the driving time "10 minutes" and the predicted driving time "16 minutes." Then, the output control unit 134 predicts the timing when the driver DX should take a rest based on the current time and the difference time "6 minutes." For example, if the current time is "2:00 PM," the output control unit 134 predicts that the timing when the driver DX should take a rest is "2:06 PM," which is "6 minutes" after the current time of "2:00 PM."

[0105] 6(b) shows an example in which a driving load ratio of "0:100" is input and a predicted driving time of "78 minutes" for the target vehicle VEx is predicted from the output information output by the prediction model M. This corresponds to an estimation that, assuming that the driver DX continues driving continuously with a driving load ratio of "0:100", it would be optimal for him to take a break "78 minutes" after the start of driving, since there is a statistical tendency to take a break at "78 minutes" when the driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, is "0:100" (in other words, when the driver drives continuously through the "FREE" section).

[0106] For example, if the driving time of the target vehicle VEx from the start of driving to the present time due to continuous driving is "10 minutes," the output control unit 134 calculates "68 minutes," which is the difference between the driving time "10 minutes" and the predicted driving time "78 minutes." Then, the output control unit 134 predicts the timing when the driver DX should take a rest based on the current time and the difference time "68 minutes." For example, if the current time is "2:00 PM," the output control unit 134 predicts that the timing when the driver DX should take a rest is "3:08 PM," which is "68 minutes" after the current time "2:00 PM."

[0107] In the above example, the actual driving route traveled by the continuous driving of the target vehicle VEx from the start of driving to the present time is handled. However, for example, when the planned driving route that the driver DX plans to drive in the target vehicle VEx is handled, the output control unit 134 may predict the timing of the rest by using “0 minutes” as the driving time of the target vehicle VEx.

[0108] 7, a description will be given of a processing procedure performed between the first server device 100A and the second server device 200. Fig. 7 is a sequence diagram showing a processing procedure performed between the server devices included in the system SyA according to the first embodiment.

[0109] First, the estimation unit 131 of the first server device 100A acquires driving records (driving histories) of an unspecified number of vehicles VEn (step S701). The estimation unit 131 also extracts information on a driving route for one trip from the driving routes indicated by the driving records, and estimates a WL type for each link included in the driving route for one trip (step S702).

[0110] The estimation unit 131 transmits the estimated WL type for each of the vehicle VEn's travel routes for one trip to the second server device 200 (step S703). The transmitted WL type is acquired by the WL type acquisition unit 231 of the second server device 200.

[0111] Once the WL type is obtained, the prediction model generation unit 232 calculates a model ratio, which is the ratio of links included in the driving route for one trip of each vehicle VEn, for which the WL type is estimated to be "BUSY", to links for which the WL type is estimated to be "FREE" (step S704).

[0112] Next, the prediction model generation unit 232 aggregates, for each model ratio, the time that the vehicle VEn corresponding to that model ratio has been continuously driven (the driving time required for the vehicle VEn to travel the driving route for one trip) (step S705).

[0113] The prediction model generation unit 232 calculates the average driving time, which is the average of the continuous driving time of the vehicle VEn, for each model ratio (step S706).The prediction model generation unit 232 then expresses the relationship between the model ratio and the average driving time in a three-dimensional graph (step S707), and generates a prediction model based on the three-dimensional graph (step S708).

[0114] The transmission unit 233 transmits the prediction model to the first server device 100A (step S709). The transmitted prediction model is acquired by the operating load ratio acquisition unit 133 (step S710).

[0115] 4. Rest Timing Prediction Processing Procedure Next, a procedure for predicting rest timings using a prediction model will be described. Fig. 8 is a flowchart showing the procedure for predicting rest timings.

[0116] First, the estimation unit 131 determines whether or not the target vehicle VEx has started to drive (step S801). If the target vehicle VEx has not started to drive (step S801; No), the estimation unit 131 waits until it can determine that the target vehicle VEx has started to drive.

