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

Abstract

An acquisition unit (131) acquires data related to vehicle or road conditions. A situation estimation unit (132) estimates a driving situation on the basis of the data related to vehicle or road conditions acquired by the acquisition unit (131). An attention degree calculation unit (133) calculates an index indicating the degree to which an operator is attentive to driving on the basis of the driving situation estimated by the situation estimation unit (132). A monotony degree calculation unit (134) calculates an index indicating the degree of monotony in driving on the basis of the driving situation estimated by the situation estimation unit (132). A load estimation unit (136) estimates a driving load on the basis of the index indicating the degree to which the operator is attentive to driving calculated by the attention degree calculation unit (133) and the index indicating the degree of monotony in driving calculated by the monotony degree calculation unit (134).

Description

[Technical Field]

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

[0002] Conventionally, there is a technology that sends a notification to a user urging them to use a driving assistance device when the user enters a notification-enabled section, and also sends a notification to encourage use of an accessory device when the user is not operating the accessory device even while driving in a notification-enabled section (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-092412 Summary of the Invention [Problem to be solved by the invention]

[0004] The conventional technology has a problem in that it is not possible to accurately determine the driver's driving load depending on the situation. For example, when the driver is driving on a narrow road or when operations such as sudden steering or sudden acceleration are performed, the driver's driving load is high, and if a notification is provided in such a case, the driver may be hindered from driving. Thus, the above-mentioned problem is cited as an example of the problem that the present invention aims to solve. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems and achieve the object, the invention described in claim 1 is characterized by having an acquisition unit that acquires data regarding vehicle or road conditions, a situation estimation unit that estimates a driving situation based on the data regarding the vehicle or road conditions acquired by the acquisition unit, a consideration calculation unit that calculates an index indicating the degree to which a driver is considerate in driving based on the driving situation estimated by the situation estimation unit, a monotony calculation unit that calculates an index indicating the degree of monotony in driving based on the driving situation estimated by the situation estimation unit, and a load estimation unit that estimates a driving load based on the index indicating the degree to which a driver is considerate in driving calculated by the consideration calculation unit and the index indicating the degree of monotony in driving calculated by the monotony calculation unit.

[0006] The invention described in claim 6 is an information processing method implemented by an information processing device, comprising: an acquisition process for acquiring data regarding vehicle or road conditions; a situation estimation process for estimating a driving condition based on the data regarding the vehicle or road conditions acquired by the acquisition process; a consideration calculation process for calculating an index indicating the degree to which a driver is considerate in driving based on the driving condition estimated by the situation estimation process; a monotony calculation process for calculating an index indicating the degree of monotony in driving based on the driving condition estimated by the situation estimation process; and a load estimation process for estimating a driving load based on the index indicating the degree to which a driver is considerate in driving calculated by the consideration calculation process and the index indicating the degree of monotony in driving calculated by the monotony calculation process.

[0007] The invention described in claim 7 is characterized in that the computer is made to execute the following steps: an acquisition step for acquiring data regarding vehicle or road conditions; a situation estimation step for estimating a driving condition based on the data regarding the vehicle or road conditions acquired by the acquisition step; a consideration calculation step for calculating an index indicating the degree to which the driver is considerate in driving based on the driving condition estimated by the situation estimation step; a monotony calculation step for calculating an index indicating the degree of monotony in driving based on the driving condition estimated by the situation estimation step; and a load estimation step for estimating a driving load based on the index indicating the degree to which the driver is considerate in driving calculated by the consideration calculation step and the index indicating the degree of monotony in driving calculated by the monotony calculation step. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing the configuration of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a diagram showing the configuration of the information processing apparatus according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 6] FIG. 6 is a flowchart illustrating an example of processing by the information processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 8] FIG. 8 is a flowchart illustrating an example of processing by the information processing device according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 10]FIG. 10 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating an outline of processing by the information processing device according to the embodiment. [Figure 12] FIG. 12 is a flowchart illustrating an example of processing by the information processing device according to the embodiment. [Figure 13] FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of a form for implementing an information processing device, an information processing method, and an information processing program (hereinafter referred to as an "embodiment") will be described in detail below with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program are not limited to this embodiment. Furthermore, the same components in the following embodiments will be assigned the same reference numerals, and duplicated descriptions will be omitted.

[0010] [System Configuration] First, the configuration of an information processing system according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of an information processing system according to an embodiment. Fig. 1 shows an information processing system 1 as an example of an information processing system according to an embodiment.

[0011] 1, the information processing system 1 may include an in-vehicle device 10 and an information processing device 100. The in-vehicle device 10 and the information processing device 100 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. The information processing system 1 shown in FIG. 1 may include any number of in-vehicle devices 10 and any number of information processing devices 100.

[0012] The in-vehicle device 10 may be a dedicated navigation device built into or externally attached to the vehicle VEx, or may be a recording device (drive recorder) or other device installed in the vehicle VEx for crime prevention, anti-tailgating measures, or driving assistance.

[0013] The in-vehicle device 10 may also be configured with a navigation device and a recording device. As an 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] Furthermore, a user can install a predetermined application into a portable terminal device (for example, a smartphone, tablet terminal, notebook PC, desktop PC, PDA, etc.) that they use on a daily basis, and use the device as the in-vehicle device 10. For example, a portable terminal device on which a predetermined navigation application or a predetermined recording application is installed can be understood as the in-vehicle device 10 referred to here. When the portable terminal device is used as the in-vehicle device 10, it is installed, for example, on the dashboard of the vehicle VEx while driving.

[0015] The in-vehicle device 10 may also include various sensors, such as a GPS sensor, an acceleration sensor, a gyro sensor, a camera, and an air pressure sensor.

[0016] The information processing device 100 may acquire various data based on sensor information detected by these sensors (for example, by analyzing the sensor information). For example, the information processing device 100 acquires position information from a GPS sensor. For example, the information processing device 100 acquires acceleration from an acceleration sensor. Furthermore, for example, the information processing device 100 acquires angular velocity from a gyro sensor. Furthermore, for example, the information processing device 100 acquires video data captured from inside the vehicle VEx by a camera of the outside. Note that the information processing device 100 may acquire sensor information detected not only by sensors provided in the in-vehicle device 10 but also by sensors provided in the vehicle VEx itself.

[0017] The information processing device 100 is a device that performs information processing according to the embodiment. For example, the information processing device 100 receives data related to a vehicle or road conditions as input and outputs an estimated driver's driving load (in other words, the degree of difficulty in driving, driving burden, or workload). The information processing device 100 can also deliver predetermined content based on the estimated driving load.

[0018] Here, if the in-vehicle device 10 is an edge computer that performs edge processing near the user, the information processing device 100 may be, for example, a cloud computer that performs processing on the cloud side. In other words, the information processing device 100 may be a server device.

