JP2024041590A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

[Problem] To provide an information processing device that performs notification by considering the difference between the driver's current driving load and past or future driving load. [Solution] An acquisition unit (131) acquires dynamic data related to the vehicle or road conditions, a situation estimation unit (132) estimates the driving situation based on the dynamic data related to the vehicle or road conditions acquired by the acquisition unit (131), a load estimation unit (136) estimates the driving load based on the driving situation estimated by the situation estimation unit (132), and a delivery unit (137) delivers predetermined content based on the change in the driving load estimated by the load estimation unit (136). [Selected Figure] Figure 2

Description

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

Previously, a notification was sent to the user prompting the user to use a driving assistance device on the condition that the user entered a notification area, and a notification was sent to the user on the condition that the user was not operating an accessory device even while driving in the notification area. There is a technology that provides notification to encourage use (for example, Patent Technical Document 1).

JP2018-092412A

In the conventional technology, there is a problem in that the notification cannot be given in consideration of the difference between the driver's current operating load and the past or future operating load. For example, if there are sections with low and high driving loads on the driving route, it is desirable to notify not only the driving load of the currently traveling section but also the difference between the driving load of the scheduled section. . As described above, the above-mentioned problem can be cited as an example of the problem to be solved by the present invention.

In order to solve the above-mentioned problem and achieve the object, the invention according to claim 1 includes an acquisition unit that acquires dynamic data regarding a vehicle or road condition, and a dynamic data acquisition unit that acquires dynamic data regarding the vehicle or road condition acquired by the acquisition unit. a situation estimating unit that estimates a driving situation based on the driving situation data; a load estimating unit that estimates a driving load based on the driving situation estimated by the situation estimating unit; and a driving load estimated by the load estimating unit. and a distribution unit that distributes predetermined content based on changes in the content.

The invention according to claim 5 is an estimation method executed by a computer, the estimation method comprising: an acquisition step of acquiring dynamic data regarding a vehicle or a road condition; a situation estimation step for estimating the driving situation based on dynamic data regarding the vehicle or road conditions; a load estimation step for estimating the driving load based on the driving situation estimated by the situation estimation step; and a load estimation step. The method is characterized in that it includes a distribution step of distributing predetermined content based on the change in operating load estimated by.

The invention according to claim 6 includes an acquisition step of acquiring dynamic data regarding the vehicle or the road condition, and estimating the driving condition based on the dynamic data regarding the vehicle or the road condition acquired by the acquisition unit. a situation estimation step; a load estimation step of estimating a driving load based on the driving situation estimated by the situation estimating section; and a distribution of distributing predetermined content based on a change in the driving load estimated by the load estimating section. The method is characterized by causing a computer to execute the steps.

FIG. 1 is a diagram showing the configuration of an information processing system according to an embodiment. FIG. 2 is a diagram showing the configuration of the information processing device according to the embodiment. FIG. 3 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 4 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 5 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 6 is a flowchart illustrating an example of processing by the information processing apparatus according to the embodiment. FIG. 7 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 8 is a flowchart illustrating an example of processing by the information processing apparatus according to the embodiment. FIG. 9 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 10 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 11 is a diagram illustrating an overview of processing by the information processing apparatus according to the embodiment. FIG. 12 is a flowchart illustrating an example of processing by the information processing apparatus according to the embodiment. FIG. 13 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device.

Hereinafter, an example of an information processing apparatus, an information processing method, and a form for implementing an information processing program (hereinafter referred to as an "embodiment") will be described in detail with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program are not limited to this embodiment. In addition, in the following embodiments, the same parts are given the same reference numerals, and redundant explanations will be omitted.

[System configuration]
First, the configuration of an information processing system according to an embodiment will be described using FIG. 1. FIG. 1 is a diagram illustrating 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.

As shown in FIG. 1, the information processing system 1 may include an in-vehicle device 10 and an information processing device 100. Further, the in-vehicle device 10 and the information processing device 100 are connected via a network N so that they can communicate by wire or wirelessly. Furthermore, 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.

The in-vehicle device 10 may be a dedicated navigation device built into or externally attached to the vehicle VEx, or a device such as a recording device (drive recorder) installed in the vehicle VEx for crime prevention, countermeasures against aggressive driving, or driving support. It may be.

Furthermore, the in-vehicle device 10 may include a navigation device and a recording device. As an example of this, the in-vehicle device 10 may be a composite device in which a navigation device and a recording device that are independent of each other are communicably connected. Furthermore, as another example, the in-vehicle device 10 may be a single device having a navigation function and a recording function.

In addition, users can install predetermined applications on the portable terminal devices they use on a daily basis (e.g., smartphones, tablet terminals, notebook PCs, desktop PCs, PDAs, etc.) and use them as in-vehicle devices. 10 can also be substituted. For example, a portable terminal device in which a predetermined navigation application or a predetermined recording application is installed can be understood as the in-vehicle device 10 herein. When a portable terminal device is utilized as the in-vehicle device 10, it is installed, for example, on the dashboard of the vehicle VEx during driving.

Further, the in-vehicle device 10 may include various sensors. For example, the in-vehicle device 10 may include various sensors such as a GPS sensor, an acceleration sensor, a gyro sensor, a camera, and an atmospheric pressure sensor.

The information processing device 100 may acquire various data based on the 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. Also, for example, angular velocity is acquired from a gyro sensor. Further, for example, the information processing device 100 acquires data of a moving image captured from inside the vehicle VEx using a camera. Note that the information processing device 100 may acquire sensor information detected not only by a sensor provided in the vehicle-mounted device 10 but also by a sensor provided in the vehicle VEx itself.

The information processing device 100 is a device that performs information processing according to the embodiment. For example, the information processing device 100 inputs data regarding the vehicle or road conditions and outputs the estimated driving load of the driver (in other words, the severity of driving, driving burden, or workload). Furthermore, the information processing device 100 can distribute predetermined content based on the estimated operating load.

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. That is, the information processing device 100 may be a server device.

