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

Abstract

To personalize a driving load in a predetermined driving scene.SOLUTION: An information processing device includes an acquisition section and an estimation section. The acquisition section acquires first speed information being the statistical speed information of a mobile body obtained from a plurality of drivers in a predetermined driving scene, and second speed information being the speed information of the mobile body in the predetermined driving scene of an object driver. The estimation section estimates information of a driving load on the object driver in the predetermined driving scene based on comparison between the first speed information and the second speed information.SELECTED DRAWING: Figure 5

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 are known techniques for controlling the timing and content of information presented to a driver according to the driver's driving load. For example, there is a technique for determining information that can be presented to a driver based on a first index value related to a driving risk and a second index value related to a driving load. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-118838 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, even in the same driving scenario, one driver may feel that the driving load is low, while another driver may feel that the driving load is high. In this way, even if the driving scenario is common, the driving load experienced by each driver may differ.

[0005] However, the above-mentioned conventional technology merely corrects the second index value, such as the inverse of the inter-vehicle time, vehicle speed x steering amount, continuous driving time, or time of day, with a correction amount corresponding to the driver's driving load, and there is room for improvement in terms of personalizing the driving load in a specific driving situation.

[0006] The present invention has been made in view of the above, and provides an information processing device, an information processing method, and an information processing program that can personalize the driving load in a predetermined driving situation. [Means for solving the problem]

[0007] The information processing device of claim 1 includes an acquisition unit that acquires first speed information, which is statistical speed information of a moving object obtained from a plurality of drivers in a specified driving scene, and second speed information, which is speed information of a moving object in the specified driving scene of a target driver, and an estimation unit that estimates information on the driving load of the target driver in the specified driving scene based on a comparison between the first speed information and the second speed information.

[0008] The information processing method of claim 9 is an information processing method executed by an information processing device, and includes an acquisition step of acquiring first speed information, which is statistical speed information of a moving object obtained from a plurality of drivers in a specified driving scene, and second speed information, which is speed information of a moving object in the specified driving scene of a target driver, and an estimation step of estimating the driving load of the target driver in the specified driving scene based on a comparison between the first speed information and the second speed information.

[0009] The information processing program of claim 10 is an information processing program executed by an information processing device, and causes the information processing device to execute an acquisition procedure of acquiring first speed information, which is statistical speed information of moving objects obtained from multiple drivers in a specified driving scene, and second speed information, which is speed information of moving objects in the specified driving scene of a target driver, and an estimation procedure of estimating the driving load of the target driver in the specified driving scene based on a comparison between the first speed information and the second speed information. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of a server device according to the embodiment. [Figure 3] FIG. 3 is a diagram showing the relationship between the WL value and the WL type. [Figure 4] FIG. 4 is a diagram showing the correspondence between scene types and speed information. [Figure 5] FIG. 5 is a diagram illustrating a correction method for calculating the second index value. [Figure 6] FIG. 6 is a diagram showing a graph (1) in which the calculation results of the hyperbolic tangent are plotted. [Figure 7] FIG. 7 is a diagram illustrating a correction method for calculating the third index value. [Figure 8] FIG. 8 is a diagram showing a graph (2) in which the calculation results of the hyperbolic tangent are plotted. [Figure 9] FIG. 9 is a flowchart illustrating an example of the operation of the server device according to the embodiment. [Figure 10] FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the server device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0012] One or more embodiments (including examples, modifications, and application examples) described below can be implemented independently. However, at least a portion of the embodiments described below may be implemented in appropriate combination with at least a portion of another embodiment. These embodiments may include novel features that are different from each other. Therefore, these embodiments may contribute to solving different purposes or problems and may produce different effects from each other.

[0013] In the following embodiments, the information processing according to the embodiment will be described using an "automobile" as an example of a "mobile body." However, the information processing according to the embodiment can also be applied to various vehicles other than automobiles.

[0014] 1. Introduction Conventionally, statistical driving load has been estimated as the driving load felt by a driver on a road section in a driving scene from the results of statistical processing of an unspecified number of driving situations in the driving scene. In other words, conventionally, the driving load corresponding to each driving scene has been uniformly determined for each driving scene.

[0015] However, even in the same driving scene, one driver may actually feel that the driving load is low, while another driver may feel that the driving load is high. In other words, even if the driving scene is common, the driving load that each driver perceives in that driving scene may differ from one driver to another.