[0019] Furthermore, in the following embodiment, an example is shown in which information processing according to the embodiment is realized in the information processing system 1 by transmitting and receiving information between the in-vehicle device 10 and the information processing device 100. However, the information processing according to the embodiment may be realized only on the edge side, i.e., by the in-vehicle device 10. In this case, the in-vehicle device 10 may be configured to behave like the information processing device 100 by, for example, an information processing program according to the embodiment.

[0020] [Configuration of information processing device] Next, an information processing device 100 according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing device 100 according to an embodiment. As shown in Fig. 2, the information processing device 100 has a communication unit 110, a storage unit 120, and a control unit 130. Each unit of the information processing device 100 will be described below.

[0021] The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the in-vehicle device 10, for example.

[0022] The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk, an optical disk, etc. The storage unit 120 has a workload DB 121.

[0023] The workload DB121 is a database that stores road link IDs, data on the driver's living area, data on the driver's sensitivity, an index corresponding to the road link ID indicating the degree of care the driver takes in driving, an index indicating the degree of monotony in driving, driving load, other information necessary for estimating driving load, and other information necessary for content distribution.

[0024] The control unit 130 is realized using a CPU (Central Processing Unit), an NP (Network Processor), an FPGA (Field Programmable Gate Array), or the like, and executes a processing program stored in memory. As shown in Fig. 2, the control unit 130 has an acquisition unit 131, a situation estimation unit 132, a consideration level calculation unit 133, a monotonicity calculation unit 134, a reference unit 135, a load estimation unit 136, and a distribution unit 137. Each unit of the control unit 130 will be described below.

[0025] The acquisition unit 131 acquires data related to the vehicle or road conditions. For example, the acquisition unit 131 acquires map information, guidance information, and vehicle behavior as data related to the vehicle or road conditions. Here, the map information includes information such as the road type, width classification, number of lanes, and in-link attributes (railroad crossing, toll gate, tunnel, etc.) as a road link ID. Furthermore, the guidance information includes information such as the distance and route to the guidance point. Furthermore, the vehicle behavior includes information such as GPS data, acceleration data, and angular velocity data.

[0026] Furthermore, the acquisition unit 131 acquires data on the driver's living area in addition to data on the vehicle or road conditions. Here, the data on the driver's living area includes map information that maps a predetermined area calculated based on information such as the driver's residence and frequently visited spots. Furthermore, the acquisition unit 131 acquires data on the driver's sensitivity in addition to data on the vehicle or road conditions. Here, the data on the driver's sensitivity includes, for example, the results of a questionnaire on the driver's sensitivity.

[0027] The acquisition unit 131 acquires static data related to the vehicle or road conditions. For example, the acquisition unit 131 acquires map information such as a road link ID. The acquisition unit 131 also acquires dynamic data related to the vehicle or road conditions. For example, the acquisition unit 131 acquires guidance information such as the distance to the guidance point and the route, and information related to the vehicle behavior such as position information, acceleration, and angular velocity. The acquisition unit 131 also acquires dynamic data related to the vehicle or road conditions in addition to the static data related to the vehicle or road conditions. For example, the acquisition unit 131 acquires a road link ID as static data related to the vehicle or road conditions, and acquires position information, acceleration, and angular velocity as dynamic data related to the vehicle or road conditions.

[0028] The situation estimation unit 132 estimates the driving situation based on data related to the vehicle or road situation acquired by the acquisition unit 131. For example, the situation estimation unit 132 estimates that the situation is an intersection, merging, or branching situation and that a curve maneuver has been performed, based on the road link ID, position information, acceleration, and angular velocity acquired by the acquisition unit 131. Furthermore, the situation estimation unit 132 estimates a dynamic driving situation based on dynamic data related to the vehicle or road situation acquired by the acquisition unit 131. For example, the situation estimation unit 132 estimates that a sudden steering operation has been performed, based on the position information, acceleration, and angular velocity.

[0029] The consideration degree calculation unit 133 calculates an index indicating the degree to which the driver is careful about driving, based on the driving situation estimated by the situation estimation unit 132. For example, based on the driving situation estimated by the situation estimation unit 132 that the road is a narrow road, the consideration degree calculation unit 133 calculates that the degree to which the driver is careful about driving is 0.7.

[0030] Furthermore, the consideration degree calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving, based on the driving situation estimated by the situation estimation unit 132 and data related to the driver's living area acquired by the acquisition unit 131. For example, the consideration degree calculation unit 133 calculates that the index indicating the degree to which the driver is considerate in driving is 0.6, based on the driving situation estimated by the situation estimation unit 132 as being a narrow road and data acquired by the acquisition unit 131 as being within the driver's living area.

[0031] Furthermore, the consideration degree calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving, based on the driving situation estimated by the situation estimation unit 132 and the data relating to the driver's sensitivity acquired by the acquisition unit 131. For example, the consideration degree calculation unit 133 calculates that the index indicating the degree to which the driver is considerate in driving is 0.8, based on the driving situation estimated by the situation estimation unit 132 as being a narrow road and the data acquired by the acquisition unit 131 as being that the driver is highly sensitive.

[0032] The monotonicity calculation unit 134 calculates an index indicating the degree of monotonicity for driving, based on the driving situation estimated by the situation estimation unit 132. For example, based on the driving situation of a low speed limit estimated by the situation estimation unit 132, the monotonicity calculation unit 134 calculates that the index indicating the degree of monotonicity for driving is 0.7.

[0033] Furthermore, the monotony calculation unit 134 calculates an index indicating the degree of monotony for driving, based on the driving situation estimated by the situation estimation unit 132 and data related to the driver's living area acquired by the acquisition unit 131. For example, the monotony calculation unit 134 calculates that the index indicating the degree of monotony for driving is 0.8, based on the driving situation that is a low speed limit estimated by the situation estimation unit 132 and data related to the driver's living area acquired by the acquisition unit 131.

[0034] Furthermore, the monotony calculation unit 134 calculates an index indicating the degree of monotony for driving, based on the driving situation estimated by the situation estimation unit 132 and the data related to the driver's sensitivity acquired by the acquisition unit 131. For example, the monotony calculation unit 134 calculates that the index indicating the degree of monotony for driving is 0.6, based on the driving situation of a low speed limit estimated by the situation estimation unit 132 and the data related to the driver's sensitivity acquired by the acquisition unit 131.

[0035] The reference unit 135 references an index indicating the degree to which the driver pays attention to driving and an index indicating the degree of monotony of driving, based on the static data related to the vehicle or road conditions acquired by the acquisition unit 131. Furthermore, the reference unit 135 references an index indicating the degree to which the driver pays attention to driving, an index indicating the degree of monotony of driving, and a driving load, based on the static data related to the vehicle or road conditions acquired by the acquisition unit 131. For example, based on the road link ID acquired by the acquisition unit 131, the reference unit 135 references information on the index indicating the degree to which the driver pays attention to driving, the index indicating the degree of monotony of driving, and the driving load, which are stored in the workload DB 121 and correspond to the road link ID.

