Information processing apparatus and information processing method

Abstract

To provide an information processor and an information processing method capable of analyzing and evaluating driving behavior.SOLUTION: An estimation unit configured to estimate a driving behavior of a driver of a vehicle based on information including an acceleration or a speed acquired via a network, using a learning model generated or updated using observation information regarding the driving behavior of the driver of the vehicle and the information including the acceleration or the speed, wherein the information regarding the driving behavior of the driver of the vehicle estimated by the estimation unit is information used to generate information regarding the occurrence positions of the dangerous driving incidents of the drivers of the plurality of vehicles.SELECTED DRAWING: Figure 3

Description

[Technical Field]

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

[0002] Recently, machine learning algorithms have been used in various fields, such as systems that use machine learning to evaluate the driving behavior of automobile drivers. Patent Document 1 (Japanese Patent No. 6264492) discloses a system that evaluates the driver's level of concentration on driving based on a captured image of the driver's face. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6264492 Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the present disclosure is to provide an information processing device and an information processing method that enable analysis and evaluation of driving behavior, for example. [Means for solving the problem]

[0005] A first aspect of the present disclosure provides: an estimation unit that estimates the driving behavior of the vehicle driver based on the information including the acceleration or speed acquired via a network, using a learning model that is generated or updated using observation information regarding the driving behavior of the vehicle driver and information including the acceleration or speed; Preparation, The information about the driving behavior of the vehicle driver estimated by the estimation unit is information used to generate information about the locations where dangerous driving by the drivers of multiple vehicles occurs. It is located in the information processing device.

[0006] Furthermore, a second aspect of the present disclosure is The information processing device Using a learning model generated or updated using observation information on the driving behavior of the vehicle driver and information including acceleration or speed, estimating the driving behavior of the vehicle driver based on information including acceleration or speed acquired via a network; The information on the estimated driving behavior of the vehicle driver is information used to generate information on the locations where dangerous driving by the drivers of multiple vehicles occurs. It's in the way information is processed.

[0007] The program of the present disclosure is a program that can be provided, for example, via a storage medium or communication medium in a computer-readable format to an information processing device or computer system capable of executing various program codes. By providing such a program in a computer-readable format, processing according to the program is realized on the information processing device or computer system.

[0008] Further objects, features, and advantages of the present disclosure will become apparent from the following detailed description of the embodiments of the present disclosure and the accompanying drawings. Note that in this specification, a system refers to a logical collective configuration of multiple devices, and is not limited to devices that are located within the same housing. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an overview of processing according to the present disclosure. [Figure 2] FIG. 10 is a diagram illustrating an example of information acquired by a mobile terminal. [Figure 3] FIG. 10 is a diagram illustrating a learning model generation process performed by the management server. [Figure 4] FIG. 10 is a diagram illustrating an example of observation information. [Figure 5] 10A and 10B are diagrams illustrating an example of driving behavior data, and a learning model generation process executed by a learning processing unit of a management server will be described. [Figure 6]FIG. 10 is a diagram illustrating an example of learning data. [Figure 7] 10A and 10B are diagrams illustrating an example of processing by a management server that executes a driving behavior estimation process using a learning model. [Figure 8] FIG. 10 is a flowchart illustrating a processing sequence of a driving behavior estimation process using a learning model executed by a management server. [Figure 9] 10A and 10B are diagrams illustrating a specific example of an estimation reliability calculation process. [Figure 10] FIG. 2 is a diagram illustrating a driving behavior estimation application stored in a mobile terminal. [Figure 11] FIG. 2 is a diagram illustrating main functions of a driving behavior estimation application. [Figure 12] FIG. 10 is a flowchart illustrating a processing sequence of a driving behavior estimation process using a learning model executed by a mobile terminal and a management server. [Figure 13] FIG. 10 is a flowchart illustrating a processing sequence of a score calculation process using a driving behavior estimation result. [Figure 14] FIG. 2 is a diagram illustrating data stored in a driving behavior analysis result DB (database) generated by the management server. [Figure 15] FIG. 2 is a diagram illustrating data stored in a driving behavior analysis result DB (database) generated by the management server. [Figure 16] FIG. 10 is a diagram illustrating score analysis data for each category. [Figure 17] FIG. 10 is a flowchart illustrating a processing sequence of a road area setting process based on score analysis data for each category. [Figure 18] FIG. 10 is a flowchart illustrating a processing sequence before starting driving using a driving behavior estimation application executed on a mobile terminal. [Figure 19] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 20] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 21] FIG. 10 is a flowchart illustrating a processing sequence during driving using a driving behavior estimation application executed on a mobile terminal. [Figure 22] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 23] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 24] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 25] FIG. 10 is a flowchart illustrating a post-driving processing sequence using a driving behavior estimation application executed on a mobile terminal. [Figure 26] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 27] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 28] FIG. 10 is a flowchart illustrating a post-driving processing sequence using a driving behavior estimation application executed on a mobile terminal. [Figure 29] FIG. 10 is a diagram illustrating an example of a display screen of a mobile terminal. [Figure 30] FIG. 1 is a diagram illustrating an example of a hardware configuration of an information processing device that can be used as a mobile terminal or a management server. DETAILED DESCRIPTION OF THE INVENTION

[0010] The information processing device, information processing system, information processing method, and program of the present disclosure will be described in detail below with reference to the drawings. The description will be made according to the following items. 1. Overview of the Disclosure Process 2. Process for generating learning models to estimate driving behavior from device-acquired information 3. Driving behavior estimation process using learning models 4. Processing using a driving behavior estimation app on a mobile device 5. Processing using the driving behavior estimation app after building the driving behavior analysis database 5-(1) Processing before starting driving using the driving behavior estimation app 5-(2) Regarding processing while driving using a driving behavior estimation app 5-(3) Post-driving processing using the driving behavior estimation app 6. Configuration examples of information processing devices 7. Summary of the Disclosure

[0011] [1. Overview of the Disclosure Process] The present disclosure makes it possible to analyze and evaluate driving behavior based on information acquired by a mobile device, such as a smartphone, carried by a vehicle driver or passenger.

[0012] An overview of the processing of the present disclosure will be described with reference to FIG. 1 shows a vehicle 10. The vehicle 10 is driven by a driver 11. A driver 11 or a passenger (not shown) owns a mobile terminal such as a smartphone. This mobile terminal is a mobile terminal 20 shown in FIG.

[0013] The vehicle 10 has an ECU (Electrical Control Unit), which is a control unit that performs control processing of the vehicle 10 and processing for acquiring operation information. The ECU has an OBD (On-Board Diagnostics) as one of its components. The OBD is one of the functions of the ECU and is a program that mainly provides a diagnostic function for the vehicle 10. The OBD of the ECU of the vehicle 10 sequentially transmits information about the vehicle 10, such as the vehicle's speed and acceleration information, to the management server 30 via the network.

[0014] A mobile terminal 20 owned by a driver 11 or a passenger has a configuration capable of communicating with a management server 30, a plurality of information providing servers 41, 42, . . . and service providing servers 43, 44, . . . via a network. The information providing servers 41, 42, etc. are servers that provide various information, such as a traffic information providing server, a weather information providing server, etc. The service providing servers 43, 44, etc. are servers that provide various services, such as an insurance company server, a product sales server, etc.

[0015] An information acquisition application 21 is installed in advance on the mobile terminal 20. The information acquisition application 21 acquires various pieces of information that can be used to analyze and evaluate the driving behavior of the driver 11. The information acquired by the mobile terminal 20 includes, for example, the following information: (1) Information obtained from the acceleration sensor and GPS on the mobile device itself, (2) Information acquired via the information providing servers 41 and 42 (traffic information, etc.) The mobile terminal 20 can acquire these various pieces of information.

[0016] 2 shows an example of information acquired by the mobile terminal 20. As shown in FIG. 2, the mobile terminal 20 acquires, for example, the following information. (a1) Acceleration information (a2) Rotation speed information (a3) GPS information (longitude, latitude, speed information, etc.) (a4) Atmospheric pressure information (a5) Direction information (direction of travel (north, south, east, west, etc.)) (a6) Terminal operation information (a7) Traffic information

[0017] (a1) The acceleration information is acquired from, for example, an acceleration sensor of the mobile terminal 20 itself. (a2) The rotation speed information is acquired from, for example, a gyro sensor of the mobile terminal 20 itself. (a3) GPS information (longitude, latitude, speed information, etc.) is acquired from, for example, a GPS sensor of the mobile terminal 20 itself. (a4) The atmospheric pressure information is acquired from, for example, an atmospheric pressure sensor of the mobile terminal 20 itself. (a5) Direction information (direction of travel (north, south, east, west, etc.)) is acquired from a geomagnetic sensor of the mobile terminal 20 itself, for example. (a6) The terminal operation information is acquired, for example, from an operation information detection sensor of the mobile terminal 20 itself. (a7) Traffic information is obtained, for example, from an external traffic information providing server (information providing server). In this way, the mobile terminal 20 can acquire various information from its own sensors and external servers. The acquired information is sequentially transmitted from the mobile terminal 20 to the management server 30.

