Information processing apparatus

Abstract

To extract data suitable for analyzing a driving taste of a driver from original data.SOLUTION: The information processor executes a step (S110) of setting a plurality of time windows for cutting out data from original data, a step (S120) of cutting out data from the original data, a step (S130) of calculating a frequency distribution of the original data and a frequency distribution of the extracted data for each feature amount, and steps (S140 and S150) of determining whether the original data and the extracted data are similar to each other by using errors of the frequency distributions. The information processor repeatedly executes steps S110 to S150 while changing the setting of the plurality of time windows, and extracts the extracted data similar to the original data from the original data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device. [Background technology]

[0002] Patent Document 1 discloses an information processing device that reduces the size of original data for analysis by compressing the original data for analysis. The original data for analysis is data collected over a predetermined period of time using a sensor mounted on a vehicle.

[0003] The information processing device disclosed in Patent Document 1 compresses data by extracting from the original data data acquired when a certain vehicle speed is reached and data acquired at an inflection point in the vehicle speed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-108247 Summary of the Invention [Problem to be solved by the invention]

[0005] When analyzing a driver's driving preferences, if data suitable for analyzing the driver's driving preferences can be extracted from the original data, the extracted data can be used to analyze the driver's driving preferences in a shorter time than when using the original data. The information processing device analyzes the driving speed pattern using the extracted data. Therefore, the information processing device cannot extract data suitable for analyzing the driver's driving preferences from the original data. [Means for solving the problem]

[0006] An information processing device for solving the above problem acquires original data collected over a predetermined period of time using a sensor mounted on a vehicle, and extracts data from the original data to be used for analyzing the driving preferences of a driver who drives the vehicle. This information processing device includes a processing circuit. the processing circuit repeatedly executes the following steps by changing the settings of the multiple time windows: a first step of calculating a frequency distribution in the original data for each of a plurality of feature quantities including information about a plurality of vehicle operations by the driver, which are included in the original data; a second step of setting a plurality of time windows to extract data for a portion of the original data so that the total period of all of the time windows is shorter than the predetermined period; a third step of extracting data from the original data using the multiple time windows; a fourth step of calculating a frequency distribution in extracted data obtained by combining all of the data extracted using the multiple time windows for the plurality of feature quantities; a fifth step of calculating errors between the frequency distribution for each of the feature quantities in the original data and the frequency distribution for each of the feature quantities in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on the errors; and when it is determined that the original data and the extracted data are similar, the processing circuit extracts the extracted data as data to be used for analyzing the driving preferences of the driver. [Effects of the Invention]

[0007] According to the information processing device, data suitable for analyzing the driving preferences of the driver can be extracted from the original data. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram showing the relationship between a data center, which is an embodiment of an information processing device, a vehicle, and an information processing terminal. [Figure 2]Figure 2 shows graphs of part of the original data. (a) shows the transition of driving mode, (b) shows the transition of accelerator operation amount, (c) shows the transition of brake operation amount, and (d) shows the transition of vehicle speed. [Figure 3] FIG. 3 is a flowchart showing the flow of processing executed by the processing circuit of the data center. [Figure 4] Figure 4 shows the frequency distribution of driving modes in the original data. [Figure 5] FIG. 5 shows the frequency distribution of accelerator operation amount in the original data. [Figure 6] FIG. 6 shows the frequency distribution of the brake operation amount in the original data. DETAILED DESCRIPTION OF THE INVENTION

[0009] An embodiment of an information processing device will be described below with reference to FIGS. <Configuration of information processing system> 1 shows the configuration of an information processing system. The information processing system includes a data center 500 equipped with an information processing device, an information processing terminal 600, a plurality of vehicles 10, and a communication network 400. The data center 500 is capable of communicating with the plurality of vehicles 10 and the information processing terminal 600 via the communication network 400.

[0010] <Configuration of Data Center 500> As shown in Fig. 1, the data center 500 includes a processing circuit 510, a storage device 520, and a communication device 530. The processing circuit 510 is an information processing device, and includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 520 is capable of storing large amounts of data. The communication device 530 performs wired or wireless communication via the communication network 400. The communication device 530 includes hardware such as a network adapter, various types of communication software, or a combination of these.

[0011] <Configuration of information processing terminal 600> As shown in FIG. 1, the information processing terminal 600 includes a processing circuit 610, a storage device 620, and a communication device 630. The processing circuit 610 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 620 is capable of storing large amounts of data. The communication device 630 performs wired or wireless communication via the communication network 400. The communication device 630 includes hardware such as a network adapter, various types of communication software, or a combination of these. The information processing terminal 600 is, for example, a personal computer.