Further, in the following embodiment, an example will be 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, that is, on the in-vehicle device 10. In this case, the in-vehicle device 10 may be configured to behave like the information processing device 100, for example, using the information processing program according to the embodiment.

[Configuration of information processing device]
Next, the information processing device 100 according to the embodiment will be described using FIG. 2. FIG. 2 is a diagram illustrating a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 2, the information processing device 100 includes a communication section 110, a storage section 120, and a control section 130. Each unit included in the information processing device 100 will be described below.

The communication unit 110 is realized by, for example, a NIC (Network Interface Card). 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.

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

The workload DB 121 includes road link IDs, data regarding the driver's living area, data regarding the driver's sensitivity, an index corresponding to the road link ID that indicates the degree to which the driver cares about driving, and monotony regarding driving. This is a database that stores indicators indicating the degree of work load, operating load, other information necessary for estimating operating load, and other information necessary for content distribution.

The control unit 130 is implemented 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 a memory. As shown in FIG. 2, the control unit 130 includes an acquisition unit 131, a situation estimation unit 132, a consideration level calculation unit 133, a monotony level calculation unit 134, a reference unit 135, a load estimation unit 136, and a distribution unit 137. and has. Each section included in the control section 130 will be described below.

The acquisition unit 131 acquires data related to vehicles or road conditions. For example, the acquisition unit 131 acquires map information, guidance information, and vehicle behavior as data related to vehicles or road conditions. Here, the map information includes information such as the road type, width division, number of lanes, and attributes within the link (level crossing, toll booth, tunnel, etc.) as the road link ID. Further, the guidance information includes information such as the distance and route to the guidance point. Further, the vehicle behavior includes information such as GPS data, acceleration data, and angular velocity data.

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

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

The situation estimating unit 132 estimates the driving situation based on the data regarding the vehicle or road situation acquired by the acquiring unit 131. For example, the situation estimating unit 132 estimates from the road link ID, position information, acceleration, and angular velocity acquired by the acquiring unit 131 that the situation is an intersection, merging, or branching, and a curve operation is being performed. do. Furthermore, the situation estimating unit 132 estimates a dynamic driving situation based on the dynamic data regarding the vehicle or road situation acquired by the acquiring 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.

The consideration level calculation unit 133 calculates an index indicating the degree to which the driver cares about driving based on the driving situation estimated by the situation estimation unit 132. For example, the consideration level calculation unit 133 calculates that the degree of consideration of the driver regarding driving is 0.7 based on the driving situation that the road is narrow as estimated by the situation estimation unit 132.

Further, the consideration level calculation unit 133 determines whether the operator is concerned about driving based on the driving situation estimated by the situation estimation unit 132 and the data regarding the driver's living area acquired by the acquisition unit 131. Calculate an index that indicates the degree. For example, the consideration level calculation unit 133 determines whether the driver is on a narrow road based on the driving situation estimated by the situation estimation unit 132 and the data that the road is within the driver's living area acquired by the acquisition unit 131. It is calculated that the index indicating the degree of consideration for driving is 0.6.

Further, the consideration level calculation unit 133 calculates the degree to which the operator cares about driving based on the driving situation estimated by the situation estimation unit 132 and the data regarding the driver's sensitivity acquired by the acquisition unit 131. Calculate an index that shows the For example, the consideration level calculation unit 133 calculates whether the driver is sensitive based on the driving situation that the road is narrow, which is estimated by the situation estimation unit 132, and the data that the driver is sensitive, which is acquired by the acquisition unit 131. It is calculated that the index indicating the degree of care given to driving is 0.8.

The monotony calculation unit 134 calculates an index indicating the degree of monotony of driving based on the driving situation estimated by the situation estimation unit 132. For example, the monotony calculation unit 134 calculates that the index indicating the degree of monotony with respect to driving is 0.7, based on the driving situation of low speed restriction estimated by the situation estimation unit 132.

Furthermore, the monotony calculation unit 134 calculates an index indicating the degree of monotony with respect to driving based on the driving situation estimated by the situation estimation unit 132 and the data regarding the driver's living area acquired by the acquisition unit 131. calculate. For example, the monotony calculation unit 134 calculates the monotony of driving based on the driving situation of a low speed limit estimated by the situation estimation unit 132 and the data that the driving situation is within the living area of the driver acquired by the acquisition unit 131. The index indicating the degree of is calculated to be 0.8.

Further, the monotony calculation unit 134 calculates an index indicating the degree of monotony with respect to driving based on the driving situation estimated by the situation estimation unit 132 and the data regarding the driver's sensitivity acquired by the acquisition unit 131. do. For example, the monotony calculation unit 134 calculates the monotony of driving based on the driving situation of a low speed limit estimated by the situation estimation unit 132 and the data indicating that the driver is sensitive, which is acquired by the acquisition unit 131. The index indicating the degree of darkness is calculated to be 0.6.

The reference unit 135 generates an index indicating the degree to which the driver cares about driving and an index indicating the degree of monotony regarding driving, based on the static data regarding the vehicle or road conditions acquired by the acquisition unit 131. and refer to. Further, the reference unit 135 provides an index indicating the degree to which the driver cares about driving and an index indicating the degree of monotony regarding driving based on the static data regarding the vehicle or road conditions acquired by the acquisition unit 131. The indicated index and operating load are referred to. For example, based on the road link ID acquired by the acquisition unit 131, the reference unit 135 generates an index indicating the degree to which the driver cares about driving, which is stored in the workload DB 121 and corresponds to the road link ID. , an index indicating the degree of monotony of driving, and information on driving load.

The load estimating unit 136 calculates an index indicating the degree to which the driver cares about driving, which is calculated by the consideration level calculating unit 133, and an index indicating the degree of monotony of driving, which is calculated by the monotony level calculating unit 134. The operating load is estimated based on the For example, the load estimation unit 136 uses an index of 0.6, which indicates the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, and the monotony of driving, which is calculated by the monotony calculation unit 134. It is estimated that the operating load is 0.8 based on the index 0.4 indicating the degree of.