[0117] On the other hand, when the estimation unit 131 determines that driving of the target vehicle VEx has started (step S801; Yes), it determines whether it is time to predict the timing of a rest break (step S802). For example, the estimation unit 131 may determine that it is time to predict the timing of a rest break when a predetermined time has elapsed since driving of the target vehicle VEx started, or when a predetermined distance has been driven since driving of the target vehicle VEx started and a certain amount of driving history has been accumulated. When it is not time to predict the timing of a rest break (step S802; No), the estimation unit 131 waits until it is time to predict the timing of a rest break.

[0118] Furthermore, when it is time to predict the rest timing (step S802; Yes), the estimation unit 131 acquires driving route information indicating the driving route of the target vehicle VEx (step S803). For example, the estimation unit 131 acquires information on the actual driving route traveled by the target vehicle VEx during continuous driving from the start of driving to the present time.

[0119] Then, the estimation unit 131 estimates the WL type for each link included in the travel route of the target vehicle VEx (step S804). The method for estimating the WL type is as described with reference to FIG.

[0120] The calculation unit 132 calculates a first cumulative time, which is the cumulative time that the target vehicle VEx has traveled through the "BUSY" section during continuous driving from the start of driving to the present time (step S805). The calculation unit 132 also calculates a second cumulative time, which is the cumulative time that the target vehicle VEx has traveled through the "FREE" section during continuous driving from the start of driving to the present time (step S806).

[0121] The calculation unit 132 calculates a driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, and transmits the driving load ratio to the driving load ratio acquisition unit 133 (step S807).

[0122] 7, at this point, the driving load ratio acquisition unit 133 has already acquired the prediction model from the second server device 200. Therefore, the driving load ratio acquisition unit 133 inputs the driving load ratio into the prediction model and calculates the predicted traveling time of the target vehicle VEx (step S808).

[0123] The output control unit 134 predicts the timing when the driver DX should take a rest based on the difference between the driving time of the target vehicle VEx up to now, i.e., the time spent continuously driving from the start of driving to the present time, and the predicted driving time, and controls the output so that information about the predicted rest timing is notified from the in-vehicle device 10 (step S809). The notification may be by voice or by a screen display.

[0124] (Second embodiment) <1. System configuration> A second embodiment will now be described. In the information processing according to the first embodiment, an appropriate timing for the driver DX to take a rest was predicted. In the information processing according to the second embodiment, the purpose is to use information on the rest timing predicted by the information processing according to the first embodiment to present a range that the driver DX can reach without taking a rest by the time the rest is due. By presenting the range that can be reached without taking a rest, the driver DX can, for example, grasp the approximate location where he or she should take a rest before driving, making it easier to create a driving plan.

[0125] First, the configuration of a system according to the second embodiment will be described with reference to Fig. 9. Fig. 9 is a diagram showing an example of a system according to the second embodiment. Fig. 9 shows a system SyB as an example of a system according to the second embodiment. Information processing according to the second embodiment is realized in the system SyB.

[0126] Compared to system SyA (Figure 1), system SyB differs in that it has a first server device 100B according to the second embodiment instead of the server device 100A according to the first embodiment, but is otherwise similar.

[0127] The first server device 100B is a central device responsible for the information processing according to the second embodiment. For example, the first server device 100B determines a reachable range, which is a range that the target vehicle VEx can reach from the travel start point, based on the prediction result obtained by the information processing according to the first embodiment, i.e., the timing when the driver DX should take a rest, and a predetermined route centered on the travel start point of the target vehicle VEx. Specifically, the first server device 100B determines, as the reachable range, a range that the driver DX is estimated to be able to reach without taking a rest by the time it is time to take a rest.

[0128] 2. Functional Configuration Next, a configuration example of the first server device 100B will be described. The first server device 100B is configured by adding a processing unit that realizes the information processing according to the second embodiment to the first server device 100A.

[0129] [First Server Device 100B] Fig. 10 is a diagram showing an example of the configuration of a first server device 100B according to the second embodiment. As shown in Fig. 10, the first server device 100B includes a communication unit 110, a storage unit 120B, and a control unit 130B.

[0130] [Storage Unit 120B] The storage unit 120B 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 120B may store, for example, data and programs related to the information processing according to the embodiment. Furthermore, according to the example of FIG. 10 , the storage unit 120B may further include a range information storage unit 124.