[0012] <Vehicle 10 Configuration> The vehicle 10 includes a communication device 99. The communication device 99 transmits data acquired by the vehicle 10 and identification information for identifying the vehicle 10 to the data center 500 via the communication network 400.

[0013] The vehicle 10 includes an internal combustion engine 21, two motor generators 23, a planetary gear mechanism 50 constituting a power split device, a power control unit (hereinafter referred to as a "PCU (Power Control Unit)") 24, a battery 25, and a vehicle control unit 90. In the following description, the two motor generators 23 will be referred to as a first motor generator 23A and a second motor generator 23B, respectively.

[0014] The crankshaft of the internal combustion engine 21 is mechanically connected to the carrier C of the planetary gear mechanism 50. A rotating shaft of a first motor generator 23A, which is an electric motor, is mechanically connected to a sun gear S of the planetary gear mechanism 50. Furthermore, a rotating shaft of a second motor generator 23B, which is an electric motor, and drive wheels 60 of the vehicle 10 are mechanically connected to a ring gear R of the planetary gear mechanism 50.

[0015] The battery 25 exchanges electric power with the motor generators 23. That is, when the first motor generator 23A or the second motor generator 23B functions as a motor that outputs torque, the electric power of the battery 25 is converted by the PCU 24 and then supplied to each motor generator. On the other hand, when the first motor generator 23A or the second motor generator 23B functions as a generator, the electric power regenerated by each motor generator is converted by the PCU 24 and then supplied to the battery 25.

[0016] The vehicle control unit 90 includes a first control device 91 that controls the internal combustion engine 21 and a second control device 92 that controls the PCU 24 . The vehicle control unit 90 includes a plurality of sensors that collect data. The first control device 91 includes a CPU that controls the operating state of the internal combustion engine 21. The first control device 91 controls the internal combustion engine 21 based on the data collected by the sensors. The second control device 92 includes a CPU that controls the PCU 24. The second control device 92 controls the PCU 24 based on the data collected by the sensors.

[0017] An example of data collected by the vehicle control unit 90 is the vehicle speed and position information of the vehicle 10. Another example of data collected by the vehicle control unit 90 is an accelerator operation amount, which is an operation amount of an accelerator pedal operated by the driver of the vehicle 10 to adjust the acceleration of the vehicle 10. Another example of data collected by the vehicle control unit 90 is a brake operation amount, which is an operation amount of a brake pedal operated by the driver to adjust the braking force of the vehicle 10. Another example of data collected by the vehicle control unit 90 is a steering angle, which is a steering angle of a steering wheel that operates the steered wheels of the vehicle 10. Another example of data collected by the vehicle control unit 90 is a driving mode of the vehicle 10 selected by an operation by the driver.

[0018] The driving modes include, for example, D mode, Ds mode, and B mode. D mode is the basic driving mode. Ds mode is a driving mode that enhances the sporty driving characteristics of the vehicle 10 compared to D mode. B mode is a driving mode that increases the deceleration of the vehicle 10 compared to D mode. The driver selects each driving mode by operating a mode select switch.

[0019] <Data Extraction> The information processing terminal 600 is used to analyze the driving preferences of a driver who drives the vehicle 10. When analyzing the driving preferences, the information processing terminal 600 transmits an instruction to the data center 500. Upon receiving the instruction, the processing circuit 510 of the data center 500 performs analysis using a portion of the vast amount of data stored in the storage device 520 of the data center 500. The data to be used is selected from the vast amount of data stored in the storage device 520 according to the purpose of the analysis. This data includes multiple pieces of data related to driving preferences collected using multiple sensors mounted on the vehicle 10. These pieces of data are called feature quantities. The processing circuit 510 uses the feature quantities to analyze the driving preferences of a driver who drives a specific vehicle 10. In this case, the feature quantities are information related to multiple vehicle operations performed by the driver who drives the vehicle 10 to be analyzed, and the vehicle speed. The information related to the multiple vehicle operations includes a driving mode, an accelerator operation amount, and a brake operation amount.

[0020] Processing circuitry 510 utilizes large amounts of data collected over a long period of time to programmatically analyze the driving preferences of a driver of a particular vehicle 10. In this analysis, processing circuitry 510 performs a large amount of calculations, which can take a long time.