In addition, the load estimating unit 136 uses the index indicating the degree to which the operator cares about driving, which is calculated by the reference unit 135, and the index indicating the degree of monotony with respect to driving, and the calculation unit 133, which calculates the The driving load is estimated based on the index indicating the degree to which the driver cares about driving, and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134. For example, the load estimating unit 136 calculates the index 0.4, which indicates the degree to which the driver cares about driving, and the index 0.2, which indicates the degree of monotony of the driver, calculated by the reference unit 135. An index of 0.8, which indicates the degree to which the driver cares about driving, calculated by the monotony calculation unit 133, and an index of 0.2, which indicates the degree of monotony of driving, which was calculated by the monotony calculation unit 134. Based on this, the operating load is estimated to be 0.9.

The load estimating unit 136 also uses the driving load calculated by the reference unit 135, an index indicating the degree to which the operator cares about driving, which is calculated by the consideration level calculation unit 133, and the monotony calculation unit 134. The operating load is estimated based on the index indicating the degree of monotony with respect to driving calculated by. For example, the load estimating unit 136 calculates the operating load of 0.3 calculated by the reference unit 135 and the index of 0.8 indicating the degree to which the driver cares about driving, which is calculated by the consideration level calculating unit 133. , and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134, which is 0.4, the operating load is estimated to be 0.9.

In addition, the load estimating unit 136 uses the index indicating the degree to which the operator cares about driving, referenced by the reference unit 135, and the indicator that the operator cares about driving, which is calculated by the consideration level calculation unit 133. If either one of the indicators indicating the degree to which the system is used is greater than or equal to a predetermined value, it is estimated that the operating load is greater than or equal to the predetermined value. For example, the load estimating unit 136 uses an index of 0.4, which indicates the degree to which the driver cares about driving, which is referenced by the reference unit 135, and an index of 0.4, which indicates the degree to which the driver cares about driving, and which is calculated by the degree of care calculation unit 133. When either one of the indicator 0.8 indicating the degree of consideration for the vehicle is equal to or greater than the predetermined value of 0.6, it is estimated that the operating load is equal to or greater than the predetermined value of 0.8.

Further, the load estimating unit 136 estimates the operating load based on the driving situation estimated by the situation estimating unit 132. For example, the load estimating unit 136 estimates that the driving load is 0.2 based on the driving situation that the roads have the same tendency as estimated by the situation estimating unit 132.

Further, the load estimating unit 136 estimates the driving load based on the driving situation estimated by the situation estimating unit 132 and the static data regarding the vehicle or road condition acquired by the acquiring unit 131. For example, the load estimating unit 136 determines that the driving load is 0.7 based on the driving situation that the road has the same tendency as estimated by the situation estimating unit 132 and the information that the road is a railroad crossing acquired by the acquiring unit 131. We estimate that.

The distribution unit 137 provides an index indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, and an index indicating the degree of monotony regarding driving, calculated by the monotony level calculation unit 134. Based on the above, predetermined content is distributed. For example, the distribution unit 137 indicates that the index indicating the degree to which the driver cares about driving calculated by the consideration level calculation unit 133 is high, and the index indicating the degree of monotony with respect to driving calculated by the monotony degree calculation unit 134 is high. If the index is low, an announcement such as ``We will propose a wider path.'' is made.

Furthermore, for example, the distribution unit 137 may receive information indicating that the index indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, is low, and the monotony of driving calculated by the monotony degree calculation unit 134 is low. If the index indicating the degree is high, an announcement such as ``You can earn points by playing quiz games about the region.'' is made.

Further, for example, the distribution unit 137 may receive information indicating that the index indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, is high, and the degree of monotony of driving calculated by the monotony calculation unit 134 is high. If the index indicating the degree of parking is also high, an announcement such as ``There is a rest point where it is easy to park, would you like to do so?'' is made.

The distribution unit 137 distributes predetermined content based on the change in operating load estimated by the load estimation unit 136. For example, the distribution unit 137 distributes predetermined content based on the change in driving load associated with the change in the driving position estimated by the load estimation unit 136. Further, for example, the distribution unit 137 makes an announcement such as “You will pass a point where the driving load is high” at a point where the driving load changes due to a change in the driving position estimated by the load estimating unit 136.

Further, for example, when the vehicle being operated passes through a point where the driving load changes due to a change in the driving position estimated by the load estimating unit 136, the distribution unit 137 sends the message “passing a point where the driving load is high”. An announcement such as ``I have done this.'' is made. Further, for example, if there is a point on the scheduled travel route of the vehicle being operated where the driving load increases due to a change in the driving position estimated by the load estimating unit 136, the distribution unit 137 transmits the information about the change in the driving load. A few seconds before reaching the point, an announcement is made such as ``Please be careful of driving conditions.''

Further, the distribution unit 137 distributes the predetermined content based on a change in the driving load 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 is burdensome” at a point where the driving load changes based on a change in the road link ID on the route estimated by the load estimating unit 136. . Further, for example, if there is a point on the scheduled travel route of the vehicle to be operated where the driving load increases due to a change in the road link ID estimated by the load estimating unit 136, the distribution unit 137 may transmit a change in the driving load. A few seconds before the vehicle reaches the desired point, an announcement such as "Please be careful of driving conditions" is made.

Note that the content distribution carried out by the distribution unit 137 includes the distribution of announcements, navigation, etc. made from the information processing device 100 or the in-vehicle device 10 to the driver, as well as the distribution of the content from the information processing device 100 to the in-vehicle device 10. Includes deliveries that take place.

[Estimation of operating load]
Next, an overview of the processing performed by the information processing apparatus 100 will be described using FIG. 3. First, the acquisition unit 131 acquires information such as map information, guidance information, and vehicle behavior.