[0018] [2. Regarding the process of generating a learning model for estimating driving behavior from terminal-acquired information] The present disclosure makes it possible to analyze and evaluate the driving behavior of a driver 11 who drives a vehicle 10 based on information acquired by a mobile terminal 20. To enable this process, it is first necessary to generate a learning model. The learning model generation process will be described with reference to FIG. 3 and subsequent figures.

[0019] The management server 30 executes the process of generating the learning model. FIG. 3 is a diagram illustrating the process of generating the learning model 81 by the management server 30. That is, this is a diagram illustrating the process of generating a learning model 81 that is applied to analyze and evaluate the driving behavior of the driver 11 who drives the vehicle 10, based on information acquired by the mobile terminal 20.

[0020] As shown in FIG. 3, the learning processing unit 80 of the management server 30 acquires terminal acquisition information 50 from the mobile terminal 20. Furthermore, the learning processing unit 80 of the management server 30 acquires observation information 60 that is configured from the OBD of the ECU of the vehicle 10 and other input information.

[0021] (a) terminal-acquired information 50 from the mobile terminal 20; (b) Observation information 60 consisting of OBD of the ECU of the vehicle 10 and other input information; These two types of information become learning data to be applied to the learning process executed by the learning processing unit 80 of the management server 30. A learning model 81 is generated by the learning process using these learning data.

[0022] The terminal acquired information 50 acquired from the mobile terminal 20 is, for example, the various pieces of information (a1) to (a7) previously described with reference to FIG. The observation information 60, which is composed of the OBD of the ECU of one vehicle 10 and other input information, will be described with reference to FIG.

[0023] Fig. 4 shows an example of the observation information 60. As shown in Fig. 4, the observation information 60 is made up of, for example, the following information. (b1) Vehicle longitudinal acceleration information (b2) Vehicle lateral acceleration information (b3) Device operation information The observed information is actual observed information of the driving behavior of the driver 11, and corresponds to actual driving behavior information.

[0024] (b1) Vehicle longitudinal acceleration information is actual longitudinal acceleration information of the vehicle 10 acquired from the OBD of the ECU of the vehicle 10. (b2) Vehicle lateral acceleration information is actual lateral acceleration information of the vehicle 10 acquired from the OBD of the ECU of the vehicle 10. (b3) The terminal operation information is, for example, information input from a terminal carried by a passenger other than the driver of the vehicle 10, and is actual observation information indicating whether the driver is operating the mobile terminal 20 or not. This information is acquired when processing to generate the learning model 81 is performed, and is transmitted to the management server 30.

[0025] After the learning model 81 is generated, the process of acquiring these observation information is no longer necessary. After the learning model 81 is generated, it is possible to apply the generated learning model 81 to perform an estimation process of the driving behavior of the driver 11 from the information acquired by the mobile terminal 20.

[0026] In addition, when the learning processing unit 80 of the management server 30 updates the learning model 81, it acquires new terminal acquisition information 50 and observation information 60, and performs learning processing using these as new learning data to update the learning model 81.

[0027] A specific example of the process of generating the learning model 81 executed by the learning processing unit 80 of the management server 30, that is, the learning process, will be described with reference to FIG. FIG. 5 shows a learning processing unit 80 of the management server 30 and a learning model 81 generated as a result of the learning processing in the learning processing unit 80. First, the learning processing unit 80 of the management server 30 collects learning data 70 to be applied to the learning process. The collected learning data 70 is made up of the following data. (A) Device Acquisition Information (B) Observation information (= driving behavior information)

[0028] (A) Terminal-acquired information is terminal-acquired information 50 acquired by mobile terminal 20 shown in FIG. 3, and is, for example, the various pieces of information (a1) to (a7) previously described with reference to FIG. On the other hand, (B) observation information is observation information 60 composed of the OBD of the ECU of vehicle 10 shown in Figure 3 and other input information, for example, various observation information (= driving behavior information) of (b1) to (b3) previously described with reference to Figure 4. Each of these pieces of information is time-series data and is acquired as data corresponding to the time axis.

[0029] The learning processing unit 80 of the management server 30 executes learning processing based on the learning data 70. That is, a machine learning algorithm is trained using the collected learning data 70. As the machine learning algorithm, an algorithm that can calculate the reliability (estimation reliability) of the estimation result using a learning model, such as a Gaussian process or a Bayesian neural network, is optimal.

[0030] The estimation reliability is an index that indicates how accurate the estimation result is. For example, in machine learning, the higher the degree of match between the patterns contained in the training data and the behavior pattern at the time of estimation, the higher the reliability. The estimation reliability uses a value between 1 and 0, for example. The highest estimation reliability is 1, and the lowest estimation reliability is 0.

[0031] In this embodiment, the estimation reliability is the estimation reliability of the driver behavior estimated value estimated by applying a learning model based on the terminal-acquired information. In order to increase the reliability of estimation, it is effective to perform a learning process using a larger amount of learning data.

[0032] 5 shows an example of generation of a (machine) learning model using a Gaussian neural network as an example of learning processing executed by the learning processing unit 80. There are various methods for designing a learning model, and one example is a method in which all types of terminal-acquired information (e.g., (a1) to (a7) shown in FIG. 2) are input into one model, and all driving behavior information (e.g., (b1) to (b3) shown in FIG. 4) is simultaneously estimated as estimation data. Furthermore, for example, if correlation analysis has been performed to show that specific terminal-acquired information is highly correlated with specific driving behavior information, there is also a method of preferentially selecting and estimating specific driving behavior from terminal-acquired information that is highly correlated with that behavior.

[0033] In this embodiment, as an example of a learning model, we will explain an example of performing a learning model generation process in which multiple pieces of information selected from terminal-acquired information are simultaneously input into the learning processing unit 80, and one or more pieces of driving behavior information can be output as output information.

[0034] The learning process sequence will be briefly described. (S1) Machine learning model design First, in the process of step S1, a (machine) learning model to be used in the learning process is designed. Machine learning models are based on a predetermined theoretical model (such as a Gaussian process or a Bayesian neural network), and various parameters are designed to match the corresponding input and output signals. In the case of a Gaussian process, the parameters are the mean function and covariance function, while in the case of a Bayesian neural network, the parameters are the number of network layers and the activation function.

[0035] (S2) Learning process using machine learning models Next, in step S2, a learning process is executed using the machine learning model. This learning process uses the above-mentioned learning data 70. The collected learning data 70 is the following data. (A) Device Acquisition Information (B) Observation information (driving behavior information) As described above, each piece of information is time-series data and is acquired as data corresponding to the time axis.

[0036] An example of the training data 70 is shown in FIG. As shown in Figure 6, the training data is (A) Device Acquisition Information (B) Observation information (driving behavior information) It is composed of these correspondence data. 6 shows a plurality of entries (e1) to (en), each of which is made up of correspondence data between one or more pieces of terminal-acquired information and observed information (driving behavior information).

[0037] During the learning process, the parameters of the machine learning model are optimized using time-series synchronized learning data, i.e., the entries (e1) to (en) shown in Figure 6. The optimization method depends on the theoretical model used.

[0038] As a result of these learning processes, a learning model 81 is generated that can output an output signal (=driving behavior estimated value) based on various input signals (=terminal acquired information). By using this learning model 81, it is possible to output an optimal output signal, i.e., a driving behavior estimation value, even for an input signal (= terminal acquired information) that does not match the input signal (= terminal acquired information) of an entry included in the learning data (see Figure 6) applied to the learning process.

[0039] The learning model 81 is a model that applies an algorithm that can calculate the reliability (estimation reliability) of the estimation results using the learning model, such as a Gaussian process or a Bayesian neural network, and outputs an estimation reliability that indicates the reliability of the driving behavior estimation value along with the driving behavior estimation value.

[0040] [3. Driving behavior estimation process using learning models] Next, a driving behavior estimation process using the learning model generated by the above-described learning process will be described. In this process, the management server 30 acquires information acquired by the mobile terminal 20 held by the driver 11 of the vehicle 10 or the passenger, and estimates the driving behavior of the driver 11 using the learning model 81 generated by the aforementioned learning process. Furthermore, in this embodiment, as described above, the estimation reliability, which is the reliability of the driving behavior estimated value, is also generated and output. The estimation reliability uses a value between 1 and 0, for example. The highest estimation reliability is 1, and the lowest estimation reliability is 0.

[0041] FIG. 7 shows an example of processing by the management server 30 that executes a driving behavior estimation process using a learning model. The driving behavior estimation unit 90, which is a data processing unit of the management server 30, receives terminal-acquired information from the mobile terminal of the user riding in the vehicle via the network. This terminal-acquired information is the following information that was previously described with reference to FIG. (a1) Acceleration information (a2) Rotation speed information (a3) GPS information (longitude, latitude, speed information, etc.) (a4) Atmospheric pressure information (a5) Direction information (direction of travel (north, south, east, west, etc.)) (a6) Terminal operation information (a7) Traffic information It is not necessary to input all of these pieces of information, and only some of them may be input.