[0021] Therefore, it is possible to extract extracted data that captures the overall characteristics of the original data from the large amount of data that is the original data. If such extracted data can be extracted, the processing circuit 510 can use the extracted data to perform analysis in a shorter time.

[0022] Fig. 2 shows a portion of original data of features related to driving preferences. The original data shown in Fig. 2 is a portion of data for 100,000 hours for one vehicle 10. The original data shown in Fig. 2 includes, as features, for example, a driving mode, an accelerator operation amount, a brake operation amount, and a vehicle speed.

[0023] Fig. 2(a) shows the driving mode, Fig. 2(b) shows the accelerator operation amount, Fig. 2(c) shows the brake operation amount, and Fig. 2(d) shows the vehicle speed. The driving mode, accelerator operation amount, brake operation amount, and vehicle speed are data related to the driver's driving preferences. Therefore, the processing circuit 510 extracts data for analyzing the driving preferences from data including the driving mode, accelerator operation amount, brake operation amount, and vehicle speed as feature amounts.

[0024] The extracted data is created by extracting data from the original data using multiple time windows. In Figure 2, three time windows are shown as examples of multiple time windows: a first time window W_1, a second time window W_2, and a third time window W_3, each indicated by a two-dot chain line. The start and end of each time window are set so that they do not overlap. In this example, 20,000 hours of data is extracted as the extracted data. Therefore, the start and end of each time window are set so that the total length of all the time windows is 20,000 hours.

[0025] The data center 500 searches for settings of the start and end of each time window that indicate a cutout pattern for extracting extracted data that captures the characteristics of the entire original data. The data center 500 stores information on the cutout pattern for extracting the extracted data described above in the storage device 520. The stored cutout pattern information is information on the settings of each time window found by the search.

[0026] The processing circuit 510 extracts data from the original data based on the information of the extraction pattern stored in the storage device 520 . <Searching for extraction patterns> 3 is a flowchart showing the flow of a series of processes related to the extraction pattern search process, which are executed by the processing circuit 510 of the data center 500 in accordance with a program.

[0027] 3, the processing circuit 510 acquires original data in the process of step S100. The original data is a portion of data selected from the vast amount of data stored in the storage device 520 of the data center 500 according to the purpose of analysis.

[0028] The original data for analyzing the driving preferences of the driver of one vehicle 10 is data of the target vehicle 10 selected from a huge amount of data of a plurality of vehicles 10. Next, in step S110, the processing circuit 510 sets a plurality of time windows to extract extracted data from the original data.

[0029] In the example shown in Fig. 2, the duration of each time window is the same. As shown in Fig. 2, the data extracted by each extraction window is data of each feature amount for the same period.

[0030] Each time the processing circuit 510 executes the process of step S110, it randomly sets the number of time windows, the start time of each time window, and the end time of each time window. At this time, the processing circuit 510 sets each time window so that the time windows do not overlap. In this way, the processing circuit 510 randomly sets multiple time windows so that the total duration of all the time windows is a predetermined duration. In the process of step S110, the processing circuit 510 may set multiple time windows by fixing the duration of each time window to a constant value, as shown in FIG. 2. In the process of step S110, the processing circuit 510 may set multiple time windows by fixing the number of time windows to a constant value.

[0031] In this way, a cut-out pattern for cutting out data from the original data is determined by setting a plurality of time windows through the process of step S110. After determining the cut-out pattern in this way, processing circuit 510 advances the process to step S120.

[0032] In the process of step S120, the processing circuit 510 extracts data from the original data using the determined extraction pattern. That is, in the process of step S120, the processing circuit 510 extracts data from the original data using the multiple time windows that have been set. Then, the processing circuit 510 combines all of the data extracted using the multiple time windows to create extracted data.

[0033] Next, in the process of step S130, the processing circuit 510 calculates the frequency distribution of the original data and the extracted data for each feature amount. The frequency distribution classifies the data for each feature into multiple classes, and shows the distribution of frequencies, which are the number of data in each class. In this embodiment, the feature data are the driving mode, the accelerator operation amount, the brake operation amount, and the vehicle speed. Therefore, the frequency distribution of the driving mode, the accelerator operation amount, the brake operation amount, and the vehicle speed are calculated.