Next, the situation estimation unit 132 estimates the driving situation from information such as the map information, guidance information, and vehicle behavior acquired by the acquisition unit 131. In other words, the situation estimating unit 132 estimates whether the external situation is such that the driver is concerned about driving or feels monotonous. For example, the situation estimation unit 132 estimates that it is an intersection, merging, or branching point from the map information. Further, for example, the situation estimation unit 132 estimates from the guidance information that a road with the same tendency will continue. Further, for example, the situation estimation unit 132 estimates that a sudden start operation has been performed from the vehicle behavior.

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, intersections/merging/branching, narrow roads, railroad crossings, toll plazas, temporary stops, etc. , tunnels, low speed limits, expressways, narrow roads to wide roads, wide roads to narrow roads, curved roads, roads with the same tendency, curves, sudden steering, sudden starts, sudden braking, shocks, stops, slow driving, etc. is included. Note that the driving situations that can be estimated by the situation estimation unit 132 are not limited to those described above.

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

The consideration level calculation unit 133 calculates an index indicating the degree to which the driver cares about driving, for example, based on the driving situation estimated by the situation estimation unit 132, by calculating Equation 1. Pbusy indicates the probability of feeling cared for. Xn indicates the objective variable. αx indicates a partial regression coefficient for Xn. The index indicating the degree to which the driver cares about driving, which is calculated by the care level calculation unit 133, takes a value from 0 to 1, and the larger this value is, the more care the driver cares about driving. Show that.

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

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

Then, the load estimating unit 136 indicates the index indicating the degree to which the operator cares about driving determined by the attentiveness level calculating unit 133 and the degree of monotony of driving determined by the monotony level calculating unit 134. The operating load is estimated based on the index. Here, the operating load calculated by the load estimation unit 136 takes a value between 0 and 1, and the closer the operating load is to 0, the more monotonous the driving is, and the closer the operating load is to 1, the more careful the operator is in driving. shows.

The load estimation process by the load estimator 136 will be explained using FIG. 4. The load estimating unit 136 first calculates that the index indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, is equal to the degree of monotony of driving calculated by the monotony calculation unit 134. It is determined whether or not the index is greater than or equal to an index indicating (step S101).

If the degree of consideration is equal to or higher than the degree of monotony (step S101 "YES"), the load estimating unit 136 calculates the level of consideration for driving based on the degree of consideration of the operator regarding driving, which is calculated by the degree of consideration calculation unit 133. Calculate the load (step S102). For example, the load estimating unit 136 estimates the driving load by adding the degree to which the driver cares about driving, calculated by the consideration level calculating unit 133, to a predetermined value. Specifically, the load estimating unit 136 divides the index 0.8, which indicates the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, by 2, and divides the value 0.4 into a predetermined value. By adding to the value 0.5, the operating load is estimated to be 0.9.

Further, if the degree of consideration is not equal to or higher than the degree of monotony (step S101 "NO"), the load estimating section 136 calculates the degree of monotony of driving based on the degree of monotony of driving calculated by the degree of monotony calculating section 134 from the predetermined value. is calculated (step S103). For example, the load estimating unit 136 estimates the operating load by subtracting the degree of monotony with respect to driving from a predetermined value. Specifically, the load estimating unit 136 subtracts 0.3, which is obtained by dividing an index 0.6 indicating the degree of monotony of driving by 2, from the predetermined value 0.5, so that the operating load becomes 0. It is estimated that it is 2.

Returning to FIG. 3 again. The distribution unit 137 then sends an index indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, and an index indicating the monotony of driving, calculated by the monotony degree calculation unit 134. Based on this, predetermined content is distributed.

Distribution of predetermined content based on the level of consideration and monotony by the distribution unit 137 will be explained using FIG. 5. For example, in a situation where the index indicating the degree to which the driver is attentive to driving is high, the distribution unit 137 may make an announcement such as "We will suggest a wider road" from the navigation application or "Parking" from the rest point application. There is an easy break point.Would you like to take a break?''

Further, for example, when the index indicating the degree of monotony with driving is high, the distribution unit 137 makes an announcement such as "You can get points by playing a quiz game about the region" using the location information game application. For example, if the index indicating the degree to which the driver cares about driving is not high, and the index indicating the degree of monotony with driving is not high, the distribution unit 137 may transmit " We will read out a message from Mr. ○○.'' and the scenic point app will make announcements such as ``The view from the Kokusai Bridge on a sunny day is spectacular.Would you like to pass?''

Further, the distribution unit 137 may make the announcement based on the value of the operating load and the distribution priority set for each content type information given to the content in advance. For example, if the operating load is 0.25 or less, content related to communication information notifications from communication apps, etc., and recommendation notifications from scenic view point apps, photography apps, etc. will have a higher distribution priority (compared to other content). If the operating load is 0.6 or more and less than 0.8, the distribution priority of content related to important notifications is set high, and if the operating load is 0.8 or higher, the distribution priority of content related to cautions and warnings is set high. Delivery priority is set high. This allows the user to receive content that is appropriate for the driving situation. Note that the above-mentioned communication information, recommendation notification, important notification, caution information, and warning information are examples of type information.

Furthermore, the distribution unit 137 may perform distribution based on distribution prohibition information that prohibits announcement of predetermined content depending on the value of the operating load. For example, when the driving load is 0.8 or higher, content announcements related to communication information notifications, recommendation notifications, and important notifications are prohibited from being distributed, and only content related to cautions and warnings is announced, thereby preventing driving. It is possible to suppress the impact on users caused by announcements under high load conditions.

[flowchart]
Next, processing by the information processing apparatus 100 having the above-described configuration will be described with reference to the flowchart of FIG. 6. The flowchart in FIG. 6 is mainly executed by the control unit 130. Further, by configuring this flowchart as a program executed by the CPU included in the control unit 130, it can be made into an information processing program. Note that steps S201 to S206 below can also be executed in a different order. Also, some of the steps S201 to S206 described below may be omitted.

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

Next, the situation estimating unit 132 estimates the driving situation based on the data regarding the vehicle or road situation acquired by the acquiring unit 131 (step S202). For example, the situation estimating unit 132 estimates that the situation is a low speed limit based on the data regarding the vehicle or road situation acquired by the acquiring unit 131.