[0042] When the driving behavior estimation unit 90, which is a data processing unit of the management server 30, receives the terminal-acquired information, it uses the learning model 81 generated in advance to estimate driving behavior information from the received terminal-acquired information. If the learning model 81 contains a data set (entry) that completely matches the input terminal-acquired information, the driving behavior information associated with that entry of the learning model can be output as a driving behavior estimation value. In this case, the estimation reliability of the output (driving behavior estimation value) will be close to 1 (highest reliability).

[0043] However, in reality, it is unlikely that the learning model 81 will have a data set (entry) that perfectly matches the input terminal-acquired information. In the actual estimation process, the final driving behavior estimation value is calculated and output by appropriately combining learning models similar to the input device-acquired information. In this case, for example, the estimation reliability is calculated according to the similarity between the input device-acquired information and the dataset of the learning model used.

[0044] The processing sequence of the driving behavior estimation process using a learning model executed by the management server 30 will be described with reference to the flowchart shown in FIG. The processing according to this flow is executed in accordance with a program stored in a storage unit in the management server 30 under the control of a control unit (data processing unit) equipped with a CPU or the like having a program execution function. The processing of each step of the flow shown in Fig. 8 will be explained in order.

[0045] (Step S101) First, in step S101, the management server 30 inputs the terminal acquisition information acquired by the user terminal (mobile terminal), which is the following information described above with reference to FIG. (a1) Acceleration information (a2) Rotation speed information (a3) GPS information (longitude, latitude, speed information, etc.) (a4) Atmospheric pressure information (a5) Direction information (direction of travel (north, south, east, west, etc.)) (a6) Terminal operation information (a7) Traffic information It is not necessary to input all of these pieces of information, and only some of them may be input.

[0046] In addition, the user terminal (mobile terminal) also sends attribute data such as the driving date and time, vehicle type, driver ID, and mobile terminal ID along with the above terminal acquisition information, and the management server acquires this data and records it in the database along with the estimation results obtained by the next estimation process to be executed.

[0047] (Step S102) Next, in step S102, the driving behavior estimation unit 90, which is a data processing unit of the management server 30, applies the learning model to calculate a driving behavior estimation value based on the terminal-acquired information, and also calculates the reliability (estimation reliability) of the calculated driving behavior estimation value.

[0048] As described above, the driving behavior estimation unit 90 of the management server 30 inputs input information, i.e., terminal-acquired information, into a learning model that executes an algorithm such as a Gaussian process or a Bayesian neural network, and outputs a driving behavior estimation value as an output value. Furthermore, it calculates and outputs the estimation reliability of the driving behavior estimation value, which is the output value.

[0049] The reliability (estimation reliability) is calculated for each estimated driving behavior item, and as described above, has a value ranging from 0 (low reliability) to 1 (high reliability), for example. A specific example of the estimation reliability calculation process will be described with reference to FIG. Figure 9 shows the distribution data of the dataset (entries) of the learning data used to build the learning model. The coordinates are N-dimensional coordinates corresponding to the N-dimensional feature space of the machine learning model.

[0050] The black dots correspond to the training data sets (entries), and the dotted lines indicate the areas where the training data sets (entries) exist. Here, for example, when the input terminal acquired information ((a1) to (a7)) is arranged in an N-dimensional feature space, it is assumed that the corresponding point of one piece of terminal acquired information ((a1) to (a7)) is the position of point A. Also, suppose that the corresponding point in another piece of terminal acquired information ((a1) to (a7)) is the position of point B.

[0051] In this case, point A exists in N-dimensional space close to the learning data set (entry) indicated by the black dot. That is, point A exists at a position close to the learning data set (entry). In this case, a highly reliable output using the learning data set (entry) close to point A, that is, a driving behavior estimation with a high estimation reliability, is possible. That is, the reliability of the driving behavior information estimated based on point A (estimation reliability) is calculated as a high value (a value close to 1).

[0052] On the other hand, point B exists in N-dimensional space far from the training data set (entry) indicated by the black dot. That is, point B exists at a position far away from the training data set (entry). In this case, even if the training data set (entry) closest to point B is used, the similarity between that training data set (entry) and point B is low. In this case, an output with low reliability, that is, a driving behavior estimation with low estimation reliability, is performed. That is, the reliability of the driving behavior information estimated based on point B (estimation reliability) is calculated as a low value (a value close to 0).

[0053] (Step S103) Next, in step S103, the driving behavior estimation unit 90 of the management server 30 transmits the driving behavior estimation value and the reliability to the user terminal (mobile terminal) and other information utilization servers. Note that the transmission data is preferably transmitted as encrypted data.

[0054] The information utilization server is, for example, an automobile company that collects automobile driving behavior data, a police force that collects traffic violation information, or an insurance company that calculates insurance premiums according to driving behavior.

[0055] (Step S104) Finally, in step S104, the driving behavior estimation unit 90 of the management server 30 records the driving behavior estimation value and reliability in the DB in association with attribute data such as the driving date and time, vehicle type, driver ID, and mobile terminal ID.

[0056] [4. Processing using a mobile device driving behavior estimation app] Next, a process of installing a driving behavior estimating app on the mobile terminal 20 owned by the driver or a passenger in the vehicle 10, and activating and executing the driving behavior estimating app will be described.

[0057] One of the main functions of the driving behavior estimation application in the mobile terminal 20 is to perform driving behavior estimation processing based on terminal-acquired information, but it also has various other functions. These processes will be described below.

[0058] When the driving behavior estimation application of the mobile terminal 20 is used to estimate the driving behavior based on the terminal-acquired information, one of the following processes is executed. (1) The acquired information of the mobile terminal 20 is transmitted to the management server 30, and the management server 30 estimates the driving behavior using a learning model. (2) The mobile terminal 20 acquires the learning model generated by the management server 30, and calculates a driving behavior estimate based on the terminal-acquired information within the mobile terminal 20. When driving behavior estimation is performed in the manner (2), the mobile terminal 20 also transmits the terminal-acquired information and the driving behavior estimation value to the management server 30.

[0059] Fig. 10 shows a diagram similar to Fig. 1 described above. A vehicle 10 is driven by a driver 11. The driver 11 or a passenger (not shown) owns a mobile terminal such as a smartphone. This mobile terminal is shown as mobile terminal 20 in Fig. 10.

[0060] A driving behavior estimation application 22 is installed on the mobile terminal 20. The driving behavior estimation application 22 applies a learning model to perform various processes for estimating driving behavior based on terminal-acquired information. The driving behavior estimation application 22 is an application that also includes the functions of the information acquisition application 21 described above with reference to FIG. 1. The driving behavior estimating application 22 also performs processing such as transmitting terminal-acquired information to the management server 30 and displaying data (maps, score information, etc.) received from the management server 30. The processing performed by the driving behavior estimating application 22 will be described in detail below.

[0061] First, the main functions of the driving behavior estimation application 22 will be described with reference to FIG. As shown in FIG. 11, the driving behavior estimation application 22 has, for example, the following functions. (1) Initial settings (register the car model and mobile device model name) (2) Notification of approaching dangerous driving zones (notification mode can also be configured) (3) Map display and car navigation functions (4) Displaying and notifying drivers in advance of dangerous areas and other points requiring caution based on driving risk scores and driving reliability scores (5) Display of road areas for which driving scores are to be scored based on the estimated reliability of driving behavior estimates (6) Display of road areas eligible for reward points based on the reliability of estimated driving behavior values (7) Output and correction of driving diagnosis results The driving behavior estimation application 22 has, for example, these functions. Details of these functions will be explained in the following description of the embodiments.

[0062] The above functions (1) to (7) include functions that use the estimation reliability of the driving behavior estimation value and functions that do not. For example, when the estimation reliability is used, processing that uses the estimation reliability is performed within the app. In addition, the use of some functions is restricted to users. Note that some of the functions that utilize the estimation reliability will be made available to users through in-app function release processing by the service provider after the driving behavior analysis result DB (database) described below is constructed. Details will be described later.

[0063] The following describes processing using the driving behavior estimation application 22, and analysis processing using the processing results of the driving behavior estimation application 22. These processes will be explained below in order.

[0064] (Process 1) Download and initial settings by the user First, when using the driving behavior estimation application 22 on the mobile terminal 20, it is necessary to download the driving behavior estimation application 22 to the mobile terminal 20 and perform initial settings. The user of the mobile terminal 20 registers driver information (gender, age, etc.), information about the type of vehicle they will be driving, and information about the model of the mobile terminal they will be using on the initial setting screen. This registration information is recorded in the database of the management server 30.