[0131] [Range Information Storage Unit 124] The range information storage unit 124 stores information on a reachable range. Specifically, the range information storage unit 124 stores information on a range that the driver DX is estimated to be able to reach without taking a break until it is time to take a break.

[0132] [Control unit 130B] The control unit 130B is realized by a CPU or the like executing various programs (e.g., the information processing program according to the embodiment) stored in a storage device inside the first server device 100B using RAM as a work area. The control unit 130B is also realized by an integrated circuit such as an ASIC or FPGA.

[0133] 10 , like the first server device 100A, the control unit 130B includes an estimation unit 131, a calculation unit 132, an operating load ratio acquisition unit 133, an output control unit 134, and a search unit 135. In addition, the control unit 130B includes a start point acquisition unit 136, a prediction unit 137, and a range specification unit 138 as additional functions compared to the first server device 100A.

[0134] These processing units realize or execute the functions and actions of the information processing described below. Note that the internal configuration of the control unit 130B is not limited to the configuration shown in Fig. 10, and may be other configurations as long as they perform the information processing described below. Furthermore, the connection relationships between the processing units in the control unit 130B are not limited to the connection relationships shown in Fig. 10, and may be other connection relationships.

[0135] The start point acquisition unit 136 corresponds to the first acquisition unit, and acquires information on a travel start point, which is a point where the travel of the target vehicle VEx starts. For example, when the engine of the target vehicle VEx is started, the start point acquisition unit 136 may acquire information on the position where the engine is started as the travel start point information.

[0136] [Estimation unit 131] The estimation unit 131 estimates the WL type for each link included in a searched route (hereinafter abbreviated as a "searched route") obtained by a route search when a destination is set as a predetermined route centered on the travel start point, the destination being points in each direction centered on the travel start point and located a predetermined distance from the travel start point. The method for estimating the WL type is as described in FIG. 4. Furthermore, a route search when a destination is set as a point located a predetermined distance from the travel start point is performed by the search unit 135.

[0137] [Calculation unit 132] The calculation unit 132 calculates a first cumulative time, which is the cumulative time that the target vehicle VEx has traveled through links included in the route search that have an estimated WL type of "BUSY" (a first type indicating that the WL is higher than a reference value). The calculation unit 132 also calculates a second cumulative time, which is the cumulative time that the target vehicle VEx has traveled through links included in the searched route of the target vehicle VEx that have an estimated WL type of "FREE" (a second type indicating that the WL is lower than a reference value).

[0138] The calculation unit 132 also calculates a driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, and transmits the calculated driving load ratio to the driving load ratio acquisition unit 133 .

[0139] The calculation unit 132 may calculate the first cumulative distance by accumulating the distances of links included in the route search whose WL type is estimated to be "BUSY" (a first type indicating that the WL is higher than a reference value).The calculation unit 132 may calculate the second cumulative distance by accumulating the distances of links included in the route search whose WL type is estimated to be "FREE" (a second type indicating that the WL is higher than a reference value).

[0140] [Driving load ratio acquisition unit 133] The driving load ratio acquisition unit 133 corresponds to the second acquisition unit, and acquires a driving load ratio indicating the ratio between a high driving load state and a low driving load state for each route in the search results obtained by route search when a destination is set to a point in each direction centered on the driving start point and located a predetermined distance from the driving start point.

[0141] Specifically, the driving load ratio acquisition unit 133 acquires a driving load ratio indicating the ratio between the first cumulative time (information indicating a high driving load state) calculated by the calculation unit 132 for each search route and the second cumulative time (information indicating a low driving load state) calculated by the calculation unit 132 for each search route.

[0142] As another example, the driving load ratio acquisition unit 133 acquires a driving load ratio indicating the ratio between the first cumulative distance (information indicating a high driving load state) calculated by the calculation unit 132 for each search route and the second cumulative distance (information indicating a low driving load state) calculated by the calculation unit 132 for each search route.

