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

Abstract

To improve the safety of operation of a mobile object.SOLUTION: An information processing device comprises a diagnosis unit that performs a diagnosis on driving aptitude of a user who drives a mobile object on the basis of the user's states before and during driving. The present technique can be applied to an information processing system, an information processing device, or a mobile body that gives assistance when a user drives a mobile object such as a vehicle, a motor cycle, a bicycle, a personal mobility, an airplane, a ship, a construction machine and an agricultural machine.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] This technology is Information processing system, The present invention relates to an information processing device, an information processing method, and a program, which are particularly suitable for use in providing driving assistance for a moving object. Information processing system, The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Conventionally, biometric information of a driver has been used to detect the driver's condition while driving.

[0003] For example, it has been proposed to correct the measured value of a driver's skin impedance using correction data based on the average of the measured values ​​of the driver's skin impedance when at rest with eyes closed and when engaged in normal activity, and to judge the driver's level of alertness based on the corrected value (see, for example, Patent Document 1).

[0004] For example, it has been proposed to transmit biometric data measured by a vehicle's biometric sensor to a data processing device via a portable terminal installed in a charger inside the vehicle and a public network line, and for the data processing device to transmit the received biometric data to the driver's portable terminal (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 10-80405 [Patent Document 2] JP 2016-140377 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, the safety of driving a moving object such as a vehicle is affected by various factors other than the state of the driver while driving. It is desirable to improve the safety of driving a moving object by taking these influences into account as much as possible.

[0007] The present technology has been made in consideration of such circumstances, and aims to improve the safety of driving a moving body. [Means for solving the problem]

[0008] The information processing system according to the first aspect of the present technology includes a data acquisition unit that receives a registration of a state before driving, a diagnosis unit that performs a driving diagnosis to diagnose a user's aptitude for driving based on the state or driving behavior of the user while driving, and , by the diagnosis unit Driving diagnosis results , and the basis for the results of the driving diagnosis A presentation control unit that feeds back the Accepts instructions to correct the basis for driving diagnosis results.

[0009] The information processing device according to the second aspect of the present technology includes a data acquisition unit that receives registration of a state before driving, a diagnosis unit that performs driving diagnosis to diagnose a user's aptitude for driving based on the state or driving behavior of the user while driving, and , by the diagnosis unit Driving diagnosis results , and the basis for the results of the driving diagnosis A presentation control unit that feeds back the Accepts instructions to correct the basis for driving diagnosis results.

[0023] In an information processing method according to a first aspect of the present technology, an information processing system receives a registration of a state before driving, performs a driving diagnosis to diagnose a user's aptitude for driving based on the state or driving behavior of the user while driving, and , by the driving diagnosis Driving diagnosis results , and the basis for the results of the driving diagnosis Feedback and accepts instructions for correction of the basis for the driving diagnosis results.

[0024] A program according to a first aspect of the present technology includes an information processing system that receives registration of a state before driving, performs driving diagnosis to diagnose a user's aptitude for driving based on the state or driving behavior of the user while driving, and , by the driving diagnosis Driving diagnosis results , and the basis for the results of the driving diagnosis Feedback and accepts instructions for correction of the basis for the driving diagnosis results. The processing is executed by a computer.

[0025] In the first or second aspect of the present technology, registration of a state before driving is accepted, and a user's aptitude for driving is diagnosed based on the state or driving behavior of the user while driving, and the state before driving is 、 Driving diagnosis results , and the basis for the results of the driving diagnosis is fed back , and instructions to correct the basis for the driving diagnosis result are accepted. Effect of the Invention

[0026] According to one aspect of the present technology, the accuracy of diagnosing a user's aptitude for driving a moving object is improved. As a result, the safety of driving a moving object is improved.

[0027] Note that the effects described herein are not necessarily limited to those described herein, and may be any of the effects described in this disclosure. [Brief description of the drawings]

[0028] [Figure 1] 1 is a block diagram showing an embodiment of an information processing system to which the present technology is applied. [Diagram 2] FIG. 2 is a block diagram showing a configuration example of a user terminal unit. [Diagram 3] FIG. 1 is a block diagram showing an example of the configuration of a vehicle. [Figure 4] FIG. 2 is a block diagram showing an example of the configuration of a server. [Diagram 5] 4 is a flowchart illustrating a driving assistance process executed by a server. [Figure 6] 4 is a flowchart illustrating a driving assistance process executed by a server. [Figure 7] FIG. 11 is a diagram for explaining a process of estimating a user's state. [Figure 8] FIG. 11 is a diagram for explaining a method of learning a standard state pattern of a user. [Figure 9] FIG. 11 is a diagram for explaining a method of learning a user group state pattern. [Figure 10] FIG. 11 is a diagram for explaining an example of a driving diagnosis method. [Figure 11] FIG. 1 illustrates an example of the configuration of a computer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Hereinafter, an embodiment of the present technology will be described in the following order. 1. Embodiment 2. Variations 3.Other

[0030] <<1. Preferred embodiment>> <Example of information processing system configuration> FIG. 1 is a block diagram showing an embodiment of an information processing system to which the present technology is applied.

[0031] The information processing system 10 is a system that uses telematics technology to provide services such as vehicle driving assistance and automobile insurance.

[0032] The information processing system 10 includes a user terminal unit 11, a vehicle 12, and a server 13. The user terminal unit 11 and the vehicle 12 communicate with each other directly or via a network 14. The server 13 communicates with the user terminal unit 11 and the vehicle 12 via the network 14.

[0033] The user terminal unit 11 is composed of one or more information processing terminals owned by a user who uses the information processing system 10. For example, the user terminal unit 11 may include a mobile terminal and a wearable terminal.

[0034] The vehicle 12 is a vehicle driven by a user who uses the information processing system 10 .

[0035] The server 13 communicates with the user terminal unit 11 and the vehicle 12 via the network 14, thereby providing services such as driving assistance and automobile insurance to the user who uses the information processing system 10.

[0036] 1, for ease of understanding, one each of the user terminal unit 11, vehicle 12, and server 13 is illustrated, but two or more each may be provided. For example, the number of user terminal units 11 and vehicles 12 provided is approximately equal to the number of users who use the information processing system 10.

[0037] In addition, in the following, for simplicity of explanation, when the user terminal unit 11, the vehicle 12, and the server 13 communicate with each other via the network 14, the phrase "via the network 14" will be omitted.

[0038] <Example of user terminal configuration> 2 is a block diagram showing an example of the configuration of the user terminal unit 11. In this example, the user terminal unit 11 includes a mobile terminal 51 and a wearable terminal 52.

[0039] The mobile terminal 51 is, for example, a portable information processing terminal such as a smartphone, a mobile phone, a tablet, a notebook personal computer, a portable game machine, or a portable video or music playback device.

[0040] The mobile terminal 51 includes a Global Navigation Satellite System (GNSS) receiver 61, an inertial sensor 62, an environmental sensor 63, a biosensor 64, an input unit 65, an output unit 66, a control unit 67, and a communication unit 68.

[0041] The GNSS receiver 61 measures the current position of the mobile terminal 51 (or the user who owns the mobile terminal 51) by receiving radio waves from positioning satellites, and supplies the control unit 67 with position data indicating the measured current position.

[0042] The inertial sensor 62 detects various inertial data related to the mobile terminal 51 (or the user who has the mobile terminal 51), and supplies the detected inertial data to the control unit 67. The inertial data detected by the inertial sensor 62 includes, for example, one or more of acceleration, angular velocity, and the like.

[0043] The environmental sensor 63 detects various types of environmental data around the mobile terminal 51 (or the user having the mobile terminal 51), and supplies the detected environmental data to the control unit 67. The environmental data detected by the environmental sensor 63 includes, for example, one or more of geomagnetism, atmospheric pressure, and carbon dioxide concentration.

[0044] The biosensor 64 detects various types of biodata of the user and supplies the detected biodata to the control unit 67. The biodata detected by the biosensor 64 includes, for example, one or more of heart rate, sweat rate, blood pressure, blood oxygen concentration, myoelectricity, body temperature, body composition, breath alcohol concentration, maximum oxygen intake, calories burned, tone of voice, speed of conversation, and the like.

[0045] The input unit 65 includes an input device for inputting various data to the mobile terminal 51. For example, the input unit 65 includes one or more of a button, a switch, a key, a touch panel, a microphone, etc. The input unit 65 supplies the input data to the control unit 67.

[0046] The output unit 66 includes an output device for outputting various information and data. For example, the output unit 66 includes one or more of a display, a speaker, a buzzer, a vibrator, and the like.

[0047] The control unit 67 includes, for example, control devices such as various processors. The control unit 67 controls each unit of the mobile terminal 51 and performs various processes based on data supplied from the GNSS receiver 61, the inertial sensor 62, the environmental sensor 63, the biosensor 64, and the input unit 65, and data received from the outside via the communication unit 68. The control unit 67 also supplies data obtained by various processes to the output unit 66 and transmits the data to other devices via the communication unit 68.

[0048] The communication unit 68 communicates with other devices (for example, the vehicle 12, the server 13, the wearable terminal 52, etc.) using a predetermined communication method. Any wireless or wired method can be adopted as the communication method of the communication unit 68. The communication unit 68 can also be compatible with multiple communication methods.

[0049] The wearable terminal 52 may be of any type, such as a glasses type, a wristwatch type, a bracelet type, a necklace type, a neckband type, an earphone type, a headset type, or a head-mounted type.

[0050] The wearable terminal 52 includes a biosensor 81, an input unit 82, an output unit 83, a control unit 84, and a communication unit 85.

[0051] The biosensor 81, like the biosensor 64 of the mobile terminal 51, detects various types of biometric data of the user and supplies the detected biometric data to the control unit 84. Note that the type of biometric data detected by the biosensor 81 and the type of biometric data detected by the biosensor 64 of the mobile terminal 51 may overlap.