[0065] (Process 2) Calculation of driving behavior estimates and prediction reliability using a learning model based on information acquired from the device while the vehicle is traveling Once the driving behavior estimation application 22 is downloaded to the mobile terminal 20 and initial settings are completed, the driving behavior estimation process using the driving behavior estimation application 22 becomes executable.

[0066] That is, when a user carries the mobile terminal 20 and drives a vehicle, a driving behavior estimation value is calculated using a learning model based on terminal-acquired information from the mobile terminal 20, and a calculation process for prediction reliability is executed.

[0067] The processing sequence of the driving behavior estimation process using a learning model executed by the mobile terminal 20 and the management server 30 will be described with reference to the flowchart shown in FIG. The processing according to this flow is executed by the driving behavior estimation application 22 of the mobile terminal 20. The processing of each step of the flow shown in Fig. 12 will be explained in order.

[0068] (Step S201) First, in step S201, the mobile terminal 20 inputs terminal-acquired information acquired by the mobile terminal 20. This information is as follows, which was previously described with reference to FIG. (a1) Acceleration information (a2) Rotation speed information (a3) GPS information (longitude, latitude, speed information, etc.) (a4) Atmospheric pressure information (a5) Direction information (direction of travel (north, south, east, west, etc.)) (a6) Terminal operation information (a7) Traffic information It is not necessary to input all of these pieces of information, and only some of them may be input.

[0069] In addition, the user terminal (mobile terminal) also sends attribute data such as the driving date and time, vehicle type, driver ID, and mobile terminal ID along with the above terminal acquisition information, and the management server acquires this data and records it in the database along with the estimation results obtained by the next estimation process to be executed.

[0070] (Step S202) Next, in step S202, the driving behavior estimation application 22 of the mobile terminal 20 applies the learning model to calculate a driving behavior estimation value based on the terminal-acquired information, and also calculates the reliability (estimation reliability) of the calculated driving behavior estimation value.

[0071] As described above, the learning model in the mobile terminal 20 can be used in one of the following ways. (1) A form in which the mobile terminal 20 acquires the learning model generated by the management server 30, stores it in the memory of the mobile terminal 20, and uses it. (2) When the mobile terminal 20 estimates the driving behavior, the learning model stored in the management server 30 is referenced and used. The driving behavior estimation application 22 of the mobile terminal 20 uses the learning model generated by the management server 30 in any of the above modes to estimate driving behavior based on terminal-acquired information.

[0072] The driving behavior estimation application 22 of the mobile terminal 20 calculates the driving behavior estimated value and also calculates the estimation reliability of the driving behavior estimated value.

[0073] (Step S203) Next, in step S203, the driving behavior estimation application 22 of the mobile terminal 20 records the driving behavior estimation value and reliability in the memory of the mobile terminal 20 in association with attribute data such as the driving date and time, vehicle type, driver ID, and mobile terminal ID.

[0074] (Step S204) Finally, in step S204, the driving behavior estimation application 22 of the mobile device 20 transmits the data stored in the memory in step S203, i.e., the driving behavior estimation value, reliability, and attribute data such as the driving date and time, driving location, vehicle type, driver ID, mobile device ID, etc. to the management server. Note that the transmitted data is preferably transmitted as encrypted data.

[0075] The data transmission process may be configured to transmit data sequentially, or may be configured to transmit data collectively at regular intervals. As will be further explained in (Process 6) below, the server transmission process in step S204 may be configured to transmit the score information calculated in the following (Processes 3 to 5) together with the score information.

[0076] (Process 3) Calculation of risk score using estimated driving behavior Next, a process of calculating a risk score using a driving behavior estimation value executed by the driving behavior estimation application 22 of the mobile terminal 20 will be described.

[0077] The driving behavior estimation application 22 of the mobile terminal 20 uses the driving behavior estimation value calculated in the above-mentioned (Process 2) to calculate a risk score, which is an index indicating the driving risk of the user (driver).

[0078] The driving behavior estimation application 22 calculates the risk score Dt at time t according to the following calculation formula (Formula 1). Dt=f D (d 1t ,d 2t ,···,d mt ))...(Formula 1) In the above (Equation 1), f D is the risk score calculation function, d 1t ,d 2t ,···,d mtis a set of estimated driving behavior values ​​calculated by applying a learning model. Specifically, it is a data set of estimated driving behavior values ​​at a certain time (t) estimated based on terminal-acquired information at that time (t). Each value included in the data set is an estimated value of various driving behavior information, such as (b1) to (b3) shown in FIG. 4.

[0079] The risk score calculation function f D The service operator designs the risk score calculation function f so that it increases as the driver behaves in a risky manner. Specifically, for example, as shown in the following (Equation 2), the risk score calculation function f is calculated by taking a weighted average of each estimated driving behavior. D is calculated. Dt=f D (d 1t ,d 2t ,···,d mt )) =w1d 1t +w2d 2t +···+w m d mt ...(Formula 2) However, w i (i=1, ,m) is the weighting coefficient.

[0080] (Process 4) Calculation of reliability score using driving behavior estimation value Next, a process of calculating a reliability score using a driving behavior estimation value executed by the driving behavior estimation application 22 of the mobile terminal 20 will be described.

[0081] The driving behavior estimation application 22 of the mobile terminal 20 uses the driving behavior estimation value and estimation reliability calculated in the above-mentioned (Process 2) to calculate a reliability score, which is an index value of the overall estimation reliability of the driving behavior estimation value calculated at a certain time (t).

[0082] The driving behavior estimation application 22 calculates the reliability score Rt at time t according to the following calculation formula (Formula 3). Rt=f R (r 1t ,r 2t ,···,r mt ))...(Formula 3) In the above (Equation 3), f R is the confidence score calculation function, r 1t ,r 2t ,···,r mt is a set of estimation reliabilities corresponding to estimated values ​​of driving behavior calculated by applying a learning model. Specifically, it is a data set of estimation reliabilities corresponding to estimated values ​​of driving behavior at a certain time (t) estimated based on terminal-acquired information at that time (t). Each value included in the data set is an estimation reliability corresponding to each of the estimated values ​​of various driving behavior information, such as (b1) to (b3) shown in FIG. 4.

[0083] The reliability score calculation function f R The service operator designs the reliability score calculation function f so that it increases as the estimation reliability of the driving behavior estimation value calculated by applying the learning model increases. Specifically, for example, as shown in the following (Equation 4), the reliability score calculation function f is calculated by taking a weighted average of each estimation reliability. R is calculated. Rt=f R (r 1t ,r 2t ,···,r mt )) =v1r 1t +v2r 2t +···+v m r mt ...(Formula 4) However, v i (i=1, ,m) is the weighting coefficient.

[0084] (Process 5) Calculation of the overall score using the risk score and the reliability score Next, a process of calculating the overall score using the risk score and the reliability score executed by the driving behavior estimation application 22 of the mobile terminal 20 will be described.

[0085] The risk score calculated in the above (Process 3) and the reliability score calculated in the above (Process 4) are used to calculate a total score indicating the driving diagnosis result of the driver. The driving behavior estimation application 22 calculates the total score St at time t according to the following calculation formula (Formula 5).

[0086] S t =f S (R t ,D t )...(Formula 5) In the above (Equation 5), f S is the overall score calculation function, R t is the confidence score at time t, D t is the risk score at time t, is.

[0087] Function f S is designed by the service operator. For example, the function f S is calculated as the reliability score R as shown in the following (Equation 6): t and a risk score of D. t A function that calculates the product of these and normalizes it to fall between 0 and 100 can be applied. S t =f S (R t ,D t ) =min(0,max(100,(R t D t ) / Z))...(Formula 6) where Z is a normalization constant. This calculation formula is an example, and various other calculation processes are possible.

[0088] According to the above (Equation 6), the overall score S t By calculating the above, it is possible to calculate a total score of 0 to 100 points depending on the level of risk in the user's (driver's) driving, for example. The lower the risk of the user's (driver's) driving, the closer to 100 points the score will be, and the higher the risk, the closer to 0 points the score will be.

[0089] (Process 6) Transmission of estimated driving behavior values ​​and calculated scores to the management server Next, a process of transmitting the driving behavior estimation value and the calculated score to the management server executed by the driving behavior estimation application 22 of the mobile terminal 20 will be described.

[0090] The driving behavior estimation application 22 of the mobile terminal 20 calculates the following data in the above (Process 2) to (Process 5) and stores it in memory. (1) Estimated driving behavior (2) Estimated reliability (3) Risk score (4) Trust score (5) Overall score Hereinafter, these data (1) to (5) are collectively referred to as "driving behavior analysis results."