[0034] Furthermore, because the total frequencies of the features contained in the original data and extracted data differ, the frequency distribution of the original data and the frequency distribution of the extracted data cannot be simply compared. If 20,000 hours of extracted data are extracted from 100,000 hours of original data, the total frequency of the extracted data will be approximately one-fifth of the total frequency of the original data. In this case, by multiplying the frequency of each class in the extracted data by five, a frequency distribution of the extracted data with a total frequency equivalent to that of the original data can be obtained. Even without using the above method, the data distribution in the original data and the data distribution in the extracted data can be compared by calculating a relative frequency distribution as the frequency distribution of the original data and the extracted data. A relative frequency distribution is a frequency distribution that indicates what percentage of the frequency of a class is made up of the total frequency.

[0035] Figure 4 shows the frequency distribution of driving modes in the original data. In the frequency distribution of driving modes, D mode, Ds mode, and B mode are classified into classes. The frequency distribution of the extracted driving mode data is also calculated by dividing it into classes corresponding to the original data.

[0036] Figure 5 shows the frequency distribution of accelerator operation amount in the original data. In the frequency distribution of accelerator operation amount, the accelerator operation amount is divided into m classes from "1" to "m". The frequency distribution of the extracted data of accelerator operation amount is also calculated by dividing it into classes corresponding to the original data.

[0037] Figure 6 shows the frequency distribution of brake operation amounts in the original data. In the frequency distribution of brake operation amounts, the brake operation amounts are divided into m classes from "1" to "m". The frequency distribution of the extracted data of brake operation amounts is also calculated by dividing it into classes corresponding to the original data.

[0038] Similarly, in the frequency distribution of vehicle speed in the original data, vehicle speed is divided into m classes from "1" to "m." The frequency distribution of the extracted vehicle speed data is also calculated by dividing it into classes corresponding to the original data.

[0039] After executing the process of step S130 shown in Fig. 3, the processing circuit 510 then executes the process of step S140. In step S140, the processing circuit 510 calculates the error between the frequency distribution of each feature amount in the original data and the frequency distribution of each feature amount in the extracted data. For example, the processing circuit 510 calculates the mean absolute error (MAE). The mean absolute error (MAE) is expressed by the following formula:

[0040]

number

[0041] In the above formula, "n" is the number of feature quantities. In this embodiment, the feature quantities are the driving mode, accelerator operation amount, brake operation amount, and vehicle speed, so "n" is "4". "m" is the total number of classes in the frequency distribution. "Y" is the frequency of the corresponding feature quantity in the corresponding class in the original data. "y" is the frequency of the corresponding feature quantity in the corresponding class in the extracted data.

[0042] As shown in the above formula, the processing circuit 510 calculates the error as the sum of the errors in the frequencies in each class for each feature amount between the frequency distribution in the original data and the frequency distribution in the extracted data.

[0043] After calculating the error, processing circuit 510 proceeds to step S150. In step S150, processing circuit 510 determines whether the calculated error is equal to or less than a threshold value. The threshold value is a value for determining whether extracted data having a frequency distribution similar to that in the original data has been extracted using the set extraction pattern. The magnitude of this threshold value is set in advance so that it can be determined that extracted data having a frequency distribution similar to that in the original data has been extracted based on the error being equal to or less than the threshold value.

[0044] If it is determined in the process of step S150 that the error is equal to or less than the threshold value (step S150: YES), the processing circuit 510 records the cut-out pattern. Specifically, the processing circuit 510 stores data on the start and end of each time window in the cut-out pattern in the storage device 520 as information that identifies the cut-out pattern. After recording the cut-out pattern in this way, the processing circuit 510 proceeds to the process of step S160.

[0045] On the other hand, if it is determined in the process of step S150 that the error is greater than the threshold value (step S150: NO), the processing circuit 510 returns the process to step S110. That is, the processing circuit 510 starts the process of setting new multiple time windows in order to reset the time windows and extract extracted data from the original data.

[0046] In this way, processing circuit 510 repeats the processes of steps S110 to S150 using the frequency distribution for each feature amount until extracted data similar to the original data is extracted. As a result, storage device 520 stores an extraction pattern whose error is equal to or smaller than the threshold value.

[0047] In the process of step S160, the processing circuit 510 extracts extracted data by extracting data from the original data based on an extraction pattern of extracted data that is similar to the original data and that is stored in the storage device 520.

[0048] After executing the process of step S160, the processing circuit 510 ends the above series of processes based on the program. In this manner, processing circuitry 510 extracts data used to analyze the driver's driving preferences.

[0049] <Operation of this embodiment> The data center 500, which is an information processing device of this embodiment, acquires original data that is collected over a predetermined period of time using a plurality of sensors mounted on the vehicle 10. Then, the data center 500 extracts data to be used for analyzing the driving preferences of the driver from the original data.