[0036] The load estimation unit 136 estimates the driving load based on the index indicating the degree of consideration the driver takes in driving, calculated by the consideration degree calculation unit 133, and the index indicating the degree of monotony of driving, calculated by the monotony calculation unit 134. For example, the load estimation unit 136 estimates the driving load to be 0.8 based on the index 0.6 indicating the degree of consideration the driver takes in driving, calculated by the consideration degree calculation unit 133, and the index 0.4 indicating the degree of monotony of driving, calculated by the monotony calculation unit 134.

[0037] Furthermore, the load estimation unit 136 estimates the driving load based on the index indicating the degree to which the driver pays attention to driving, calculated by the reference unit 135, the index indicating the degree of monotony of driving, calculated by the consideration degree calculation unit 133, and the index indicating the degree of monotony of driving, calculated by the monotony calculation unit 134. For example, the load estimation unit 136 estimates the driving load to be 0.9 based on the index 0.4 indicating the degree to which the driver pays attention to driving, calculated by the reference unit 135, the index 0.2 indicating the degree of monotony of driving, calculated by the consideration degree calculation unit 133, and the index 0.2 indicating the degree of monotony of driving, calculated by the monotony calculation unit 134.

[0038] Furthermore, the load estimation unit 136 estimates the driving load based on the driving load calculated by the reference unit 135, the index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133, and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134. For example, the load estimation unit 136 estimates the driving load to be 0.9 based on the driving load of 0.3 calculated by the reference unit 135, the index of 0.8 calculated by the consideration degree calculation unit 133 indicating the degree of consideration the driver takes in driving, and the index of 0.4 calculated by the monotony calculation unit 134 indicating the degree of monotony of driving.

[0039] Furthermore, the load estimation unit 136 estimates that the driving load is equal to or greater than a predetermined value when either the index indicating the degree to which the driver is considerate in driving, referred to by the reference unit 135, or the index indicating the degree to which the driver is considerate in driving, calculated by the consideration degree calculation unit 133, is equal to or greater than a predetermined value. For example, the load estimation unit 136 estimates that the driving load is equal to or greater than a predetermined value of 0.8, when either the index 0.4, indicating the degree to which the driver is considerate in driving, referred to by the reference unit 135, or the index 0.8, indicating the degree to which the driver is considerate in driving, calculated by the consideration degree calculation unit 133, is equal to or greater than a predetermined value of 0.6.

[0040] Furthermore, the load estimation unit 136 estimates the driving load based on the driving situation estimated by the situation estimation unit 132. For example, the load estimation unit 136 estimates that the driving load is 0.2 based on the driving situation that is the same road tendency estimated by the situation estimation unit 132.

[0041] Furthermore, the load estimation unit 136 estimates the driving load based on the driving situation estimated by the situation estimation unit 132 and static data related to the vehicle or road situation acquired by the acquisition unit 131. For example, the load estimation unit 136 estimates that the driving load is 0.7 based on the driving situation that the roads have the same tendency estimated by the situation estimation unit 132 and the information that the roads are at a railroad crossing acquired by the acquisition unit 131.

[0042] The distribution unit 137 distributes predetermined content based on the index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133 and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134. For example, when the index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133 is high and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134 is low, the distribution unit 137 makes an announcement such as "We will suggest a wider road."

[0043] Also, for example, if the index calculated by the consideration degree calculation unit 133 indicating the degree of consideration the driver takes in driving is low and the index calculated by the monotony calculation unit 134 indicating the degree of monotony in driving is high, the distribution unit 137 will make an announcement such as, "You can get points by playing a quiz game about the area."

[0044] Also, for example, if the index calculated by the consideration degree calculation unit 133 indicating the degree of consideration the driver takes in driving is high and the index calculated by the monotony degree calculation unit 134 indicating the degree of monotony in driving is also high, the distribution unit 137 will make an announcement such as, "We've got a rest spot that's easy to park at. Would you like to see it?"

[0045] The distribution unit 137 distributes predetermined content based on a change in the driving load estimated by the load estimation unit 136. For example, the distribution unit 137 distributes predetermined content based on a change in the driving load associated with a change in the driving position estimated by the load estimation unit 136. Furthermore, for example, the distribution unit 137 makes an announcement such as "You are passing a point where driving becomes stressful" at a point where the driving load changes associated with a change in the driving position estimated by the load estimation unit 136.

[0046] Furthermore, for example, the distribution unit 137 makes an announcement such as "A point where driving load increases has been passed" when the vehicle being operated passes a point where the driving load changes due to a change in the driving position estimated by the load estimation unit 136. Furthermore, for example, when there is a point on the planned driving route of the vehicle being operated where the driving load increases due to a change in the driving position estimated by the load estimation unit 136, the distribution unit 137 makes an announcement such as "Pay attention to the driving conditions" several seconds before the point where the driving load changes is reached.

[0047] Furthermore, the distribution unit 137 distributes predetermined content based on a change in the driving load that is based on a change in the road link ID on the route estimated by the load estimation unit 136. For example, the distribution unit 137 makes an announcement such as "You will pass a point where driving load will be increased" at a point where the driving load changes based on a change in the road link ID on the route estimated by the load estimation unit 136. Furthermore, for example, if there is a point on the route planned for the vehicle to be driven where the driving load increases due to a change in the road link ID estimated by the load estimation unit 136, the distribution unit 137 makes an announcement such as "Pay attention to the driving conditions" several seconds before the point where the driving load changes is reached.

[0048] The content distribution performed by the distribution unit 137 includes distribution from the information processing device 100 to the in-vehicle device 10, as well as distribution of announcements and navigation from the information processing device 100 or the in-vehicle device 10 to the driver.

[0049] [Estimation of driving load] Next, an overview of the processing performed by the information processing device 100 will be described with reference to Fig. 3. First, the acquisition unit 131 acquires information such as map information, guidance information, and vehicle behavior.

[0050] Next, the situation estimation unit 132 estimates the driving situation from information such as map information, guidance information, and vehicle behavior acquired by the acquisition unit 131. That is, the situation estimation unit 132 estimates whether an external situation is causing the driver to pay attention to driving or to feel monotonous. For example, the situation estimation unit 132 estimates from map information that the vehicle is at an intersection, junction, or branching point. Also, for example, the situation estimation unit 132 estimates from guidance information that the road continues with the same tendency. Also, for example, the situation estimation unit 132 estimates from vehicle behavior that a sudden acceleration operation has been performed.