Then, the consideration level calculation unit 133 calculates an index indicating the degree of consideration of the driver regarding 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 low speed limit situation estimated by the situation estimation unit 132.

Subsequently, the monotony calculation unit 134 calculates an index indicating the degree of monotony of driving based on the driving situation estimated by the situation estimation unit 132 (step S204). For example, the monotony calculation unit 134 calculates that the index indicating the degree of monotony is 0.8 based on the low speed restriction situation estimated by the situation estimation unit 132. Note that step S203 and step S204 may be processed in the reverse order of FIG. 6, or may be processed in parallel.

Thereafter, the load estimating unit 136 calculates the index indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, and the degree of monotony with respect to driving, calculated by the monotony calculation unit 134. The operating load is estimated based on the indicated index (step S205). For example, the load estimation unit 136 uses an index of 0.8, which indicates the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, and the monotony of driving, which is calculated by the monotony calculation unit 134. It is estimated that the operating load is 0.9 based on the index 0.4 indicating the degree of.

Then, the distribution unit 137 indicates the index indicating the degree to which the driver cares about driving, which is calculated by the consideration level calculation unit 133, and the degree of monotony with respect to driving, which is calculated by the monotony degree calculation unit 134. Based on the index, predetermined content is distributed (step S206). For example, the distribution unit 137 indicates that the index indicating the degree to which the driver cares about driving calculated by the consideration level calculation unit 133 is high, and the index indicating the degree of monotony with respect to driving calculated by the monotony degree calculation unit 134 is high. If the index is low, an announcement such as ``We will propose a wider path.'' is made.

[effect]
In the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires data related to vehicles or road conditions, and the situation estimation unit 132 is configured based on the data related to vehicles or road conditions acquired by the acquisition unit 131. Based on the driving situation estimated by the situation estimation unit 132, the attentiveness level calculation unit 133 calculates an index indicating the degree to which the driver is attentive to driving, and calculates the degree of monotony. The load estimating unit 136 calculates an index indicating the degree of monotony of driving based on the driving situation estimated by the situation estimating unit 132, and the load estimating unit 136 calculates an index indicating the degree of monotony of driving based on the driving situation estimated by the situation estimating unit 132. The driving load is estimated based on an index indicating the degree of care given to the driver and an index indicating the degree of monotony of the driving calculated by the monotony calculation unit 134.

With this aspect, by estimating the driving load based on the degree of attentiveness and monotony calculated from the driving situation, the driving load of the operator can be determined with high accuracy according to the situation.

In the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires data regarding the driver's living area in addition to data regarding the vehicle or road conditions, and the consideration level calculation unit 133 acquires data regarding the driver's living area. Based on the driving situation estimated by the estimation unit 132 and the data regarding the driver's living area acquired by the acquisition unit 131, an index indicating the degree to which the operator cares about driving is calculated, and the degree of monotony is calculated. The calculation unit 134 calculates an index indicating the degree of monotony with respect to driving based on the driving situation estimated by the situation estimation unit 132 and the data regarding the driver's living area acquired by the acquisition unit 131.

With this aspect, by estimating the driving load based on the level of consideration and monotony calculated from the driving situation and data regarding the driver's living area, the driving load of the operator can be determined with high accuracy according to the situation. I can do it.

Further, in the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires data regarding the driver's sensitivity in addition to data regarding the vehicle or road conditions, and the consideration level calculation unit 133 performs situation estimation. Based on the driving situation estimated by the unit 132 and the data regarding the driver's sensitivity acquired by the acquisition unit 131, an index indicating the degree to which the operator cares about driving is calculated, and the monotony calculation unit 134 calculates an index indicating the degree of monotony with respect to driving based on the driving situation estimated by the situation estimating unit 132 and the data regarding the driver's sensitivity acquired by the acquiring unit 131.

According to this aspect, by estimating the driving load based on the degree of consideration and monotony calculated from the driving situation and data regarding the sensitivity of the operator, it is possible to determine the driving load of the operator with high accuracy according to the situation. can.

Further, the information processing device 100 according to an embodiment of the present invention uses an index indicating the degree to which the driver cares about driving, which is calculated by the consideration level calculation unit 133, and an index calculated by the monotony degree calculation unit 134. The vehicle further includes a distribution unit 137 that distributes predetermined content based on an index indicating monotony of driving.

With this aspect, by estimating the driving load based on the degree of attentiveness and monotony calculated from the driving situation, the driving load of the operator can be determined with high accuracy according to the situation, and content can be created according to the situation. distribution.

[Estimation of operating load combining static and dynamic]
Next, a combination of static and dynamic estimation processing will be described using FIG. 7. In the estimation of driving load explained using Figure 3, static and dynamic data regarding vehicles or road conditions were not distinguished. However, in estimating driving load that combines static and dynamic data, Distinguish between static data regarding road conditions and dynamic data regarding vehicles or road conditions.

First, the acquisition unit 131 acquires static data and dynamic data. For example, the acquisition unit 131 distinguishes and acquires static data related to vehicles or road conditions and dynamic data related to vehicles or road conditions. Here, the static data regarding vehicles or road conditions includes a road link ID. Further, the dynamic data regarding vehicles or road conditions includes vehicle information and guidance information.

Next, the reference unit 135 generates an index indicating the degree to which the driver cares about driving, an index indicating the degree of monotony of driving, and a driving load based on static data regarding the vehicle or road conditions. and refer to. That is, the reference unit 135 refers to necessary information from the workload DB 121, which is a database that stores the degree of consideration, monotony, and driving load, using static data such as the road link ID as a key.

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

Subsequently, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver cares about driving based on the dynamic driving situation estimated by the situation estimation unit 132. For example, the consideration level calculation unit 133 estimates that the index indicating the degree to which the driver cares about driving is 0.8, based on the situation of sudden steering operation estimated by the situation estimation unit 132. .