[0143] The driving load ratio acquisition unit 133 may also perform a process of calculating a predicted driving time of the target vehicle VEx for each search route based on the driving load ratio and the prediction model generated by the second server device 200. Specifically, the driving load ratio acquisition unit 133 predicts the time for which the target vehicle VEx will be continuously driven (the time for which the driver DX will drive continuously without a break) for each search route based on the driving load ratio and the prediction model. The prediction model used here is the one generated by the second server device 200.

[0144] [Prediction Unit 137] The prediction unit 137 predicts the arrival time of the target vehicle VEx at each change point where the WL type changes on each searched route. Specifically, the prediction unit 137 predicts the arrival time of the target vehicle VEx at each change point where the WL type changes on each searched route, based on the distance from the travel start point to each link included in the searched route and the WL type estimated for each link. This point will be explained using the example of Figure 4.

[0145] If the travel route RT shown in FIG. 4 is the search route, the position PT1 is the travel start point of the target vehicle VEx, and the position PT2 is the destination determined based on a predetermined distance from the travel start point.

[0146] For example, the prediction unit 137 detects a node connecting links of different WL types as a change point where the WL type changes. According to the example of FIG. 4B, node ND23 is a node that connects link 102 of WL type "FREE" with link 103 of WL type "BUSY". Node ND34 is a node that connects link 103 of WL type "BUSY" with link 104 of WL type "FREE". Node ND45 is a node that connects link 104 of WL type "FREE" with link 105 of WL type "BUSY". Therefore, the prediction unit 137 detects nodes ND23, ND34, and ND45 as nodes that connect links of different WL types as change points where the WL type changes.

[0147] The prediction unit 137 then predicts the arrival time at which the target vehicle VEx will reach node ND23 based on the average speed from position PT1 to node ND23 (connection point) and the distance from position PT1 to node ND23 (connection point). The prediction unit 137 also predicts the arrival time at which the target vehicle VEx will reach node ND34 based on the average speed from position PT1 to node ND34 (connection point) and the distance from position PT1 to node ND34 (connection point). The prediction unit 137 also predicts the arrival time at which the target vehicle VEx will reach node ND45 based on the average speed from position PT1 to node ND45 (connection point) and the distance from position PT1 to node ND45 (connection point). Furthermore, the prediction unit 137 predicts the arrival time at which the target vehicle VEx will arrive at the position PT2 based on the average speed from the position PT1 to the position PT2 and the distance from the position PT1 to the position PT2.

[0148] [Range Identification Unit 138] The range identification unit 138 extracts, from among the change points detected by the prediction unit 137, change points for which the calculated arrival time is within a predetermined range of the timing when it is predicted that the driver DX should take a rest, as rest candidate points. Then, the range identification unit 138 identifies a polygon formed by connecting the rest candidate points as a reachable range. As an example, the range identification unit 138 may extract, from among the change points, change points for which the calculated arrival time is later than the timing when it is predicted that the driver DX should take a rest, as rest candidate points.

[0149] [Output Control Unit 134] The output control unit 134 controls output so that the in-vehicle device 10 displays a screen on which information about the reachable range is superimposed on a map.

[0150] 3. Range Identification Processing Procedure> The procedure of the range identification processing for identifying a reachable range will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the procedure of the range identification processing. A specific example of the range identification processing will be described with reference to Fig. 12. Fig. 12 is a diagram showing a specific example of the range identification processing.

[0151] First, the start point acquisition unit 136 acquires information on the travel start point BA of the target vehicle VEx (step S1101). Fig. 12A illustrates an example in which the start point acquisition unit 136 detects the travel start point BA as the travel start point of the target vehicle VEx and acquires information on the travel start point BA (e.g., location information).

[0152] Next, the search unit 135 sets destinations at a predetermined distance in each direction of 360 degrees from the travel start point BA (step S1102). Fig. 12(a) shows an example in which the search unit 135 sets seven destinations (destinations G1 to G7), namely, destination G1, destination G2, destination G3, destination G4, destination G5, destination G6, and destination G7.

[0153] In this state, the search unit 135 executes a route search to search for a route from the travel start point BA to each of the destinations G1 to G7 (step S1103).