[0051] Here, the driving situations that the situation estimation unit 132 can estimate from the map information, guidance information, and vehicle behavior acquired by the acquisition unit 131 include, for example, situations such as intersections, junctions, and forks, narrow roads, railroad crossings, toll booths, temporary stops, tunnels, low speed limits, expressways, narrow roads to wide roads, wide roads to narrow roads, curving roads, roads with similar trends, curves, sudden steering, sudden starts, sudden braking, impacts, stops, slow driving, etc. Note that the driving situations that the situation estimation unit 132 can estimate are not limited to those mentioned above.

[0052] Then, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver is careful about driving, based on the driving situation estimated by the situation estimation unit 132. In other words, the consideration level calculation unit 133 estimates whether the situation requires the driver to be careful about driving.

[0053] The consideration degree calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving, for example, by calculation of Equation 1 based on the driving situation estimated by the situation estimation unit 132. Pbusy indicates the probability that the driver feels that he or she is considerate. Xn indicates the objective variable. αx indicates the partial regression coefficient for Xn. The index indicating the degree to which the driver is considerate in driving, calculated by the consideration degree calculation unit 133, takes a value between 0 and 1, and the larger this value, the more considerate the driver is in driving.

[0054]

number

[0055] Next, the monotonicity calculation unit 134 calculates an index indicating the degree of monotony for the driving, based on the driving situation estimated by the situation estimation unit 132. In other words, the monotonicity calculation unit 134 estimates whether the driving situation is monotonous.

[0056] The monotonicity calculation unit 134 calculates an index indicating the degree of monotonicity of driving by, for example, calculating Equation 2 based on the driving situation estimated by the situation estimation unit 132. Pfree indicates the probability of feeling monotonic. Xn indicates the objective variable. αx indicates the partial regression coefficient for Xn. The index indicating the degree of monotonicity of driving calculated by the monotonicity calculation unit 134 takes a value between 0 and 1, and the larger this value, the greater the monotonicity of driving.

[0057]

number

[0058] Then, the load estimation unit 136 estimates the driving load based on the index indicating the degree of consideration the driver takes in driving determined by the consideration degree calculation unit 133 and the index indicating the degree of monotony in driving determined by the monotony calculation unit 134. Here, the driving load calculated by the load estimation unit 136 takes a value between 0 and 1, and the closer the driving load is to 0, the more monotonous the driving is, and the closer the driving load is to 1, the more consideration the driver takes in driving.

[0059] The load estimation process by the load estimation unit 136 will be described with reference to Fig. 4. First, the load estimation unit 136 determines whether the index calculated by the consideration level calculation unit 133, which indicates the degree of consideration the driver takes in driving, is equal to or greater than the index calculated by the monotony level calculation unit 134, which indicates the degree of monotony in driving (step S101).

[0060] If the consideration level is equal to or greater than the monotonicity level (step S101 "YES"), the load estimation unit 136 calculates the driving load based on the degree to which the driver is considerate in driving calculated by the consideration level calculation unit 133 (step S102). For example, the load estimation unit 136 estimates the driving load by adding the degree to which the driver is considerate in driving calculated by the consideration level calculation unit 133 to a predetermined value. Specifically, the load estimation unit 136 estimates the driving load to be 0.9 by adding 0.4, which is obtained by dividing 0.8, an index indicating the degree to which the driver is considerate in driving calculated by the consideration level calculation unit 133, by 2, to a predetermined value of 0.5.

[0061] Furthermore, if the consideration level is not equal to or greater than the monotony level (step S101 "NO"), the load estimation unit 136 calculates the driving load based on the degree of monotony of driving calculated by the monotonicity calculation unit 134 from a predetermined value (step S103). For example, the load estimation unit 136 estimates the driving load by subtracting the degree of monotony of driving from the predetermined value. Specifically, the load estimation unit 136 estimates the driving load to be 0.2 by subtracting 0.3, which is a value obtained by dividing 0.6, an index indicating the degree of monotony of driving, by 2, from the predetermined value 0.5.

[0062] Returning to Fig. 3 again, the distribution unit 137 then distributes predetermined content based on the index calculated by the consideration level calculation unit 133, which indicates the degree to which the driver is considerate in driving, and the index calculated by the monotony level calculation unit 134, which indicates the monotony of driving.

[0063] The distribution of predetermined content based on the consideration level and monotony level by the distribution unit 137 will be described with reference to Fig. 5. For example, in a situation where an index showing the degree to which the driver is considerate in driving is high, the distribution unit 137 makes an announcement such as "We will suggest a wide road" through a navigation app, or "We have a rest spot where it is easy to park. Would you like to see it?" through a rest spot app.

[0064] Furthermore, for example, when the index indicating the degree of monotony of driving is high, the distribution unit 137 makes an announcement through a location information game app such as "You can earn points by playing a quiz game about the area." Furthermore, when the index indicating the degree of care the driver takes in driving is not high and the index indicating the degree of monotony of driving is not high, the distribution unit 137 makes an announcement through a communication app such as "A message from Mr. / Ms. XX will be read out," or an announcement through a scenic spot app such as "The view from Kokusai Bridge on a clear day is amazing. Would you like to pass by?"

[0065] The distribution unit 137 may also make announcements based on the driving load value and a distribution priority set for each content type information previously assigned to the content. For example, when the driving load is 0.25 or less, the distribution priority of content related to communication information notifications from a communication app or the like and recommendation notifications from scenic spot apps, photography apps, etc. is set high (compared to other content). When the driving load is 0.6 or more but less than 0.8, the distribution priority of content related to important notifications is set high. When the driving load is 0.8 or more, the distribution priority of content related to cautions and warnings is set high. This allows the user to receive content suited to the driving situation. The above-mentioned communication information, recommendation notifications, important notifications, caution information, and warning information are examples of type information.

[0066] Furthermore, the distribution unit 137 may perform distribution based on distribution prohibition information that prohibits the announcement of predetermined content depending on the value of the driving load. For example, when the driving load is 0.8 or more, the distribution of announcements of content related to communication information notifications, recommendation notifications, and important notifications is set to be prohibited, and only content related to cautions and warnings is announced, thereby reducing the impact of announcements on the user when the driving load is high.

[0067] [flowchart] Next, processing by the information processing device 100 configured as described above will be described with reference to the flowchart in Fig. 6. The flowchart in Fig. 6 is mainly executed by the control unit 130. Furthermore, this flowchart can be configured as a program executed by the CPU of the control unit 130 to form an information processing program. Note that the following steps S201 to S206 can also be executed in a different order. Furthermore, some processing of the following steps S201 to S206 may be omitted.

[0068] First, the acquisition unit 131 acquires data relating to the vehicle or road conditions (step S201). For example, the acquisition unit 131 acquires data such as position information, acceleration, angular velocity, and road link ID.

[0069] Next, the situation estimation unit 132 estimates the driving situation based on the data related to the vehicle or road conditions acquired by the acquisition unit 131 (step S202). For example, the situation estimation unit 132 estimates that the situation is one in which a low speed limit is imposed based on the data related to the vehicle or road conditions acquired by the acquisition unit 131.