[0016] Conventional speech agents are designed to control the output of content to the driver based on the statistical driving load associated with the driving scene. For this reason, even if the driver actually feels that the driving load is low on the road section of the current driving scene and has the capacity to accept the content, if the statistical driving load associated with the driving scene is high, the content will not be output and speech opportunities will be limited.

[0017] Therefore, the inventors of the present invention thought that if the driving load in a driving scene could be personalized for each driver, it would be possible to realize a speech agent that can increase speech opportunities by using information on the personalized driving load.

[0018] Based on this concept, the server device 100 (an example of an information processing device) according to the embodiment described herein performs information processing for personalizing the driving load in a predetermined driving scene. Specifically, the server device 100 acquires first speed information, which is statistical speed information of the vehicle VEx obtained from a plurality of drivers (an unspecified number of drivers Dx) in the predetermined driving scene, and second speed information, which is speed information of the vehicle VE1 in the predetermined driving scene of the target driver D1. Then, the server device 100 estimates information about the driving load of the target driver D1 in the predetermined driving scene based on a comparison between the first speed information and the second speed information.

[0019] Vehicle VEx refers to the automobiles of an unspecified number of drivers Dx. Vehicle VE1 refers to the automobiles of an individual driver D1 whose driving load is to be personalized. Note that when there is no need to distinguish between an unspecified number of drivers Dx and an individual driver D1 whose driving load is to be personalized, it will simply be referred to as "driver D." Furthermore, when there is no need to distinguish between vehicle VEx and vehicle VE1, it will simply be referred to as "vehicle VE."

[0020] [2. System Configuration] The configuration of an information processing system 1 will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration of the information processing system 1 according to an embodiment. As shown in Fig. 1, the information processing system 1 includes a terminal device 10 and a server device 100. The terminal device 10 and the server device 100 are connected to each other via a predetermined communication network (network N) so as to be able to communicate with each other via wired or wireless communication.

[0021] In the information processing system 1, the terminal device 10 is an edge computer located on the edge side, while the server device 100 is a cloud computer located on the cloud side.

[0022] The terminal device 10 is an information processing terminal used by the driver D, and may be, for example, a smartphone, a tablet terminal, or a personal computer. The terminal device 10 may also be an in-vehicle device (car navigation system) mounted on the vehicle VE. In this embodiment, the terminal device 10 is described as an in-vehicle device, as shown in FIG. 1. The terminal device 10 includes a sensor that detects various conditions related to the vehicle VE. For example, the sensor may detect the driving speed of the vehicle VE by the driver D every second, and transmit the driving speed to the server device 100 each time it is detected.

[0023] The server device 100 can acquire speed information of the vehicle VE based on the driving speed acquired from the terminal device 10. For example, the server device 100 may acquire, for each road section included in the travel route on which the vehicle VE travels, an average speed according to the driving scene corresponding to the road section, The server device 100 calculates the standard deviation of the driving speed. Furthermore, the server device 100 executes a driving load personalization process, which estimates information on the driving load of each target driver D1, as information processing according to the embodiment based on the acquired driving speed.

[0024] 3. Server Device Configuration The server device 100 according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Fig. 7, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0025] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. For example, the communication unit 110 transmits and receives information to and from the terminal device 10.

[0026] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 may store, for example, data and programs related to the information processing according to the embodiment.

[0027] (control unit 130) The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs (for example, the information processing program according to the embodiment) stored in a storage device inside the server device 100 using RAM as a work area. The control unit 130 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0028] As shown in Fig. 2, the control unit 130 has a calculation unit 131, an acquisition unit 132, a correction unit 133, and an estimation unit 134, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Fig. 2, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship shown in Fig. 3, and may be other connection relationships.

[0029] (Calculation unit 131) The calculation unit 131 calculates an average value of the driving speed in a predetermined driving scene. The calculation unit 131 calculates an average value of the driving speed in each predetermined driving scene. The calculation unit 131 can calculate the average value of the driving speed based on the driving speed detected by the sensor of the terminal device 10.

[0030] Specifically, the calculation unit 131 calculates the first average speed for each road section included in the travel route of the vehicle VEx by averaging the driving speeds of the vehicle VEx driven by an unspecified number of drivers Dx in the driving scene indicated by the road section. The calculation unit 131 may also calculate a first standard deviation indicating the variation in the driving speed of the vehicle VEx in the driving scene indicated by the road section from the first average speed. The calculation unit 131 may then store first speed information, which is information that associates a link ID identifying the road section with the first average speed and the first standard deviation, in the storage unit 120. As a result, the server device 100 can collect the first average speeds and first standard deviations in road sections of various driving scenes.