[0154] 12(a) shows an example in which the search unit 135 performs a route search from the driving start point BA to the destination G1, and acquires a searched route SR1 as the route of the search result. Also, FIG. 12(a) shows an example in which the search unit 135 performs a route search from the driving start point BA to the destination G2, and acquires a searched route SR2 as the route of the search result. Also, FIG. 12(a) shows an example in which the search unit 135 performs a route search from the driving start point BA to the destination G3, and acquires a searched route SR31 and a searched route SR32 as the routes of the search result.

[0155] 12(a) shows an example in which the search unit 135 performs a route search from the driving start point BA to the destination G4, and acquires searched routes SR41, SR42, and SR43 as routes resulting from the search. Also, FIG. 12(a) shows an example in which the search unit 135 performs a route search from the driving start point BA to the destination G5, and acquires searched routes SR51 and SR52 as routes resulting from the search.

[0156] 12(a) shows an example in which the search unit 135 acquires a searched route SR6 as a route resulting from a route search from the driving start point BA to the destination G6. Also, FIG. 12(a) shows an example in which the search unit 135 acquires a searched route SR7 as a route resulting from a route search from the driving start point BA to the destination G7.

[0157] The estimation unit 131 estimates the WL type for each link included in each searched route acquired by the searching unit 135 (step S1104).

[0158] Next, the prediction unit 137 predicts the arrival time of the target vehicle VEx at each change point where the WL type changes on each searched route (step S1105). The method for detecting the change point and the method for predicting the arrival time are as described using the example of FIG. 4(b).

[0159] Here, the calculation unit 132 calculates the driving load ratio for each searched route (step S1106). For example, the calculation unit 132 calculates a first cumulative time, which is the cumulative time estimated when the target vehicle VEx travels through links included in the route search that are estimated to have a WL type of "BUSY." The calculation unit 132 also calculates a second cumulative time, which is the cumulative time estimated when the target vehicle VEx travels through links included in the route search that are estimated to have a WL type of "FREE." For example, the calculation unit 132 can calculate the first cumulative time and the second cumulative time based on statistical information known in advance about busy sections.

[0160] The calculation unit 132 calculates a driving load ratio, which is the ratio between the first cumulative time and the second cumulative time, and transmits the driving load ratio to the driving load ratio acquisition unit 133 (step S1107).

[0161] At this point, the driving load ratio acquisition unit 133 has already acquired the prediction model from the second server device 200. Therefore, the driving load ratio acquisition unit 133 inputs the driving load ratio into the prediction model and calculates the predicted driving time of the target vehicle VEx for each searched route (step S1108). Specifically, the driving load ratio acquisition unit 133 calculates the predicted driving time for each searched route in which the driver DX is predicted to drive the searched route continuously without taking a break.

[0162] The output control unit 134 predicts, for each searched route, the timing of a rest period (the timing at which the driver DX should take a rest period) for that searched route based on the predicted travel time obtained for that searched route (step S1109). Specifically, the output control unit 134 predicts the time of the rest period based on the current time and the predicted travel time.

[0163] The range specifying unit 138 extracts, from among the change points detected for each searched route, change points whose arrival times are predicted to exceed within a predetermined time range based on the break timing time predicted for that searched route as break candidate points (step S1110). Note that if there is no arrival time that exceeds within the predetermined time range from the break timing time, the range specifying unit 138 does not need to extract break candidate points.

[0164] As another example, the range determination unit 138 may extract, from among the change points detected for each search route, change points whose arrival time is predicted to be below within a predetermined time range based on the time of the predicted rest timing for that search route, as candidate rest points.

[0165] 12(a), the candidate rest points extracted for each of the 11 searched routes, including searched route SR11, are indicated by "x" marks. In this state, the range specification unit 138 specifies a reachable range based on the 11 candidate rest points (step S1111). For example, as shown in FIG. 12(b), the range specification unit 138 specifies an area AR obtained by connecting the 11 candidate rest points as the reachable range, which is a range that the driver DX is estimated to be able to reach without taking a break by the time the break timing predicted in step S1109 arrives.