[0070] Then, the consideration degree calculation unit 133 calculates an index indicating the degree of consideration the driver takes in driving, based on the driving situation estimated by the situation estimation unit 132 (step S203). For example, the consideration degree calculation unit 133 calculates that the index indicating the degree of consideration is 0.3, based on the situation of a low speed limit estimated by the situation estimation unit 132.

[0071] Next, the monotonicity calculation unit 134 calculates an index indicating the degree of monotonicity for driving based on the driving situation estimated by the situation estimation unit 132 (step S204). For example, the monotonicity calculation unit 134 calculates that the index indicating the degree of monotonicity is 0.8 based on the situation of a low speed limit estimated by the situation estimation unit 132. Note that steps S203 and S204 may be processed in the reverse order to that in FIG. 6, or may be processed in parallel.

[0072] Thereafter, the load estimation unit 136 estimates the driving load based on the index indicating the degree of consideration the driver takes in driving, calculated by the consideration degree calculation unit 133, and the index indicating the degree of monotony of driving, calculated by the monotony calculation unit 134 (step S205). For example, the load estimation unit 136 estimates the driving load to be 0.9 based on the index 0.8 indicating the degree of consideration the driver takes in driving, calculated by the consideration degree calculation unit 133, and the index 0.4 indicating the degree of monotony of driving, calculated by the monotony calculation unit 134.

[0073] Then, the distribution unit 137 distributes predetermined content based on the index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133 and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134 (step S206). For example, when the index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133 is high and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134 is low, the distribution unit 137 makes an announcement such as "We will suggest a wider road."

[0074] [effect] In an information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires data relating to vehicle or road conditions, the situation estimation unit 132 estimates the driving situation based on the data relating to the vehicle or road conditions acquired by the acquisition unit 131, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving based on the driving situation estimated by the situation estimation unit 132, the monotony calculation unit 134 calculates an index indicating the degree of monotony in driving based on the driving situation estimated by the situation estimation unit 132, and the load estimation unit 136 estimates the driving load based on the index indicating the degree to which the driver is considerate in driving calculated by the consideration level calculation unit 133 and the index indicating the degree of monotony in driving calculated by the monotony calculation unit 134.

[0075] According to this aspect, the driving load is estimated based on the degree of consideration and the degree of monotony calculated from the driving situation, so that the driving load of the driver can be determined with high accuracy depending on the situation.

[0076] Furthermore, in the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires data on the driver's living area in addition to data on the vehicle or road conditions, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving based on the driving situation estimated by the situation estimation unit 132 and the data on the driver's living area acquired by the acquisition unit 131, and the monotony calculation unit 134 calculates an index indicating the degree of monotony in driving based on the driving situation estimated by the situation estimation unit 132 and the data on the driver's living area acquired by the acquisition unit 131.

[0077] In this manner, the driving load is estimated based on the degree of consideration and monotony calculated from the driving situation and data related to the driver's living area, making it possible to accurately determine the driver's driving load depending on the situation.

[0078] Furthermore, in the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires data on the driver's sensitivity in addition to data on the vehicle or road conditions, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving based on the driving conditions estimated by the situation estimation unit 132 and the data on the driver's sensitivity acquired by the acquisition unit 131, and the monotony calculation unit 134 calculates an index indicating the degree of monotony in driving based on the driving conditions estimated by the situation estimation unit 132 and the data on the driver's sensitivity acquired by the acquisition unit 131.

[0079] In this manner, the driving load is estimated based on the degree of consideration and monotony calculated from data relating to the driving situation and the driver's sensitivity, thereby making it possible to accurately determine the driver's driving load depending on the situation.

[0080] In addition, the information processing device 100 according to one embodiment of the present invention further includes a distribution unit 137 that distributes predetermined content based on an index calculated by the consideration level calculation unit 133 indicating the degree to which the driver is considerate in driving and an index calculated by the monotony level calculation unit 134 indicating the monotony of driving.

[0081] In this manner, by estimating the driving load based on the degree of consideration and monotony calculated from the driving situation, it is possible to accurately determine the driver's driving load according to the situation, and also to deliver content according to the situation.

[0082] [Estimation of driving load by combining static and dynamic loads] Next, the estimation process combining static and dynamic data will be explained using Fig. 7. In the estimation of driving load explained using Fig. 3, no distinction was made between static and dynamic data regarding the vehicle or road conditions, but in the estimation of driving load combining static and dynamic data, static data regarding the vehicle or road conditions and dynamic data regarding the vehicle or road conditions are handled separately.

[0083] First, the acquisition unit 131 acquires static data and dynamic data. For example, the acquisition unit 131 distinguishes between static data related to vehicle or road conditions and dynamic data related to vehicle or road conditions when acquiring the data. Here, the static data related to vehicle or road conditions includes a road link ID. Furthermore, the dynamic data related to vehicle or road conditions includes vehicle information and guidance information.

[0084] Next, the reference unit 135 references an index indicating the degree of consideration the driver gives to driving, an index indicating the degree of monotony of driving, and the driving load based on static data related to the vehicle or road conditions. That is, the reference unit 135 uses static data such as a road link ID as a key to reference necessary information from the workload DB 121, which is a database that stores the degree of consideration, monotony, and driving load.

[0085] Then, the situation estimation unit 132 estimates the dynamic driving situation based on the dynamic data related to the vehicle or road situation acquired by the acquisition unit 131. For example, the situation estimation unit 132 estimates that a sudden steering operation has been performed based on GPS information, acceleration information, and angular velocity information.

[0086] Next, the consideration degree calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving, based on the dynamic driving situation estimated by the situation estimation unit 132. For example, the consideration degree calculation unit 133 estimates that the index indicating the degree to which the driver is considerate in driving is 0.8, based on the situation of abrupt steering operation estimated by the situation estimation unit 132.

[0087] Then, the monotonicity calculation unit 134 calculates an index indicating the degree of monotonicity for driving, based on the dynamic driving situation estimated by the situation estimation unit 132. For example, the monotonicity calculation unit 134 estimates that the index indicating the degree of monotonicity for driving is 0.1, based on the situation of abrupt steering operation estimated by the situation estimation unit 132.

[0088] Thereafter, the load estimation unit 136 estimates the driving load based on the consideration degree and the monotony degree referenced or calculated from the static data and the dynamic data, respectively. For example, the load estimation unit 136 estimates the driving load based on an index indicating the degree of consideration the driver takes in driving and an index indicating the degree of monotony of driving referenced by the reference unit 135, an index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133, and an index indicating the degree of monotony of driving calculated by the monotony calculation unit 134.

[0089] More specifically, the load estimation unit 136 estimates the driving load to be 0.9 based on an index of 0.4 indicating the degree of care the driver takes in driving, calculated by the reference unit 135, an index of 0.2 indicating the degree of monotony in driving, an index of 0.8 calculated by the consideration calculation unit 133 indicating the degree of care the driver takes in driving, and an index of 0.2 calculated by the monotony calculation unit 134 indicating the degree of monotony in driving.