[0031] Furthermore, the calculation unit 131 may calculate a second average speed by averaging, for each road section included in the travel route of the vehicle VE1, the driving speed of the target driver D1 in the driving scene indicated by the road section. The calculation unit 131 may also calculate a second standard deviation indicating the variation in the driving speed of the vehicle VE1 in the driving scene indicated by the road section from the second average speed. The calculation unit 131 may then store second speed information in the storage unit 120, which is information that associates a driver ID that identifies the target driver D1, a link ID that identifies the road section, the second average speed, and the second standard deviation. As a result, the server device 100 can collect the second average speed and the second standard deviation for each target driver D1 in road sections of various driving scenes.

[0032] (Acquisition part 132) The acquisition unit 132 acquires first speed information, which is statistical speed information obtained from an unspecified number of drivers Dx in a specified driving scene, and second speed information, which is speed information of the target driver D1 in a specified driving scene.

[0033] For example, the acquisition unit 132 acquires, as the first speed information, a first average speed obtained by averaging the driving speeds of an unspecified number of drivers Dx in a predetermined driving scene. Furthermore, the acquisition unit 132 may acquire, as the first speed information, a first standard deviation corresponding to an unspecified number of drivers Dx in a predetermined driving scene, which is calculated from the first average speed. In a case where the first average speed and the first standard deviation are calculated by the calculation unit 131 and stored in the storage unit 120, the acquisition unit 132 may acquire the first average speed and the first standard deviation from the storage unit 120.

[0034] Furthermore, the acquisition unit 132 acquires, as the second speed information, a second average speed obtained by averaging the driving speeds of the target driver D1 in a predetermined driving scene. Furthermore, the acquisition unit 132 may acquire, as the second speed information, a second standard deviation corresponding to the driving speed of the target driver D1 in a predetermined driving scene and calculated from the second average speed. In a case where the second average speed and the second standard deviation are calculated by the calculation unit 131 and stored in the storage unit 120, the acquisition unit 132 may acquire the second average speed and the second standard deviation from the storage unit 120.

[0035] (Correction unit 133, estimation unit 134) The correction unit 133 corrects statistical driving load information in a predetermined driving scene based on a comparison between the first speed information and the second speed information. The estimation unit 134 estimates driving load information of the target driver D1 in the predetermined driving scene based on a comparison between the first speed information and the second speed information.

[0036] For example, when the second average speed is higher than the first average speed in a predetermined driving scene, the correction unit 133 corrects the statistical driving load information so that the information on the driving load of the target driver D1 in the predetermined driving scene becomes lower compared to the information on the statistical driving load in the predetermined driving scene. On the other hand, when the second average speed is lower than the first average speed in the predetermined driving scene, the correction unit 133 corrects the statistical driving load information so that the information on the driving load of the target driver D1 in the predetermined driving scene becomes higher compared to the information on the statistical driving load in the predetermined driving scene.

[0037] Specifically, the correction unit 133 calculates a second index value, which is an index value indicating the driving load of the target driver D1, based on the difference between the first average speed and the second average speed, a first index value, which is an index value indicating a statistical driving load, and a predetermined correction coefficient. The estimation unit 134 estimates the driving load of the target driver D1 based on the second index value. More specifically, the correction unit 133 may calculate the second index value based on an output y output by performing a hyperbolic tangent operation using the difference between the first average speed and the second average speed as an input x, the first index value, and a correction coefficient set according to a range of the index value.

[0038] Furthermore, when the second standard deviation is larger than the first standard deviation in a predetermined driving scene, the correction unit 133 corrects the statistical driving load information so that the information of the driving load of the target driver D1 in the predetermined driving scene becomes higher compared to the information of the statistical driving load in the predetermined driving scene. On the other hand, when the second standard deviation is smaller than the first standard deviation in the predetermined driving scene, the correction unit 133 corrects the statistical driving load information so that the information of the driving load of the target driver D1 in the predetermined driving scene becomes lower compared to the information of the statistical driving load in the predetermined driving scene.