[0052] The input unit 82 includes an input device for inputting various data to the wearable terminal 52. For example, the input unit 82 includes one or more of a button, a switch, a key, a touch panel, a microphone, etc. The input unit 82 supplies the input data to the control unit 67.

[0053] The output unit 83 includes an output device for outputting various information and data. For example, the output unit 83 includes one or more of a display, a speaker, a buzzer, a vibrator, and the like.

[0054] The control unit 84 includes, for example, control devices such as various processors. The control unit 84 controls each unit of the wearable terminal 52 and performs various processes based on data supplied from the biosensor 81 and the input unit 82, and data received from the outside via the communication unit 85. The control unit 84 also supplies data obtained by various processes to the output unit 83 and transmits the data to other devices via the communication unit 85.

[0055] The communication unit 85 communicates with other devices (for example, the vehicle 12, the server 13, the mobile terminal 51, etc.) using a predetermined communication method. Any wireless or wired method can be adopted as the communication method of the communication unit 85. The communication unit 85 can also be compatible with multiple communication methods.

[0056] <Vehicle configuration example> 3 is a block diagram showing an example of a portion of the configuration of the vehicle 12. The vehicle 12 includes an in-vehicle system 101. The in-vehicle system 101 includes a vehicle data acquisition unit 111, a video and audio acquisition unit 112, an input unit 113, an output unit 114, a control unit 115, and a communication unit .

[0057] The vehicle data acquisition unit 111 includes, for example, various sensors, communication devices, control devices, etc. The vehicle data acquisition unit 111 acquires vehicle data related to the vehicle 12, and supplies the acquired vehicle data to the control unit 115. The vehicle data acquired by the vehicle data acquisition unit 111 includes, for example, one or more of vehicle speed, torque, steering angle, yaw angle, gear state, handbrake state, accelerator pedal depression amount, brake pedal depression amount, turn signal state, light state, tire rotation angle and rotation speed, data indicating diagnosis results of OBD (On-board Diagnostics) (hereinafter referred to as OBD data), and sensor data such as millimeter wave radar and laser radar.

[0058] The video / audio acquisition unit 112 includes, for example, a camera and a microphone. The camera included in the video / audio acquisition unit 112 may be a special camera such as a ToF (Time Of Flight) camera, a stereo camera, an infrared camera, or the like, in addition to a normal camera. The video / audio acquisition unit 112 acquires, for example, video and audio around and inside the vehicle 12, and supplies the control unit 115 with video data and audio data indicating the acquired video and audio.

[0059] The input unit 113 includes an input device for inputting various data to the vehicle 12. For example, the input unit 113 includes one or more of a button, a switch, a key, a touch panel, etc. The input unit 113 supplies the input data to the control unit 115.

[0060] The output unit 114 includes an output device for outputting various information and data. For example, the output unit 114 includes one or more of a display (e.g., a head-up display), a speaker, a buzzer, a vibrator, an instrument panel, and the like.

[0061] The control unit 115 includes a control device such as an ECU (Electronic Control Unit). The control unit 115 controls each part of the vehicle 12 and performs various processes based on data supplied from the vehicle data acquisition unit 111, the video / audio acquisition unit 112, and the input unit 113, and data received from the outside via the communication unit 116. The control unit 115 also supplies data obtained by various processes to the output unit 114 and transmits the data to other devices via the communication unit 116.

[0062] The communication unit 116 communicates with other devices (for example, the server 13, the mobile terminal 51, the wearable terminal 52, etc.) using a predetermined communication method. Any wireless or wired method can be adopted as the communication method of the communication unit 116. The communication unit 116 can also support multiple communication methods.

[0063] <Server configuration example> 4 is a block diagram showing a configuration example of the server 13. The server 13 includes a communication unit 151, a state estimation unit 152, a surrounding data acquisition unit 153, a diagnosis unit 154, a driving behavior detection unit 155, a risk prediction unit 156, a loss prediction unit 157, a presentation control unit 158, an evaluation unit 159, a learning unit 160, an insurance premium calculation unit 161, and a storage unit 162.

[0064] The communication unit 151 communicates with other devices (for example, the vehicle 12, the mobile terminal 51, the wearable terminal 52, other servers (not shown), etc.) via the network 14 using a predetermined communication method. Any wireless or wired method can be adopted as the communication method of the communication unit 151. The communication unit 151 can also be compatible with multiple communication methods.

[0065] The state estimation unit 152 acquires data related to the user's state from the user terminal unit 11 and the vehicle 12 via the communication unit 151. The state estimation unit 152 generates and updates a state data log, which is a log of data related to the user's state, and stores the state data log in the storage unit 162.

[0066] Furthermore, the state estimation unit 152 performs a process of estimating the state of the user using a state estimation model stored in the storage unit 162 based on the state data log. Here, the state estimation model is a model used to estimate the state of the user, and is generated for each user, for example. The state estimation unit 152 generates and updates an estimated state history, which is a history of the estimation results of the state of the user, and stores it in the storage unit 162.

[0067] The surrounding data acquisition unit 153 acquires surrounding data indicating the state of the surroundings of the vehicle 12 based on data received from the user terminal unit 11, the vehicle 12, and another server (not shown) via the communication unit 151. The surrounding data acquisition unit 153 supplies the acquired surrounding data to the diagnosis unit 154 and the risk prediction unit 156.

[0068] The diagnosis unit 154 acquires the user's estimated state history, driving behavior history, and driving diagnosis model from the storage unit 162. Here, the driving behavior history is a history of detection results of driving behavior, which is the behavior of the user or the vehicle 12 while driving, and is generated for each user, for example. Also, the driving diagnosis model is a model used for driving diagnosis that diagnoses the user's aptitude for driving a vehicle, and is generated for each user, for example. The diagnosis unit 154 performs driving diagnosis of the user using the driving diagnosis model based on the acquired history. The diagnosis unit 154 generates and updates a driving diagnosis history, which is a history of the user's driving diagnosis results, and stores it in the storage unit 162.

[0069] The driving behavior detection unit 155 receives data from the user terminal unit 11 and the vehicle 12 via the communication unit 151. The driving behavior detection unit 155 also acquires the user's estimated state history and driving behavior detection model from the storage unit 162. Here, the driving behavior detection model is a model used to detect driving behavior, and is generated for each user, for example. Furthermore, the driving behavior detection unit 155 acquires the risk related to the user's driving of the vehicle and the predicted result of the damage caused by the risk from the damage prediction unit 157. The driving behavior detection unit 155 performs detection processing of the user's driving behavior using the driving behavior detection model based on the acquired history, data, etc. The driving behavior detection unit 155 generates and updates a driving behavior history, which is a history of the detection result of the user's driving behavior, and stores it in the storage unit 162.

[0070] The risk prediction unit 156 acquires the user's estimated state history, driving diagnosis history, driving behavior history, and risk prediction model from the storage unit 162. Here, the risk prediction model is a model used to predict risks related to the user's driving of the vehicle 12, and is generated for each user, for example. The risk prediction unit 156 performs risk prediction using the risk prediction model based on the acquired history. The risk prediction unit 156 supplies the risk prediction result to the damage prediction unit 157.

[0071] The damage prediction unit 157 predicts damage caused by the risk predicted by the risk prediction unit 156, using the user's obedience evaluated by the evaluation unit 159 as necessary. Here, the obedience level is the degree to which the user obediently follows suggestions from the server 13. The damage prediction unit 157 supplies the risk and damage prediction results to the driving behavior detection unit 155 and the presentation control unit 158.

[0072] The presentation control unit 158 ​​acquires the user's driving diagnosis history and the insurance premium calculated by the insurance premium calculation unit 161 from the storage unit 162. The presentation control unit 158 ​​generates feedback information to be presented to the user, including information on predicted risks based on the risk and damage prediction results, and the user's driving diagnosis history and insurance premiums. The presentation control unit 158 ​​controls the presentation of the feedback information to the user by transmitting the generated feedback information to the user terminal unit 11 or the vehicle 12 of the user via the communication unit 151. The presentation control unit 158 ​​also supplies the feedback information to the evaluation unit 159.

[0073] The evaluation unit 159 acquires the user's estimated state history and driving behavior history from the storage unit 162. Then, the evaluation unit 159 evaluates the user's obedience level based on the acquired history and feedback information. The evaluation unit 159 supplies the user's obedience level to the damage prediction unit 157 and stores it in the storage unit 162.

[0074] The learning unit 160 acquires the user's estimated state history from the storage unit 162. The learning unit 160 learns a standard state pattern of the user (hereinafter, referred to as a standard state pattern) based on the acquired estimated state history, and stores data indicating the acquired standard state pattern in the storage unit 162.

[0075] Furthermore, the learning unit 160 acquires an estimated state history of each user included in a predetermined user set from the storage unit 162. The learning unit 160 learns an average state pattern of users in the user set (hereinafter referred to as a user set state pattern) based on the acquired estimated state history, and stores data indicating the acquired user set state pattern in the storage unit 162.

[0076] Furthermore, the learning unit 160 acquires the user's state data log, estimated state history, driving diagnosis history, and driving behavior history from the storage unit 162. The learning unit 160 learns a state estimation model, a driving diagnosis model, a driving behavior detection model, and a risk prediction model based on the acquired logs and histories, and stores them in the storage unit 162.

[0077] The insurance premium calculation unit 161 acquires the user's driving diagnosis history, driving behavior history, and obedience level from the storage unit 162. The insurance premium calculation unit 161 calculates the user's automobile insurance premium based on the acquired history and obedience level. The insurance premium calculation unit 161 stores data indicating the calculated insurance premium in the storage unit 162.

[0078] <Driving assistance processing> Next, the driving support process executed by the server 13 will be described with reference to the flowcharts of FIGS.

[0079] In the following, the processing for one particular user (hereinafter, referred to as the user of interest) will be mainly described, but in reality, processing for users other than the user of interest is carried out in parallel.

[0080] In step S1, the server 13 starts a process of estimating the state of a user (a user of interest) when not driving. Specifically, for example, the following process is started.