Then, the monotony calculation unit 134 calculates an index indicating the degree of monotony of driving based on the dynamic driving situation estimated by the situation estimation unit 132. For example, the monotony calculation unit 134 estimates that the index indicating the degree of monotony with respect to driving is 0.1, based on the situation of sudden steering operation estimated by the situation estimation unit 132.

Thereafter, the load estimation unit 136 estimates the operating load based on the degree of consideration and the degree of monotony, which are referenced or calculated from the static data and the dynamic data, respectively. For example, the load estimating unit 136 uses the index indicating the degree to which the operator cares about driving, which is referred to by the reference unit 135, and the index indicating the degree of monotony regarding driving, and calculates the amount using the attentiveness level calculating unit 133. The driving load is estimated based on the index indicating the degree to which the driver cares about driving, and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134.

More specifically, the load estimating unit 136 uses the index 0.4, which indicates the degree to which the operator cares about driving, and the index 0.4, which indicates the degree of monotony with respect to driving, calculated by the reference unit 135. 2, an index of 0.8 indicating the degree to which the driver cares about driving, calculated by the consideration level calculation unit 133, and an index of 0.8, which indicates the degree of monotony with respect to driving, calculated by the monotony level calculation unit 134. Based on the index 0.2, the operating load is estimated to be 0.9.

Further, the load estimation unit 136 may adjust the operating load referenced from static data based on dynamic data. For example, the load estimating unit 136 uses the driving load calculated by the reference unit 135, an index indicating the degree to which the operator cares about driving, which is calculated by the consideration level calculation unit 133, and the monotony calculation unit 134. The operating load is estimated based on the index indicating the degree of monotony with respect to driving calculated by.

More specifically, the load estimating unit 136 uses the operating load 0.3 calculated by the reference unit 135 and an index indicating the degree to which the operator is attentive to driving, which is calculated by the attentiveness level calculating unit 133. The operating load is estimated to be 0.9 based on 0.8 and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134, which is 0.4.

Further, if the load estimating unit 136 is in a situation where consideration is given to either static data or dynamic data, the load estimating unit 136 may output a higher operating load without making any adjustment. For example, the load estimating unit 136 uses the index indicating the degree to which the driver cares about driving, which is referenced by the reference unit 135, and the indicator, which indicates the degree to which the driver cares about driving, which is calculated by the care level calculation unit 133. If either one of the indicators indicating the degree to which the system is used is greater than or equal to a predetermined value, it is estimated that the operating load is greater than or equal to the predetermined value.

More specifically, the load estimating unit 136 uses the index 0.4, which indicates the degree to which the driver cares about driving, which is referred to by the reference unit 135, and the driver's control value, which is calculated by the consideration level calculation unit 133. When either one of the index 0.8 indicating the degree to which a person cares about driving is a predetermined value of 0.6 or more, it is estimated that the driving load is a predetermined value of 0.8 or more.

Further, the load estimation unit 136 may place emphasis on either static data or dynamic data and perform adjustment using the other. For example, even if the operating load is high based on static data alone, the load estimating unit 136 estimates that the operating load is low using information from dynamic data that the operating load is in a stopped state such as waiting at a traffic light. Good too. Further, even if the operating load is medium or low based on static data alone, it may be estimated that the operating load is high using information from the dynamic data that a sudden braking operation was performed.

The distribution unit 137 then distributes predetermined content based on the operating load estimated by the load estimation unit 136. For example, the distribution unit 137 plays relaxing music to suppress speech based on the operating load of 0.9 estimated by the load estimation unit 136.

[flowchart]
Next, processing by the information processing apparatus 100 having the above-described configuration will be described with reference to the flowchart of FIG. 8. The flowchart in FIG. 8 is mainly executed by the control unit 130. Further, by configuring this flowchart as a program executed by the CPU included in the control unit 130, it can be made into an information processing program. Note that steps S301 to S307 below can also be executed in a different order. Furthermore, some processes may be omitted from steps S301 to S307 below.

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

Next, the reference unit 135 generates, based on the static data regarding the vehicle or road conditions acquired by the acquisition unit 131, an index indicating the degree to which the driver cares about driving and the degree of monotony regarding driving. (Step S302). For example, based on the road link ID acquired by the acquisition unit 131, the reference unit 135 generates an index indicating the degree to which the driver cares about driving, which is stored in the workload DB 121 and corresponds to the road link ID. , an index indicating the degree of monotony of driving, and information on driving load.

Then, the situation estimation unit 132 estimates a dynamic driving situation based on the dynamic data regarding the vehicle or road situation acquired by the acquisition unit 131 (step S303). For example, the situation estimating unit 132 estimates that the driving situation is a sudden steering operation based on the GPS sensor information, acceleration information, and angular velocity information acquired by the acquisition unit 131.

Next, the consideration level calculation unit 133 calculates an index indicating the degree to which the driver cares about driving based on the dynamic driving situation estimated by the situation estimation unit 132 (step S304). For example, the consideration level calculation unit 133 calculates that the index indicating the degree to which the driver cares about driving is 0.8, based on the driving situation in which a sudden steering wheel operation is performed, which is estimated by the situation estimation unit 132. We estimate that there is.

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

Thereafter, the load estimating unit 136 uses the index indicating the degree to which the operator cares about driving, which has been referenced by the reference unit 135, and the index indicating the degree of monotony regarding driving, and calculates the amount using the attentiveness level calculating unit 133. The driving load is estimated based on the index indicating the degree to which the operator cares about driving, and the index indicating the degree of monotony of driving calculated by the monotony calculation unit 134 (step S306). ).

For example, the load estimating unit 136 uses an index of 0.4, which indicates the degree to which the driver cares about driving, which is referenced by the reference unit 135, and an index of 0.4, which indicates the degree to which the driver cares about driving, and which is calculated by the degree of care calculation unit 133. When either one of the indicator 0.8 indicating the degree of consideration for the vehicle is equal to or greater than the predetermined value of 0.6, it is estimated that the operating load is equal to or greater than the predetermined value of 0.8.