[0039] Specifically, the correction unit 133 calculates a third index value, which is an index value indicating the driving load of the target driver D1, based on the difference between the first standard deviation and the second standard deviation, the second index value, and a predetermined correction coefficient. The estimation unit 134 estimates the driving load of the target driver D1 based on the third index value. More specifically, the correction unit 133 calculates the third index value based on an output y obtained by performing a hyperbolic tangent calculation using the difference between the first standard deviation and the second standard deviation as an input x, the second index value, and a correction coefficient set according to the range of the index value.

[0040] According to the description above, the correction unit 133 may perform a two-stage calculation for correction. For example, in the first stage of calculation, the correction unit 133 calculates a second index value, which is an index value indicating the driving load of the target driver D1, based on the difference between the first average speed report and the second average speed, a first index value, which is an index value indicating a statistical driving load, and a correction coefficient. Next, the correction unit 133 performs a second stage of calculation using the second index value calculated in the first stage of calculation. Specifically, the correction unit 133 calculates a third index value, which is an index value indicating the driving load of the target driver D1, based on the difference between the first standard deviation and the second standard deviation, the second index value, and the correction coefficient. The driving load of the target driver D1 may be estimated based on this finally calculated third index value. Note that the driving load of the target driver D1 may be estimated only from the result of the first stage of calculation, i.e., the second index value. Therefore, the information processing according to the embodiment may be completed only by the result of the first stage calculation, or may be based on the result of the second stage calculation. The first stage calculation corresponds to equation (1) described later, and the second stage calculation corresponds to equation (2) described later.

[0041] Furthermore, the driving load refers to the degree of difficulty (driving difficulty) of the driver D, and is also called workload (WL). Therefore, the information on the driving load includes two meanings: the "WL value," which is an index value indicating the degree of difficulty of the driver D, and the "WL type" estimated from the WL value.

[0042] Here, the relationship between the WL value and the WL type will be described with reference to Fig. 3. Fig. 3 is a diagram showing the relationship between the WL value and the WL type. Fig. 3 shows a correspondence table TB in which the WL value and the WL type are associated with each other. As shown in Fig. 3, the WL value may be classified into five ranges in the range of 0.0 to 1.0. Specifically, as shown in Fig. 3, the WL value may be classified into five ranges: "0.00 to 0.49", "0.50", "0.51 to 0.60", "0.61 to 0.90", and "0.91 to 1.00". Of course, the classification method is not limited to the above example.

[0043] According to the correspondence table TB, each range of WL values ​​is associated with a corresponding WL type. For example, the range "0.00 to 0.49" is associated with the WL type "FREE." A WL value included in the range "0.00 to 0.49" indicates a low driving load compared to a reference value (e.g., a WL value of 0.50). A road section of a scene for which a WL value within this range is calculated is a road section that the driver D may find monotonous and boring (FREE), and is associated with the WL type "FREE" in the sense that a variety of content can be uttered.

[0044] The range "0.50" is associated with the WL type "IDEAL." The WL value "0.50" means that the driving load is neither high nor low, but normal, and the road section in the scene where this WL value was calculated is a road section that driver D would perceive as normal (IDEAL), and the WL type "IDEAL" is associated with it in the sense that content other than guidance-related content (warning notification, caution notification, important notification) may also be spoken.

[0045] A WL value within the range "0.51 to 0.60" means that the driving load is slightly higher than a reference value (for example, a WL value of "0.50"). Road sections in scenes where a WL value within this range is calculated are road sections where driver D may feel that he needs to be careful (BUSY) while driving, and are associated with the WL type "BUSY" in the sense that only warning notifications, caution notifications, and important notifications should be issued.

[0046] A WL value within the range "0.61 to 0.90" means that the driving load is significantly higher than a reference value (for example, a WL value of "0.50"). Road sections in scenes where a WL value within this range is calculated are road sections where driver D may feel that driving requires significant attention (BUSY+), and are associated with the WL type "BUSY+", meaning that only warning and caution notifications should be issued.

[0047] A WL value within the range "0.91 to 1.00" means that the driving load is at its highest compared to a reference value (for example, a WL value of "0.50"). A road section in a scene where a WL value within this range is calculated is a road section where driver D may feel that he or she needs to be extremely careful (BUSY_MAX), and is associated with the WL type "BUSY_MAX" in the sense that only a warning notification should be issued.