[0091] The driving behavior analysis results, which are made up of the above data (1) to (5), are first stored in the memory of the mobile terminal 20. Furthermore, the driving behavior estimation application 22 of the mobile terminal 20 transmits the data stored in the memory, i.e., the "driving behavior analysis result" consisting of the data (1) to (5) above, as well as attribute data such as the driving date and time, the driving location, the vehicle type, the driver ID, and the mobile terminal ID to the management server. Note that the transmitted data is preferably transmitted as encrypted data. Note that the data transmission process may be configured to transmit data sequentially, or may be configured to be executed collectively at regular intervals.

[0092] The sequence of the above-mentioned (Process 3) to (Process 6) will be described with reference to the flowchart shown in Fig. 13. The flowchart shown in Fig. 13 is a flowchart for explaining the processing sequence of the score calculation process using the driving behavior estimation result. The processing of each step in the flowchart shown in FIG. 13 will be described below.

[0093] (Step S301) First, in step S301, the driving behavior estimation application 22 of the mobile terminal 20 calculates a driving risk score indicating a driving risk level based on the driving behavior estimation value. This process is the process for calculating the risk score Dt explained above in (Process 3).

[0094] (Step S302) Next, in step S302, the driving behavior estimation application 22 calculates a reliability score based on the driving behavior estimation value and the estimation reliability. This process is the calculation process of the reliability score Rt explained above in (Process 4).

[0095] (Step S303) Next, in step S303, the driving behavior estimation application 22 calculates a total score St of the driving diagnosis using the risk score Dt calculated in step S301 and the reliability score Rt calculated in step S302. This process is the calculation process of the total score St explained above in (Process 5).

[0096] (Step S304) Next, in step S304, the driving behavior estimation application 22 records the driving behavior estimation value, the estimation reliability, the driving risk score, the estimation reliability score, and the overall score in memory in association with attribute data such as the driving date and time, the driving location, the vehicle type, the driver ID, and the mobile terminal ID.

[0097] (Step S305) Next, in step S305, the driving behavior estimation application 22 transmits the data stored in the memory in step S304 to the management server. That is, the driving behavior estimation value, estimation reliability, driving risk score, estimation reliability score, and overall score are transmitted to the management server 30 along with attribute data such as driving date and time, driving location, vehicle type, driver ID, and mobile terminal ID. The processing in steps S304 and S305 is the processing explained in (Process 6) above.

[0098] (Process 7) Construction of a database of driving behavior analysis results Next, as Process 7, a process for constructing a driving behavior analysis result database executed by the management server 30 will be described.

[0099] The management server 30 receives the "driving behavior analysis results" described above (Process 6) and the accompanying attribute data (driving date and time, driving location, vehicle type, driver ID, mobile terminal ID, etc.) from multiple users.

[0100] The management server 30 constructs a driving behavior analysis result DB (database) based on this received data. The data stored in the driving behavior analysis result DB (database) 82 generated by the management server 30 will be described with reference to FIGS.

[0101] The driving behavior analysis result DB (database) 82 of the management server 30 includes: (1) Driver-specific vehicle model and terminal data, (2) driver-specific driving data shown in FIG. 14, and further (3) driver behavior information analysis data corresponding to driving data shown in FIG. 15 are stored.

[0102] 14, (1) vehicle model and terminal data for each driver (driver ID) includes vehicle model information and mobile terminal information. This information is registered when each user initially configures the driving behavior estimation application 22.

[0103] 14, a driving number and a driving table ID are recorded as driving information for each driver ID. The driving number and the driving table ID are a number and an ID that are automatically assigned by the driving behavior estimation application 22 for each driving unit when, for example, a user (driver) performs a driving process while executing the driving behavior estimation application 22. One travel unit is, for example, a period from when the user starts the engine until when the user stops the engine, or a period from when the user starts the driving behavior estimation application 22 until when the user stops the driving behavior estimation application 22.

[0104] For each driving table ID, (3) driving data-based driver behavior analysis data shown in FIG. 15 is generated and stored in the database. The (3) driving data-based driver behavior analysis data shown in FIG. 15 is made up of two tables. (3a) Driving data-based driver behavior analysis data a is a table that records correspondence data between a plurality of driving behavior estimation values ​​calculated by applying a learning model based on terminal-acquired information and estimation reliability.

[0105] (3b) Driver behavior analysis data b corresponding to driving data is a table that records the following information in addition to (1) risk score, (2) reliability score, and (3) overall score calculated based on the driving behavior estimate and estimation reliability recorded in (3a) driver behavior analysis data a corresponding to driving data. (4) Weather, (5) Driving location: Driving conditions (weather, driving location) at the time of the drive that was used to calculate the score (6) Scoring section: Information indicating whether the driving point is a scoring section for the user's (driver's) driving behavior, where 1 = scoring section and 0 = non-scoring section (7) Reward acquisition section: Information indicating whether the driving point is a section in which the user's (driver's) driving behavior is scored, 1 = reward acquisition section, 0 = non-reward acquisition section

[0106] (Process 8) Category-based score analysis processing for data stored in the driving behavior analysis result database Next, as Process 8, a category-by-category score analysis process for data stored in the driving behavior analysis result database 82, which is executed by the management server 30, will be described.

[0107] The management server 30 executes a score analysis process for each category using the data stored in the driving behavior analysis result database 82, which stores the data described with reference to FIGS. Specifically, for example, as shown in FIG. 16, the following category-based score analysis data is generated. (1) Analysis data of scores (risk score, reliability score, overall score) for each driving location (2) Analysis data of scores (risk score, reliability score, overall score) for each vehicle type (3) Analysis data of scores for each mobile device model (risk score, reliability score, overall score) These score data are also stored in the driving behavior analysis result database 82.

[0108] As shown in Fig. 16, (1) the score analysis data for each driving point is a table that stores the risk score, reliability score, and average data (statistics) of the total score corresponding to each driving point. These average scores are calculated by averaging the data received from the mobile terminals of multiple vehicles.

[0109] (2) The score analysis data for each vehicle type is a table that stores the risk score, reliability score, and average value data (statistics) of the total score corresponding to each vehicle type. (3) The score analysis data for each mobile terminal model is a table that stores the risk score, reliability score, and average data (statistics) of the total score corresponding to each mobile terminal model.

[0110] In the example shown in Figure 16, only the driving location, vehicle type, and mobile terminal model are shown as categories, but it is also possible to generate analysis data in various categories, such as information such as the driver's gender and age, driving time, weather, etc. In the example shown in FIG. 16, the average value is calculated as the statistical value of the score, but various values ​​such as the median value and variance of the score can be used as the statistical value.

[0111] (Process 9) Road area setting process based on category-based score analysis data Next, the road area setting process based on the score analysis data for each category, which is executed by the management server 30, will be described.

[0112] From the category-based score analysis data generated in the above (Process 8), "(1) Score analysis data per driving point" is obtained, and the reliability score and the statistical value (= average value, etc.) of the overall score per latitude and longitude coordinate (x, y) of the driving point are calculated, respectively. Confidence score statistic = R place (x,y), Overall score statistics = S place (x,y) Let's say.

[0113] Confidence score statistic R place (x,y) and the overall score statistic S place (x, y) are each a predetermined threshold: R thres ,S thres Point group A is larger than check ,A danger Search for.

[0114] in particular, Confidence score statistic > confidence score threshold, i.e., R place (x,y)>R thres Check out the points that meet the above conditions. Point A check Search as. Checkpoint A found by this search process check is set as the "road area for driving score scoring."

[0115] Also, Overall score statistic > overall score threshold, i.e., S place (x,y)>S thres The point that satisfies the above conditions is called danger point A. danger Search as. Danger point A found by this search process danger is set as the "road area where dangerous driving occurs."

[0116] Additionally, the confidence score statistic R place (x, y) and a predetermined reward point threshold: R2 thresPoint group A is smaller than reward Search for. in particular, Confidence score statistic < reward point threshold, i.e., R place (x,y) <R2 thres The point that satisfies the above conditions is called reward point A. reward Search as. Reward point awarding point A found by this search process reward is set as the "road area eligible for reward points acquisition."

[0117] The management server 30 stores the area information, i.e., (1) Road areas subject to driving score scoring (2) Road areas where dangerous driving occurs (3) Road areas eligible for reward points This area information is stored in a map information database managed by the management server 30. The information in this map information database is made available to users based on the judgment of the management server 30.

[0118] In addition, (1) Road area for driving score scoring: A check (2) Road area where dangerous driving occurs: A danger (3) Road area eligible for reward points: A reward These areas are expressed by the following equation (Equation 7):

[0119] A check ={(x,y)|R place (x,y)>R thres} A danger ={(x,y)|S place (x,y)>S thres} A reward ={(x,y)|R place (x,y) <R2 thres} ...(Formula 7)

[0120] A flowchart showing the procedure of this (Process 9) is shown in FIG. The processing of each step in the flow shown in FIG. 17 will be described.

[0121] (Step S401) First, in step S401, the management server 30 calculates the reliability score and the total score statistical value for each latitude and longitude coordinate (x, y) of the travel point, i.e., Confidence score statistic = R place (x,y), Overall score statistics = S place (x,y) These data are obtained.