[0081] The state estimation unit 152 acquires data on the state of the noted user from data (e.g., inertial data, environmental data, biological data, input data, etc.) received by the communication unit 151 from the user terminal unit 11 of the noted user. The state estimation unit 152 stores each piece of data together with the time of acquisition in the storage unit 162. This updates the state data log of the noted user.

[0082] It should be noted that the status data log of the noted user may include not only data regarding the status of the noted user himself, but also data regarding the status of the noted user's surroundings.

[0083] The state estimation unit 152 estimates the current state of the noted user based on the state data log of the noted user within a recent predetermined period, using a state estimation model of the noted user stored in the storage unit 162. For example, the state estimation unit 152 estimates the current biological state, behavior, and emotion of the noted user as the current state of the noted user.

[0084] 7, the state estimation unit 152 estimates the biological state and behavior of the noted user for each frame of a predetermined time based on a time-series change in a group of sensor data indicating the state of the noted user. For example, various biological states of the noted user, such as concentration level, awakening level, fatigue level, stress level, tension level, and intensity of exercise, are estimated based on the noted user's heart rate and sweat rate. For example, the type of behavior of the noted user (e.g., stationary, walking, running, cycling, climbing up and down stairs, eating, sleeping, etc.) is estimated based on the noted user's acceleration and angular velocity, as well as the surrounding air pressure.

[0085] Furthermore, for example, the state estimation unit 152 estimates the movement distance, movement speed, movement range, and the like of the noted user based on the position data, acceleration, and the like of the noted user.

[0086] Furthermore, the state estimation unit 152 estimates the emotion of the noted user based on the biometric data of the noted user and the estimation result of the biometric state of the noted user, etc. For example, the state estimation unit 152 estimates the degree of joy, anger, sorrow, pleasure, excitement, impatience, anxiety, etc. of the noted user.

[0087] Note that the biological state and the emotion of the noted user are not necessarily all clearly separated, and some may overlap. For example, an excitement level may be both a biological state and an emotion of the noted user.

[0088] In addition, any method can be adopted as a method for estimating the user's state. Furthermore, the types of user's states to be estimated are not limited to the above-mentioned examples, and can be added or deleted as necessary.

[0089] The state estimation unit 152 stores the estimated state of the noted user together with the time of estimation in the storage unit 162. This updates the estimated state history of the noted user.

[0090] For example, as illustrated in FIG. 7, the estimated state history includes data in which numerical values ​​for each frame, such as the level of alertness, are arranged in chronological order, and data in which labels (indicating the type of behavior) assigned to each frame, such as behavior, are arranged in chronological order.

[0091] In step S2, the learning unit 160 determines whether or not to learn a standard state pattern (standard state pattern) of the user (the noted user). For example, learning of the standard state pattern of the noted user is performed at a predetermined timing, such as when the noted user starts using the information processing system 10, every time a predetermined time elapses, or every time the amount of data of the noted user's estimated state history increases by a predetermined amount or more. Then, if it is now time to learn the standard state pattern of the noted user, the learning unit 160 determines to learn the standard state pattern of the noted user, and the process proceeds to step S3.

[0092] In step S3, the learning unit 160 learns a standard state pattern (standard state pattern) of the user (the user of interest). For example, as illustrated in Fig. 8, the learning unit 160 learns the standard state pattern of the user of interest based on the estimated state history of the user of interest over a relatively long period of time (e.g., on a monthly or yearly basis).

[0093] For example, the learning unit 160 calculates the average and variance for each of the following items for a predetermined time period (for example, 1 minute, 10 minutes, 30 minutes, or 1 hour) as the standard state pattern of the noted user, thereby learning a transition pattern showing a standard transition of each item for a predetermined period (for example, 1 day). For example, standard transition patterns for a day of the noted user's concentration level, alertness level, fatigue level, stress level, tension level, intensity of exercise, heart rate, amount of sweat, amount of exercise, degree of joy, anger, sadness, and happiness, excitement level, impatience level, anxiety level, etc. are learned.

[0094] Furthermore, for example, the learning unit 160 calculates the average and variance of each of the following items for each day (each day of the week) as the standard state pattern of the noted user, thereby learning the standard transition pattern of each item for a predetermined period (for example, one week). For example, standard transition patterns for one week of sleep time, wake-up time, bedtime, amount of exercise, range of movement, number of meals, meal times, meal time periods, driving time, driving time periods, commuting time periods, commuting time periods, etc. are learned.

[0095] Furthermore, the learning unit 160 learns an average standard state pattern in a user set (user group state pattern) based on the estimated state history of each user in a predetermined user set as a comparison target for the standard state pattern of the user of interest.

[0096] Here, the user set may include all users of the information processing system 10, or may include only some of the users. In the latter case, the user set may be made up of users similar to the noted user, for example. Here, a user similar to the noted user is, for example, a user whose attributes (e.g., age, sex, occupation, address, etc.), behavioral patterns, preferences, etc. are similar to those of the noted user. In addition, the noted user may or may not be included in the user set.

[0097] For example, as illustrated in Fig. 9, the learning unit 160 learns the user group state pattern by tallying up the average of the standard state pattern of each user in the user group. For example, the average of the standard transition pattern of each item in one day is tallied in the user group, so that the average transition pattern of each item in one day in the user group is learned. Also, for example, the average of the standard transition pattern of each item in one week is tallied in the user group, so that the average transition pattern of each item in the user group in one week is learned.

[0098] The learning unit 160 stores in the storage unit 162 data indicating the learning results of the standard state pattern of the noted user and the user group state pattern.

[0099] It should be noted that the timing of learning the standard state pattern of the target user and the timing of learning the user group state pattern do not necessarily need to be synchronized, and they may be performed at different times.

[0100] The process then proceeds to step S4.

[0101] On the other hand, if it is determined in step S2 that the standard state pattern of the noted user is not to be learned, the process of step S3 is skipped and the process proceeds to step S4.

[0102] In step S4, the diagnosis unit 154 performs driving diagnosis while not driving. For example, the diagnosis unit 154 detects a transition pattern (hereinafter referred to as a most recent state pattern) within a relatively short period (for example, one day and one week) for the same items as the above-mentioned standard state pattern based on the estimated state history of the noted user.

[0103] Next, the diagnosis unit 154 calculates a deviation vector x indicating the deviation between the most recent state pattern of the noted user and the standard state pattern, and a deviation vector y indicating the deviation between the most recent state pattern of the noted user and the user group state pattern, as illustrated in Fig. 10. For example, the deviation vector x and the deviation vector y are vectors including normalized values ​​of the deviation for each item of the two state patterns to be compared.

[0104] Then, the diagnosis unit 154 calculates the driving aptitude u of the noted user when not driving, using a driving diagnosis model of the noted user represented by a function f in the following formula (1).

[0105] u = f(x, y, wx, wy) (1)

[0106] Here, wx is a weight for the deviation vector x, and wy is a weight for the deviation vector y.

[0107] The driving suitability u becomes larger as the deviation of each item included in the deviation vector x becomes smaller. That is, the closer the most recent state pattern of the noted user is to the standard state pattern, in other words, the smaller the difference between the most recent state of the noted user and the standard state, the more suitable the state is determined to be. On the other hand, the driving suitability u becomes smaller as the deviation of each item included in the deviation vector x becomes larger. That is, the farther the most recent state pattern of the noted user is from the standard state pattern, in other words, the larger the difference between the most recent state of the noted user and the standard state, the more unsuitable the state is determined to be.

[0108] In addition, the driving suitability u becomes larger as the deviation of each item included in the deviation vector y becomes smaller. That is, the closer the most recent state pattern of the noted user is to the user group state pattern, in other words, the smaller the difference between the most recent state of the noted user and the average state of the user group, the more suitable the state is determined to be for driving. On the other hand, the driving suitability u becomes smaller as the deviation of each item included in the deviation vector y becomes larger. That is, the farther the most recent state pattern of the noted user is from the user group state pattern, in other words, the larger the difference between the most recent state of the noted user and the average state of the user group, the more unsuitable the state is determined to be for driving.

[0109] In addition, the larger the weight wx, the greater the influence of the deviation vector x (i.e., the difference between the most recent state pattern and the standard state pattern of the noted user) on the driving suitability u. On the other hand, the larger the weight wy, the greater the influence of the deviation vector y (i.e., the difference between the user group state pattern and the standard state pattern of the noted user) on the driving suitability u.

[0110] Also, for example, when the driving suitability u is less than a predetermined threshold, the diagnosis unit 154 estimates the cause of the decrease in the driving suitability u. For example, the diagnosis unit 154 extracts items in the deviation vector x, for which the product of the weight wx and the deviation is equal to or greater than a predetermined value. Also, the diagnosis unit 154 extracts items in the deviation vector y, for which the product of the weight wy and the deviation is equal to or greater than a predetermined value. Then, the diagnosis unit 154 estimates the cause of the decrease in the driving suitability u by comparing the most recent state pattern with the standard state pattern or the user set average pattern for each extracted item.

[0111] For example, if the sleep time in the most recent state pattern of the noted user is significantly lower than the standard state pattern or the average pattern of a user group, it is estimated that lack of sleep is the cause of the decline.

[0112] For example, if one or more of the exercise time, heart rate, and sweat amount in the most recent condition pattern of the user of interest significantly exceed the standard condition pattern or the average pattern of a user group, it is estimated that physical fatigue due to intense exercise is the cause of the decline.

[0113] For example, if one or more of the stress level, tension level, and impatience level in the most recent state pattern of the noted user is significantly higher than the standard state pattern or the average pattern of a user group, it is estimated that the noted user's impatience is the cause of the decline.

[0114] When the driving suitability u is equal to or greater than a predetermined threshold, for example, there is no particular cause for the decrease in the driving suitability u.

[0115] The diagnosis unit 154 stores the driving aptitude u and the estimated deterioration factor together with the diagnosis time as a diagnosis result of the driving aptitude of the noted user in the storage unit 162. This updates the driving diagnosis history of the noted user.