The distribution unit 137 then distributes predetermined content based on the operating load estimated by the load estimation unit 136 (step S307). For example, the distribution unit 137 plays relaxing music to suppress speech based on the operating load of 0.9 estimated by the load estimation unit 136.

[effect]
In the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires static data regarding the vehicle or road conditions, and the reference unit 135 acquires static data regarding the vehicle or road conditions acquired by the acquisition unit 131. An index indicating the degree to which the driver cares about driving, an index indicating the degree of monotony of driving, and a driving load are referenced based on the above data.

With this aspect, the driver's driving load can be determined with high accuracy according to the situation by referring to the level of consideration, monotony, and driving load from static data.

In the information processing device 100 according to an 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. A situation estimating unit 132 estimates a dynamic driving situation based on dynamic data related to the vehicle or road situation, and a situation estimating unit 132 estimates a dynamic driving situation based on dynamic data regarding the vehicle or road situation. Based on the dynamic driving situation estimated by the consideration level calculation unit 133 that calculates an index indicating the degree of consideration for driving, and the situation estimation unit 132, an index indicating the degree of monotony regarding driving is calculated. The monotony degree calculation unit 134 calculates the index indicating the degree of care that the driver gives to driving, which is calculated by the reference unit 135; a load estimating unit that estimates the driving load based on the index indicating the degree to which the operator cares about driving, and the index indicating the degree of monotony with respect to driving calculated by the monotony calculating unit 134; 136.

With this aspect, the operating load is estimated by integrating static data and dynamic data using the degree of consideration and monotony referred to from static data and the degree of consideration and monotony calculated from dynamic data. This allows the driver's driving load to be determined with high accuracy depending on the situation.

In the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires a road link ID as static data related to a vehicle or road condition. With this aspect, by referring to the degree of consideration, monotony, and driving load from the road link ID, the driving load of the driver can be determined with high accuracy according to the situation.

In the information processing device 100 according to an embodiment of the present invention, the load estimating unit 136 uses the driving load calculated by the reference unit 135 and the driver's care level when driving, which is calculated by the consideration level calculation unit 133. The driving load is estimated based on the index indicating the degree of monotony of the driving and the index indicating the degree of monotony of the driving calculated by the monotony calculation unit 134. With this aspect, by adjusting the operating load referenced from static data using the degree of consideration and monotony calculated from dynamic data, the operating load of the operator can be determined with high accuracy according to the situation. I can do it.

In the information processing device 100 according to an embodiment of the present invention, the load estimating unit 136 uses the index indicating the degree of care of the operator regarding driving, which is referred to by the reference unit 135, and the consideration level calculation unit 134. If either one of the calculated indicators indicating the degree of consideration of the driver in driving is greater than or equal to a predetermined value, it is estimated that the driving load is greater than or equal to the predetermined value. With this aspect, if the degree of consideration is calculated to be high in either static data or dynamic data, the driving load will be outputted so that the operator's driving load will be higher without making any adjustments, thereby adjusting the driving load of the operator to the situation. Depending on the situation, it can be determined with high accuracy.

[Content distribution based on changes in operating load]
Next, content distribution based on changes in operating load will be explained using FIG. 9. Unlike the operating load estimation explained in Fig. 3 and the combined static/dynamic operating load estimation explained in Fig. 7, in content distribution based on changes in operating load, when estimating operating load, It is not necessary to use an index that indicates the degree to which the driver cares about driving or an index that indicates the degree of monotony with respect to driving.

FIG. 9(1) shows a vehicle operated by an operator. FIG. 9(2) is a section where the operating load is high. FIG. 9(3) is a section where the operating load is moderate. That is, at the boundary between FIG. 9(2) and FIG. 9(3), the operating load estimated by the load estimation unit 136 changes. The distribution unit 137 distributes predetermined content based on the change in operating load due to the movement of the vehicle position.

For example, the distribution unit 137 transmits information based on the change in the operating load caused by the vehicle moving from the section with a high operating load shown in FIG. 9(2) to the section with a medium operating load shown in FIG. 9(3). , makes an announcement such as ``You have passed a point where driving becomes taxing.''

Next, content distribution based on changes in operating load will be explained using FIG. 10. FIG. 10(1) shows a vehicle operated by an operator. FIG. 10(2) is a section where the operating load is medium to low. FIG. 10(3) is a section where the operating load is high.

Here, FIG. 10(2) and FIG. 10(3) are the scheduled travel routes of the vehicle operated by the operator. If the planned travel route includes a point where the driving load changes (for example, the boundary between FIG. 10 (2) and FIG. 10 (3)), the distribution unit 137 will transmit the distance to the changing point and the driving load. The system predicts the time it will take to reach the point where the driving load changes based on vehicle speed information, and makes an announcement, such as ``Please be careful of driving conditions,'' several seconds before reaching the point. In addition, announcements may be made in accordance with the degree of change in operating load. For example, at a point where the driving load changes from a low (monotonous) state to a high driving load (careful) state, a warning will be displayed saying, ``The driving situation will change significantly from now on, so please be careful when driving.'' An announcement may be made that calls for more attention than when the change is small.

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 "Dangerous speed. Please be careful." Further, if risky driving events occur frequently near a point where the driving load changes, the distribution unit 137 issues a warning by setting that point as a near-miss point.

Next, content distribution based on changes in operating load will be explained using FIG. 11. FIG. 11(1) shows a vehicle operated by an operator. FIG. 11(2) is a section where the operating load is moderate. FIG. 11(3) is a section where the operating load is high. FIG. 11(4) is a table including road link IDs existing on the route on which the vehicle of FIG. 11(1) is scheduled to travel, and information based on the road link IDs.

If the planned travel route includes a point where the operating load changes (for example, the boundary between FIG. 11(2) and FIG. 11(3)), the information processing device 100 may, for example, From information such as the road link ID, driving load, distance, expected driving speed, and expected driving time as shown in the figure, the distribution unit 137 predicts the arrival time to the point where the driving load changes, and several seconds before reaching the point, the distribution unit 137 Please be careful of driving conditions.'' An announcement is made.