[0122] (Step S402) Next, in step S402, the management server 30 performs a comparison process with a predetermined threshold value to determine: (1) Road area for driving score scoring: A check (2) Road area where dangerous driving occurs: A danger (3) Road area eligible for reward points: A reward Establish these areas.

[0123] That is, as described above, each area is defined by the following formula: A check ={(x,y)|R place (x,y)>R thres} A danger ={(x,y)|S place (x,y)>S thres} A reward ={(x,y)|R place (x,y) <R2 thres}

[0124] (Step S403) Next, in step S403, the management server 30 (1) Road area for driving score scoring: Acheck (2) Road area where dangerous driving occurs: A danger (3) Road area eligible for reward points: A reward This area information is registered in the map information database. As mentioned above, the information in this map information database is made available to users based on the judgment of the management server 30.

[0125] In this way, the management server 30 calculates statistics of risk scores, reliability scores, and overall scores corresponding to various vehicle types, models, locations, weather, and dates and times based on multiple driving data, and further sets each of the above-mentioned areas based on these statistics. The area setting information can be referenced by the user via the mobile terminal 20.

[0126] [5. Processing using the driving behavior estimation app after building the driving behavior analysis database] Next, a process that is executed by a user (such as a driver) using a driving behavior estimating application installed on the mobile terminal 20 after the driving behavior analysis DB 82 is constructed in the management server 30 will be described.

[0127] The following items will be explained in order. (1) Processing before starting driving using a driving behavior estimation app (2) Processing while driving using a driving behavior estimation app (3) Post-driving processing using a driving behavior estimation app

[0128] [5-(1) Processing before starting driving using the driving behavior estimation app] First, the processing before starting driving using the driving behavior estimation application will be described. With reference to the flowchart shown in FIG. 18, a processing sequence before starting driving using the driving behavior estimation application 22 executed on the mobile terminal 20 will be described.

[0129] (Step S501) First, in step S501, the user of the mobile terminal 20 starts the driving behavior estimation application 22 installed on the mobile terminal 20, displays the initial screen, and inputs the mobile terminal model information and the vehicle model information to be used, which are then transmitted to the management server 30.

[0130] (Step S502) Next, in step S502, the mobile terminal 20 receives from the management server 30 estimated reliability information () corresponding to the combination of the mobile terminal model information and vehicle model information input in step S501, and displays it on the mobile terminal 20.

[0131] FIG. 19 shows an example of the display screen of the mobile terminal 20. Terminal model: abcpohne-x Car model:xyz-czr These are the mobile terminal model information and vehicle model information entered by the user in step S501.

[0132] Estimation reliability: 87 (Comment: Highly accurate driving behavior estimation possible) This is the estimated reliability information received from the management server 30 in step S502, and corresponds to the combination of the mobile terminal model information and the vehicle model information input by the user. Furthermore, a comment according to the value of the estimated reliability is transmitted from the management server 30 and displayed on the mobile terminal 20. The estimated reliability of 87 is a relatively high value, and the combination of the mobile terminal model and vehicle type used by the user is one that allows for highly reliable estimation of driving behavior, and a comment informing the user of this is provided by the management server 30.

[0133] The estimated reliability information corresponding to the combination of the mobile terminal model information and the vehicle model information is data stored in the driving behavior analysis DB 82 managed by the management server 30. The management server 30 executes driving behavior estimation processing according to various mobile terminal models and vehicle types, and based on the results of verifying this data, generates estimation reliability information corresponding to the combination of the mobile terminal model information and vehicle type information used, and stores this information in the driving behavior analysis DB 82. In step S502, this data is provided from the management server 30 to the mobile terminal 20 and displayed on the mobile terminal 20.

[0134] (Step S503) Next, in step S503, the user of the mobile terminal 20 sets the fluctuation range of the score based on the driving behavior estimation process, and transmits the setting information to the management server 30. 13 to 16, the management server 30 calculates the driving behavior estimated value based on the terminal-acquired information, and also calculates various scores based on the driving behavior estimated value, such as (1) a risk score, (2) a reliability score, and (3) a total score.

[0135] Here, (1) the risk score and (3) the overall score are scores that can be used as index values ​​that indicate the user's (driver's) safe driving level, and these scores can be used for various services, such as insurance premium calculations and point awarding.

[0136] Specifically, (1) the risk score and (3) the overall score are provided to insurance companies, and are used to calculate premiums, such as setting lower insurance premiums if the user (driver) is deemed to be driving safely and not dangerously.

[0137] As mentioned above, for example, the overall score is calculated as a score from 0 to 100 by calculation based on the risk score and the reliability score, with 0 corresponding to dangerous driving and 100 corresponding to safe driving. However, this score (total score) is high if the estimated reliability is high, but is low if the estimated reliability is low.

[0138] The user sets the score fluctuation range taking this into consideration. If the score fluctuation range set by the user is small, the score (total score) calculated by the calculation process based on the risk score and the reliability score will remain around the average score, for example, 50 points. On the other hand, if the score fluctuation range set by the user is large, the score (total score) calculated by the calculation process based on the risk score and the reliability score may fluctuate significantly between 0 and 100 points.

[0139] Therefore, a user who is confident in their driving skills can set a larger swing in the score calculation range to obtain a higher score. However, conversely, if their driving behavior is poor, there is a risk that they will receive a lower score. Conversely, users who are not confident in their driving skills can expect to receive a stable score by reducing the fluctuation range of the scoring results.

[0140] (Step S504) Next, in step S504, the user of the mobile terminal 20 sets the notification frequency for notifications (advance notification, post-notification) to the user, and transmits the setting information to the management server 30.

[0141] Notifications to the user include, for example, advance notifications informing the user that they are approaching a "road area where dangerous driving occurs," and subsequent notifications such as warnings about the user's dangerous driving behavior, such as sudden braking, determined based on driving behavior estimates. The user can set the frequency of this notification. Figure 20 shows an example of the notification frequency setting screen.

[0142] As shown in FIG. 20, the user can set the frequency of advance notification and the frequency of post-notification separately. This setting information is transmitted to the management server 30, and the management server 30 determines whether or not to notify the user based on this setting information, and executes notification processing according to the determination result.

[0143] [5-(2) Processing while driving using the driving behavior estimation app] Next, processing performed while driving using the driving behavior estimation application will be described. A processing sequence during driving using the driving behavior estimation application 22 executed on the mobile terminal 20 will be described with reference to the flowchart shown in FIG. The processing of each step in the flow shown in FIG. 21 will be explained in order.

[0144] (Step S601) First, in step S601, current location information and map information about the area surrounding the current location are transmitted from the management server 30 to the mobile terminal 20 and displayed on the display unit of the mobile terminal 20. The management server 30 has a map information DB 83, and based on the current location information received from the mobile terminal 20, acquires a map including the area surrounding the current location from the map information DB 83, transmits it to the mobile terminal 20, and outputs it on the display unit.

[0145] (Step S602) Furthermore, the management server 30 displays the following road area information superimposed on the map information displayed on the mobile terminal 20. (1) Road area for driving score scoring: A check (2) Road area where dangerous driving occurs: A danger (3) Road area eligible for reward points: A reward

[0146] As described above, the road area information is registered in the map information DB 83 managed by the management server 30.

[0147] FIG. 22 shows an example of display data on the display unit of the mobile terminal 20 after the processing of step S602. As shown in FIG. 22, a map including the current location is displayed on the display unit of the mobile terminal 20. Furthermore, roads on the map are marked with the following symbols: (1) Road area for driving score scoring: A check (2) Road area where dangerous driving occurs: Adanger (3) Road area eligible for reward points: A reward These three types of road segment information are displayed in a distinguishable manner.

[0148] (Step S603) Next, the user (driver) sets a driving route and starts driving in step S603. After starting driving, the mobile terminal 20 starts to execute a process of calculating a driving behavior estimated value based on terminal-acquired information.

[0149] As described above, the calculation process of the driving behavior estimated value based on the terminal-acquired information is executed in one of the following ways. (1) The acquired information of the mobile terminal 20 is transmitted to the management server 30, and the management server 30 estimates the driving behavior using a learning model. (2) The mobile terminal 20 acquires the learning model generated by the management server 30, and calculates a driving behavior estimate based on the terminal-acquired information within the mobile terminal 20. When driving behavior estimation is performed in the manner (2), the mobile terminal 20 also transmits the terminal-acquired information and the driving behavior estimation value to the management server 30. The server 30 records the acquired information, which includes the terminal acquired information, and information such as the driving behavior estimated value and estimation reliability based on the terminal acquired information, in the driving behavior analysis result DB 82.

[0150] (Steps S604 to S605) After starting to travel, in step S604, it is determined whether the vehicle is traveling in a road section for which a driving score is to be scored. If it is determined that the vehicle is traveling on a road section for which driving scores are to be assessed, the distance traveled on that road section is recorded in the driving behavior analysis result DB 82 in step S605.