[0090] Furthermore, the load estimation unit 136 may adjust the driving load referred to from static data based on dynamic data. For example, the load estimation unit 136 estimates the driving load based on the driving load calculated by the reference unit 135, the index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133, and the index indicating the degree of monotony of driving calculated by the monotony degree calculation unit 134.

[0091] More specifically, the load estimation unit 136 estimates the driving load to be 0.9 based on the driving load of 0.3 calculated by the reference unit 135, the index of 0.8 calculated by the consideration calculation unit 133 indicating the degree of consideration the driver gives to driving, and the index of 0.4 calculated by the monotony calculation unit 134 indicating the degree of monotony of driving.

[0092] Furthermore, if the situation requires consideration of either the static data or the dynamic data, the load estimation unit 136 may output a higher driving load without making any adjustment. For example, if either the index indicating the degree to which the driver is considerate in driving, referred to by the reference unit 135, or the index indicating the degree to which the driver is considerate in driving, calculated by the consideration degree calculation unit 133, is equal to or greater than a predetermined value, the load estimation unit 136 estimates that the driving load is equal to or greater than a predetermined value.

[0093] More specifically, the load estimation unit 136 estimates that the driving load is a predetermined value of 0.8 or more when either the index 0.4 indicating the degree to which the driver is careful about driving, referenced by the reference unit 135, or the index 0.8 indicating the degree to which the driver is careful about driving, calculated by the consideration calculation unit 133, is a predetermined value of 0.6 or more.

[0094] Furthermore, the load estimation unit 136 may place emphasis on either the static data or the dynamic data and make adjustments using the other. For example, even if the driving load is high based on static data alone, the load estimation unit 136 may estimate that the driving load is low by using information from dynamic data that the vehicle is stopped, such as waiting at a traffic light. Furthermore, even if the driving load is medium to low based on static data alone, the load estimation unit 136 may estimate that the driving load is high by using information from dynamic data that the vehicle has performed a sudden braking operation.

[0095] Then, the distribution unit 137 distributes predetermined content based on the driving load estimated by the load estimation unit 136. For example, based on the driving load of 0.9 estimated by the load estimation unit 136, the distribution unit 137 plays relaxing music that suppresses speech.

[0096] [flowchart] Next, processing by the information processing device 100 configured as described above will be described with reference to the flowchart in Fig. 8. The flowchart in Fig. 8 is mainly executed by the control unit 130. Furthermore, this flowchart can be configured as a program executed by the CPU of the control unit 130 to form an information processing program. Note that the following steps S301 to S307 can also be executed in a different order. Furthermore, some of the following steps S301 to S307 may be omitted.

[0097] First, the acquisition unit 131 acquires static data related to the vehicle or road conditions and dynamic data related to the vehicle or road conditions (step S301). For example, the acquisition unit 131 acquires a road link ID as the static data related to the vehicle or road conditions, and vehicle information and guidance information as the dynamic data related to the vehicle or road conditions.

[0098] Next, the reference unit 135 calculates an index indicating the degree to which the driver pays attention to driving and an index indicating the degree of monotony of driving based on the static data related to the vehicle or road conditions acquired by the acquisition unit 131 (step S302). For example, based on the road link ID acquired by the acquisition unit 131, the reference unit 135 refers to the index indicating the degree to which the driver pays attention to driving, the index indicating the degree of monotony of driving, and driving load information stored in the workload DB 121 and corresponding to the road link ID.

[0099] Then, the situation estimation unit 132 estimates a dynamic driving situation based on the dynamic data related to the vehicle or road situation acquired by the acquisition unit 131 (step S303). For example, the situation estimation unit 132 estimates that the driving situation is one in which a sudden steering operation has been performed based on the information of the GPS sensor, the information of the acceleration, and the information of the angular velocity acquired by the acquisition unit 131.

[0100] Next, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver is considerate in driving, based on the dynamic driving situation estimated by the situation estimation unit 132 (step S304). For example, the consideration level calculation unit 133 estimates that the index indicating the degree to which the driver is considerate in driving is 0.8, based on the driving situation in which an abrupt steering operation was performed, estimated by the situation estimation unit 132.

[0101] Then, the monotonicity calculation unit 134 calculates an index indicating the degree of monotonicity of driving based on the dynamic driving situation estimated by the situation estimation unit 132 (step S305). For example, the monotonicity calculation unit 134 estimates that the index indicating the degree of monotonicity of driving is 0.1 based on the situation of abrupt steering operation estimated by the situation estimation unit 132. Note that steps S304 and S305 may be processed in the reverse order to that in FIG. 8 or may be processed in parallel.

[0102] Thereafter, the load estimation unit 136 estimates the driving load based on an index indicating the degree of care the driver takes in driving, referenced by the reference unit 135, an index indicating the degree of monotony in driving, calculated by the consideration calculation unit 133, and an index indicating the degree of monotony in driving, calculated by the monotony calculation unit 134 (step S306).

[0103] For example, if either the index 0.4 indicating the degree to which the driver is careful about driving, referenced by the reference unit 135, or the index 0.8 indicating the degree to which the driver is careful about driving, calculated by the consideration degree calculation unit 133, is a predetermined value 0.6 or greater, the load estimation unit 136 estimates that the driving load is a predetermined value 0.8 or greater.

[0104] Then, the distribution unit 137 distributes predetermined content based on the driving load estimated by the load estimation unit 136 (step S307). For example, the distribution unit 137 plays relaxing music that suppresses speech based on the driving load of 0.9 estimated by the load estimation unit 136.

[0105] [effect] In the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires static data relating to the vehicle or road conditions, and the reference unit 135 refers to an index indicating the degree to which the driver pays attention to driving, an index indicating the degree to which driving is monotonous, and the driving load, based on the static data relating to the vehicle or road conditions acquired by the acquisition unit 131.

[0106] In this manner, the degree of consideration, monotony, and driving load can be referenced from static data, and the driving load of the driver can be determined with high accuracy depending on the situation.

[0107] In the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires dynamic data regarding the vehicle or road conditions in addition to static data regarding the vehicle or road conditions, and further includes a situation estimation unit 132 that estimates a dynamic driving situation based on the dynamic data regarding the vehicle or road conditions acquired by the acquisition unit 131, a consideration degree calculation unit 133 that calculates an index indicating the degree to which the driver is careful about driving based on the dynamic driving situation estimated by the situation estimation unit 132, a monotony calculation unit 134 that calculates an index indicating the degree of monotony of driving based on the dynamic driving situation estimated by the situation estimation unit 132, and a load estimation unit 136 that estimates a driving load based on the index indicating the degree to which the driver is careful about driving and the index indicating the degree of monotony of driving calculated by the reference unit 135, the index indicating the degree to which the driver is careful about driving calculated by the consideration degree calculation unit 133, and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134.