[0116] In step S5, the risk prediction unit 156 performs risk prediction. Specifically, the risk prediction unit 156 acquires the driving diagnosis history and the risk prediction model of the noted user from the storage unit 162. Based on the driving diagnosis history of the noted user, the risk prediction unit 156 predicts the risk when the noted user drives at the present time using the risk prediction model of the noted user.

[0117] For example, if the cause of the decrease in driving aptitude u is lack of sleep or physical fatigue, it is estimated that there is a risk of drowsy driving.

[0118] For example, if the cause of the decline in driving suitability u is the attention user's impatience, it is estimated that there is a risk of collision or contact with other vehicles due to speeding or reckless overtaking, as well as a risk of collision or contact with obstacles (e.g., other vehicles, bicycles, pedestrians, etc.) due to the attention user's carelessness.

[0119] In addition, the risk prediction unit 156 estimates the occurrence probability of the predicted risk. For example, the shorter the sleeping time of the noted user, the higher the estimated occurrence probability of drowsy driving.

[0120] In general, the lower the driving aptitude level u, the greater the predicted risk and occurrence probability.

[0121] The risk prediction unit 156 supplies the risk prediction result to the damage prediction unit 157. The risk prediction result includes the predicted risk, the basis for predicting the risk (for example, factors that cause a decrease in the driving suitability u), and the occurrence probability of the risk.

[0122] In step S6, the damage prediction unit 157 performs damage prediction. Specifically, the damage prediction unit 157 predicts damage (e.g., risk / penalty value) that will occur due to the risk predicted by the risk prediction unit 156. At this time, the occurrence probability of the risk is taken into consideration. That is, the higher the occurrence probability, the larger the predicted damage will be, and the lower the occurrence probability, the smaller the predicted damage will be. The damage prediction unit 157 supplies the predicted results of risk and damage to the driving behavior detection unit 155 and the presentation control unit 158.

[0123] In step S7, the driving behavior detection unit 155 adjusts detection parameters for driving behavior. For example, the driving behavior detection unit 155 adjusts parameters for detecting dangerous driving among various driving behaviors in accordance with predicted risks and damages.

[0124] Here, driving behaviors related to the detection of dangerous driving include, for example, sudden operations such as sudden starts, sudden acceleration, sudden braking, and sudden steering, erratic driving, drowsy driving, reduced alertness and concentration, inattentive driving, not paying attention to the road ahead, speeding, reckless overtaking, insufficient distance between vehicles, and approaching obstacles.

[0125] In addition, the detection parameters are adjusted so that the level (riskiness) of dangerous driving to be detected becomes lower as the estimated risk and damage increases, so that dangerous driving can be detected at an earlier and less severe stage.

[0126] For example, when the occurrence of drowsy driving is predicted, the threshold value of the number of blinks in drowsiness judgment is made small so that drowsiness of the user can be detected earlier.For example, when the occurrence of inattentive driving, inattention to the road ahead, speeding, etc. is predicted, the threshold value for detecting sudden acceleration or sudden braking is made small so that sudden acceleration or sudden braking can be easily detected.

[0127] Furthermore, the lower the level of risky driving to be detected, the lower the level of risk to be predicted, i.e., the earlier and less severe the risk is predicted.

[0128] Here, the level of dangerous driving to be detected and the level of risk to be predicted change depending on the driving aptitude u of the noted user. For example, as described above, generally, the lower the driving aptitude u, the higher the predicted risk and probability of damage occurrence, and the higher the predicted damage. Therefore, the detection parameters are adjusted so that the level of dangerous driving to be detected becomes lower. As a result, the lower the driving aptitude u, the lower the level of dangerous driving to be detected and the level of risk to be predicted.

[0129] In step S8, the presentation control unit 158 ​​determines whether or not to provide feedback to the user (the noted user). Feedback to the noted user is performed at a predetermined timing. For example, feedback is performed when a feedback request is transmitted from the user terminal unit 11 of the noted user, every time a predetermined time has elapsed, or when a serious risk is predicted. Then, if it is now the timing to provide feedback to the noted user, the presentation control unit 158 ​​determines to provide feedback to the noted user, and the process proceeds to step S9.

[0130] In step S9, the server 13 provides feedback to the user (noted user). Specifically, the presentation control unit 158 ​​generates feedback information to be presented to the noted user. This feedback information includes, for example, one or more of the results of the driving diagnosis of the noted user, the contents of the predicted risk, the basis of the driving diagnosis result or the basis of the predicted risk, and a proposal for avoiding the risk (hereinafter referred to as a risk avoidance proposal). The presentation control unit 158 ​​transmits the generated feedback information to the user terminal unit 11 of the noted user via the communication unit 151.

[0131] For example, when the mobile terminal 51 of the noted user receives feedback information, the output unit 66 of the mobile terminal 51 presents the feedback information to the noted user using at least one of visual information (e.g., video) and audio information (e.g., audio). Also, when the wearable terminal 52 of the noted user receives feedback information, the output unit 83 of the wearable terminal 52 presents the feedback information to the noted user using at least one of visual information and audio information.

[0132] For example, if the user of interest is estimated to be sleep-deprived, a voice message such as "You are sleep-deprived. We recommend that you get some sleep before driving" is output.

[0133] Furthermore, for example, as a driving diagnosis result of the noted user, the driving aptitude level u is presented in a predetermined scale (for example, 10 scales) and the basis thereof is presented.

[0134] Furthermore, for example, an estimation result of the state of the noted user may be presented. For example, the emotional quotient of the noted user, such as joy, anger, sadness, excitement, and aggression, may be presented as a value on a predetermined scale (for example, 10 scales).

[0135] The process then proceeds to step S10.

[0136] On the other hand, if it is determined in step S8 that feedback is not to be provided to the noted user, the process of step S9 is skipped and the process proceeds to step S10.

[0137] In step S10, the state estimation unit 152 determines whether or not the user (the noted user) has started driving. Specifically, the state estimation unit 152 determines whether or not the noted user has started driving, based on data received from at least one of the user terminal unit 11 and the vehicle 12 of the noted user via the communication unit 151.

[0138] The driving start determination process may be performed by any of the user terminal unit 11, the vehicle 12, and the server 13. For example, the mobile terminal 51 may perform the driving start determination process by performing driving recognition process of the noted user or by performing beacon synchronization with the vehicle 12. In addition, for example, a measuring device integrated with an in-vehicle display device of the vehicle 12 may perform the driving start determination process.

[0139] In addition, when the judgment process is performed by the user terminal unit 11 or the vehicle 12, the judgment result is included in the data transmitted from the user terminal unit 11 or the vehicle 12, and the state estimation unit 152 judges whether the user of interest has started driving based on the judgment result.

[0140] If it is determined that the noted user has not started driving, the process returns to step S2. Thereafter, the processes of steps S2 to S10 are repeatedly executed until it is determined in step S10 that the noted user has started driving. As a result, the noted user's driving diagnosis, risk prediction, and damage prediction are appropriately performed, and based on the results, the detection parameters for risky driving are adjusted and feedback is provided to the noted user. In addition, the noted user's standard state pattern and user group state pattern are appropriately updated.

[0141] On the other hand, if it is determined in step S10 that the noted user has started driving, the process proceeds to step S11.

[0142] In step S11, the server 13 starts an estimation process of the state of the user (the user of interest) while driving. This estimation process is significantly different from the estimation process in step S1 in that the estimation process is performed based on data transmitted from the vehicle 12 in addition to the user terminal unit 11 of the user of interest.

[0143] For example, the gaze, blinking, facial expression, etc. of the noted user are detected based on the video data from the vehicle 12 and are used to estimate the noted user's concentration level, awakening level, fatigue level, emotions, etc. In addition, for example, the content of the noted user's driving operation, etc. are estimated based on the vehicle data from the vehicle 12.

[0144] Furthermore, the state of the noted user is estimated based on, for example, the driving route of the vehicle 12 and the time period during which the noted user is driving. For example, if the noted user's heart rate or amount of exercise increases rapidly and then the noted user drives at a time period different from normal (for example, late at night or early in the morning) or the vehicle 12 is traveling outside the range of the noted user's daily living area, it is estimated that the noted user is in a hurry due to some kind of emergency.

[0145] Furthermore, the state estimation unit 152 appropriately updates the state data log and the estimated state history of the noted user, similarly to the process in step S1.

[0146] In step S12, the surrounding data acquisition unit 153 starts acquiring surrounding data of the vehicle 12. For example, the surrounding data acquisition unit 153 detects the state of structures, roads, traffic jams, weather, etc., around the vehicle 12 of the noted user based on the position information transmitted from the user terminal unit 11 or the vehicle 12 and map information, etc. received from another server, etc. via the communication unit 151. In addition, for example, the surrounding data acquisition unit 153 detects objects (for example, vehicles, people, obstacles, structures, roads, traffic lights, traffic signs, road markings, etc.) around the vehicle 12 based on the video data, audio data, and sensor data, etc. transmitted from the user terminal unit 11 or the vehicle 12. Note that the user terminal unit 11 or the vehicle 12 may perform a process of detecting objects around the vehicle 12 and transmit the detection results to the server 13.

[0147] The surrounding data acquisition unit 153 supplies the acquired surrounding data of the vehicle 12 to the diagnosis unit 154 and the risk prediction unit 156.

[0148] In step S13, the server 13 starts the detection process of the driving behavior. Specifically, for example, the following process is started.

[0149] The driving behavior detection unit 155 acquires data related to driving behavior (for example, the behavior of the noted user or the vehicle 12 while driving) from the communication unit 151 among the data received by the communication unit 151 from the user terminal unit 11 and the vehicle 12 of the noted user. In addition, the driving behavior detection unit 155 detects driving behavior using a driving behavior detection model of the noted user stored in the storage unit 162 based on the acquired data related to driving behavior, the estimated state history of the noted user stored in the storage unit 162, and the peripheral data of the vehicle 12 acquired from the peripheral data acquisition unit 153. That is, the behavior of the noted user while driving and the behavior of the vehicle 12 are detected. For example, the speed, acceleration, deceleration, braking operation, steering angle, driving route, etc. of the vehicle 12 are detected.