[0151] The driving behavior analysis result DB82 records acquired information including terminal acquired information, driving behavior estimates based on the terminal acquired information, estimation reliability, etc., as well as the distance traveled in the road area where the driving score is scored. When calculating the driving score, the score is calculated taking this travel distance into consideration.

[0152] (Steps S606 to S607) Furthermore, in step S606, it is determined whether the vehicle is traveling in a road section for earning reward points. If it is determined that the vehicle is traveling on a road section eligible for reward point acquisition, the distance traveled on that road section is recorded in the driving behavior analysis result DB 82 in step S607.

[0153] The driving behavior analysis result DB 82 records acquired information including terminal acquired information, driving behavior estimates based on the terminal acquired information, estimation reliability, etc., as well as the distance traveled in road areas eligible for reward point acquisition. When calculating reward points, the reward points are calculated taking into account this travel distance.

[0154] (Steps S609 to S611) Furthermore, in step S609, it is determined whether the vehicle is approaching a road area where dangerous driving occurs.

[0155] If it is determined that the vehicle is approaching a road area where dangerous driving occurs, in step S610, a notification that the vehicle is approaching a dangerous road is sent to the user as needed via the mobile terminal 20. Note that this notification is executed taking into consideration the user's setting level (setting frequency). An example of the notification process is shown in Fig. 23. As shown in Fig. 23, when it is determined that the vehicle is approaching a road area where dangerous driving has occurred, a notification that the vehicle is approaching a dangerous road is executed.

[0156] If it is determined that the vehicle is not approaching a road area where dangerous driving has occurred, then in step S611, a post-event notification is given as necessary, such as a post-event notification that dangerous driving such as sudden braking or sudden steering has been detected. This notification is also given taking into consideration the user's setting level (setting frequency). An example of the notification process is shown in Fig. 24. As shown in Fig. 24, when abrupt steering is detected, for example, display data for notifying the user that abrupt steering has been detected is output.

[0157] (Step S612) The final step S612 is a step for determining whether driving has ended. If driving has ended, the driving behavior estimation process based on terminal-acquired information from the mobile terminal is terminated. If the travel has not ended, the process returns to step S601, updates the map, etc., and continues to execute the processes from step S601 onwards.

[0158] In this way, while driving, the driving behavior estimation process is continuously performed based on the terminal-acquired information of the mobile terminal, and the management server 30 performs the calculation process of the driving behavior estimation value and estimation reliability, as well as the calculation of each score, and continuously performs the process of storing the calculated data in the driving behavior analysis result DB82.

[0159] [5-(3) Post-driving processing using the driving behavior estimation app] Next, post-driving processing using the driving behavior estimation application will be described. With reference to the flowchart shown in FIG. 25, a post-driving processing sequence using the driving behavior estimation application 22 executed on the mobile terminal 20 will be described. The processing of each step in the flow shown in FIG. 25 will be explained in order.

[0160] (Step S701) First, in step S701, map information including the route that has been traveled is transmitted from the management server 30 to the mobile terminal 20 and displayed on the display unit of the mobile terminal 20. As described above, the management server 30 has the map information DB 83, and further records the vehicle's travel route based on the current location information received from the mobile terminal 20.

[0161] (Step S702) Furthermore, in step S702, the management server 30 displays on the map information displayed on the mobile terminal 20 the locations where reckless driving was determined to have occurred based on the driving behavior estimated values ​​and the details of the reckless driving. A specific example is shown in FIG.

[0162] For example, as shown in display data example a in FIG. 26(a), the locations where reckless driving was determined to have occurred based on the driving behavior estimated values ​​and the details of the reckless driving are displayed on top of map information displayed on the mobile terminal 20.

[0163] (Step S703) Furthermore, in step S703, the management server 30 displays on the mobile terminal 20 points where the estimation reliability of the driving behavior estimated value is equal to or less than a specified threshold and where correction by the user is permitted. A specific example is shown in FIG.

[0164] For example, as shown in display data example b in FIG. 26(b), points where the estimation reliability of the driving behavior estimated value is equal to or less than a specified threshold and where correction by the user is permitted are displayed on the map information displayed on the mobile terminal 20. For example, if the specified threshold value is set to 0.3, points with an estimated reliability of 0.3 or less are displayed, and a message is displayed asking the user whether or not to request correction.

[0165] (Steps S704 to S705) In step S704, the management server 30 determines whether or not there is a correction request from the user. When the user touches the [Yes] area shown in the display data example b of FIG. 26(b), a correction request is sent to the management server 30. The management server 30 will receive a large number of correction requests from mobile terminals owned by a large number of users of vehicles that have completed their journeys.

[0166] In the example described with reference to step S703 of the flow in Figure 25 and Figure 26, information is displayed only for points whose estimation reliability is below a threshold value, but the estimation reliability of all points may be displayed in response to a user request, regardless of the estimation reliability.

[0167] For example, as shown in display data example a in Figure 27(a), the locations where dangerous driving has been determined to have occurred based on driving behavior estimates and the details of the dangerous driving are displayed on top of map information displayed on the mobile terminal 20, and the user touches the displayed area. This process displays the estimation reliability value corresponding to the driving behavior estimated value, as shown in Figure 27(b). This estimation reliability is 0.81, which is greater than the specified threshold value of 0.3, so a correction request cannot be made. In this case, a message indicating that a correction request is not possible is displayed.

[0168] Next, with reference to the flowchart shown in FIG. 28, a process sequence that the management server 30 executes after receiving a modification request from a mobile terminal will be described. The processing of each step in the flow shown in FIG. 28 will be explained in order.

[0169] (Step S721) First, in step S721, the management server 30 receives a modification request from the mobile terminal 20 of each user.

[0170] (Step S722) Next, in step S722, the management server 30 determines whether the number of correction requests received from the mobile terminal 20 is equal to or greater than a specified threshold number. If the number of correction requests is less than the specified threshold number, the process ends. On the other hand, if it is determined that the number of correction requests is equal to or greater than the specified threshold number, the process proceeds to step S723.

[0171] (Step S723) If it is determined in the determination process of step S722 that the number of correction requests is equal to or greater than the specified threshold number, the process proceeds to step S723. In step S723, the management server 30 corrects the driving behavior estimated value and the score calculation result based on the driving behavior estimated value.

[0172] (Step S724) Furthermore, in step S724, the management server 30 transmits the correction result and the reward points to the mobile terminal that made the correction request. A specific example is shown in FIG.

[0173] As shown in Figure 29, the location where the user's driving behavior was determined to be dangerous and the user requested a correction is displayed, along with the estimated driving behavior value for that location and a message indicating that the score has been corrected. In addition, a message indicating that the user has been awarded reward points because the correction was approved is also displayed. Specifically, reward points are points for discounts on products, points that can be applied to discounts on insurance premiums, etc. The management server 30 also manages the allocation and use of these points in cooperation with other information providing servers and service providing servers.

[0174] (Step S725) Furthermore, in step S725, the management server 30 performs a process of reflecting the correction results in the learning data. For example, the management server 30 corrects the driving behavior estimated values ​​stored in the driving behavior analysis result database 82 and the score calculation results based on the driving behavior estimated values, and reflects the correction results in the learning data.

[0175] [6. Configuration example of information processing device] Next, an example of the hardware configuration of an information processing device that can be used as the mobile terminal 20 or the management server 30 will be described with reference to FIG. An information processing device applicable as the mobile terminal 20 or the management server 30 has, for example, the hardware configuration shown in FIG.

[0176] A CPU (Central Processing Unit) 301 functions as a data processing unit that executes various processes in accordance with programs stored in a ROM (Read Only Memory) 302 or a storage unit 308. For example, it executes processes in accordance with the sequences described in the above-mentioned embodiments. A RAM (Random Access Memory) 303 stores programs and data executed by the CPU 301. The CPU 301, ROM 302, and RAM 303 are interconnected by a bus 304.

[0177] The CPU 301 is connected to an input / output interface 305 via a bus 304, and the input / output interface 305 is connected to an input unit 306 consisting of various switches, a keyboard, a touch panel, a mouse, a microphone, etc., and an output unit 307 consisting of a display, a speaker, etc.

[0178] The input unit of the mobile terminal 20 includes an information acquisition unit that acquires information used to estimate driving behavior, such as an acceleration sensor, a speed sensor, a GPS sensor, and a rotation speed sensor. The management server 30 or the CPU 301 of the mobile terminal 20 performs driving behavior estimation based on the terminal-acquired information.

[0179] A storage unit 308 connected to the input / output interface 305 is formed of, for example, a hard disk, and stores various data and programs executed by the CPU 301. A communication unit 309 functions as a transmitting / receiving unit for data communication via a network such as the Internet or a local area network, and also as a transmitting / receiving unit for broadcast waves, and communicates with external devices.