[0166] The output control unit 134 controls the in-vehicle device 10 to provide the driver DX with the reachable range AR superimposed on the map (step S1112). While FIG. 12B illustrates an example in which the reachable range AR is determined to be a shape generated by simply connecting the candidate rest points, the method for generating the reachable range AR is not limited to this example. For example, the range determination unit 138 may determine, as the reachable range AR, a shape that is smoother than the shape generated by simply connecting the candidate rest points. Alternatively, the range determination unit 138 may determine, as the reachable range AR, a polygon obtained by applying a convex hull algorithm to the candidate rest points.

[0167] In the above example, the range specification unit 138 specifies a reachable range, which is a range that the driver DX is estimated to be able to reach without taking a break. However, the range specification unit 138 may specify a range that the driver DX is estimated to be able to reach with one break in between. This point will be described with reference to FIG. 12 .

[0168] 12(a), the range specifying unit 138 extracts one rest candidate point for each of the 11 searched routes, but the range specifying unit 138 regards these rest candidate points as travel start points and executes steps S1101 to S1111 again. Specifically, the range specifying unit 138 assumes that the rest candidate points are travel start points, and further specifies a reachable range that is a range that can be reached from the rest candidate points by the target vehicle VEx, and specifies a range that is estimated to be reachable by the driver DX with one rest break in between, based on the reachable range.

[0169] According to this example, for example, among the change points on the multiple search routes further extended from each of the eleven candidate rest points, the change points whose estimated arrival times are later than the rest timing within a predetermined time range are extracted as the candidate rest points. Thus, in a simple example, eleven reachable ranges are identified according to the eleven candidate rest points. In this state, the range identification unit 138 may, for example, identify one polygon covering the eleven reachable ranges as the range that the driver DX is estimated to be reachable with one rest break in between.

[0170] (Hardware Configuration) The above-described information processing devices (e.g., first server device 100A, first server device 100B, second server device 200) may be realized, for example, by a computer 1000 configured as shown in Fig. 13. Fig. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, a HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] For example, when the computer 1000 functions as the first server device 100A according to the first embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130A by executing programs loaded onto the RAM 1200. 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.

[0176] Furthermore, when the computer 1000 functions as the first server device 100B according to the second embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130B by executing programs loaded onto the RAM 1200. 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.

[0177] Furthermore, when the computer 1000 functions as the second server device 200 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 230. 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.

[0178] (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.

[0179] 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 the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0180] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0181] 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.

[0182] SyA System 100A First server device 120A Storage unit 121 Map information storage unit 122 WL type estimation result storage unit 123 Ratio information storage unit 130A Control unit 131 Estimation unit 132 Calculation unit 133 Driving load ratio acquisition unit 134 Output control unit 135 Search unit SyB System 100B First server device 120B Storage unit 124 Range information storage unit 130B Control unit 136 Start point acquisition unit 137 Prediction unit 138 Range identification unit

Claims

1. A calculation unit that calculates a first cumulative time, which is the cumulative time that the target vehicle has traveled, for road sections included in the driving route of the target vehicle, where a first type of driving load is estimated to be a first type indicating that the driving load is high compared to a reference value, and a second cumulative time, which is the cumulative time that the target vehicle has traveled, for road sections included in the driving route of the target vehicle, where a second type of driving load is estimated to be a second type indicating that the driving load is low compared to a reference value; an acquisition unit that acquires a driving load ratio indicating a ratio between the first cumulative time when a driving load of a driver on a travel route of the target vehicle is high and the second cumulative time when a driving load of the driver is low; an output control unit that uses at least the driving load ratio to output a timing when the driver should take a break; An information processing device comprising:

2. The travel route of the target vehicle is a travel route that has been traveled by continuous driving of the target vehicle from the start of travel to the present time.

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

3. The travel route of the target vehicle is a planned travel route that the driver plans to travel with the target vehicle.