[0048] Next, the correspondence between scene types and speed information will be explained using FIG. 4. FIG. 4 is a diagram showing the correspondence between scene types and speed information. First, as shown in FIG. 4, there are various types of predetermined driving scenes. For example, a predetermined driving scene may be information indicating the attributes of a driving section. According to the example of FIG. 4, a road section having an attribute of narrow road width (driving scene SC1) can be defined as a narrow road section (SC1 section). Furthermore, a road section having an attribute of having an intersection (driving scene SC2) can be defined as an intersection section (SC2 section).

[0049] A road section having the attribute of a road inside a tunnel (driving scene SC3) can be defined as a tunnel section (SC3 section). A road section having the attribute of a highway (driving scene SC4) can be defined as a highway section (SC4 section).

[0050] In addition, the above-mentioned first average speed, first standard deviation, second average speed, and second standard deviation are defined according to the type of driving scene, as shown in Fig. 4(a). According to the example of Fig. 4(a), the first average speed, first standard deviation, second average speed, and second standard deviation are defined for each driving scene (each road section of the attributes indicated by the driving scene).

[0051] Similarly, the above-mentioned first index value, second index value, and third index value are defined according to the type of driving scene, as shown in Fig. 4(b). According to the example of Fig. 4(b), the first index value, second index value, and third index value are defined for each driving scene (each road section of the attributes indicated by the driving scene).

[0052] 4 will be described taking as an example a case where the driving load of the driver D1 is personalized for a specific road section having an attribute called a driving scene SC1. In this case, the correction unit 133 calculates a second index value indicating the driving load of the target driver D1 in the "narrow road section" based on the difference between the "first average speed report" and the "second average speed" corresponding to the "narrow road section," the "first index value" corresponding to the "narrow road section," and the correction coefficient α. Since the WL value ranges from 0.0 to 1.0, the value of the correction coefficient α may also be set in the range of 0.0 to 1.0.

[0053] Next, the correction unit 133 calculates a third index value, which is an index value indicating the driving load of the target driver D1 in the "narrow road section," based on the difference between the "first standard deviation" and the "second standard deviation" corresponding to the "narrow road section," the previously calculated "second index value," and the correction coefficient β. Since the WL value ranges from 0.0 to 1.0, the value of the correction coefficient β may also be set in the range of 0.0 to 1.0.

[0054] In this correction method, when the second average speed is higher than the first average speed in a target driving scene, the first index value is corrected so that the second index value of the target driver D1 in that driving scene is calculated as a lower value compared to the first index value corresponding to that driving scene. Also, when the second average speed is lower than the first average speed in a target driving scene, the first index value is corrected so that the second index value of the target driver D1 in that driving scene is calculated as a higher value compared to the first index value corresponding to that driving scene. This correction method is based on the idea that speeds tend to decrease in scenes that are perceived as requiring attention, and speeds tend to increase in scenes that are perceived as boring.

[0055] As another example, when the second standard deviation is larger than the first standard deviation in a target driving scene, the first index value is corrected so that the second index value of the target driver D1 in that driving scene is calculated as a higher value compared to the first index value corresponding to that driving scene. Also, when the second standard deviation is smaller than the first standard deviation in a target driving scene, the first index value is corrected so that the second index value of the target driver D1 in that driving scene is calculated as a lower value compared to the first index value corresponding to that driving scene. This correction method is based on the idea that if a driver is not good at driving and repeatedly accelerates and decelerates, they are being careful when driving.

[0056] [4. Specific examples of correction methods] Next, specific examples of the correction method by the correction unit 133 will be described. Fig. 5 and Fig. 6 explain a correction method corresponding to the first stage of calculation, in which the second index value of the target driver D1 is calculated based on the difference between the first average speed report and the second average speed. Fig. 7 and Fig. 8 explain a correction method corresponding to the second stage of calculation, in which the third index value of the target driver D1 is calculated based on the difference between the first standard deviation and the second standard deviation and the second index value.

[0057] [4-1. Specific example of correction method (1)] 5A and 5B are diagrams illustrating a correction method for calculating the second index value. Fig. 5A illustrates a situation in which a first average speed among an unspecified number of drivers Dx is compared with a second average speed of a target driver D1 on a travel route including an SC1 section RD1 as one road section having an attribute of a narrow road and an SC3 section RD3 as another road section having an attribute of a road inside a tunnel. Fig. 5B illustrates a situation in which the second index value is controlled based on the relationship between the first index value and the second index value in response to a comparison between the first average speed and the second average speed.