[0150] Note that a part of the behavior of the noted user while driving may be detected (estimated) by the state estimation unit 152 in step S11.

[0151] Furthermore, the detection of the driving behavior may be performed by any of the user terminal unit 11, the vehicle 12, and the server 13, or may be shared among the user terminal unit 11, the vehicle 12, and the server 13.

[0152] Next, the driving behavior detection unit 155 performs a process of detecting dangerous driving based on the detected driving behavior, the estimated state history of the noted user, and the surrounding data of the vehicle 12.

[0153] For example, a sudden operation is detected by a sudden change in the speed, steering angle, torque, or the like of the vehicle 12 based on OBD information, or a sudden change in the acceleration or angular velocity, or the like detected by the user terminal unit 12.

[0154] For example, swerving is detected by periodic fluctuations in the speed, steering angle, torque, etc. of the vehicle 12 based on OBD information, etc., or periodic fluctuations in acceleration or angular velocity, etc. detected by the user terminal unit 12.

[0155] For example, a lack of inter-vehicle distance is detected based on the position of the preceding vehicle detected using a stereo camera, a laser radar, or a millimeter wave radar.

[0156] Here, the detection parameters adjusted in step S7 described above or step S20 described below are used in the dangerous driving detection process. Therefore, as described above, the lower the driving aptitude u of the noted user is, the lower the level of dangerous driving to be detected, and the earlier and at a milder stage the detection target is detected as dangerous driving.

[0157] The driving behavior detection unit 155 stores the detection result of the driving behavior together with the detection time in the storage unit 162. This updates the driving behavior history of the noted user.

[0158] In step S14, the diagnosis unit 154 performs driving diagnosis during driving. For example, the diagnosis unit 154 corrects the driving aptitude u by using a driving diagnosis model of the noted user stored in the storage unit 162 based on the estimated state history and driving behavior history of the noted user after starting driving.

[0159] For example, when the concentration or alertness of the noted user decreases, or when the fatigue, stress, or tension of the noted user increases, the driving suitability u is lowered. On the other hand, when the concentration or alertness of the noted user increases, or when the fatigue, stress, or tension of the noted user decreases, the driving suitability u is raised. Also, for example, when dangerous driving is detected, the driving suitability u is lowered according to the detection frequency. On the other hand, when a state in which dangerous driving is not detected continues, the driving suitability u is raised according to the duration.

[0160] In addition, the driving aptitude level u may be corrected based on the quality of the driving operations of the noted user, such as the smoothness of the brake and accelerator pedals, the way the steering wheel is handled when cornering, and the smoothness of acceleration and deceleration.

[0161] The diagnosis unit 154 stores the corrected driving aptitude u and the estimated deterioration factor together with the diagnosis time as a diagnosis result of the driving aptitude of the noted user in the storage unit 162. This updates the driving diagnosis history of the noted user.

[0162] In step S15, the risk prediction unit 156 performs risk prediction. Here, unlike the process of step S5, the risk prediction unit 156 performs risk prediction by further using the estimated state history and driving behavior history of the noted user stored in the storage unit 162 in addition to the driving diagnosis history of the noted user. For example, even if the driving aptitude u of the noted user is high, the predicted risk increases as the detection frequency of dangerous driving increases and the risk of the detected dangerous driving increases. On the other hand, for example, even if the driving aptitude u of the noted user is low, if dangerous driving is not detected, the predicted risk decreases. In this way, the risk prediction is performed by further using the actual driving state and driving behavior of the noted user, so the accuracy of the risk prediction is improved compared to the process of step S5.

[0163] The risk prediction unit 156 can predict not only the risk and its occurrence probability, but also the time when the probability of the risk occurrence will become higher in the future (hereinafter referred to as the risk increase time). For example, based on the time series change in the concentration level, tension level, or awakening level of the noted user, the risk prediction unit 156 estimates the time when the level falls below a predetermined threshold as the risk increase time.

[0164] The risk prediction unit 156 supplies the risk prediction result to the damage prediction unit 157. This risk prediction result includes the content of the predicted risk, the basis for predicting the risk (for example, factors that cause a decrease in the driving suitability u or the detection result of dangerous driving), and the probability of risk occurrence. In addition, the time of risk rise is included as necessary.

[0165] In step S16, damage prediction is performed in the same manner as in the process of step S6. However, unlike the process of step S6, the damage prediction unit 157 performs damage prediction by further using the obedience of the noted user. That is, the higher the obedience of the noted user, the higher the possibility of avoiding risk, and the lower the obedience, the lower the possibility of avoiding risk. Therefore, the higher the obedience, the smaller the predicted damage, and the lower the obedience, the larger the predicted damage. The damage prediction unit 157 supplies the predicted results of risk and damage to the driving behavior detection unit 155 and the presentation control unit 158.

[0166] Here, the level of dangerous driving to be detected and the level of risk to be predicted change depending on the obedience of the noted user. For example, as described above, the lower the obedience of the noted user, the greater the damage of the predicted risk. Therefore, the detection parameters are adjusted so that the level of dangerous driving to be detected becomes lower. As a result, the lower the obedience, the lower the level of dangerous driving to be detected and the lower the level of risk to be predicted.

[0167] In step S17, the presentation control unit 158 ​​determines whether to provide feedback to the user (the noted user). For example, when the occurrence of a risk that needs to be notified to the noted user is predicted, the presentation control unit 158 ​​determines to provide feedback to the noted user, and the process proceeds to step S18.

[0168] In step S18, feedback is given to the noted user in the same manner as in the process of step S8. A specific example of feedback information presented to the noted user will now be described.

[0169] For example, when the attention user is estimated to be sleep-deprived and abrupt operation or erratic driving is detected, a warning against dangerous driving is issued. For example, a voice message such as "You are sleep-deprived. Please drive carefully" is output in the user terminal unit 11 or vehicle 12 of the attention user. For example, the part "Please drive carefully" becomes a risk avoidance proposal, and the part "You are sleep-deprived" becomes the basis for presenting the risk avoidance proposal.

[0170] For example, when the noted user is estimated to be outside the range of daily life or is estimated to be under strong stress, and the approach to a vehicle ahead is detected, a warning against dangerous driving is issued. For example, a voice message such as "Are you in a hurry? Let's drive calmly" is output in the user terminal unit 11 or vehicle 12 of the noted user. For example, the part "Let's drive calmly" is the risk avoidance proposal, and the part "Are you in a hurry?" is the basis for presenting the risk avoidance proposal.

[0171] For example, when it is estimated that the noted user engaged in vigorous exercise before driving, and when the noted user's gaze is detected to be concentrated on one point, or when a large number of pedestrians are detected around the vehicle 12, a warning is issued against possible dangerous driving. For example, a voice message such as "Are you feeling sleepy? Please drive while paying attention to your surroundings" is output in the user terminal unit 11 of the noted user or the vehicle 12. For example, the part "Please drive while paying attention to your surroundings" is the risk avoidance proposal, and the part "Are you feeling sleepy?" is the basis for presenting the risk avoidance proposal.

[0172] For example, when the noted user is estimated to be outside the range of daily life, or when the noted user is estimated to be sleep-deprived, and a sudden operation or erratic driving is detected while driving on a highway, a warning against dangerous driving is issued. For example, a voice message such as "Why don't you take a break at the next service area? You didn't sleep enough yesterday, and you are currently driving erratic" is output in the user terminal unit 11 or vehicle 12 of the noted user. For example, the part "Why don't you take a break at the next service area?" is the risk avoidance proposal, and the part "You didn't sleep enough yesterday, and you are currently driving erratic" is the basis for presenting the risk avoidance proposal.

[0173] For example, when the noted user is estimated to be sleep deprived or in a state of extreme tension, and dangerous driving is frequently detected, a warning of an increase in automobile insurance premiums and a presentation of the cause are made. For example, a voice message such as "Dangerous driving due to lack of sleep (or tension) is occurring frequently. If x more dangerous driving incidents are detected, you will lose your right to insurance premium cashback" is output on the user terminal unit 11 or vehicle 12 of the noted user. For example, the part "If x more dangerous driving incidents are detected, you will lose your right to insurance premium cashback" is the warning to the noted user, and the part "Dangerous driving due to lack of sleep (or tension) is occurring frequently" is the basis for issuing the warning.

[0174] For example, when it is estimated that the noted user is driving on an unfamiliar road outside the range of daily life, and the time of risk increase due to fatigue is estimated, a voice message such as "Are you tired? Why don't you take a break at service area A?" is output. Note that service area A is a service area that is estimated to be reachable by the noted user's vehicle 12 before the risk increase time. For example, the part "Why don't you take a break at service area A?" becomes a risk avoidance proposal, and the part "Are you tired?" becomes the basis for presenting the risk avoidance proposal.

[0175] In addition, risk avoidance measures can be more specific, such as "Please slow down to 60 km / h" or "Please turn forward immediately."

[0176] In addition, the presentation control unit 158 ​​supplies feedback information to the evaluation unit 159.

[0177] In step S19, the evaluation unit 159 evaluates the obedience level of the user (the noted user). Specifically, the evaluation unit 159 acquires the estimated state history and the driving behavior history of the noted user after feedback to the noted user is provided from the storage unit 162. Then, the evaluation unit 159 detects the reaction of the noted user to the feedback (e.g., the details of driving) based on the acquired history.