[0108] In this manner, the driving load is estimated by integrating static data and dynamic data using the consideration level and monotony level referenced from static data and the consideration level and monotony level calculated from dynamic data, thereby making it possible to accurately determine the driver's driving load depending on the situation.

[0109] In the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires a road link ID as static data relating to the vehicle or road conditions. With this configuration, the driver's driving load can be accurately determined depending on the situation by referring to the consideration level, monotony, and driving load from the road link ID.

[0110] In the information processing device 100 according to one embodiment of the present invention, the load estimation unit 136 estimates the driving load based on the driving load calculated by the reference unit 135, an index indicating the degree of consideration the driver takes in driving calculated by the consideration degree calculation unit 133, and an index indicating the degree of monotony of driving calculated by the monotony calculation unit 134. According to this aspect, the driving load referred to from static data is adjusted using the consideration degree and monotony degree calculated from dynamic data, thereby making it possible to accurately determine the driving load of the driver depending on the situation.

[0111] In the information processing device 100 according to one embodiment of the present invention, the load estimation unit 136 estimates that the driving load is equal to or greater than a predetermined value when either the index indicating the degree of consideration the driver takes in driving, referenced by the reference unit 135, or the index indicating the degree of consideration the driver takes in driving, calculated by the consideration level calculation unit 134, is equal to or greater than a predetermined value. According to this aspect, when the consideration level is calculated to be high in either the static data or the dynamic data, the driving load is output to be high without adjustment, thereby making it possible to accurately determine the driver's driving load according to the situation.

[0112] [Content delivery based on changes in driving load] Next, content distribution based on changes in driving load will be explained using Fig. 9. Unlike the driving load estimation explained in Fig. 3 and the combined static and dynamic driving load estimation explained in Fig. 7, content distribution based on changes in driving load does not require the use of an index indicating the degree of attention a driver pays to driving or an index indicating the degree of monotony in driving when estimating the driving load.

[0113] FIG. 9(1) shows a vehicle operated by a driver. FIG. 9(2) shows a section where the driving load is high. FIG. 9(3) shows a section where the driving load is medium. In other words, the driving load estimated by the load estimation unit 136 changes at the boundary between FIG. 9(2) and FIG. 9(3). The distribution unit 137 distributes predetermined content based on the change in driving load accompanying this movement of the vehicle position.

[0114] For example, the distribution unit 137 makes an announcement such as "You have passed a point where driving becomes stressful" based on the change in driving load that occurs when the vehicle moves from the section with high driving load in Figure 9(2) to the section with medium driving load in Figure 9(3).

[0115] Next, content distribution based on changes in driving load will be explained using Fig. 10. Fig. 10(1) shows a vehicle operated by a driver. Fig. 10(2) shows a section where the driving load is medium to low. Fig. 10(3) shows a section where the driving load is high.

[0116] Here, Figures 10(2) and 10(3) show the planned driving route of the vehicle operated by the driver. If the planned driving route includes a point where the driving load changes (for example, the boundary between Figures 10(2) and 10(3)), the distribution unit 137 predicts the arrival time to the point where the driving load changes from the distance to the change point and the speed information of the operated vehicle, and makes an announcement, for example, "Please be careful of the driving conditions." several seconds before the arrival. In addition, an announcement may be made according to the degree of change in the driving load. For example, at a point where the driving load changes from a low (monotonous) state to a high (careful) state, an announcement may be made that calls for more caution than when the change is small, such as "The driving conditions will change significantly from here on. Please be careful when driving."

[0117] At this time, if there is a risk that the vehicle will pass through a point where the driving load changes at a dangerous speed, the distribution unit 137 makes an announcement such as, "This is a dangerous speed. Please be careful." Furthermore, if there are many risky driving events occurring near a point where the driving load changes, the distribution unit 137 calls attention to that point as a near-miss point.

[0118] Next, content distribution based on changes in driving load will be explained using Fig. 11. Fig. 11(1) shows a vehicle operated by a driver. Fig. 11(2) shows a section where the driving load is medium. Fig. 11(3) shows a section where the driving load is high. Fig. 11(4) is a table containing road link IDs that exist on the route that the vehicle in Fig. 11(1) is scheduled to travel, and information based on the road link IDs.

[0119] If the planned route includes a point where the driving load changes (for example, the boundary between Figure 11(2) and Figure 11(3)), the information processing device 100 predicts the arrival time to the point where the driving load changes from information such as the road link ID, driving load, distance, expected driving speed, and expected driving time, as shown in Figure 11(4), and the distribution unit 137 makes an announcement such as, "Please pay attention to the driving conditions," a few seconds before the arrival.

[0120] [flowchart] Next, processing by the information processing device 100 configured as described above will be described with reference to the flowchart in Fig. 12. The flowchart in Fig. 12 is mainly executed by the control unit 130. This flowchart can be configured as a program executed by the CPU of the control unit 130 to form an information processing program. Note that the following steps S401 to S404 can also be executed in a different order. Also, some of the following steps S401 to S404 may be omitted.

[0121] First, the acquisition unit 131 acquires dynamic data related to the vehicle or road conditions (step S401). The acquisition unit 131 also acquires dynamic data related to the vehicle or road conditions and static data related to the vehicle or road conditions. For example, the acquisition unit 131 acquires dynamic data such as position information from a GPS or acceleration from an acceleration sensor, and static data such as a road link ID.

[0122] Next, the situation estimation unit 132 estimates the driving situation based on the dynamic data related to the vehicle or road situation acquired by the acquisition unit 131 (step S402). For example, the situation estimation unit 132 estimates that a sudden steering operation has been performed based on the position information from the GPS, the acceleration from the acceleration sensor, and the angular velocity from the gyro sensor.

[0123] Then, the load estimation unit 136 estimates a driving load according to the driving situation estimated by the situation estimation unit 132 (step S403). For example, the load estimation unit 136 estimates that the driving load is 0.2 based on the driving situation that is estimated by the situation estimation unit 132 as being on a road with the same tendency.

[0124] The distribution unit 137 distributes predetermined content based on the change in driving load estimated by the load estimation unit 136 (step S404). The distribution unit 137 also distributes predetermined content based on a change in driving load accompanying a change in driving position. The distribution unit 137 also distributes predetermined content based on a change in driving load based on the road link ID on the route.

[0125] [effect] In the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires dynamic data relating to vehicle or road conditions, the situation estimation unit 132 estimates the driving situation based on the dynamic data relating to the vehicle or road conditions acquired by the acquisition unit 131, the load estimation unit 136 estimates the driving load based on the driving situation estimated by the situation estimation unit 132, and the distribution unit 137 distributes predetermined content based on changes in the driving load estimated by the load estimation unit 136.