[0050] The data center 500 includes a processing circuit 510. The original data includes, as a plurality of feature quantities, information on a plurality of vehicle operations by the driver and the vehicle speed. In the data center 500, the processing circuit 510 executes a search process. The search process includes a first step (step S130) of calculating a frequency distribution in the original data for each of the plurality of feature quantities.

[0051] The search process includes a second step (step S110) of setting a plurality of time windows that extract data for a partial period of the original data so that the total period of all the time windows is shorter than the period of the entire original data.

[0052] The search process includes a third step (step S120) of extracting data from the original data using a plurality of time windows. The extracted data is the data obtained by combining all of the data extracted using the plurality of time windows.

[0053] The search process includes a fourth step (step S130) of calculating a frequency distribution in the extracted data for each feature amount. The search process includes a fifth step (step S140) of calculating each error between the frequency distribution of each feature amount in the original data and the frequency distribution of each feature amount in the extracted data.

[0054] The search process includes a sixth step (step S150) of determining whether the original data and the extracted data are similar based on the above errors. After executing step 1, processing circuit 510 executes a search process in which steps 2 to 6 are repeatedly executed by changing the settings of multiple time windows. Then, processing circuit 510 extracts the extracted data when it is determined that the original data and the extracted data are similar as data to be used for analyzing the driver's driving preferences (step S160).

[0055] The data center 500 can analyze driving preferences using extracted data whose distribution of features related to driving preferences is similar to that of the original data. Therefore, the data center 500 can obtain analysis results similar to those of an analysis of driving preferences using the original data.

[0056] The extracted data extracted by the data center 500 is a portion of the original data. Therefore, the amount of extracted data is smaller than that of the original data. The processing time required to analyze driving preferences increases as the amount of data used for the analysis increases. By using the extracted data, the data center 500 can shorten the analysis time compared to when using the original data.

[0057] <Effects of this embodiment> (1) According to the data center 500, whether the original data and the extracted data are similar to each other is determined based on a plurality of feature quantities including information about a plurality of vehicle operations by the driver. Therefore, data suitable for analyzing the driver's driving preferences can be extracted from the original data.

[0058] (2) The plurality of feature quantities includes vehicle speed in addition to information about a plurality of vehicle operations, so that data suitable for analyzing the relationship between the driver's driving preferences and vehicle speed can be extracted.

[0059] (3) One of the plurality of pieces of information about vehicle operations is the driving mode of the vehicle selected by the driver. Therefore, data suitable for analyzing the driver's driving preferences, such as preference for sporty driving, can be extracted.

[0060] (4) One of the pieces of information related to the vehicle operations is the accelerator operation amount of the driver. Therefore, data suitable for analyzing the driver's driving preferences regarding the acceleration of the vehicle 10 can be extracted.

[0061] (5) One of the pieces of information related to the vehicle operations is the amount of braking performed by the driver. Therefore, data suitable for analyzing the driver's driving preferences regarding deceleration of the vehicle 10 can be extracted.

[0062] <Example of change> This embodiment can be modified as follows: This embodiment and the following modifications can be combined and implemented within the scope of technical compatibility.

[0063] The above plurality of feature amounts do not have to include vehicle speed. The information relating to the plurality of vehicle operations may include at least one of a driving mode, an accelerator operation amount, and a brake operation amount.

[0064] The information about the plurality of vehicle operations may include a steering angle. In this case, data suitable for analyzing the driver's driving preferences regarding steering of the vehicle 10 can be extracted.

[0065] The plurality of feature quantities may include location information of the vehicle 10 in addition to information about the vehicle operations. In this case, data suitable for analyzing the driver's driving preferences related to the vehicle's driving area, which is determined from the location information, can be extracted. Examples of driving areas include mountainous areas and urban areas.

[0066] The accelerator operation amount, brake operation amount, and vehicle speed included in the original data and the extracted data are divided into three categories: a category for driving mode D, a category for driving mode Ds, and a category for driving mode B. The processing circuit 510 may then calculate the frequency distribution described above for each of the three driving mode categories.

[0067] The driving modes, accelerator operation amounts, and brake operation amounts included in the original data and the extracted data may be divided into two categories, for example, a category for vehicle speeds below a predetermined value and a category for vehicle speeds equal to or greater than the predetermined value. The processing circuit 510 may then calculate the frequency distribution described above for each of the two vehicle speed categories.