[0178] Furthermore, the evaluation unit 159 updates the evaluation value of the obedience level of the noted user based on the reaction of the noted user. For example, if the currently presented feedback information includes a risk avoidance plan and the noted user follows the risk avoidance plan, the obedience level of the noted user increases. Furthermore, the shorter the time it takes for the noted user to follow the risk avoidance plan (the faster the reaction speed), or the smaller the difference between the noted user's reaction and the risk avoidance plan, the larger the increase in the obedience level of the noted user. Conversely, the longer the time it takes for the noted user to follow the risk avoidance plan (the slower the reaction speed), or the larger the difference between the noted user's reaction and the risk avoidance plan, the smaller the increase in the obedience level of the noted user.

[0179] On the other hand, if the noted user does not follow the risk avoidance plan, for example, if the noted user ignores the risk avoidance plan, or if the noted user shows a reaction different from the risk avoidance plan, the noted user's obedience level decreases. In particular, if the noted user does not follow the risk avoidance plan and drives dangerously, the degree of decrease in the noted user's obedience level is large.

[0180] The evaluation unit 159 supplies the updated obedience degree of the noted user to the damage prediction unit 157 and also stores it in the storage unit 162.

[0181] The process then proceeds to step S20.

[0182] On the other hand, if it is determined in step S17 that feedback is not to be provided to the noted user, the processes of steps S18 and S19 are skipped and the process proceeds to step S20.

[0183] In step S20, the detection parameters for the driving behavior are adjusted in the same manner as in the process in step S7.

[0184] In step S21, the state estimation unit 152 determines whether the user has stopped driving. That is, similar to the determination process of the start of driving in step S10, the state estimation unit 152 determines whether the noted user has stopped driving based on data received from at least one of the user terminal unit 11 and the vehicle 12 of the noted user via the communication unit 151.

[0185] If it is determined that the noted user has not stopped driving, the process returns to step S14. Thereafter, the processes of steps S14 to S21 are repeatedly executed until it is determined in step S21 that the noted user has stopped driving. As a result, the noted user's driving diagnosis, risk prediction, and damage prediction are appropriately performed, and based on the results, the detection parameters for dangerous driving are adjusted and feedback is given to the noted user. In addition, the degree of obedience of the noted user is appropriately updated.

[0186] On the other hand, if it is determined in step S21 that the noted user has stopped driving, the process proceeds to step S22.

[0187] In step S22, similar to the process in step S1, the process of estimating the state of the noted user when not driving is started.

[0188] In step S23, the surrounding data acquisition unit 153 stops acquiring data about the surroundings of the vehicle 12.

[0189] In step S24, the driving behavior detection unit 155 stops the detection process of the driving behavior.

[0190] In step S25, the learning unit 160 performs a learning process. For example, the learning unit 160 learns a state estimation model, a driving diagnosis model, a driving behavior detection model, and a risk prediction model based on the state data log, the estimated state history, the driving diagnosis history, and the driving behavior history of the noted user stored in the storage unit 162.

[0191] For example, if safe driving is performed despite driving aptitude u being evaluated as low, the weights wx and wy in formula (1) representing the driving diagnosis model described above are set small. This allows the driving aptitude u of the noted user to be evaluated higher than before. Conversely, if dangerous driving is frequently performed despite driving aptitude u being evaluated as high, the weights wx and wy in formula (1) are set large. This allows the driving aptitude u of the noted user to be evaluated lower than before.

[0192] Also, for example, if the difference between the most recent state pattern of the noted user and the user group state pattern is large and the driving aptitude u is evaluated low, but the user drives safely, the weight wx in the above formula (1) is increased and the weight wy is decreased. That is, in the driving diagnosis, the difference between the most recent state pattern of the noted user and the standard state pattern is given more importance.

[0193] For example, when the user of interest starts using the service, a model corresponding to an average user obtained through prior experiments is used. After that, each model is updated (personalized) to a model more suitable for the user of interest through a learning process.

[0194] This learning process does not necessarily have to be performed every time the target user finishes driving, but may be performed at any timing, for example, every time a predetermined period of time passes, every time driving is performed a predetermined number of times, every time the driving time increases by a predetermined time or more, etc. Also, it is not necessarily required to perform the learning process for all models at the same time, but the learning process may be performed at different timings for each model.

[0195] Also, for example, the learning unit 160 may learn a fatigue level estimation model that predicts the fatigue level of the noted user based on the transition of the noted user's fatigue level before and during driving shown in the estimated state history. For example, the state estimation unit 152 can estimate the fatigue level of the noted user at each point on the planned driving route using the fatigue level estimation model. Furthermore, for example, the learning unit 160 can improve the estimation accuracy of the fatigue level by learning the fatigue level estimation model while taking into account the surrounding conditions of the vehicle 12, such as the time of day, weather, traffic congestion, and type of road (for example, general road or expressway).

[0196] The learning process may be carried out using machine learning such as a neural network or any other method.

[0197] In step S26, the insurance premium calculation unit 161 calculates the insurance premium of the user (noted user). For example, when the automobile insurance premium of the noted user or the cashback amount for the insurance premium changes in real time, the insurance premium calculation unit 161 updates the insurance premium or the cashback amount based on the driving diagnosis history, driving behavior history, and obedience of the noted user stored in the storage unit 162. The insurance premium calculation unit 161 stores the updated insurance premium or cashback amount of the noted user in the storage unit 162.

[0198] For example, the lower the average value of driving suitability u, the higher the insurance premium (or the lower the cash back amount), and the higher the average value of driving suitability u, the lower the insurance premium (or the higher the cash back amount).

[0199] Also, for example, the greater the frequency or cumulative number of dangerous driving incidents, the higher the insurance premium (or the lower the cashback amount), and the smaller the frequency or cumulative number of dangerous driving incidents, the lower the insurance premium (or the higher the cashback amount).Furthermore, for example, the higher the average or total predicted damages for dangerous driving, the higher the insurance premium (or the lower the cashback amount), and the lower the average or total predicted damages for dangerous driving, the lower the insurance premium (or the higher the cashback amount).

[0200] Also, for example, the lower the obedience level, the lower the risk reduction is expected, so the insurance premium will increase (or the cashback amount will decrease), and the higher the obedience level, the lower the risk reduction is expected, so the insurance premium will decrease (or the cashback amount will increase). In particular, if the user of interest does not follow the risk avoidance plan and drives dangerously, for example, the increase in the insurance premium (or the decrease in the cashback amount) will be large as a penalty.

[0201] It should be noted that this insurance premium calculation process does not necessarily have to be performed every time the target user finishes driving, but may be performed at any time, such as every specified period of time, every time driving is performed a specified number of times, or every time the driving time increases by more than a specified amount of time.

[0202] This insurance premium calculation process may also be performed, for example, to calculate an estimated insurance premium amount at the time of the next insurance renewal.

[0203] Furthermore, for example, in the case of automobile insurance per driving or per day, the insurance premium may be calculated by executing this insurance premium calculation process before driving. In this case, for example, the insurance premium is calculated using at least one of the driving aptitude u and obedience of the noted user.

[0204] In addition, the calculated insurance premium or cashback amount may be presented to the noted user as feedback information.

[0205] Thereafter, the process returns to step S2, and the processes from step S2 onwards are executed.

[0206] For example, driving behavior is influenced not only by the user's state while driving, but also by the user's state before driving (e.g., behavior, biological state, emotions, etc.). In response to this, as described above, by comprehensively considering the user's state before driving in addition to the user's state and driving behavior while driving, the driving aptitude u is more appropriately evaluated. Furthermore, by improving the accuracy of the driving aptitude u, the accuracy of risk prediction and damage prediction is improved. As a result, appropriate feedback information can be presented at a more appropriate time, safety is improved, and the occurrence of accidents can be prevented.

[0207] In addition, since the feedback information shows the basis of the driving diagnosis result or the basis of the predicted risk, the user feels more convinced and is more likely to follow the risk avoidance plan. In addition, if the presented basis is incorrect, for example, the user can instruct the server 13 to correct it and reflect it in the learning process, thereby improving the accuracy of the driving diagnosis model and the risk prediction model.

[0208] Furthermore, by evaluating the user's level of compliance and changing the level of dangerous driving to be detected and the level of risk to be predicted based on the level of compliance, the user can more reliably avoid risks.

[0209] In addition, by calculating the car insurance premium (including the cashback amount) based on not only the user's driving behavior but also the driving aptitude u and obedience, it is possible to set a more appropriate premium for each user. This makes it possible, for example, to motivate the user to follow a risk avoidance plan, allowing the user to avoid risks more reliably.

[0210] <<2. Modifications>> Below, a description will be given of modified examples of the above-described embodiment of the technology according to the present disclosure.

[0211] <Modifications regarding system configuration> The configuration examples of the information processing system 10 shown in FIGS. 1 to 4 are merely examples, and can be changed as necessary.

[0212] For example, in the above explanation, an example has been shown in which the server 13 performs most of the processing based on the data acquired from the user terminal unit 11 and the vehicle 12 (in-vehicle system 101), but it is also possible for the processing to be shared among the user terminal unit 11, the vehicle 12, and the server 13, or for the user terminal unit 11 or the vehicle 12 to perform the processing independently.

[0213] For example, it is possible for at least one of the user terminal unit 11 and the vehicle 12 to perform part or all of the processing of the server 13.

[0214] For example, at least one of the user terminal unit 11 and the vehicle 12 may perform some or all of the processing of the state estimation unit 152, the driving behavior detection unit 155, and the evaluation unit 159, and transmit the estimation results and detection results to the server 13.

[0215] For example, at least one of the user terminal unit 11 and the vehicle 12 may perform part or all of the processing of the surrounding data acquisition unit 153 and transmit the acquired surrounding data to the server 13.

[0216] For example, at least one of the user terminal unit 11 and the vehicle 12 may perform some or all of the processing of the diagnosis unit 154, the risk prediction unit 156, and the damage prediction unit 157, and transmit the diagnosis results and prediction results to the server 13.