[0058] According to FIG. 5(a), the second average speed is calculated to be higher than the first average speed in the SC1 section RD1. This example indicates that in the driving scene SC1, the target driver D1 has the luxury of increasing his speed and feels that driving is not difficult (FREE) compared to an unspecified number of drivers Dx. In other words, the target driver D1 increases his speed more than usual in the driving scene SC1 because he feels that driving is easy. Therefore, in this case, as shown in FIG. 5(b), the correction unit 133 corrects the first index value so that the second index value of the target driver D1 in the driving scene SC1 is calculated to be lower than the first index value corresponding to the driving scene SC1. That is, the correction unit 133 corrects the WL type toward FREE.

[0059] Also, according to FIG. 5(a), the second average speed is calculated to be lower than the first average speed in the SC3 section RD3. This example indicates that in the driving scene SC3, the target driver D1 feels that he has no room to increase his speed and that driving is difficult (BUSY) compared to the unspecified number of drivers Dx. In other words, it can be said that the target driver D1 is driving slower than usual in the driving scene SC3 because he feels that driving is difficult. Therefore, in this case, as shown in FIG. 5(b), the correction unit 133 corrects the first index value so that the second index value of the target driver D1 in the driving scene SC3 is calculated to be higher than the first index value corresponding to the driving scene SC3. That is, the correction unit 133 corrects the WL type toward BUSY.

[0060] In the correction method shown in FIG. 5, the correction coefficient α, the first average speed v in a predetermined driving scene SC (such as driving scene SC1), all , a second average speed v in a predetermined driving scene SC, a first index value w corresponding to the predetermined driving scene SC, and a difference vv between the first average speed and the second average speed. all Then, the second index value w of the target driver D1 in the predetermined driving scene SC is p is calculated using the following formula (1):

[0061]

number

[0062] In this way, the correction unit 133 performs a hyperbolic tangent operation with the difference v - v between the first average speed v all and the second average speed v as the input x, and based on the output y obtained by the operation, the first index value w (0 < w < 1), and the correction coefficient α (0 < α < 1), calculates the second index value w all (0 < w p (0 < w p (0 < w < 1). Note that the estimation unit 134 may estimate the WL type as information on the driving load of the target driver D1 in a predetermined driving scene SC based on the second index value w p and the correspondence table TB in FIG. 3.

[0063] Here, FIG. 6 shows a graph (1) when the operation result of the hyperbolic tangent is plotted. FIG. 6 also shows a graph when the formula (1) (excluding the part of the first index value w) is plotted. In such a graph, focusing on the part of the correction coefficient α (0 < α < 1), when the value of the difference v - v between the first average speed v all and the second average speed v is positive, that is, the more the speed is increased compared to others, it is suggested that the second index value w all becomes smaller and approaches a situation (FREE) where driving is not difficult. p

[0064] [4 - 2. Specific Example of Correction Method (2)] FIG. 7 is a diagram showing a correction method for calculating the third index value. FIG. 7(a) shows a scene in which the first standard deviation of an unspecified number of drivers Dx and the second standard deviation of the target driver D1 are compared on a driving route including a SC1 section RD1 as a road section 1 having an attribute of a narrow road and a SC3 section RD3 as another road section 1 having an attribute of a road in a tunnel. FIG. 7(b) shows a scene in which the third index value is controlled based on the relationship between the first index value and the third index value according to the comparison between the first standard deviation and the second standard deviation.

[0065] According to FIG. 7(a), the second standard deviation is calculated to be larger than the first standard deviation in the SC1 section RD1. This example indicates that in the driving scene SC1, the target driver D1 feels that his driving operation is unstable and he is under pressure (BUSY) compared to an unspecified number of drivers Dx. In other words, the target driver D1 feels that driving is difficult in the driving scene SC1, and therefore his driving operation varies compared to others. Therefore, in this case, as shown in FIG. 7(b), the correction unit 133 corrects the first index value so that the third index value of the target driver D1 in the driving scene SC1 is calculated to be higher than the first index value corresponding to the driving scene SC1. In other words, the correction unit 133 corrects the WL type toward BUSY.