[0068] When the information relating to the plurality of vehicle operations includes a steering angle, the frequency distribution described above may be calculated as follows. That is, the driving mode, accelerator operation amount, brake operation amount, and vehicle speed included in the original data and the extracted data may be divided into two categories, for example, a category where the steering angle is within a predetermined range and a category where it is outside the predetermined range. Then, the processing circuit 510 may calculate the frequency distribution described above for each of the two categories of steering angle.

[0069] When the plurality of feature quantities includes location information of the vehicle 10 in addition to information related to the plurality of vehicle operations, the frequency distribution described above may be calculated as follows. That is, the processing circuit 510 divides the driving area, for example, into urban areas and mountainous areas based on the location information. The processing circuit 510 divides the driving mode, accelerator operation amount, brake operation amount, and vehicle speed contained in the original data and the extracted data into two categories, an urban area category and a rural area category. Then, the processing circuit 510 may calculate the frequency distribution described above for each of the two driving area categories.

[0070] In the above embodiment, an example has been shown in which the information processing device is embodied as the data center 500. However, the above information processing device may also be embodied as the information processing terminal 600. In this case, the processing circuit 610 of the information processing terminal 600 acquires the original data from the data center 500 in the process of step S100 shown in FIG. 3 . Then, the processing circuit 610 executes the processes from step S110 onwards shown in FIG. 3 . The above information processing device may also be embodied as a control device of the vehicle 10. In this case, for example, the processing circuit included in the vehicle control unit 90 of the vehicle 10 acquires the original data from the data center 500 in the process of step S100 shown in FIG. 3 . Then, the processing circuit of the vehicle control unit 90 executes the processes from step S110 onwards shown in FIG. 3 .

[0071] The data center 500 determines the similarity between the original data and the extracted data by calculating the error in the frequency distribution. Alternatively, the data center 500 may determine whether the original data and the extracted data are similar without calculating the error. For example, by using a statistical method such as a goodness-of-fit test, it can be determined that the original data and the extracted data are similar when the difference between them is not significant.

[0072] The number of motor generators 23 provided in the vehicle 10 can be changed as appropriate. The vehicle 10 is not limited to a hybrid vehicle equipped with the series-parallel system shown in FIG. 1, but may be a hybrid vehicle equipped with another system.

[0073] The vehicle 10 may be a vehicle equipped with only an electric motor as a prime mover. The vehicle 10 may be a vehicle equipped with only an internal combustion engine as a prime mover. [Explanation of symbols]

[0074] 10...vehicle 21...internal combustion engine 23A...first motor generator 23B...second motor generator 24...PCU 25...battery 50...planetary gear mechanism 60...drive wheels 90...vehicle control unit 91...first control device 92...second control device 99...communication device 400...communication network 500...data center 510...processing circuit 520...storage device 530...communication device 600...information processing terminal 610...processing circuit 620...storage device 630...communication device

Claims

1. An information processing device that acquires original data collected over a predetermined period of time using a sensor mounted on a vehicle, and extracts data from the original data to be used for analyzing driving preferences of a driver who drives the vehicle, a processing circuit; The processing circuitry a first step of calculating a frequency distribution in the original data for each of a plurality of feature amounts including information about a plurality of vehicle operations by the driver included in the original data; a second step of setting a plurality of time windows to extract data for a portion of the original data such that the sum of the periods of all the time windows is shorter than the predetermined period; a third step of extracting data from the original data using the plurality of time windows; a fourth step of calculating, for each of the plurality of feature quantities, a frequency distribution in extracted data obtained by combining all of the data extracted using the plurality of time windows for the plurality of feature quantities; a fifth step of calculating each error between the frequency distribution for each of the feature quantities in the original data and the frequency distribution for each of the feature quantities in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on each error, while changing the settings of the plurality of time windows; When it is determined that the original data and the extracted data are similar, the extracted data is extracted as data to be used for analyzing the driving preferences of the driver. Information processing device.

2. The plurality of feature amounts includes a vehicle speed in addition to information about the plurality of vehicle operations. The information processing device according to claim 1 .

3. One of the plurality of pieces of information about vehicle operations is a driving mode of the vehicle selected by an operation of the driver.

3. The information processing device according to claim 1.

4. One of the plurality of pieces of information about vehicle operations is the accelerator operation amount of the driver.

3. The information processing device according to claim 1.

5. One of the plurality of pieces of information about vehicle operations is the amount of braking operation by the driver.

3. The information processing device according to claim 1.

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