[0180] A drive 310 connected to the input / output interface 305 drives removable media 311 such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory such as a memory card, and executes recording or reading of data.

[0181] 7. Summary of the Disclosure The embodiments of the present disclosure have been described in detail above with reference to specific examples. However, it is obvious that those skilled in the art can modify or substitute the embodiments without departing from the gist of the present disclosure. In other words, the present invention has been disclosed in the form of examples and should not be interpreted as being limited. To determine the gist of the present disclosure, the claims should be taken into consideration.

[0182] The technology disclosed in this specification can be configured as follows. (1) A data processing unit that receives terminal-acquired information, which is information acquired by a mobile terminal in a vehicle, and executes a driving behavior estimation process of a driver of the vehicle; The data processing unit An information processing device that applies a pre-generated learning model to calculate an estimated value of the driver's driving behavior based on the terminal-acquired information.

[0183] (2) The learning model is An information processing device as described in (1), which is a learning model generated by inputting terminal-acquired information and vehicle observation information, and which is a learning model that takes various terminal-acquired information as input and outputs the driving behavior estimation value and its estimation reliability, which is its reliability.

[0184] (3) The terminal acquisition information is The information processing device according to (1) or (2), which includes at least one of acceleration information, rotational speed information, and position information.

[0185] (4) The data processing unit The information processing device according to any one of (1) to (3) executes a score calculation process using the driving behavior estimated value and its reliability as an estimation reliability.

[0186] (5) The data processing unit (1) A risk score, which is an index showing the driver's driving risk; (2) A reliability score, which is an index value of the overall estimation reliability of the driving behavior estimation value; (3) a total score indicating the driver's driving diagnosis results; The information processing device according to (4) executes a calculation process for at least one of the scores.

[0187] (6) The data processing unit The information processing device according to (5), wherein the total score is calculated by performing an arithmetic process on the risk score and the reliability score.

[0188] (7) The data processing unit The information processing device according to (5) or (6), which calculates a score according to at least one of the vehicle type and the mobile terminal model.

[0189] (8) The data processing unit The information processing device according to any one of (5) to (7), wherein information in which road area information determined based on the score is superimposed on a map is generated and output to the mobile terminal.

[0190] (9) The road area information is (1) Information on road areas where driving scores are assessed; (2) Information on road areas where dangerous driving occurs; (3) Information on road areas eligible for reward points; The information processing device according to (8), wherein the information is any one of the road area information.

[0191] (10) The data processing unit The information processing device according to (9) executes advance notification processing for notifying that a dangerous driving occurrence road area is approaching.

[0192] (11) The data processing unit The information processing device according to any one of (1) to (9) executes a post-notification process for notifying that a dangerous driving behavior has been performed.

[0193] (12) The data processing unit The information processing device according to any one of (1) to (10) receives a request from the mobile terminal to correct a driving behavior estimation result or a score calculation result based on the driving behavior estimation result, and executes a correction process.

[0194] (13) The data processing unit The information processing device according to (12), wherein when the correction process based on the correction request is executed, reward points are given to the user of the mobile terminal that sent the correction request.

[0195] (14) An information processing system having a management server and a mobile terminal, the mobile terminal is a mobile terminal in a vehicle; transmitting terminal acquisition information acquired by the mobile terminal to the management server; The management server An information processing system that inputs the terminal-acquired information received from the mobile terminal into a learning model and outputs an estimated value of the driving behavior of the driver of the vehicle.

[0196] (15) The management server The information processing system according to (14), wherein the terminal-acquired information received from the mobile terminal is input into a learning model, and the driving behavior estimation value and its estimation reliability, which is its reliability, are output.

[0197] (16) The management server Applying the driving behavior estimation value and its reliability, (1) A risk score, which is an index showing the driver's driving risk; (2) A reliability score, which is an index value of the overall estimation reliability of the driving behavior estimation value; (3) a total score indicating the driver's driving diagnosis results; The information processing system according to (14) or (15) executes a calculation process for at least one of the scores.

[0198] (17) An information processing method executed in an information processing device, the information processing device has a data processing unit that receives terminal-acquired information, which is information acquired by a mobile terminal in a vehicle, and executes a driving behavior estimation process of a driver of the vehicle; The data processing unit An information processing method for calculating an estimated value of the driver's driving behavior based on the terminal-acquired information by applying a learning model generated in advance.

[0199] (18) An information processing method executed in an information processing system having a management server and a mobile terminal, the mobile terminal is a mobile terminal in a vehicle; transmitting terminal acquisition information acquired by the mobile terminal to the management server; The management server An information processing method that inputs the terminal-acquired information received from the mobile terminal into a learning model and outputs an estimated value of the driving behavior of the driver of the vehicle.

[0200] (19) A program for causing an information processing device to execute information processing, the information processing device has a data processing unit that receives terminal-acquired information, which is information acquired by a mobile terminal in a vehicle, and executes a driving behavior estimation process of a driver of the vehicle; The program causes the data processing unit to A program that applies a pre-generated learning model to calculate an estimated value of the driver's driving behavior based on the terminal-acquired information.

[0201] Furthermore, the series of processes described in this specification can be executed by hardware, software, or a combination of both. When executing processes by software, a program recording the processing sequence can be installed and executed in the memory of a computer incorporated in dedicated hardware, or the program can be installed and executed on a general-purpose computer capable of executing various processes. For example, the program can be pre-recorded on a recording medium. In addition to installing the program on a computer from the recording medium, the program can also be received via a network such as a LAN (Local Area Network) or the Internet and installed on a recording medium such as an internal hard disk.

[0202] The various processes described in this specification may not only be executed in chronological order as described, but may also be executed in parallel or individually depending on the processing capabilities of the devices executing the processes or as needed. Furthermore, in this specification, a system refers to a logical collective configuration of multiple devices, and is not limited to devices that are all located in the same housing. [Industrial Applicability]

[0203] As described above, according to the configuration of one embodiment of the present disclosure, a configuration is realized in which terminal-acquired information from a mobile terminal in a vehicle is input into a learning model to estimate the driver's driving behavior, and score calculation and notification processing, etc. are performed based on the estimation results. Specifically, the system inputs terminal-acquired information, such as acceleration information acquired by a mobile device inside the vehicle, and executes a driving behavior estimation process for the vehicle driver. A learning model is applied to calculate an estimated value of the driver's driving behavior and its estimation reliability based on the terminal-acquired information. Furthermore, the system executes calculation processes for a risk score, which is an index of the driver's driving risk level; a reliability score, which is an index of the overall estimated reliability of the driving behavior estimate; and a total score, which indicates the driver's driving diagnosis results, and executes a notification process for the mobile device user based on the scores. This configuration realizes a configuration in which terminal-acquired information from a mobile terminal inside a vehicle is input into a learning model to estimate the driver's driving behavior, and score calculation and notification processing, etc. are performed based on the estimation results. [Explanation of symbols]

[0204] 10 vehicles 11 Driver 20 Mobile devices 21 Information Acquisition App 22 Driving behavior estimation app 30 Management Server 41,42 Information Server 43,44 Service provider server 50 Device Acquisition Information 60 Observation Information 70 training data 80 Learning processing unit 81 Learning Model 90 Driving behavior estimation unit 301 CPU 302 ROM 303 RAM 304 Bus 305 Input / Output Interface 306 Input section 307 Output section 308 Storage section 309 Communications Department 310 Drive 311 Removable Media

Claims

1. an estimation unit that estimates the driving behavior of the vehicle driver based on the information including the acceleration or speed acquired via a network, using a learning model that is generated or updated using observation information regarding the driving behavior of the vehicle driver and information including the acceleration or speed; Preparation, The information about the driving behavior of the vehicle driver estimated by the estimation unit is information used to generate information about the locations where dangerous driving by the drivers of multiple vehicles occurs. Information processing device.

2. The vehicle of the driver corresponding to the observation information used to generate or update the learning model and the vehicle of the driver whose driving behavior is estimated by the estimation unit are different vehicles. The information processing device according to claim 1 .

3. The information processing device according to claim 1 , wherein the vehicle from which information including acceleration or speed used to generate or update the learning model is acquired and the vehicle of the driver whose driving behavior is estimated by the estimation unit are different vehicles.

4. The information processing device Using a learning model generated or updated using observation information on the driving behavior of the vehicle driver and information including acceleration or speed, estimate the driving behavior of the vehicle driver based on the information including acceleration or speed acquired via a network; The information on the estimated driving behavior of the vehicle driver is information used to generate information on the locations where dangerous driving by the drivers of multiple vehicles occurs. Information processing methods.

5. The vehicle of the driver corresponding to the observation information used to generate or update the learning model is different from the vehicle of the driver whose driving behavior is estimated. The information processing method according to claim 4.

6. The vehicle from which the information including the acceleration or speed used to generate or update the learning model was obtained is different from the vehicle of the driver whose driving behavior is to be estimated. The information processing method according to claim 4.

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