[0066] 7(a), the second standard deviation is calculated to be smaller than the first standard deviation in the SC3 section RD3. This example indicates that in the driving scene SC3, the target driver D1 feels that his driving operation is stable and relaxed (FREE) compared to an unspecified number of drivers Dx. In other words, the target driver D1 feels that driving is easy in the driving scene SC3, and therefore his driving operation is stable compared to others. Therefore, in this case, as shown in FIG. 7(b), the correction unit 133 corrects the first index value so that the third index value of the target driver D1 in the driving scene SC3 is calculated to be lower than the first index value corresponding to the driving scene SC3. In other words, the correction unit 133 corrects the WL type toward FREE.

[0067] In the correction method shown in FIG. 7, the correction coefficient β, the first average speed v in a predetermined driving scene SC (such as driving scene SC1), all The first standard deviation σ calculated from vall , the second standard deviation σ calculated from the second average speed v in the given driving scene SC v , the difference between the first and second average standard deviations σ v -σ vall Then, the third index value w of the target driver D1 in the predetermined driving scene SC is p,σ is calculated using the following formula (2):

[0068]

number

[0069] In this way, the correction unit 133 calculates the first standard deviation σ vall and the second standard deviation σ v The difference between (σ v -σ vall ) and the second index value w p (0 <w p <1) and the correction coefficient α (0<β<1), a third index value (0 <w p,σ <1). The estimation unit 134 calculates the third index value w p,σ 3, the WL type may be estimated as information on the driving load of the target driver D1 in the predetermined driving scene SC.

[0070] Here, FIG. 8 shows a graph (2) in which the calculation result of the hyperbolic tangent is plotted. In FIG. 8, the second index value w p In this graph, when the correction coefficient β (0<β<1) is focused on, the difference σ between the first standard deviation and the second mean standard deviation v -σ vall If the value of is positive, that is, the more stable the driver's driving is compared to others, the higher the third index value w p,σ This suggests that the driving situation will become less difficult (FREE).

[0071] [5. Example of Server Device Operation] 9 is a flowchart showing an example of the operation of the server device 100 according to the embodiment. First, the calculation unit 131 determines whether it is time to estimate the driving load (step S901). The timing to estimate the driving load may be a real-time point when the vehicle VE1 of the target driver D1 enters a road section having a predetermined driving scene as an attribute, or may be an arbitrarily determined periodic timing.

[0072] If it is not time to estimate the driving load (step S901; No), the calculation unit 131 waits until it is time to estimate the driving load.

[0073] When it is time to estimate the driving load (step S901; Yes), the calculation unit 131 calculates first speed information and second speed information based on speed information accumulated for the vehicles VEx and VE1 traveling on a road section having a predetermined driving scene as an attribute, or speed information detected in real time for the vehicles VEx and VE1 traveling on a road section having a predetermined driving scene as an attribute (step S902). As a result, the acquisition unit 132 can acquire the first speed information and the second speed information.

[0074] Next, the correction unit 133 corrects the statistical driving load information based on a comparison between the first speed information and the second speed information (step S903). That is, the correction unit 133 may perform a calculation to solve equation (1) as a first-stage correction, and may perform a calculation to solve equation (2) as a second-stage correction.

[0075] The estimation unit 134 estimates the WL type felt by the target driver D1 based on the WL value (the second index value or the third index value) obtained as a result of the correction in step S903 (step S904).

[0076] Here, according to the processing of steps S901 to S904, the driving load in a predetermined driving scene is personalized, and therefore, the server device 100 may further perform speech control of the content using the WL type of step S904, which is the result of personalization (step S905). For example, even if the WL type is statistically determined to be "BUSY" in a road section having a predetermined driving scene as an attribute, if the WL type of the target driver D1 traveling on the road section is estimated to be "FREE", the server device 100 may perform speech control so that the content is output by the terminal device 10.

[0077] The information processing for personalizing the driving load in a predetermined driving scene has been described. According to the personalization processing of the embodiment, the driving load is personalized for each driving scene. Therefore, according to the personalization processing of the embodiment, for example, the target driver D1 tends to be careful in a certain driving scene but feels bored in another driving scene, and thus it is possible to personalize the way the target driver D1 feels for each driving scene.

[0078] [6. Hardware Configuration] The server device 100 according to the embodiment may be realized by, for example, a computer 1000 configured as shown in Fig. 10. Fig. 10 is a hardware configuration diagram showing an example of a computer that realizes the functions of the server device 100 according to the embodiment. 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.

[0079] 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.

[0080] The HDD 1400 stores programs executed by the CPU 1100, data used by these programs, etc. 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.

[0081] The CPU 1100 controls an output device such as a display and an input device such as a keyboard via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. The CPU 1100 also outputs generated data to the output device via the input / output interface 1600.