[flowchart]
Next, processing by the information processing apparatus 100 having the above-described configuration will be described with reference to the flowchart of FIG. 12. The flowchart in FIG. 12 is mainly executed by the control unit 130. Further, by configuring this flowchart as a program executed by the CPU included in the control unit 130, it can be made into an information processing program. Note that steps S401 to S404 below can also be executed in a different order. Furthermore, some processes may be omitted from steps S401 to S404 below.

First, the acquisition unit 131 acquires dynamic data regarding vehicles or road conditions (step S401). The acquisition unit 131 also acquires dynamic data regarding vehicles or road conditions, and static data regarding vehicles or road conditions. For example, the acquisition unit 131 acquires dynamic data such as position information by GPS and acceleration by an acceleration sensor, and static data such as a road link ID.

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

Then, the load estimating unit 136 estimates the operating load according to the driving situation estimated by the situation estimating unit 132 (step S403). For example, the load estimating unit 136 estimates that the driving load is 0.2 based on the driving situation that the roads have the same tendency as estimated by the situation estimating unit 132.

The distribution unit 137 distributes predetermined content based on the change in operating load estimated by the load estimation unit 136 (step S404). Further, the distribution unit 137 distributes predetermined content based on the conversion of the driving load due to the change in the driving position. Further, the distribution unit 137 distributes predetermined content based on changes in driving load based on road link IDs on the route.

[effect]
In the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires dynamic data regarding the vehicle or road conditions, and the situation estimation unit 132 acquires dynamic data regarding the vehicle or road conditions acquired by the acquisition unit 131. The load estimating unit 136 estimates the operating load based on the driving situation estimated by the situation estimating unit 132, and the distribution unit 137 estimates the operating situation based on the dynamic data. Predetermined content is distributed based on the estimated change in operating load.

According to this aspect, by distributing content according to changes in the operating load, notification can be performed in consideration of the difference between the current operating load of the operator and the past or future operating load.

In the information processing device 100 according to an embodiment of the present invention, the acquisition unit 131 acquires dynamic data regarding vehicles or road conditions, and static data regarding vehicles or road conditions, and the load estimating unit 136 estimates the driving load based on the driving situation estimated by the situation estimating unit 132 and static data regarding the vehicle or road situation acquired by the acquiring unit 131.

With this aspect, by distributing content according to changes in the operating load estimated from dynamic and static data, it is possible to integrate dynamic and static data and adjust it to the operator's current operating load. , the notification can be made taking into account the difference between the past and future operating loads.

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

With this aspect, by distributing content based on changes in the operating load due to changes in the operating position, the difference between the operating load at the operator's current operating position and the operating load at past or future operating positions can be calculated. notification may be given in consideration.

Furthermore, in the information processing device 100 according to an embodiment of the present invention, the distribution unit 137 distributes predetermined content based on a change in the driving load based on a change in the road link ID on the route estimated by the load estimation unit 136. To deliver.

According to this aspect, by distributing content according to changes in the driving load based on changes in the road link ID, the driving load at the driver's current road link ID and the driving load at past or future road link IDs can be adjusted. Notifications can be made taking into account the differences in

[Hardware configuration]
Further, the information processing apparatus 100 according to the embodiments described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. 13. FIG. 13 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device 100. Computer 1000 has CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I/F) 1500, input/output interface (I/F) 1600, and media interface (I/F) 1700.

CPU 1100 operates based on a program stored in ROM 1300 or HDD 1400, and controls each section. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, programs depending on the hardware of the computer 1000, and the like.

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

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.

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

For example, when the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 by executing a program loaded onto the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from another device via a predetermined communication network.

[others]
Although an example of the embodiment according to the present invention has been described so far, the present invention is not limited to the above embodiment. That is, those skilled in the art can implement various modifications based on conventionally known knowledge without departing from the gist of the present invention. Of course, such modifications fall within the scope of the present invention as long as they still include the information processing apparatus of the present invention.

1 Information processing system 100 Information processing device 110 Communication unit 120 Storage unit 121 Workload DB
130 Control section 131 Acquisition section 132 Situation estimation section 133 Concern level calculation section 134 Monotony degree calculation section 135 Reference section 136 Load estimation section 137 Distribution section

Claims (6)

an acquisition unit that acquires dynamic data regarding vehicles or road conditions;
a situation estimation unit that estimates a driving situation based on dynamic data regarding the vehicle or road conditions acquired by the acquisition unit;
a load estimating unit that estimates a driving load based on the driving situation estimated by the situation estimating unit;
An information processing device comprising: a distribution unit that distributes predetermined content based on the change in the operating load estimated by the load estimation unit. The acquisition unit acquires dynamic data regarding the vehicle or road conditions and static data regarding the vehicle or road conditions,
The load estimating unit estimates the driving load based on the driving situation estimated by the situation estimating unit and static data regarding the vehicle or road condition acquired by the acquiring unit. The information processing device according to claim 1. The information processing device according to claim 1, wherein the distribution unit distributes predetermined content based on a change in the driving load associated with a change in the driving position estimated by the load estimating unit. The information processing according to claim 1, wherein the distribution unit distributes the predetermined content based on a change in the driving load based on a change in a road link ID on the route estimated by the load estimation unit. Device. A method performed by a computer, the method comprising:
an acquisition step for acquiring dynamic data regarding vehicle or road conditions;
a situation estimation step of estimating a driving situation based on the dynamic data regarding the vehicle or road condition acquired in the acquisition step;
a load estimation step of estimating a driving load based on the driving situation estimated by the situation estimation step;
and a distribution step of distributing predetermined content based on the change in the operating load estimated by the load estimation step. an acquisition step of acquiring dynamic data about the vehicle or road conditions;
a situation estimation step of estimating a driving situation based on dynamic data regarding the vehicle or road situation acquired by the acquisition unit;
a load estimating step of estimating a driving load based on the driving situation estimated by the situation estimating unit;
and a distribution step of distributing predetermined content based on the change in the operating load estimated by the load estimating unit.

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