[0126] According to this aspect, by delivering content in accordance with changes in the driving load, it is possible to provide notification taking into consideration the difference between the driver's current driving load and past or future driving loads.

[0127] Furthermore, in the information processing device 100 according to one embodiment of the present invention, the acquisition unit 131 acquires dynamic data relating to the vehicle or road conditions and static data relating to the vehicle or road conditions, and the load estimation unit 136 estimates the driving load based on the driving conditions estimated by the condition estimation unit 132 and the static data relating to the vehicle or road conditions acquired by the acquisition unit 131.

[0128] In this manner, content is delivered in accordance with changes in driving load estimated from dynamic and static data, and notifications can be provided taking into account the difference between the driver's current driving load and past or future driving load after integrating the dynamic and static data.

[0129] Furthermore, in the information processing device 100 according to one embodiment of the present invention, the distribution unit 137 distributes predetermined content based on a change in driving load associated with a change in driving position estimated by the load estimation unit 136.

[0130] In this manner, by delivering content based on changes in driving load due to changes in driving position, notifications can be made taking into account the difference between the driving load at the driver's current driving position and the driving load at past or future driving positions.

[0131] In addition, in the information processing device 100 according to one embodiment of the present invention, the distribution unit 137 distributes predetermined content based on a change in driving load based on a change in the road link ID on the route estimated by the load estimation unit 136.

[0132] In this manner, by delivering content in accordance with changes in driving load based on changes in road link ID, notifications can be made taking into account the difference between the driver's driving load at the current road link ID and the driving load at past or future road link IDs.

[0133] [Hardware configuration] The information processing device 100 according to the embodiment described above is 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 100. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0134] 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.

[0135] The HDD 1400 stores programs executed by the CPU 1100 and data used by the programs. The communication interface 1500 receives data from other devices via a predetermined communication network and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0136] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0137] 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.

[0138] For example, when the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0139] [others] Although one example of an embodiment of the present invention has been described above, the present invention is not limited to the above example. In other words, a person skilled in the art can implement various modifications in accordance with conventionally known knowledge without departing from the gist of the present invention. As long as such modifications still include the information processing device of the present invention, they are of course included in the scope of the present invention. [Explanation of symbols]

[0140] 1. Information Processing Systems 100 Information processing device 110 Communications Department 120 Storage section 121 Workload DB 130 Control Unit 131 Acquisition Department 132 Situation Estimation Department 133 Consideration Level Calculation Unit 134 Monotonicity calculation unit 135 Reference section 136 Load estimation part 137 Distribution Department

Claims

1. an acquisition unit that acquires data related to a vehicle or road condition; a situation estimation unit that estimates a driving situation based on the data related to the vehicle or road situation acquired by the acquisition unit; a consideration level calculation unit that calculates an index indicating a degree of consideration a driver takes in driving based on the driving situation estimated by the situation estimation unit; a monotony calculation unit that calculates an index indicating a degree of monotony in driving, the index having the same numerical range as an index indicating a degree of attention paid to driving by the driver, based on the driving situation estimated by the situation estimation unit; a load estimation unit that estimates a driving load based on the index indicating the degree of consideration the driver takes in driving when the index indicating the degree of consideration the driver takes in driving determined by the consideration degree calculation unit is equal to or greater than the index indicating the degree of monotony of driving determined by the monotony calculation unit, and that estimates the driving load based on the index indicating the degree of monotony of driving when the index indicating the degree of consideration the driver takes in driving is less than the index indicating the degree of monotony of driving; An information processing device comprising:

2. the acquisition unit acquires data on the driver's living area in addition to data on the vehicle or road conditions, the consideration level calculation unit calculates an index indicating a degree of consideration the driver takes in driving based on the driving situation estimated by the situation estimation unit and data related to the driver's living area acquired by the acquisition unit; and The monotony calculation unit calculates an index indicating a degree of monotony for the driving based on the driving situation estimated by the situation estimation unit and data related to the driver's living area acquired by the acquisition unit.

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

3. the acquisition unit acquires data on the driver's sensitivity in addition to the data on the vehicle or road conditions, the consideration level calculation unit calculates an index indicating a degree of consideration the driver takes in driving based on the driving situation estimated by the situation estimation unit and data related to the driver's sensitivity acquired by the acquisition unit; and The monotony calculation unit calculates an index indicating a degree of monotony for the driving based on the driving situation estimated by the situation estimation unit and data related to the driver's sensitivity acquired by the acquisition unit.

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

4. The information processing device described in claim 1, further comprising a distribution unit that distributes specified content based on an index indicating the degree of consideration the driver gives to driving, calculated by the consideration level calculation unit, and an index indicating the monotony of driving, calculated by the monotony calculation unit.

5. 5. The information processing device according to claim 4, wherein the distribution unit distributes the content based on the driving load, type information indicating a type of content, and distribution priority or distribution prohibition information preset for each type of content.

6. An information processing method implemented by an information processing device, an acquisition step of acquiring data relating to vehicle or road conditions; a situation estimation step of estimating a driving situation based on the data related to the vehicle or road situation acquired in the acquisition step; a consideration level calculation step of calculating an index indicating a degree of consideration that the driver takes in driving based on the driving situation estimated by the situation estimation step; a monotony calculation step of calculating an index indicating a degree of monotony in driving, the index being in the same numerical range as the index indicating the degree of attention the driver pays to driving, based on the driving situation estimated by the situation estimation step; a load estimation step of estimating a driving load based on the index indicating the degree of consideration the driver takes in driving when the index indicating the degree of consideration the driver takes in driving determined in the consideration degree calculation step is equal to or greater than the index indicating the degree of monotony of driving determined in the monotony calculation step, and estimating the driving load based on the index indicating the degree of monotony of driving when the index indicating the degree of consideration the driver takes in driving is less than the index indicating the degree of monotony of driving; An information processing method comprising:

7. an acquisition step of acquiring data relating to a vehicle or road condition; a situation estimation step of estimating a driving situation based on the data related to the vehicle or road situation acquired by the acquisition step; a consideration level calculation step of calculating an index indicating a degree of consideration that the driver takes in driving based on the driving situation estimated by the situation estimation step; a monotony calculation step of calculating an index indicating a degree of monotony in driving, the index having the same numerical range as the index indicating the degree of attention the driver pays to driving, based on the driving situation estimated by the situation estimation step; a load estimation step of estimating a driving load based on the index indicating the degree of consideration the driver takes in driving when the index indicating the degree of consideration the driver takes in driving determined in the consideration degree calculation step is equal to or greater than the index indicating the degree of monotony of driving determined in the monotony calculation step, and estimating the driving load based on the index indicating the degree of monotony of driving when the index indicating the degree of consideration the driver takes in driving is less than the index indicating the degree of monotony of driving; An information processing program that causes a computer to execute the above.

Images