[0217] Also, for example, the processing related to driving assistance may be performed by the user terminal unit 11. In this case, the user terminal unit 11 may be configured with multiple devices or may be configured with one device. Also, the user terminal unit 11 may acquire various data (e.g., vehicle data, video data, audio data, etc.) from the vehicle 12 and use the data for processing, or may not use data from the vehicle 12 for processing. Also, the user group state pattern may or may not be used for the driving diagnosis processing. When the user group state pattern is used, for example, the server 13 learns the user group state pattern based on the standard state pattern of each user acquired from the user terminal unit 11 of each user. Then, the server 13 transmits data indicating the user group state pattern to the user terminal unit 11 of each user.

[0218] In this case, the insurance premium calculation process may be performed by either the user terminal unit 11 or the server 13. When the user terminal unit 11 performs the process, for example, an application program that performs the insurance premium calculation process is provided from the server 13. On the other hand, when the server 13 performs the process, for example, data required for insurance premium calculation is provided from the user terminal unit 11 to the server 13.

[0219] Furthermore, for example, among the processes related to driving assistance, almost all processes except the process of estimating the state of the user when not driving and the process of learning the standard state pattern may be performed by the vehicle 12 (the in-vehicle system 101). In this case, for example, the user terminal unit 11 performs the process of estimating the state of the user when not driving and the process of learning the standard state pattern, and transmits the resultant data indicating the estimated state history and the standard state pattern to the vehicle 12 before driving. Then, the vehicle 12 performs the remaining processes. In this case, the user group state pattern may or may not be used in the driving diagnosis process. When the user group state pattern is used, for example, the server 13 learns the user group state pattern based on the standard state pattern of each user acquired from the user terminal unit 11 of each user. Then, the server 13 transmits the data indicating the user group state pattern to the user terminal unit 11 of each user or the vehicle 12.

[0220] In this case, the insurance premium calculation process may be performed by any of the user terminal unit 11, the vehicle 12, or the server 13. When the user terminal unit 11 or the vehicle 12 performs the process, for example, an application program that performs the insurance premium calculation process is provided from the server 13. On the other hand, when the server 13 performs the process, for example, data required for insurance premium calculation is provided to the server 13 from the user terminal unit 11 and the vehicle 12.

[0221] Also, for example, the processing may be shared between the user terminal unit 11 and the server 13. In this case, data from the vehicle 12 may or may not be used in the processing.

[0222] Furthermore, for example, the processing may be shared between the vehicle 12 and the server 13. In this case, for example, data required for the processing (for example, the user's standard state pattern and most recent state pattern, etc.) is provided from the user terminal unit 11 to the vehicle 12 and the server 13.

[0223] In addition, the processes may be shared among a plurality of servers. For example, the process related to driving assistance and the process related to insurance premium calculation may be performed by different servers.

[0224] Furthermore, for example, communication between the vehicle 12 and the server 13 may be performed via the user terminal unit 11. In this case, for example, data from the vehicle 12 is once transmitted to the user terminal unit 11, and then transferred from the user terminal unit 11 to the server 13. Also, data from the server 13 is once transmitted to the user terminal unit 11, and then transferred from the user terminal unit 11 to the vehicle 12.

[0225] Also, for example, communication between the user terminal unit 11 and the server 13 may be performed via the vehicle 12. In this case, for example, data from the user terminal unit 11 is once transmitted to the vehicle 12, and then transferred from the vehicle 12 to the server 13. Also, data from the server 13 is once transmitted to the vehicle 12, and then transferred from the vehicle 12 to the user terminal unit 11.

[0226] Furthermore, for example, instead of the estimated state history, a state data log before the user's state is estimated may be directly used to perform driving diagnosis by the diagnosis unit 154, risk prediction by the risk prediction unit 156, and detection of driving behavior by the driving behavior detection unit 155. In this case, it is possible to delete the state estimation unit 152.

[0227] <Other Modifications> For example, the driving aptitude level u may be calculated using the current state of the user in addition to the deviation between the most recent state pattern of the user and the standard state pattern or the average pattern of the user group. For example, even if the difference between the most recent state pattern of the user and the standard state pattern is small, for example, if the user has low alertness, is excited, or is depressed, there is a high possibility that the user will drive dangerously. Therefore, for example, for items that have a large impact on driving among the items indicating the user's state, the current state of those items (for example, alertness, excitement, depression) may be used to calculate the driving aptitude level u.

[0228] In addition, the state of the user to be estimated is not limited to the above-mentioned three types of biological state, behavior, and emotion. For example, only one or two of the above three types may be estimated, or other types of states may be estimated.

[0229] Furthermore, for example, user behavior that is not expected to directly affect driving may be used to calculate the driving aptitude u. For example, based on the user's purchase history, if the user makes unusual purchases (for example, makes very expensive purchases), it is expected that the user's state (for example, emotions) is different from normal. Therefore, for example, the user's purchase history may be used to calculate the driving aptitude u.

[0230] In addition, for example, the driving aptitude u may be calculated based on the user's ability detected based on the user's state when not driving, and which also affects driving. For example, the driving aptitude u of a user with high judgment ability in daily life may be set high, and the driving aptitude u of a user with low judgment ability may be set low.

[0231] In this case, for example, even if a user with high and low judgment ability repeatedly changes lanes at a high frequency, the predicted risk for the user with high judgment ability will be low, and the predicted risk for the user with low judgment ability will be high. However, even if a user with high judgment ability is presented with a risk avoidance plan to reduce lane changes, if the user ignores the plan and continues to change lanes, the degree of obedience will be evaluated as low, and the predicted risk will be high.

[0232] Furthermore, for example, risk prediction may be performed based on only one or two of the estimation result of the user's state, the result of driving diagnosis, and the detection result of the driving behavior. Also, for example, when risk prediction is performed using the result of driving diagnosis, only one of the driving aptitude u before driving and the driving aptitude u during driving may be used.

[0233] Also, for example, driving diagnosis may be performed based on only one of the user's state before driving and the state during driving.

[0234] Furthermore, for example, the estimated state history, driving diagnosis history, and driving behavior history can be used to investigate the cause of an accident. For example, by recording the estimated state history, driving diagnosis history, and driving behavior history in a drive recorder, it becomes possible to investigate the cause of an accident based on the state of the user before driving in addition to the state and behavior of the user (driver) at the time of the accident.

[0235] Also, for example, the user terminal unit 11 or the vehicle 12 may be configured to acquire the driving aptitude u, risk prediction results, and the like of other users from the user terminal unit 11 or vehicle 12 of the other users, or the server 13. In this way, for example, when a reckless vehicle driven by a user with low driving aptitude u is present nearby, the user can avoid being involved in an accident caused by the reckless vehicle by acquiring the information in advance.

[0236] Furthermore, the state estimation model, driving diagnosis model, driving behavior detection model, and risk prediction model do not necessarily need to be personalized, and models using a predetermined algorithm, etc. may be used. Also, for example, the learning of the above models may not be performed for each user, but may be performed for the entire user group, and a common model may be used for each user.

[0237] Also, for example, the method of presenting feedback information, the frequency of presentation, and the content of presentation may be changed based on the user's obedience.

[0238] <Application Examples> The driving assistance processing of the present technology can be applied to driving various moving objects other than the vehicles exemplified above (for example, the present technology can be applied to driving motorcycles, bicycles, personal mobility, airplanes, ships, construction machinery, agricultural machinery (tractors), etc. In other words, by using the estimation results of the user's state before and during driving, driving diagnosis, risk prediction, damage prediction, feedback to the user, etc. can be performed. Note that moving objects to which the present technology can be applied include not only those that move from place to place, but also moving objects in which parts that perform work at fixed places, such as some construction machinery (for example, fixed cranes, etc.), move. Moving objects to which the present technology can be applied also include, for example, moving objects that are driven (operated) remotely without a user on board, such as drones and robots.

[0239] In addition, the present technology can be applied to systems and devices that provide various types of insurance, such as life insurance, non-life insurance, and medical insurance, in addition to automobile insurance. For example, various insurance premiums (including cashback amounts) can be calculated based on the estimated state history and obedience of the user. Specifically, for example, in the case of life insurance or medical insurance, the user terminal unit 11 or the server 13 performs a state estimation process for the user and accumulates the estimated state history. In addition, the user terminal unit 11 or the server 13 presents the user with a proposal (risk avoidance proposal) for avoiding risks such as illness based on the user's lifestyle and biological state based on the estimated state history, and evaluates the user's obedience based on the reaction. Then, the user terminal unit 11 or the server 13 calculates the insurance premium for life insurance or medical insurance based on the user's lifestyle and biological state, and the obedience.

[0240] For example, the better the lifestyle and biological condition of a user, the lower the insurance premium, and the worse the lifestyle and biological condition of a user, the higher the insurance premium. Also, the higher the obedience of a user, the lower the insurance premium, and the lower the obedience of a user, the higher the insurance premium. In this way, by taking into account not only the lifestyle and biological condition of a user but also the obedience of a user, it is possible to set a more appropriate insurance premium for each user, as in the case of the above-mentioned automobile insurance.

[0241] <<3.Other>> <Computer configuration example> The above-mentioned series of processes can be executed by hardware or software. When the series of processes is executed by software, the program constituting the software is installed in a computer. Here, the computer includes a computer built into dedicated hardware, and a general-purpose personal computer, for example, capable of executing various functions by installing various programs.

[0242] FIG. 11 is a block diagram showing an example of the hardware configuration of a computer that executes the above-mentioned series of processes according to a program.

[0243] In the computer, a central processing unit (CPU) 401, a read only memory (ROM) 402, and a random access memory (RAM) 403 are interconnected via a bus 404.

[0244] An input / output interface 405 is further connected to the bus 404. An input unit 406, an output unit 407, a recording unit 408, a communication unit 409, and a drive 410 are connected to the input / output interface 405.

[0245] The input unit 406 includes an input switch, a button, a microphone, an image sensor, etc. The output unit 407 includes a display, a speaker, etc. The recording unit 408 includes a hard disk, a non-volatile memory, etc. The communication unit 409 includes a network interface, etc. The drive 410 drives a removable recording medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory.