[0082] 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.

[0083] For example, when the computer 1000 functions as the server device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0084] [7. Other] Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0085] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0086] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0087] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the aspects described in the "present invention" section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0088] 1. Information Processing Systems 100 Server device 130 Control Unit 131 Calculation Unit 132 Acquisition Department 133 Correction Unit 134 Estimation Department

Claims

1. an acquisition unit that acquires first speed information, which is statistical speed information of a moving object obtained from a plurality of drivers in a predetermined driving scene, and second speed information, which is speed information of a moving object of a target driver in the predetermined driving scene; an estimation unit that estimates information about a driving load of the target driver in the predetermined driving scene based on a comparison between the first speed information and the second speed information; An information processing device comprising:

2. a correction unit that corrects statistical driving load information in the predetermined driving scene based on a comparison between the first speed information and the second speed information; Further preparation, The estimation unit estimates the driving load of the target driver from the corrected information on the driving load. The information processing device according to claim 1 .

3. The acquisition unit As the first speed information, a first average speed is obtained by averaging driving speeds of the plurality of drivers in the predetermined driving scene; As the second speed information, a second average speed is obtained by averaging the driving speed of the target driver in the predetermined driving scene; The correction unit When the second average speed is higher than the first average speed, correct the statistical driving load information so that the driving load information of the target driver in the predetermined driving scene is lower than the statistical driving load information in the predetermined driving scene; When the second average speed is lower than the first average speed, the statistical driving load information is corrected so that the information on the driving load of the target driver in the predetermined driving scene is higher than the information on the statistical driving load in the predetermined driving scene. The information processing device according to claim 2 .

4. the correction unit calculates a second index value that is an index value indicating the driving load of the target driver based on a difference between the first average speed and the second average speed, a first index value that is an index value indicating the statistical driving load, and a predetermined correction coefficient; The estimation unit estimates a driving load of the target driver based on the second index value. The information processing device according to claim 3 .

5. The correction unit calculates the second index value based on an output y obtained by performing a hyperbolic tangent operation using an input x that is a difference between the first average velocity and the second average velocity, the first index value, and the correction coefficient set according to a range of the index value. The information processing device according to claim 4 .

6. The acquisition unit As the first speed information, a first standard deviation is acquired, which is a standard deviation corresponding to the plurality of drivers in the predetermined driving scene and is calculated from the first average speed; As the second speed information, a second standard deviation is acquired, which is a standard deviation corresponding to the driving speed of the target driver in the predetermined driving scene and is calculated from the second average speed; The correction unit When the second standard deviation is larger than the first standard deviation, the statistical driving load information is corrected so that the information of the driving load of the target driver in the predetermined driving scene is higher than the information of the statistical driving load in the predetermined driving scene; When the second standard deviation is smaller than the first standard deviation, the statistical driving load information is corrected so that the information on the driving load of the target driver in the predetermined driving scene is lowered compared with the information on the statistical driving load in the predetermined driving scene. The information processing device according to claim 4 .

7. the correction unit calculates a third index value, which is an index value indicating a driving load of the target driver, based on a difference between the first standard deviation and the second standard deviation, the second index value, and a predetermined correction coefficient; The estimation unit estimates a driving load of the target driver based on the third index value. The information processing device according to claim 6 .

8. The correction unit calculates the third index value based on an output y obtained by performing a hyperbolic tangent operation on an input x that is the difference between the first standard deviation and the second standard deviation, the second index value, and the correction coefficient set according to a range of the index value. The information processing device according to claim 7 .

9. An information processing method executed by an information processing device, an acquisition step of acquiring first speed information, which is statistical speed information of a moving object obtained from a plurality of drivers in a predetermined driving scene, and second speed information, which is speed information of a moving object of a target driver in the predetermined driving scene; an estimation step of estimating a driving load of the target driver in the predetermined driving scene based on a comparison between the first speed information and the second speed information; An information processing method including:

10. An information processing program executed by an information processing device, an acquisition procedure for acquiring first speed information, which is statistical speed information of a moving object obtained from a plurality of drivers in a predetermined driving scene, and second speed information, which is speed information of a moving object of a target driver in the predetermined driving scene; an estimation step of estimating a driving load of the target driver in the predetermined driving scene based on a comparison between the first speed information and the second speed information; An information processing program that causes the information processing device to execute the above.

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