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

4. an estimation unit that estimates a type of driving load for each road section included in a travel route of the target vehicle; Further provided with 2. The information processing apparatus according to claim 1, wherein:

5. A generation unit is further provided which generates a prediction model for predicting a driving time by continuous driving based on a driving record of a predetermined vehicle, the driving record being a driving record by continuous driving from the start of driving to the end of driving, the acquisition unit calculates a predicted traveling time of the target vehicle based on the driving load ratio and the prediction model; The output control unit predicts a timing when the driver should take a rest based on a predicted running time of the target vehicle.

5. The information processing apparatus according to claim 4,

6. the estimation unit estimates a type of driving load for each road section included in the travel route indicated by the travel record; the generation unit calculates an average driving time, which is an average of the time for which the predetermined vehicle is continuously driven, for each model ratio, which is a ratio between road sections included in the driving route indicated by the driving record, in which a first type, which indicates that the driving load is high compared to a reference value, is estimated as the type of the driving load, and road sections in which a second type, which indicates that the driving load is low compared to a reference value, is estimated as the type of the driving load, and generates the prediction model based on the model ratio and the average driving time; The acquisition unit calculates a predicted driving time of the target vehicle based on output information output by the prediction model using the driving load ratio as an input.

6. The information processing apparatus according to claim 5,

7. The generation unit generates the prediction model based on a three-dimensional graph in which a relationship between the model ratio and the average running time is expressed in three dimensions.

7. The information processing apparatus according to claim 6,

8. The output control unit predicts a timing when the driver should take a break based on a difference between a traveling time of the target vehicle and the predicted traveling time.

6. The information processing apparatus according to claim 5,

9. a search unit that searches for a rest spot that the target vehicle can reach by the time the driver should take a rest, The output control unit outputs proposal information that proposes the rest spot.

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

10. The search unit searches for the rest spot within a range of a predicted travel distance, which is a distance that the target vehicle is predicted to travel until the timing to take a rest, and calculates a predicted arrival time, which is a time that the target vehicle is predicted to arrive at the rest spot.

10. The information processing apparatus according to claim 9,

11. the search unit uses the driving load ratio corresponding to a planned driving route along which the driver is scheduled to drive the target vehicle as a driving route of the target vehicle, and when a timing at which the driver should take a rest is predicted, searches for a rest spot within a range corresponding to a point on the planned driving route at which the driver is estimated to arrive at the time at which the driver should take a rest; The output control unit outputs proposal information that proposes the rest spot along with the planned driving route.

10. The information processing apparatus according to claim 9,

12. The planned driving route is a guide route searched based on the destination of the target vehicle.

12. The information processing apparatus according to claim 11,

13. An information processing method executed by an information processing device, a calculation step of calculating a first cumulative time, which is a cumulative time that the target vehicle has traveled on road sections included in the travel route of the target vehicle, for which a first type of driving load has been estimated as a type of driving load that indicates a high driving load compared to a reference value, and a second cumulative time, which is a cumulative time that the target vehicle has traveled on road sections included in the travel route of the target vehicle, for which a second type of driving load has been estimated as a type of driving load that indicates a low driving load compared to a reference value; an acquisition step of acquiring a driving load ratio indicating a ratio between the first cumulative time when the driving load of the driver on the travel route of the target vehicle is high and the second cumulative time when the driving load of the driver is low; an output control step of outputting a timing when the driver should take a rest, using at least the driving load ratio; An information processing method comprising:

14. An information processing program executed by an information processing device, a calculation step of calculating a first cumulative time, which is a cumulative time that the target vehicle has traveled on road sections included in a travel route of the target vehicle, for which a first type of driving load has been estimated as a type of driving load that indicates that the driving load is high compared to a reference value, and a second cumulative time, which is a cumulative time that the target vehicle has traveled on road sections included in a travel route of the target vehicle, for which a second type of driving load has been estimated as a type of driving load that indicates that the driving load is low compared to a reference value; an acquisition step of acquiring a driving load ratio indicating a ratio between the first cumulative time when the driving load of the driver on the travel route of the target vehicle is high and the second cumulative time when the driving load of the driver is low; an output control step of outputting a timing when the driver should take a rest, using at least the driving load ratio; An information processing program for causing the information processing device to execute the above.