[0246] In the computer configured as above, the CPU 401 loads, for example, a program recorded in the recording unit 408 into the RAM 403 via the input / output interface 405 and the bus 404, and executes the program, thereby performing the series of processes described above.

[0247] The program executed by the computer (CPU 401) can be provided by being recorded on a removable recording medium 411 such as a package medium, for example. The program can also be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.

[0248] In the computer, the program can be installed in the recording unit 408 via the input / output interface 405 by mounting the removable recording medium 411 in the drive 410. The program can also be received by the communication unit 409 via a wired or wireless transmission medium and installed in the recording unit 408. Alternatively, the program can be installed in the ROM 402 or the recording unit 408 in advance.

[0249] In addition, the program executed by the computer may be a program in which processing is performed chronologically in the order described in this specification, or it may be a program in which processing is performed in parallel or at the required timing, such as when called.

[0250] In this specification, a system refers to a collection of multiple components (devices, modules (parts), etc.), regardless of whether all the components are in the same case. Therefore, multiple devices housed in separate cases and connected via a network, and a single device in which multiple modules are housed in a single case, are both systems.

[0251] Furthermore, the embodiments of the present technology are not limited to the above-described embodiments, and various modifications are possible without departing from the gist of the present technology.

[0252] For example, the present technology can be configured as cloud computing, in which a single function is shared and processed collaboratively by multiple devices via a network.

[0253] Furthermore, each step described in the above flow chart can be executed by one device, or can be shared and executed by a plurality of devices.

[0254] Furthermore, when a single step includes multiple processes, the multiple processes included in the single step can be executed by a single device, or can be shared and executed by multiple devices.

[0255] <Examples of configuration combinations> The present technology can also be configured as follows.

[0256] (1) A diagnosis unit that diagnoses a user's aptitude for driving based on a state of the user before driving a moving object that is acquired in advance and a state of the user acquired while driving the moving object; a presentation control unit that generates feedback information based on the diagnosis obtained by the diagnosis unit; An information processing device comprising: (2) A driving behavior detection unit that detects a driving behavior, which is a behavior of the user or the moving object while driving. Further preparation, The level of dangerous driving to be detected by the driving behavior detection unit is changed based on the result of the diagnosis. The information processing device according to (1). (3) The diagnosing unit further performs the diagnosis based on the detection result of the driving behavior. The information processing device according to (2). (4) The diagnosing unit diagnoses the user's aptitude for driving before driving based on the user's state before driving, and diagnoses the user's aptitude for driving while driving based on the user's state while driving and the detection result of the driving behavior. The information processing device according to (3). (5) a risk prediction unit that predicts a risk associated with the user's driving of the mobile object based on at least one of a result of the diagnosis and a result of the detection of the driving behavior; The information processing device according to any one of (2) to (4) further comprises: (6) The feedback information includes the content of the risk and the grounds on which the risk was predicted. The information processing device according to (5). (7) The risk is predicted based on the results of the diagnosis. The information processing device according to (6). (8) The feedback information includes a plan to avoid the risk. The information processing device according to any one of (5) to (7). (9) The lower the driving aptitude of the user based on the diagnosis, the lower the level of dangerous driving that is to be detected by the driving behavior detection unit. The information processing device according to any one of (2) to (8). (10) a learning unit that learns a model for performing the diagnosis based on the result of the diagnosis and the detection result of the driving behavior; The information processing device according to any one of (2) to (9) further comprises: (11) The diagnosis unit performs the diagnosis based on a difference between a most recent state of the user and a standard state of the user. The information processing device according to any one of (1) to (10). (12) The diagnosis unit further performs the diagnosis based on a difference between the most recent state of the user and an average state of users in a user group including a plurality of users. The information processing device according to (11). (13) A state estimation unit that estimates the state of the user before and during driving. Further preparation, The diagnosis unit performs the diagnosis based on the estimation result of the user's state. 13. The information processing device according to any one of (1) to (12). (14) The state of the user includes at least one of the biological state, the behavior, and the emotion of the user. The information processing device according to (13). (15) The diagnosis unit performs the diagnosis based on data indicating a state of the user acquired before and during driving of the user. 13. The information processing device according to any one of (1) to (12). (16) A diagnostic step of diagnosing a user's aptitude for driving based on a state of the user before driving the moving object acquired in advance and a state of the user acquired during driving; a presentation control step of generating feedback information based on the diagnosis obtained by the diagnosis unit; An information processing method comprising: (17) A diagnostic step of diagnosing a user's aptitude for driving based on a state of the user before driving the moving object acquired in advance and a state of the user acquired during driving; a presentation control step of generating feedback information based on the diagnosis obtained by the diagnosis unit; A program for causing a computer to execute a process including the steps of:

[0257] It should be noted that the effects described in this specification are merely examples and are not limiting, and other effects may also be obtained. [Explanation of symbols]

[0258] 10 Information processing system, 11 User terminal unit, 12 Vehicle, 13 Server, 51 Mobile terminal, 52 Wearable terminal, 61 GNSS receiver, 62 Inertial sensor, 63 Environmental sensor, 64 Biometric sensor, 66 Output unit, 67 Control unit, 81 Biometric sensor, 83 Output unit, 84 Control unit, 101 In-vehicle system, 111 Vehicle data acquisition unit, 112 Video and audio acquisition unit, 114 Output unit, 115 Control unit, 152 State estimation unit, 154 Diagnosis unit, 155 Driving behavior detection unit, 156 Risk prediction unit, 157 Loss prediction unit, 158 Presentation control unit, 159 Evaluation unit, 160 Learning unit, 161 Insurance premium calculation unit

Claims

1. A data acquisition unit that receives registration of a state before driving; A diagnosis unit that performs a driving diagnosis to diagnose a user's aptitude for driving based on the user's state or driving behavior while driving; a presentation control unit that feeds back a state before driving, a driving diagnosis result by the diagnosis unit, and a reason for the driving diagnosis result; In an information processing system comprising: The information processing system receives an instruction to correct the basis of the driving diagnosis result. Information processing system.

2. The pre-driving state includes the user's state before driving The information processing system according to claim 1 .

3. The user's state includes at least one of a biological state, a behavior, and an emotion. The information processing system according to claim 2 .

4. The diagnosis unit performs driving diagnosis of the user before driving based on the state of the user before driving.

4. The information processing system according to claim 2 or 3.

5. The diagnosing unit performs driving diagnosis based on the user's state either before driving or during driving.

5. The information processing system according to claim 2.

6. The presentation control unit controls presentation of information regarding a state of the user before driving.

6. The information processing system according to claim 2.

7. The data acquisition unit acquires data regarding a state before driving of a plurality of users.

7. The information processing system according to claim 2.

8. The data acquisition unit stores the state of the user before driving together with time in a storage unit. The information processing system according to any one of claims 2 to 7.

9. The diagnosis unit stores the driving diagnosis result together with the time when the driving diagnosis was performed in the storage unit.

9. The information processing system according to claim 1.

10. A driving behavior detection unit detects driving behavior based on data on driving behavior and a user's state. The information processing system according to any one of claims 1 to 9 further comprises:

11. A risk prediction unit that predicts a risk related to a user's driving based on at least one of a driving diagnosis result and a driving behavior. The information processing system according to any one of claims 1 to 10, further comprising:

12. The presentation control unit feeds back the content of the risk and the reason why the risk is predicted. The information processing system according to claim 11.

13. A user terminal and a server are provided, Driving diagnosis is shared between the user terminal and the server.

13. An information processing system according to claim 1.

14. A data acquisition unit that receives registration of a state before driving; A diagnosis unit that performs a driving diagnosis to diagnose a user's aptitude for driving based on the user's state or driving behavior while driving; a presentation control unit that feeds back a state before driving, a driving diagnosis result by the diagnosis unit, and a reason for the driving diagnosis result; In an information processing device comprising: The information processing device receives an instruction to correct the basis of the driving diagnosis result. Information processing device.

15. The pre-driving state includes the user's state before driving The information processing device according to claim 14.

16. The user's state includes at least one of a biological state, a behavior, and an emotion. The information processing device according to claim 15.

17. The diagnosis unit performs driving diagnosis of the user before driving based on the state of the user before driving.

17. The information processing device according to claim 15 or 16.

18. The diagnosing unit performs driving diagnosis based on the user's state either before driving or during driving. The information processing device according to any one of claims 15 to 17.

19. The presentation control unit controls presentation of information regarding a state of the user before driving. The information processing device according to any one of claims 15 to 18.

20. The data acquisition unit acquires data regarding a state before driving of a plurality of users.

20. The information processing device according to claim 15,

21. The data acquisition unit stores the state of the user before driving together with time in a storage unit. The information processing device according to any one of claims 15 to 20.

22. The diagnosis unit stores the driving diagnosis result together with the time when the driving diagnosis was performed in the storage unit. The information processing device according to any one of claims 14 to 21.

23. A driving behavior detection unit detects driving behavior based on data on driving behavior and a user's state. The information processing device according to any one of claims 14 to 22 further comprising:

24. A risk prediction unit that predicts a risk related to a user's driving based on at least one of a driving diagnosis result and a driving behavior. The information processing device according to any one of claims 14 to 23, further comprising:

25. The presentation control unit feeds back the content of the risk and the reason why the risk is predicted. The information processing device according to claim 24.

26. The information processing system Accepts registration of pre-driving conditions, A driving diagnosis is performed to diagnose the user's aptitude for driving based on the user's state or driving behavior while driving; Feedback is given to the state before driving, the driving diagnosis result by the driving diagnosis, and the grounds for the driving diagnosis result; Accept instructions to correct the basis for driving diagnosis results Information processing methods.

27. Accepts registration of pre-driving conditions, A driving diagnosis is performed to diagnose the user's aptitude for driving based on the user's state or driving behavior while driving; Feedback is given to the state before driving, the driving diagnosis result by the driving diagnosis, and the grounds for the driving diagnosis result; Accept instructions to correct the basis for driving diagnosis results A program that causes a computer to execute a process.

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